数据采集中英文文献
关于隧道监控量测测的外文文献
关于隧道监控量测测的外文文献Title: Tunnel Monitoring and Measurement of Surveillance Introduction:隧道监控量测是现代交通运输中至关重要的一环。
通过对隧道内部环境、结构和交通流量等数据的监测和测量，可以确保隧道的安全运营和管理。
本文将介绍隧道监控量测的相关技术和方法，包括传感器技术、数据采集与传输、数据处理与分析等内容。
1. 传感器技术传感器是隧道监控量测的核心技术之一。
不同类型的传感器可以用于监测隧道内的不同参数，如温度、湿度、气体浓度、烟雾等。
其中，温度和湿度传感器可以帮助监测隧道内的环境条件，及时发现异常情况并采取相应措施。
气体浓度传感器可以用于监测隧道内的有害气体浓度，预警并防止事故发生。
烟雾传感器可以及时检测到火灾或烟雾，保证隧道内人员的安全。
2. 数据采集与传输隧道监控量测需要对传感器采集到的数据进行实时或定期的采集与传输。
采集方式可以通过有线或无线方式进行。
有线方式可以通过布设数据线缆来实现，但需要考虑线缆的布置和维护。
无线方式可以利用无线传感器网络进行数据采集与传输，具有布置灵活、维护成本低等优势。
3. 数据处理与分析采集到的数据需要进行处理与分析，以提取有价值的信息。
数据处理可以包括数据清洗、数据校正和数据融合等步骤，以确保数据的准确性和一致性。
数据分析可以利用统计学方法、机器学习和人工智能等技术，对数据进行模式识别、异常检测和预测分析，帮助提前预警和决策。
4. 隧道监控系统隧道监控量测需要建立完善的监控系统，包括传感器布置、数据采集与传输、数据处理与分析以及监控中心等组成部分。
传感器布置需要考虑隧道内的特殊环境，如高温、高湿等因素。
数据采集与传输需要确保数据的稳定传输和存储，以及数据的实时性。
数据处理与分析需要建立适当的算法和模型，以提取有用信息并辅助决策。
监控中心是隧道监控系统的核心，负责监测和管理隧道的运行情况。
完整word版基于STM32的数据采集系统英文文献
Design of the Data Acquisition System Based on STM32ABSTRACTEarly detect ion of failures in machi nery equipments is one of the most important concerns to industry. In order to monitor effective of rotating machinery, we devel opment a micro-c on trolleruC/OS-II system of sig nal acquisiti on system based on STM32 in this paper, we have give n the whole desig n scheme of system and the multi-cha nnel vibrati on sig nal in axis X, Y and Z of the rotary shaft can be acquired rap idly and dis play in real-time. Our system has the character of sim pie structure,low po wer consump ti on, mi niaturizatio n.Keywords: STM32; data acquisition; embedded system;uC/OS-ll;1.1.IntroductionThe real-time acquisition of vibration in rotating machinery can effectively p redict, assessa nd diag nose equipment op erati on state, the in dustry gets vibratio n data acquisiti on Rap idly and an alysis in real-time can mon itor the rotati ng mach inery state and guara ntee the safe running of the equipmen t. I n order to p reve nt failure, reduce maintenance time, improve the econo mic efficie ncy, The purpose of fault diag no sis system can detect these devices through the vibratio n sig nal acquisiti on of rotating machinery, and process the data acquisition, then it will make timely judgme nt of running state of equipment .While the data acquisiti on module is the core part of the fault diag no sis system [1-4].The p ractical app licati on in the in dustrial field, is the equipment operating parameters willbe acquired to monitor equipment op erati ng state. In traditi onal data acquisiti on systems, the data from acquisiti on card are gen erally send into the compu ter, and sp ecific software will be devel oped for the data acquisition. The main contribution of this paper has designed the STM32 p latform with ARM tech no logy, that has become a traditi onal main stream tech no logy in embedded systems, and thecollect ing data toward the directi on of high real-time, multi-parameter, high-precision, while data storage become large capacity, more mini aturizati on and p ortable, and the devel opment of multicom muni cati on mode and Iong-distanee for data transmission. So as to meet the actual acquisition system multitask ing requireme nts, this article has desig ned based on STM32 micro-co ntroller uC/OS-ll system of sig nal acquisiti on system. Therefore, in order to meet the actual acquisiti on system multitask requireme nts, this no velty of this article has desig ned a sig nal acquisiti on system in micro-c on troller uC/OS-II based on STM32.2・Architecture of data acquisition systemData acquisiti on as key tech no logy for mon itori ng equipmen t, rece ntly a lot of work has been done on it. An embedded parallel data acquisition system based onFPGA is Optimized designed which will make it reasonableto divide and allocate high-s peed and low-s peed A/D [5]. I nstead, it has use a high-s peed A/Dcon verier and Stratix II series of FPGA for data collecti on and p rocess ing, in which the main contribution is used of the Compact Peripheral ComponentIn terc onn ect, the system has the characters of modularizati on, sturd in ess and scalability [6].But remote control will be needed in Special Conditions, this paper introduce the embedded operating system platform based on Windows CE and uC/OS-II to desig n a remote acquisiti on and con trol system with theGPRS wireless tech no logy [7-8]」n order to achieve the data shari ng of multi-user, it has build the embedded dyn amic website for data acquisiti on man ageme nt and dissem in ati on with the ARM9 and Linux operation system [9].A data collection terminal devices is designed based on ARM7 microprocessor LPC2290and embedded real-time op erati ng system uC/OS-II to solve the real-time acquisiti on of multicha nnel small sig nal and multi-cha nnel tran smissi on[ 10].O n the other han ds, two p arallelDSP-based system dedicated to the data acquisiti on on rotati ng machi nes, and the inner sig nal conditi oner is used to ada pt the sen sor out put to the input range of the acquisiti on, and the n sig nal p ost -p rocess in gby the desig n software, while the most frequently structure is to use DAS and FPGA-based, and such programs are also dependent on the DAS cost.In order to meet market requireme nts of low po wer consump ti on, low cost, and mobility, Fig.1 in this paper presents the design overall structure diagram of data acquisiti on system. Through SPI in terface, the system gets the data collectio n withthree axis acceleration sensor into the STM32 controller of inner A/D conversion module with 12-bit, this p rocess is non-i nterferi ng p arallel acquisiti on. Our system uses 240x400 LCD and touch scree n module real-time to dis play the collected data in real time.Fig J Headway Framework of System2.1.STM32 micro-controllerA 32 bit RISC STM32F103VET6, used as the processor in our system, com pared with similar p roducts, the STM32F103VET6 work at 72MHZ, with characters of stro ng p erforma nee and low po wer consump tio n, real-time and low-cost.The processor in cludes: 512K FLASH, 64K SRAM, and it will com mun icate by using five serial p ortswhich con tai n a CAN bus, a USB2.0 SLA VE mode and a Ethernet in terface, what s more two RS232 p orts are also in cluded. The system in our paper exte nd the SST25VF016B serial memory through the SPI bus in terface, that will regard as the temporary storage whe n collect large nu mber of data, furthermore, we have the A/D con verier with 12 bits resoluti on, and the fastest con versi on up to 1us, with 3.6 Vfull-scale of the system .In additi on to desig n of the system po wer supply circuit, the reset circuit, RTC circuit and GPIO port to assura ncesystem n eeds and no rmal op erati on.2.2.Data acquisitionThe machi ne state is no rmal or not is mai niy depen ded on the vibrati on sig nal.In this paper, to acquire the vibratio n data of rotati ng machi nery rotor, we have used vibrati on accelerati on tran sducers MMA7455L which could collect the data from axis x, y, and z of the company of Free-scale. The kind of vibration acceleration transducers has advantage of low cost and small size, high sensitivity and large dynamic range with small interferenee. MMA7455L is mainIy consists of gravity sensing unit and sig nal con diti oning circuit comp ositi on, and this sen sor will amp lify the tiny data before sig nal prep rocess ing. In data acquisiti on p rocess of our system, the error of samp li ng stage is mainly caused by qua ntified, and the error is depen ded on the bits of the A/D conv erter ,whe n we regard the maximum voltage as V max , theAD converter bits is n, and the quantization Q = V max/2n, then, the quantization error is obeyed uniform distributi on in [- q / 2, q / 2] [13].丘=1 ep(e)de = 0=匚仗一已）血F）尿M *血M告£ =卩I呼=血呼=口（S皿）=]z 垃/ b- 孑- z Se is aveiase enoi\ is enor variance . and —is SKR.「并NThe desig ned STM32 could built at most three 12-bit parallel ADC in this paper , whichtheoretical in dex is 72dB and the actual dyn amic range is betwee n 54 to 60dB while 2 or 3 bits is imp acted by no ise, the dyn amic range of measureme nt can up to 1000 times with 60dB. For the vast majority of the vibratio n sig nal, the maximum samp li ng rate of 10kHZ can meet actual dema nd, and the higher freque ncy of collecti on is gen erally used in the 8-12 bits AD, therefore one of con tributi on of thiswork is to choose a built-in 12-bit A/D to meet the accuracy of vibration signal acquisiti on and lower cost in this exp erime nt.3・Software design3.1. Trans plantation of C/OSIn order to ensure real-time and safety data collection requirements, in thissystem, a kind of RTOS whose source code is open and small is prop osed. It also canbe easily to be cut dow n, repo tted and solidified, and its basic functions in clud ing task management and resource management, storage management and systemmanagement. The RTOS embedded systemcould support 64 tasks, with at most 56user tasks, and four tasks of the highest and the lowest p riorities will be reta ined insystem. The uC/OS-II assig ns p riorities of the tasks accordi ng to their imp orta nee, theoperation system executive the task from the priority sequenceand each task haveindependent p riority. The op erati ng system kernel is streamli ned, and multi-task ingfun cti on is well comp ared with others, it can be transplan ted to p rocessors that from8-bit to 64-bit.The transplant in the system are to modify the three file systemstructure: OS_CPU_C.H OS_CPU.C, OS_CPU_A.ASM. Main transplan tati on p rocedure is as follows:A. OS_C PU_C.HIt has defi ned the data typ es, the len gth and growth direct ion of stack in theprocessor. Because different microprocessors have different word length , so the uC/OS-II transplan tati on in clude a series of type defi niti on to en sure its p ortability,and the revised code as follows:typ edef un sig ned char BOOLEAN;typ edef un sig ned char INT8U;typ edef sig ned char INT8S;typ edef un sig ned short INT16U;typ edef sig ned short INT16U;typ edef un sig ned int INT32U;typ edef signed int INT32S;typ edef float FP32;typ edef double FP64;typ edef un sig ned int OS_STK;typ edef un sig ned int OS_C PU_SR;Cortex-M3 p rocessor defi nes the OS_ENTER_CRITICAL () andOS_EXIT_CRITICAL () as opening and closi ng in terru pt, and they must set to 32 bit of the stack OS_STK and CPU register len gth. In additi on, that has defi ned the stack poin ter OS_STK_GROWTH stack growth direct ion from high address to lower address.B. OS_C PU.CTo modify the function OSTaskStklnit() according to the processor, the nine rema ining user in terface fun cti ons and hook fun cti ons can be n ull without sp ecial requirements, they will produce code for these functions only when theOS_C PU_HOOKS_EN is set to 1 in the file of OS_CFG.H. The stack ini tialization fun cti on OSTaskStk Init () retu rn to the new top of the stack poin ter.OS_C PU_A.ASMMost of the transplant work are comp leted in these docume nts, and modify the followi ng functions.OsStartHighRdy() is used for running the most priority ready task, it will be respon sible for stack poin ter SP from the highest p riority task of TCB con trol block, and restore the CPU, the n the task p rocess created by the user start to con trol the p rocess.OSCtxSw () is for task switch ing, When the curre nt task ready queue have a higher p riority task, the CPU will start OSCtxSw () task switchi ng to run the higher p riority task and the curre nt task stored in task stack.OSIntCtxSw () has the similar function with OSIntSw (), in order to ensure real-time p erforma nee of the system, it will run the higher p riority task directly whe n the in terr upt come, and will not store the curre nt task.OSTickISR () is use to han dle the clock in terr upt, which n eeds in terru pt to schedule its impi eme ntati on whe n a higher p riority task is wait ing for the clock sig nal.OS_CPU_SR_Save () and OS_CPU_SR_Restore () is completed to switch in terr upt while en teri ng and leav ing the critical code both functions imp leme nt by the critical p rotectio n fun ctio n OS_ENTER_CRITICAL () and OS_EXIT_CRITICAL ().After the completion ofthe above work, uC/OS-II can run on the processors.3.2.Software architectureFig.2 shows the system software architecture, so as to dis play the data visualized,uC/GUI3.90 anduC/OS-ll is transplan ted in the system, our system contains six tasks such data acquisiti on, data tran smissi on, LCD dis play, touch scree n driver, key-press management and uC/GUI interface.First of all, we should set the task priority and the task scheduling based on the priority. It needs compiete the required driver design before the data acquisition, such as A/D driver, touch panel driver and system initialization, while the initializations include: hardware platform in itializati on, system clock in itializati on, in terr upt source con figurati on, GPIO port configuration, serial port initialization and parameter configuration, and LCD in itializati on. The p rocess is that the cha nnel module sent samp li ng comma nd to theAD channel, then to inform the receiver module it has been sent the sample start comma nd, the receiver module is ready to receive and large data will store in the storage module, after the comp leti on of the first samp li ng, cha nnel module will send the compi ete comma nd of samp li ng to the receiver module, the receiver sends an in terr upt request to the storage module to sto p the data stori ng, the n the data will dis play on the LCD touch scree n. The data acquisiti on p rocess show n in Fig.3Htii-fti Zhang ami Kang /Pfticedia Cortipitier Sciifice 222 - 22Sin-pw fCOT_U>11Liil EibnsFig,2 Software Architecture ofSyMem rig J Dnia Acquisition of Flow Chart 4・Ex perimentsThe exp erime nt of the embedded system has bee n done and data acquisiti on comes from the accelerati on of MMA7455L, which is in stalled on the bench of rotat ing mach ine. The data acquisiti on have dis played as show n in Fig.4 and Fig.5, the system can select three cha nn els to collect the vibrati on sig nal from the three directi ons of X, Y and Z-axis , and in this paper the samp li ng freque ncy is 5KHZ and we have collect the vibratio n sig nal from no rmal state of un bala need state at the same cha nn el. The result shows that our system can dis play real-time data acquisiti on andp redict the p relimi nary diag no sis rapi dly.Fig 4 Nonnal Dnta AcqiiisittonFig,5 Unbalance Data AcqmsLtion5・ConclusionThis paper has designed an embeddedsignal acquisition system for real time according to the mechanical failure occurred with high frequency of in the rotating machines. The system is based on a low cost microcontroller, Vibration signals is pi cked by the three axis acceleratio n sen sor which has the p erforma nee of low cost and high sen sitivity, and the acquisiti on data from axis x, y, and z. We have desig ned the system hardware structure, and an alyses the work ing principle of data acquisiti on module. The proposed system of uC/OS-ll realize the data task management and scheduli ng, and it is comp acted with structure and low cost, what's more the system collects the vibrati on sig nal and an alysis in real-time of the rotati ng mach in es, and then quickly gives diag no stic results.AcknowledgementsThis work was supp orted by The Nati onal Natural Scie nee Foun dati on of China(51175169); Chi na Natio nal Key Tech no logy R&D P rogram(2012BAF02B01);Planned Scie nee and Tech no logy P roject of Hunan Provin ce(2009FJ4055);Scie ntificResearch Fu nd of Hu nan P rovi ncial Education Dep artme nt(10K023).REFERENCES[1] Cheng, L., Yu, H., Research on intelligent maintenance unit of rotarymachine, Computer Integrated Manufacturing Systems, vol. 10, Issue: 10, page1196-1198, 2004.[2] Yu, C., Zhong, Ou., Zhen, D., Wei, F., .Design and ImpIementation ofMon itori ng and Man ageme nt PI atform in Embedded Fault Diag no sis System,Comp uter En gi neeri ng, vol. 34 , Issue: 8, p age 264-266, 2008.[3]Bi, D., Gui, T., Jun, S., Dynam . Behavior of a High-speed Hybrid GasBeari ng-rotor System for a Rotat ing ramjet, Journal of Vibrati on and Shock, vol. 28,Issue: 9, p age 79-80, 2009.[4] Hai, L., Jun, S., Research of Driver Based on Fault Diag no sis System DataAcquisitio n Module, Mach ine Tool& Hydraulics, vol. 38 , Issue: 13, p age 166-168,2011.⑸ Hao, W., Qin, W., Xiao, S., Op timized. Desig n of Embedded P arallel Data Acquisition System, Computer Engineering and Design, vol. 32, Issue: 5, p age 1622-1625, 2011.[6] Lei, S., Mi ng, N., Desig n and Imp leme ntati on of High Sp eed Data Acquisiti on System Based on FP GA, Compu ter Engin eeri ng, vol. 37, Issue: 19, p age221-223, 2011.[7] Chao, T., Jun, Z., Ru, G, Design of remote data acquisition and controlsystem based on Win dow CE, Microco mpu ter& Its App licati ons , vol. 30, Issue: 14,page 21-27, 2011.[8]Xiao, W., Bin, W., SMS con trolled in formatio n collectio n system based onuC/OS-II, Comp uter App licatio n, vol. 12, Issue: 31, page 29-31,2011.[9]Ti ng,Y., Zhong, C., Con structio n of Data Collectio n& Release in EmbeddedSystem, Comp uter En gi neeri ng, vol. 33, Issue: 19, p age 270-272, 2007.[10]Yo ng, W., Hao, Z., Pen g,D., Desig n and Realization of Multi-fu nction DataAcquisition System Based on ARM, Process Automation Instrumentation, vol. 32,Issue: 1, page: 13-16, 2010.[11] Betta, G., Liguori, C., Paolillo, A., A DSP-Based FFT An alyzer for the FaultDiag no sis of Rotati ng Mach ine Based on Vibrati on An alysis, IEEE Tran sacti on onIn strume ntati on and Measureme nt, vol. 51, Issue: 6, 2002.[12]Con treras-Medi na LM., Romero Tron coso RJ., Millan Almarez JR., FPGABased Mult ip le-Cha nnel Vibrati on An alyzer Embedded System for In dustrialApp licati on in Automatic Failure Detect ion, IEEE tran sacti ons on Intern ati onal and measureme nt, vol. 59, Issue: 1, p age 63-67, 2008.[13]Ch on, W., Shua ng, C., Desig n and impi eme ntati on of sig nal detecti on system based on ARM for ship borne equipment, Compu ter Engin eeri ng and Desig n, vol. 32,Issue: 4, page: 1300-1301,2011.[14]Miao, L., Tia n, W., Hong, W., Real-time An alysis of Embedded CNC SystemBased on uC/OS-ll, Comp uter En gi neeri ng, vol. 32, Issue: 22, p age 222-223, 2006.。
英语词汇研究之数据采集
英语词汇研究之数据采集作者：李梦圆来源：《中国教育技术装备》2017年第08期摘要英语词汇数据分析近年来发展较快，数据采集是词汇数据分析的基础工作。
介绍利用英语词汇分析工具专用软件采集词汇数据，包括采集范畴、数据类型和相关性质。
关键词英语词汇；英语词汇分析工具；数据采集中图分类号：H319.3 文献标识码：B文章编号：1671-489X（2017）08-0027-04Abstract Recently there has been a fairly great rapid development inthe data analysis for the English vocabulary. The data collection serves as the basis for the vocabulary data analysis. The present paperwill give an introduction to the collection of vocabulary data， inclu-ding the collection scope， the data kinds and the relative correspon-ding qualities by using the special software An Analysis Tool for the English Vocabulary.Key words English vocabulary； an analysis tool for the English vocabulary； data collection1 引言英语语言研究中词汇研究占有重要位置。
利用维普期刊资源整合服务平台[1]对国内1989—2016年期刊发表的文献进行关键词检索，英语研究类文献中词汇研究文献多达22 600篇。
其中英语词汇数据研究文献1989—1998年仅为4篇，1999—2008年增至8篇，2009—2016年则达到25篇，显示出词汇数据分析研究领域发展很快。
数据采集 英文文献
Developing conversational interfaces is a classic chicken and egg problem. In order to develop the system capabilities, one needs to have a large corpus of data for system development, training and evaluation. In order to collect data that reflect actual usage, one needs to have a system that users can speak to. Figure 1 illustrates a typical cycle of system development. For a new domain or language, one must first develop some limited natural language capabilities, thus enabling an “experimenter-in-the-loop,” or wizard-of-oz, data collection paradigm, in which an experimenter types the spoken sentences to the system, after removing spontaneous speech artifacts. This process has the advantage of eliminating potential recognition errors. The resulting data are then used for the development and training of the speech recognition and natural language components. As these components begin to mature, it becomes feasible to collect more data using the “system-in-the-loop,” or wizardless, paradigm, which is both more realistic and more cost effective. Performance evaluation using newly collected data will facilitate system refinement.
关于爬虫的外文文献
关于爬虫的外文文献爬虫技术作为数据采集的重要手段，在互联网信息挖掘、数据分析等领域发挥着重要作用。
本文将为您推荐一些关于爬虫的外文文献，以供学习和研究之用。
1."Web Scraping with Python: Collecting Data from the Modern Web"作者：Ryan Mitchell简介：本书详细介绍了如何使用Python进行网页爬取，从基础概念到实战案例，涵盖了许多常用的爬虫技术和工具。
通过阅读这本书，您可以了解到爬虫的基本原理、反爬虫策略以及如何高效地采集数据。
2."Scraping the Web: Strategies and Techniques for Data Mining"作者：Dmitry Zinoviev简介：本书讨论了多种爬虫策略和技术，包括分布式爬虫、增量式爬虫等。
同时，还介绍了数据挖掘和文本分析的相关内容，为读者提供了一个全面的爬虫技术学习指南。
3."Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, Pinterest, and More"作者：Matthew A.Russell简介：本书主要关注如何从社交媒体平台（如Facebook、Twitter 等）中采集数据。
通过丰富的案例，展示了如何利用爬虫技术挖掘社交媒体中的有价值信息。
4."Crawling the Web: An Introduction to Web Scraping and Data Mining"作者：Michael H.Goldwasser, David Letscher简介：这本书为初学者提供了一个关于爬虫技术和数据挖掘的入门指南。
内容包括：爬虫的基本概念、HTTP协议、正则表达式、数据存储和数据分析等。
数据分析外文文献+翻译
数据分析外文文献+翻译文献1：《数据分析在企业决策中的应用》该文献探讨了数据分析在企业决策中的重要性和应用。
研究发现，通过数据分析可以获取准确的商业情报，帮助企业更好地理解市场趋势和消费者需求。
通过对大量数据的分析，企业可以发现隐藏的模式和关联，从而制定出更具竞争力的产品和服务策略。
数据分析还可以提供决策支持，帮助企业在不确定的环境下做出明智的决策。
因此，数据分析已成为现代企业成功的关键要素之一。
文献2：《机器研究在数据分析中的应用》该文献探讨了机器研究在数据分析中的应用。
研究发现，机器研究可以帮助企业更高效地分析大量的数据，并从中发现有价值的信息。
机器研究算法可以自动研究和改进，从而帮助企业发现数据中的模式和趋势。
通过机器研究的应用，企业可以更准确地预测市场需求、优化业务流程，并制定更具策略性的决策。
因此，机器研究在数据分析中的应用正逐渐受到企业的关注和采用。
文献3：《数据可视化在数据分析中的应用》该文献探讨了数据可视化在数据分析中的重要性和应用。
研究发现，通过数据可视化可以更直观地呈现复杂的数据关系和趋势。
可视化可以帮助企业更好地理解数据，发现数据中的模式和规律。
数据可视化还可以帮助企业进行数据交互和决策共享，提升决策的效率和准确性。
因此，数据可视化在数据分析中扮演着非常重要的角色。
翻译文献1标题： The Application of Data Analysis in Business Decision-making The Application of Data Analysis in Business Decision-making文献2标题： The Application of Machine Learning in Data Analysis The Application of Machine Learning in Data Analysis文献3标题： The Application of Data Visualization in Data Analysis The Application of Data Visualization in Data Analysis翻译摘要：本文献研究了数据分析在企业决策中的应用，以及机器研究和数据可视化在数据分析中的作用。
数据采集外文文献翻译中英文
数据采集外文文献翻译(含：英文原文及中文译文)文献出处：Txomin Nieva. DATA ACQUISITION SYSTEMS [J]. Computers in Industry, 2013, 4(2):215-237.英文原文DATA ACQUISITION SYSTEMSTxomin NievaData acquisition systems, as the name implies, are products and/or processes used to collect information to document or analyze some phenomenon. In the simplest form, a technician logging the temperature of an oven on a piece of paper is performing data acquisition. As technology has progressed, this type of process has been simplified and made more accurate, versatile, and reliable through electronic equipment. Equipment ranges from simple recorders to sophisticated computer systems. Data acquisition products serve as a focal point in a system, tying together a wide variety of products, such as sensors that indicate temperature, flow, level, or pressure. Some common data acquisition terms are shown below.Data collection technology has made great progress in the past 30 to 40 years. For example, 40 years ago, in a well-known college laboratory, the device used to track temperature rises in bronze made of helium was composed of thermocouples, relays, interrogators, a bundle of papers, anda pencil.Today's university students are likely to automatically process and analyze data on PCs. There are many ways you can choose to collect data. The choice of which method to use depends on many factors, including the complexity of the task, the speed and accuracy you need, the evidence you want, and more. Whether simple or complex, the data acquisition system can operate and play its role.The old way of using pencils and papers is still feasible for some situations, and it is cheap, easy to obtain, quick and easy to start. All you need is to capture multiple channels of digital information (DMM) and start recording data by hand.Unfortunately, this method is prone to errors, slower acquisition of data, and requires too much human analysis. In addition, it can only collect data in a single channel; but when you use a multi-channel DMM, the system will soon become very bulky and clumsy. Accuracy depends on the level of the writer, and you may need to scale it yourself. For example, if the DMM is not equipped with a sensor that handles temperature, the old one needs to start looking for a proportion. Given these limitations, it is an acceptable method only if you need to implement a rapid experiment.Modern versions of the strip chart recorder allow you to retrieve data from multiple inputs. They provide long-term paper records of databecause the data is in graphic format and they are easy to collect data on site. Once a bar chart recorder has been set up, most recorders have enough internal intelligence to operate without an operator or computer. The disadvantages are the lack of flexibility and the relative low precision, often limited to a percentage point. You can clearly feel that there is only a small change with the pen. In the long-term monitoring of the multi-channel, the recorders can play a very good role, in addition, their value is limited. For example, they cannot interact with other devices. Other concerns are the maintenance of pens and paper, the supply of paper and the storage of data. The most important is the abuse and waste of paper. However, recorders are fairly easy to set up and operate, providing a permanent record of data for quick and easy analysis.Some benchtop DMMs offer selectable scanning capabilities. The back of the instrument has a slot to receive a scanner card that can be multiplexed for more inputs, typically 8 to 10 channels of mux. This is inherently limited in the front panel of the instrument. Its flexibility is also limited because it cannot exceed the number of available channels. External PCs usually handle data acquisition and analysis.The PC plug-in card is a single-board measurement system that uses the ISA or PCI bus to expand the slot in the PC. They often have a reading rate of up to 1000 per second. 8 to 16 channels are common, and the collected data is stored directly in the computer and then analyzed.Because the card is essentially a part of the computer, it is easy to establish the test. PC-cards are also relatively inexpensive, partly because they have since been hosted by PCs to provide energy, mechanical accessories, and user interfaces. Data collection optionsOn the downside, the PC plug-in cards often have a 12-word capacity, so you can't detect small changes in the input signal. In addition, the electronic environment within the PC is often susceptible to noise, high clock rates, and bus noise. The electronic contacts limit the accuracy of the PC card. These plug-in cards also measure a range of voltages. To measure other input signals, such as voltage, temperature, and resistance, you may need some external signal monitoring devices. Other considerations include complex calibrations and overall system costs, especially if you need to purchase additional signal monitoring devices or adapt the PC card to the card. Take this into account. If your needs change within the capabilities and limitations of the card, the PC plug-in card provides an attractive method for data collection.Data electronic recorders are typical stand-alone instruments that, once equipped with them, enable the measurement, recording, and display of data without the involvement of an operator or computer. They can handle multiple signal inputs, sometimes up to 120 channels. Accuracy rivals unrivalled desktop DMMs because it operates within a 22 word, 0.004 percent accuracy range. Some data electronic automatic recordershave the ability to measure proportionally, the inspection result is not limited by the user's definition, and the output is a control signal.One of the advantages of using data electronic loggers is their internal monitoring signals. Most can directly measure several different input signals without the need for additional signal monitoring devices. One channel can monitor thermocouples, RTDs, and voltages.Thermocouples provide valuable compensation for accurate temperature measurements. They are typically equipped with multi-channel cards. Built-in intelligent electronic data recorder helps you set the measurement period and specify the parameters for each channel. Once you set it all up, the data electronic recorder will behave like an unbeatable device. The data they store is distributed in memory and can hold 500,000 or more readings.Connecting to a PC makes it easy to transfer data to a computer for further analysis. Most data electronic recorders can be designed to be flexible and simple to configure and operate, and most provide remote location operation options via battery packs or other methods. Thanks to the A/D conversion technology, certain data electronic recorders have a lower reading rate, especially when compared with PC plug-in cards. However, a reading rate of 250 per second is relatively rare. Keep in mind that many of the phenomena that are being measured are physical in nature, such as temperature, pressure, and flow, and there are generallyfewer changes. In addition, because of the monitoring accuracy of the data electron loggers, a large amount of average reading is not necessary, just as they are often stuck on PC plug-in cards.Front-end data acquisition is often done as a module and is typically connected to a PC or controller. They are used in automated tests to collect data, control and cycle detection signals for other test equipment. Send signal test equipment spare parts. The efficiency of the front-end operation is very high, and can match the speed and accuracy with the best stand-alone instrument. Front-end data acquisition works in many models, including VXI versions such as the Agilent E1419A multi-function measurement and VXI control model, as well as a proprietary card elevator. Although the cost of front-end units has been reduced, these systems can be very expensive unless you need to provide high levels of operation, and finding their prices is prohibited. On the other hand, they do provide considerable flexibility and measurement capabilities.Good, low-cost electronic data loggers have the right number of channels (20-60 channels) and scan rates are relatively low but are common enough for most engineers. Some of the key applications include:•product features•Hot die cutting of electronic products•Test of the environmentEnvironmental monitoring•Composition characteristics•Battery testBuilding and computer capacity monitoringA new system designThe conceptual model of a universal system can be applied to the analysis phase of a specific system to better understand the problem and to specify the best solution more easily based on the specific requirements of a particular system. The conceptual model of a universal system can also be used as a starting point for designing a specific system. Therefore, using a general-purpose conceptual model will save time and reduce the cost of specific system development. To test this hypothesis, we developed DAS for railway equipment based on our generic DAS concept model. In this section, we summarize the main results and conclusions of this DAS development.We analyzed the device model package. The result of this analysis is a partial conceptual model of a system consisting of a three-tier device model. We analyzed the equipment project package in the equipment environment. Based on this analysis, we have listed a three-level item hierarchy in the conceptual model of the system. Equipment projects are specialized for individual equipment projects.We analyzed the equipment model monitoring standard package in the equipment context. One of the requirements of this system is the ability to use a predefined set of data to record specific status monitoring reports. We analyzed the equipment project monitoring standard package in the equipment environment. The requirements of the system are: (i) the ability to record condition monitoring reports and event monitoring reports corresponding to the items, which can be triggered by time triggering conditions or event triggering conditions; (ii) the definition of private and public monitoring standards; (iii) Ability to define custom and predefined train data sets. Therefore, we have introduced the "monitoring standards for equipment projects", "public standards", "special standards", "equipment monitoring standards", "equipment condition monitoring standards", "equipment project status monitoring standards and equipment project event monitoring standards, respectively Training item triggering conditions, training item time triggering conditions and training item event triggering conditions are device equipment trigger conditions, equipment item time trigger conditions and device project event trigger condition specialization; and training item data sets, training custom data Sets and trains predefined data sets, which are device project data sets, custom data sets, and specialized sets of predefined data sets.Finally, we analyzed the observations and monitoring reports in the equipment environment. The system's requirement is to recordmeasurements and category observations. In addition, status and incident monitoring reports can be recorded. Therefore, we introduce the concept of observation, measurement, classification observation and monitoring report into the conceptual model of the system.Our generic DAS concept model plays an important role in the design of DAS equipment. We use this model to better organize the data that will be used by system components. Conceptual models also make it easier to design certain components in the system. Therefore, we have an implementation in which a large number of design classes represent the concepts specified in our generic DAS conceptual model. Through an industrial example, the development of this particular DAS demonstrates the usefulness of a generic system conceptual model for developing a particular system.中文译文数据采集系统Txomin Nieva数据采集系统, 正如名字所暗示的, 是一种用来采集信息成文件或分析一些现象的产品或过程。
粤教版(2019)高中信息技术必修1 数据与计算 第二单元《知识与数字化学习》课时练习(解析版)
B.实验中的U=2905.67607341mV表示的是一个具体信息
C.通过实验，我们验证了I=U/R这一知识
D.经过实验验证，我们得出电流I与电阻R成反比例关系，这是智慧
【答案】D
【解析】
【详解】本题考查数据、信息与知识。
8.小明正在与班内其他同学共同完成一个项目作业，每个同学按照分工需完成不同的部分和任务，并最终汇总形成本组的项目报告。下列（）工具更适合于这样的团队合作任务。
A.微信B.有道云协作C.网络画板D.Xmind
【答案】B
【解析】
【详解】本题考查的是项目学习工具。有道云协作是一款基于资料管理和沟通的团队协作工具,与个人笔记无缝连接;团队成员可以对同篇文档共同编辑,让多人协作成为现实。Xmind是思维导图工具。故选项B正确。
10.网络环境下的自主探究学习主要体现了网络的（）特点。
A.共享和交流B.开放性
C.信息容量大D.信息传播交互性
【答案】A
【解析】
【详解】本题考查网络特点 相关知识点
网络环境下的自主探究学习主要体现了网络的共享和交流特点。故本题选A选项
11.在教科书中利用Python探究电流和电压、电阻的关系实验里，下列说法错误的是（）。
18.下列选项中，可以实现实时数据可视化的是（）
A.导航地图B.标签云图C.思维导图D.统计图表
【答案】A
【解析】
【分析】
【详解】本题主要考查数据可视化方法。导航地图可以实现实时数据可视化，标签云图、思维导图、统计图表是静态非实时的可视化工具，故本题选A选项。
19.传感器的主要作用是( )
A.数据采集B.数据分析C.数据传递D.数据发布
数据采集外文翻译
中文1950字附录附录A外文资料Data CollectionAt present,the management of China’s colleges and universities’apartments are developing toward standardization and market development,accidents have occurred in electricity,while some colleges and universities have installed apart ment energy metering control system,however,these systems monitor the prevale nce of low level,billing accuracy is low,electricity-sharing,the network number o f the drawbacks of low extent.Therefore，improving the Energy Measurement m onitoring device has become more urgent．The issue of student hostels in colle ges and universities to monitor energy metering system to study，design the st udent hostels in colleges and universities of the electricity data collector apartm ent.Data acquisition, also known as data acquisition, is the use of a device th at collect data from outside the system and enter into an interface within the s ystem.Data acquisition technology is widely cited in the various fields.Such as camera, microphone, all data collection tools.Data is being collected has been c onverted to electrical signals of various physical quantities such as temperature, water level, wind speed, pressure, etc., can be analog, it can be digital.Sampl e collection generally means that a certain time interval (called the sampling p eriod) to repeat the same point of data collection.The data collected are mostly instantaneous value, but also a feature within a certain period of time value.A ccurate data measurement is the basis for data collection.Data measurement met hod of contact and non-contact detection elements varied.Regardless of which method and components are measured object does not affect the status and me asurement environment as a precondition to ensure the accuracy of the data.Ver y broad meaning of data collection, including continuous physical hold the collection across the state.In computer-aided mapping, surveying and mapping, desi gn, digital graphics or image data acquisition process may also be called, this time to be collected is the geometric volume (or include physical quantities, su ch as gray)data.[1] In today's fast-growing Internet industry, data collection has been widely used in the field of Internet and distributed data acquisition field has undergone important changes.First, the distributed control applications in i ntelligent data acquisition system at home and abroad have made great progres s.Second, the bus-compatible data acquisition plug-in number is increasing, and personal computer-compatible data acquisition system the number is increasing. Various domestic and international data collection machine has come out, the d ata acquisition into a new era.Digital signal processor (DSP) to the high-speed data processing ability an d strong peripherals interface, more and more widely used in power quality an alysis field, in order to improve the real-time and reliability.The DSP and micr ocomputer as the center of the system, realize the power system signal collecti on and analysis. This paper based on the FFT algorithm with window interpola tion electric system harmonic analysis, improves the accuracy of the power qua lity parameters. In electricity parameter acquisition circuit, by highaccuracy tran sformer and improve software synchronous communication sampling method to conduct electricity parameters of the acquisition.The system consists of two main components, mainly complete data acquis ition and logic control.To synchronous sampling and A/D converter circuit pri ority . The DSP development board(SY-5402EVM),complete data processing. T HE signal after transformer, op-amp into A/D converter, using DSP multi-chann el buffer (McBSP) and serial port (A/D connected, data collection and operatio ns. At the same time, adopt PLL circuit implementation synchronous sampling, can prevent well due to sampling synchronization and cause the measuring err or. The overall system diagram of the A/D converter chooses the Analog to pr oduce stats redetect (AD) company AD73360. The chip has six analogue input channel, each channel can output 16 the digital quantity. Six channel simultan eous sampling, and conversion, timeshare transmission, effectively reduce gener ated due to the sampling time different phase error. SY - 5402EVM on-board DSP chip is TI company's 16 fixed-point digital signal processor TMS320VC54 02. It has high costperformance and provide high-speed, bidirectional, multi-channel belt cushion, be used to serial port with system of other serial devices di rectly interface.The realization method of ac sample：In the field of power quality analysi s,The fast Fourier transform (FFT) algorithm analysis of electric system harmon ic is commonly used.and the FFT algorithm to signal a strict requirements syn chronous sampling. The synchronous sampling influence: it's difficult to accomp lish synchronous sampling and integer a period truncation in the actual measur ement, so there was a affect the measurement accuracy of the frequency spectr um leakage problem. The signal has to deal with through sampling and A/D c onversion get limited long digital sequence,the original signal multiplied by A r ectangular window to truncated. Time-domain truncation will cause the detuning frequency domain, spectrum leakage occurs. In the synchronous sampling, bec ause the actual signal every harmonic component can't exactly landed in freque ncy resolution point in, but fall between the frequency resolution points. But F FT spectrum is discrete, only in all sampling points, while in other places of s pectrum is not. Such through FFT and cannot directly get every harmonic com ponent, but only the accurate value in neighboring frequency resolution point v alue to approximate instead of, can cause the fence effect error.The realization method of synchronous sampling signal：According to provide different ways of sampling signal, synchronous sampling method and divided into software sync hronous sampling method and hardware synchronous sampling method is two k inds. Software is synchronous sampling method by micro controller (MCU) or DSP provide synchronized sampling pulse, first measured the measured signal, the sa mpling interval period T Δ T = T/N (N for week of sampling points), T hus the count value determined timer,Use timing interrupt way realization sync hronous sampling. The advantage of this method is no hardware synchronous c ircuit, simple structure .This topic will be the eventual realization of access to embedded systems，the realization of the power measurement and monitoring,m onitoring system to meet the electricity network,intelligence requirement,it prom ote the development of remote monitoring services,bringing a certain degree of socio.economic effectiveness.On the fundamental reactive current and harmonic current detection, there are mainly 2 ways: First, the instantaneous reactive power theory based method, the second is based on adaptive cancellation techniques.In addition, there areother non-mainstream approach, such as fast Fourier transform method, wavelet transform.Instantaneous power theory based on the method of offensive principles ar e: a three-phase current detection and load phase voltage A, the coordinate tra nsformation, two-phase stationary coordinate system the current value, calculate the instantaneous active and instantaneous reactive power ip iq,then after coor dinate transformation, three-phase fundamental active current, with the final loa d current minus the fundamental current, active power and harmonic currents a re fundamental iah, ibhi, ich.From:Principles of Data Acquisitio数据采集目前，我国高校公寓管理正在向着正规化、市场化发展，在不断提高学生方便用电的同时，用电事故频有发生，虽然部分高校公寓已经安装了电能计量监控系统，但这些系统普遍存在着监控程度低、计费精度不高、电费均分、网络程度低等诸多端。
统计学(中英文)_ch01
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc.
Chap 1-12
∑X
n
i
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc.
Chap 1-8
Inferential Statistics 推断统计
Estimation 估计 e.g., Estimate the population mean weight using the sample mean weight 例如：利用采样的平均重量估计人口的平均体 重 Hypothesis testing 假设检验 e.g., Test the claim that the population mean weight is 120 pounds 例如：根据测试的要求，人口平均体重是120 磅
英文翻译乃自己所做， 英文翻译乃自己所做，有错误 之处请自行查证。 之处请自行查证。
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc.
Chap 1-1
Business Statistics, A First Course
Defined descriptive vs. inferential statistics 描述性统计和推理统计 Reviewed data types 回顾数据类型
♦ ♦ ♦ ♦
Categorical vs. Numerical data 绝对的和数值的数据 Discrete vs. Continuous data 离散的和连续的数据
多路数据采集与分析系统的设计及应用 外文翻译 外文文献 英文文献
附录五中英文资料Multi-channel data collection and analysisof the design and applicationAbstract:The Paper mainly introduces a multichannel data acquisition and analysis system composed of one PC and one measuring instrument. The system can test eight products parallelly. It reduces the test cost and improves work efficiency. The paper also gives the hardware structure and software flow diagr am of the system. The application in the gyro test is also introduced briefly.Key words:communication prot;data acquisition; gyro; testWith the development of computer technology and the digital measuring instrument, usually by computer and measuring instruments to communicate with each other in real-time data collection and use of computer powerful computing capability to conduct the analysis of the data processing. Particularly in the large volume of data, measuring the length of time occasions, such as the Gyro-tilt test, using computer for automatic control of measuring instruments, automatic data acquisition and analysis it is particularly important, can save a lot of manpower and material resources to improve work efficiency, reduce costs , The conventional method of testing is usually a measuring instrument at the same time can only test a product, namely a computer and a measuring instrument test system can only be composed of serial testing. To test multiple products at the same time, they need multiple systems, testing products in large volume, low efficiency, such as the composition of several sets of test system, an increase of cost. First on a machine with a PC and a measuring instrument consisting of 8-way data collection and analysissystem, which can carry out multiple sets of product testing, at no additional cost on the basis of a computer give full play to the advantages of automatic test, Improve work efficiency.1 PrincipleThe system hardware and software system. A PC through a RS232 port and a measuring instrument connected, PC-parallel port (LPT) and an 8-way channel selector attached to a 8-way connector will channel selector were connected with a number of test products.The working principle as shown in Figure 1. The course of testing, computer through the parallel port 8-way control channel selection, were open different channels, each channel for data transmission by choosing to measuring instruments, measuring instruments through the RS232 port to the computer data sent to save, A complete cycle of all channels of data collection, and this has also tested a number of product features.Figure 1 system block diagram of workThroughout the course of testing, all the control operations have completed the software automatically, without human intervention.2 hardware designThe system is mainly to use the computer onboard RS232 communication ports and digital measuring instrument of communication port connecting communications, re-use LPT parallel port on a 8-way channel selector for access control. 8-way channel of choice for an 8-elected one of analog switches and related circuit, the control signals from the computer's parallel port to provide and meet shown in table 1.Table1 The relation between channel selection and port output Communications port output Binary code Channel selection selectchannel0 000 11 001 22 010 33 011 44 100 55 101 66 110 77 111 88-way channel selector industry can use the SCM, subject to additional controls, select RS232 serial port as data transmission, because the RS232 port is the computer and measuring instruments on the standard configuration, communicate with each other without additional hardware , Easy to use. In addition, a serial communication-only a bit, with only a standard data-voltage potential, hence more difficult in data errors. In a parallel port to transfer data 8-bit, data transmission speed, but the data vulnerable to interference. Transmission distance in a shorter amount of data transmission larger circumstances, may be parallel port (such as GPIB, LPT, etc.) to communicate. In addition, since LPT parallel port may signal transmission, channel selection is suitable for the control port.System in the course of work, good access control modules and data acquisition module synchronization is particularly important because different channels of datastorage needs of the corresponding data buffer pool, which is controlled by software.3 software designThe whole system software design is the most important part. Software system from the bottom of the communication protocol can be divided into functional three-tier module and user interface. Software design in the use of multi-threaded Windows technology, the technology for data collection procedures can effectively accelerate the reaction time and increase the efficiency of implementation. The procedures used in a separate thread for data collection, so the guaranteed maximum energy collection of real-time; using another thread at the same time data processing, such procedures to avoid a single-threaded the same time only the implementation of a functional deficiencies. Especially when the amount of data collection, data processing task, using multi-threaded technology will greatly improve the efficiency of the system as a whole.3.1 Data Acquisition ModuleData acquisition modules to eight channels of data in a cycle of all the acquisition to the computer, and save the channel, and the corresponding data in the buffer. Its procedures diagram shown in Figure 2.Fig 2 Flow diagram of data acquisitionAt the beginning of procedures, with the choice of control and store data buffer at the same time to switch to the same channel, 8-way data collection cycle and command judgement, in the end not received orders, has recycling collection to do.Multi-channel data acquisition process the data vulnerable to interference, especially in the fast-channel switching, the data vulnerable to fluctuations, as shown in Figure 3. At this time if the data collection, will be collecting the wrong data, the need to add some software algorithms to prevent this from happening. If we develop the automated data tracking algorithm to automatically track each channel data to determine whether the channel in a stable state, and only the stability of dataacquisition, the volatility of other data. In addition, the software can also add some filtering algorithm (such as limiting filter, etc.) to filter out man-made interference or other factors caused by the mutation data. Limiting filter for（1）Figure 3 channel switching, the data volatilityWhen the new collected data and the data before a difference to the absolute value of more than one set of values that the data is invalid, and the previous data from the current data.3.2 Data Analysis ModuleIn the data analysis module can be added if the algorithm analysis, graphics display and print output, and other useful features, such as gyroscopes and stability in the standard deviation algorithm can function in the course of testing real-time calculation of zero stability, and through chart shows. Zero stability calculation formula as follows:（2）According to first-(2) to prepare an algorithm function, and then call in the analysis module. Analysis module diagram of the procedure shown in Figure 4.Figure 4 data analysis process flow chartBecause the system uses multi-threaded technology, in the cycle of operation and will not affect the acquisition module's operation. The module also in its algorithm in the function of any expansion, forming a algorithm to adapt to different procedures for data analysis.In addition, software design, a friendly user interface is necessary in the process of the functions from the package, through a unified interface to users, to reduce operating difficulties and enhance efficiency.4 system test resultsFigure 5 to 8 in the analysis of data acquisition systems, at the same time two three-axis gyro and a single axis gyroscope total of seven road test data of the situation. Its precise data collection, data analysis can be conducted at the same time, and through real-time charts, user-friendly, easy to operate.Figure 5 8 Data Collection and Analysis System5 ConclusionMulti-channel data acquisition and analysis system for the hardware requirements simple, easy to set up, can be applied to various tests occasions, it can also test multiple products, thereby reducing the cost and enhance efficiency. As a result of a multi-threaded technology, the speed of data acquisition systems and hardware only (instrument) and the response speed of the speed of Communication. With the collection and analysis software algorithm has nothing to do.PAD programming tools can be used to develop a data collection, data analysis, graphics display and print output, and other powerful features and friendly user interface of our software. Software modular design and easy to carry out expansion, according to different algorithm for data analysis at the request of upgrades, and hardware can remain the same. The system give full play to the use of computers and measuring instruments of mutual communication, automation and test advantage.多路数据采集与分析系统的设计及应用摘要：介绍了用一台PC机和一台测量仪表组成的8路数据采集与分析系统。
论文必备中英文献数据库大全
论文必备——中英文献数据库大全终身受用，写论文需要的参考文献都在这里了！一、中文数据库中国最大的数据库，内容较全。
收录了5000多种中文期刊，1994年以来的数百万篇文章，并且目前正以每天数千篇的速度进行更新。
阅读全文需在网站主页下载CAJ全文浏览器。
文献收录1989年以来的全文。
只是扫描质量有点差劲，1994年以后的数据不如CNKI全。
阅读全文需下载维谱全文浏览器，约7M。
目前，以下站点提供免费检索3、万方数据库收录了核心期刊的全文，文件为pdf格式，阅读全文需Acrobat Reader 浏览器。
二、外文全文站点(所有外文数据库世界上第二大免费数据库（最大的免费数据库没有生物学、农业方面的文献），该网站提供部分文献的免费检索，和所用文献的超级链接，免费文献在左边标有FREE.Elsevier Science是荷兰一家全球著名的学术期刊出版商，每年出版大量的农业和生物科学、化学和化工、临床医学、生命科学、计算机科学、地球科学、工程、能源和技术、环境科学、材料科学、航空航天、天文学、物理、数学、经济、商业、管理、社会科学、艺术和人文科学类的学术图书和期刊，目前电子期刊总数已超过1 200多种（其中生物医学期刊499种），其中的大部分期刊都是SCI、EI等国际公认的权威大型检索数据库收录的各个学科的核心学术期刊。
Wiley InterScience是John Wiely & Sons 公司创建的动态在线内容服务，1997年开始在网上开通。
通过InterScience，Wiley公司以许可协议形式向用户提供在线访问全文内容的服务。
Wiley InterScience收录了360多种科学、工程技术、医疗领域及相关专业期刊、30多种大型专业参考书、13种实验室手册的全文和500多个题目的Wiley学术图书的全文。
其中被SCI收录的核心期刊近200种。
(注册一个用户名密码，下次直接用注册的用户名密码进去，不用代理照样能看文章全文，Willey注册一个，就可以免费使用CP了，那可是绝对好的Protocols )施普林格出版集团年出新书2000多种，期刊500多种，其中400多种期刊有电子版。
基于STM32的数据采集系统英文文献
traditional mainstream technology in embedded systems, and the collecting data toward the direction of high real-time, multi-parameter, high-precision, while data storage become large capacity, more miniaturization and portable, and the development of multicommunication mode and long-distance for data transmission. So as to meet the actual acquisition system multitasking requirements, this article has designed based on STM32 micro-controller uC/OS-II system of signal acquisition system. Therefore, in order to meet the actual acquisition system multitask requirements, this novelty of this article has designed a signal acquisition system in micro-controller uC/OS-II based on STM32.
基于 STM32的数据采集系统英文 文献
ቤተ መጻሕፍቲ ባይዱ
Design of the Data Acquisition System Based on STM32
中英文参考文献
中英文参考文献
中英文参考文献是学术研究中必不可少的部分，用于向读者提供关于研究背景、方法和结果的详细信息。
以下是一些中英文参考文献的示例：
中文参考文献：
1. 张三. (2019). 机器学习算法在数据挖掘中的应用研究. 中国计算机学会.
2. 李四, 王五, & 赵六. (2018). 人工智能的发展及其应用. 北京: 电子工业出版社.
3. 吕七, 刘八, & 陈九. (2017). 自然语言处理技术的最新进展. 人工智能, 25(3), 28-35.
英文参考文献：
1. Zhang, S. (2019). Application of machine learning algorithms in data mining. China Computer Federation.
2. Li, S., Wang, W., & Zhao, L. (2018). The development and applications of artificial intelligence. Beijing: Electronics Industry Press.
3. Lyu, Q., Liu, B., & Chen, J. (2017). The latest advances in natural language processing technology. Artificial Intelligence, 25(3), 28-35.。
物联网中英文对照外文翻译文献
物联网中英文对照外文翻译文献一、引言物联网（Internet of Things，IoT）作为当今信息技术领域的热门话题，正在深刻地改变着我们的生活和工作方式。
它通过将各种物理设备与互联网连接，实现了设备之间的智能交互和数据共享，为人们带来了前所未有的便利和效率。
在这一领域，中英文对照的外文翻译文献对于推动技术的发展和交流具有重要的意义。
二、物联网的概念和特点（一）物联网的定义物联网是指通过各种信息传感设备，实时采集任何需要监控、连接、互动的物体或过程等各种需要的信息，与互联网结合形成的一个巨大网络。
其目的是实现物与物、人与物之间的智能化识别、定位、跟踪、监控和管理。
（二）物联网的特点1、全面感知通过各种传感器和智能设备，实现对物理世界的全面感知和数据采集。
2、可靠传输利用多种通信技术，确保数据的稳定、安全和快速传输。
3、智能处理运用大数据分析、人工智能等技术，对采集到的数据进行处理和分析，以实现智能化的决策和控制。
三、物联网的关键技术（一）传感器技术传感器是物联网获取信息的基础，能够将物理世界的各种信号转换为电信号。
（二）射频识别技术（RFID）通过无线电波实现对物体的自动识别和数据采集。
（三）无线通信技术包括 WiFi、蓝牙、Zigbee 等，为物联网设备之间的通信提供支持。
（四）云计算和大数据技术用于处理和存储海量的物联网数据，并从中挖掘有价值的信息。
四、物联网的应用领域（一）智能家居实现家庭设备的智能化控制和管理，提高生活的舒适性和便利性。
（二）智能交通优化交通流量，提高交通运输的安全性和效率。
（三）工业物联网提升工业生产的自动化水平和管理效率，降低成本。
（四）医疗物联网改善医疗服务质量，实现患者的远程监护和医疗资源的优化配置。
五、物联网中英文对照外文翻译文献的重要性（一）促进技术交流帮助不同国家和地区的研究人员和工程师更好地了解彼此的研究成果和技术进展。
（二）加速技术创新为国内的研究和开发提供新的思路和方法，推动物联网技术的创新发展。
数据采集系统中英文对照外文翻译文献
中英文对照外文翻译(文档含英文原文和中文翻译)Data Acquisition SystemsData acquisition systems are used to acquire process operating data and store it on，secondary storage devices for later analysis. Many or the data acquisition systems acquire this data at very high speeds and very little computer time is left to carry out any necessary, or desirable, data manipulations or reduction. All the data are stored on secondary storage devices and manipulated subsequently to derive the variables ofin-terest. It is very often necessary to design special purpose data acquisition systems and interfaces to acquire the high speed process data. This special purpose design can be an expensive proposition.Powerful mini- and mainframe computers are used to combine the data acquisition with other functions such as comparisons between the actual output and the desirable output values, and to then decide on the control action which must be taken to ensure that the output variables lie within preset limits. The computing power required will depend upon the type of process control system implemented. Software requirements for carrying out proportional, ratio or three term control of process variables are relatively trivial, and microcomputers can be used to implement such process control systems. It would not be possible to use many of the currently available microcomputers for the implementation of high speed adaptive control systems which require the use of suitable process models and considerable online manipulation of data.Microcomputer based data loggers are used to carry out intermediate functions such as data acquisition at comparatively low speeds, simple mathematical manipulations of raw data and some forms of data reduction. The first generation of data loggers, without any programmable computing facilities, was used simply for slow speed data acquisition from up to one hundred channels. All the acquired data could be punched out on paper tape or printed for subsequent analysis. Such hardwired data loggers are being replaced by the new generation of data loggers which incorporate microcomputers and can be programmed by the user. They offer an extremely good method of collecting the process data, using standardized interfaces, and subsequently performing the necessary manipulations to provide the information of interest to the process operator. The data acquired can be analyzed to establish correlations, if any, between process variables and to develop mathematical models necessary for adaptive and optimal process control.The data acquisition function carried out by data loggers varies from one to 9 in system to another. Simple data logging systems acquire data from a few channels while complex systems can receive data from hundreds, or even thousands, of input channels distributed around one or more processes. The rudimentary data loggers scan the selected number of channels, connected to sensors or transducers, in a sequential manner and the data are recorded in a digital format. A data logger can be dedicated in the sense that it can only collect data from particular types of sensors and transducers. It is best to use a nondedicated data logger since any transducer or sensor can be connected to the channels via suitable interface circuitry. This facility requires the use of appropriate signal conditioning modules.Microcomputer controlled data acquisition facilitates the scanning of a large number of sensors. The scanning rate depends upon the signal dynamics which means that some channels must be scanned at very high speeds in order to avoid aliasing errors while there is very little loss of information by scanning other channels at slower speeds. In some data logging applications the faster channels require sampling at speeds of up to 100 times per second while slow channels can be sampled once every five minutes. The conventional hardwired, non-programmable data loggers sample all the channels in a sequential manner and the sampling frequency of all the channels must be the same. This procedure results in the accumulation of very large amounts of data, some of which is unnecessary, and also slows down the overall effective sampling frequency. Microcomputer based data loggers can be used to scan some fast channels at a higher frequency than other slow speed channels.The vast majority of the user programmable data loggers can be used to scan up to 1000 analog and 1000 digital input channels. A small number of data loggers, with a higher degree of sophistication, are suitable for acquiring data from up to 15, 000 analog and digital channels. The data from digital channels can be in the form of Transistor- Transistor Logic or contact closure signals. Analog data must be converted into digital format before it is recorded and requires the use of suitable analog to digital converters (ADC)．The characteristics of the ADC will define the resolution that can be achieved and the rate at which the various channels can be sampled. An in-crease in the number of bits used in the ADC improves the resolution capability. Successive approximation ADC's arefaster than integrating ADC's. Many microcomputer controlled data loggers include a facility to program the channel scanning rates. Typical scanning rates vary from 2 channels per second to 10, 000 channels per second.Most data loggers have a resolution capability of ±0.01% or better, It is also pos-sible to achieve a resolution of 1 micro-volt. The resolution capability, in absolute terms, also depends upon the range of input signals, Standard input signal ranges are 0-10 volt, 0-50 volt and 0-100 volt. The lowest measurable signal varies form 1 t, volt to 50, volt. A higher degree of recording accuracy can be achieved by using modules which accept data in small, selectable ranges. An alternative is the auto ranging facil-ity available on some data loggers.The accuracy with which the data are acquired and logged-on the appropriate storage device is extremely important. It is therefore necessary that the data acquisi-tion module should be able to reject common mode noise and common mode voltage. Typical common mode noise rejection capabilities lie in the range 110 dB to 150 dB. A decibel (dB) is a tern which defines the ratio of the power levels of two signals. Thus if the reference and actual signals have power levels of N, and Na respectively, they will have a ratio of n decibels, wheren＝10 Log10（Na /Nr）Protection against maximum common mode voltages of 200 to 500 volt is available on typical microcomputer based data loggers.The voltage input to an individual data logger channel is measured, scaled and linearised before any further data manipulations or comparisons are carried out.In many situations, it becomes necessary to alter the frequency at which particu-lar channels are sampled depending upon the values of data signals received from a particular input sensor. Thus a channel might normally be sampled once every 10 minutes. If, however, the sensor signals approach the alarm limit, then it is obviously desirable to sample that channel once every minute or even faster so that the operators can be informed, thereby avoiding any catastrophes. Microcomputer controlledintel-ligent data loggers may be programmed to alter the sampling frequencies depending upon the values of process signals. Other data loggers include self-scanning modules which can initiate sampling.The conventional hardwired data loggers, without any programming facilities, simply record the instantaneous values of transducer outputs at a regular samplingin-terval. This raw data often means very little to the typical user. To be meaningful, this data must be linearised and scaled, using a calibration curve, in order to determine the real value of the variable in appropriate engineering units. Prior to the availability of programmable data loggers, this function was usually carried out in the off-line mode on a mini- or mainframe computer. The raw data values had to be punched out on pa-per tape, in binary or octal code, to be input subsequently to the computer used for analysis purposes and converted to the engineering units. Paper tape punches are slow speed mechanical devices which reduce the speed at which channels can be scanned. An alternative was to print out the raw data values which further reduced the data scanning rate. It was not possible to carry out any limit comparisons or provide any alarm information. Every single value acquired by the data logger had to be recorded eventhough it might not serve any useful purpose during subsequent analysis; many data values only need recording when they lie outside the pre-set low and high limits.If the analog data must be transmitted over any distance, differences in ground potential between the signal source and final location can add noise in the interface design. In order to separate common-mode interference form the signal to be recorded or processed, devices designed for this purpose, such as instrumentation amplifiers, may be used. An instrumentation amplifier is characterized by good common-mode- rejection capability, a high input impedance, low drift, adjustable gain, and greater cost than operational amplifiers. They range from monolithic ICs to potted modules, and larger rack-mounted modules with manual scaling and null adjustments. When a very high common-mode voltage is present or the need for extremely-lowcom-mon-mode leakage current exists（as in many medical-electronics applications），an isolation amplifier is required. Isolation amplifiers may use optical or transformer isolation.Analog function circuits are special-purpose circuits that are used for a variety of signal conditioning operations on signals which are in analog form. When their accu-racy is adequate, they can relieve the microprocessor of time-consuming software and computations. Among the typical operations performed are multiplications, division, powers, roots, nonlinear functions such as for linearizing transducers, rimsmeasure-ments, computing vector sums, integration and differentiation, andcurrent-to-voltage or voltage- to-current conversion. Many of these operations can be purchased in available devices as multiplier/dividers, log/antilog amplifiers, and others.When data from a number of independent signal sources must be processed by the same microcomputer or communications channel, a multiplexer is used to channel the input signals into the A/D converter.Multiplexers are also used in reverse, as when a converter must distribute analog information to many different channels. The multiplexer is fed by a D／A converter which continually refreshes the output channels with new information.In many systems, the analog signal varies during the time that the converter takes to digitize an input signal. The changes in this signal level during the conversion process can result in errors since the conversion period can be completed some time after the conversion command. The final value never represents the data at the instant when the conversion command is transmitted. Sample-hold circuits are used to make an acquisition of the varying analog signal and to hold this signal for the duration of the conversion process. Sample-hold circuits are common in multichannel distribution systems where they allow each channel to receive and hold the signal level.In order to get the data in digital form as rapidly and as accurately as possible, we must use an analog/digital (A/D) converter, which might be a shaft encoder, a small module with digital outputs, or a high-resolution, high-speed panel instrument. These devices, which range form IC chips to rack-mounted instruments, convert ana-log input data, usually voltage, into an equivalent digital form. The characteristics of A/D converters include absolute and relative accuracy, linearity, monotonic, resolu-tion, conversion speed, and stability. A choice of input ranges, output codes, and other features are available. The successive-approximation technique is popular for a large number ofapplications, with the most popular alternatives being the counter-comparator types, and dual-ramp approaches. The dual-ramp has been widely-used in digital voltmeters.D/A converters convert a digital format into an equivalent analog representation. The basic converter consists of a circuit of weighted resistance values or ratios, each controlled by a particular level or weight of digital input data, which develops the output voltage or current in accordance with the digital input code. A special class of D/A converter exists which have the capability of handling variable reference sources. These devices are the multiplying DACs. Their output value is the product of the number represented by the digital input code and the analog reference voltage, which may vary form full scale to zero, and in some cases, to negative values.Component Selection CriteriaIn the past decade, data-acquisition hardware has changed radically due to ad-vances in semiconductors, and prices have come down too; what have not changed, however, are the fundamental system problems confronting the designer. Signals may be obscured by noise, rfi，ground loops, power-line pickup, and transients coupled into signal lines from machinery. Separating the signals from these effects becomes a matter for concern.Data-acquisition systems may be separated into two basic categories:（1）those suited to favorable environments like laboratories -and（2）those required for hostile environments such as factories, vehicles, and military installations. The latter group includes industrial process control systems where temperature information may be gathered by sensors on tanks, boilers, wats, or pipelines that may be spread over miles of facilities. That data may then be sent to a central processor to provide real-time process control. The digital control of steel mills, automated chemical production, and machine tools is carried out in this kind of hostile environment. The vulnerability of the data signals leads to the requirement for isolation and other techniques.At the other end of the spectrum-laboratory applications, such as test systems for gathering information on gas chromatographs, mass spectrometers, and other sophis-ticated instruments-the designer's problems are concerned with the performing of sen-sitive measurements under favorable conditions rather than with the problem ofpro-tecting the integrity of collected data under hostile conditions.Systems in hostile environments might require components for wide tempera-tures, shielding, common-mode noise reduction, conversion at an early stage, redun-dant circuits for critical measurements, and preprocessing of the digital data to test its reliability. Laboratory systems, on the other hand, will have narrower temperature ranges and less ambient noise. But the higher accuracies require sensitive devices, and a major effort may be necessary for the required signal /noise ratios.The choice of configuration and components in data-acquisition design depends on consideration of a number of factors:1. Resolution and accuracy required in final format.2. Number of analog sensors to be monitored.3. Sampling rate desired.4. Signal-conditioning requirement due to environment and accuracy.5. Cost trade-offs.Some of the choices for a basic data-acquisition configuration include：1 ．Single-channel techniques.A. Direct conversion.B. Preamplification and direct conversion.C. Sample-hold and conversion.D. Preamplification, sample-hold, and conversion.E. Preamplification, signal-conditioning, and direct conversion.F. Preamplification, signal-conditioning, sample-hold, and conversion.2. Multichannel techniques.A. Multiplexing the outputs of single-channel converters.B. Multiplexing the outputs of sample-holds.C. Multiplexing the inputs of sample-holds.D. Multiplexing low-level data.E. More than one tier of multiplexers.Signal-conditioning may include:1. Radiometric conversion techniques.B. Range biasing.D. Logarithmic compression.A. Analog filtering.B. Integrating converters.C. Digital data processing.We shall consider these techniques later, but first we will examine some of the components used in these data-acquisition system configurations.MultiplexersWhen more than one channel requires analog-to-digital conversion, it is neces-sary to use time-division multiplexing in order to connect the analog inputs to a single converter, or to provide a converter for each input and then combine the converter outputs by digital multiplexing.Analog MultiplexersAnalog multiplexer circuits allow the timesharing of analog-to-digital converters between a numbers of analog information channels. An analog multiplexer consists of a group of switches arranged with inputs connected to the individual analog channels and outputs connected in common（as shown in Fig. 1）．The switches may be ad-dressed by a digital input code.Many alternative analog switches are available in electromechanical and solid-state forms. Electromechanical switch types include relays, stepper switches,cross-bar switches, mercury-wetted switches, and dry-reed relay switches. The best switching speed is provided by reed relays（about 1 ms）．The mechanical switches provide high do isolation resistance, low contact resistance, and the capacity to handle voltages up to 1 KV, and they are usually inexpensive. Multiplexers using mechanical switches are suited to low-speed applications as well as those having high resolution requirements. They interface well with the slower A/D converters, like the integrating dual-slope types. Mechanical switches have a finite life, however, usually expressed innumber of operations. A reed relay might have a life of 109 operations, which wouldallow a 3-year life at 10 operations/second.Solid-state switch devices are capable of operation at 30 ns, and they have a life which exceeds most equipment requirements. Field-effect transistors（FETs）are used in most multiplexers. They have superseded bipolar transistors which can introduce large voltage offsets when used as switches.FET devices have a leakage from drain to source in the off state and a leakage from gate or substrate to drain and source in both the on and off states. Gate leakage in MOS devices is small compared to other sources of leakage. When the device has a Zener-diode-protected gate, an additional leakage path exists between the gate and source.Enhancement-mode MOS-FETs have the advantage that the switch turns off when power is removed from the MUX. Junction-FET multiplexers always turn on with the power off.A more recent development, the CMOS-complementary MOS-switch has the advantage of being able to multiplex voltages up to and including the supply voltages. A±10-V signal can be handled with a ±10-V supply.Trade-off Considerations for the DesignerAnalog multiplexing has been the favored technique for achieving lowest system cost. The decreasing cost of A/D converters and the availability of low-cost, digital integrated circuits specifically designed for multiplexing provide an alternative with advantages for some applications. A decision on the technique to use for a givensys-tem will hinge on trade-offs between the following factors:1. Resolution. The cost of A/D converters rises steeply as the resolution increases due to the cost of precision elements. At the 8-bit level, the per-channel cost of an analog multiplexer may be a considerable proportion of the cost of a converter. At resolutions above 12 bits, the reverse is true, and analog multiplexing tends to be more economical.2. Number of channels. This controls the size of the multiplexer required and the amount of wiring and interconnections. Digital multiplexing onto a common data bus reduces wiring to a minimum in many cases. Analog multiplexing is suited for 8 to 256 channels; beyond this number, the technique is unwieldy and analog errors be-come difficult to minimize. Analog and digital multiplexing is often combined in very large systems.3. Speed of measurement, or throughput. High-speed A/D converters can add a considerable cost to the system. If analog multiplexing demands a high-speedcon-verter to achieve the desired sample rate, a slower converter for each channel with digital multiplexing can be less costly．4. Signal level and conditioning. Wide dynamic ranges between channels can be difficult with analog multiplexing. Signals less than 1V generally require differential low-level analog multiplexing which is expensive, with programmable-gain amplifiers after the MUX operation. The alternative of fixed-gain converters on each channel, with signal-conditioning designed for the channel requirement, with digital multi-plexing may be more efficient.5. Physical location of measurement points. Analog multiplexing is suitedfor making measurements at distances up to a few hundred feet from the converter, since analog lines may suffer from losses, transmission-line reflections, and interference. Lines may range from twisted wire pairs to multiconductor shielded cable, depending on signal levels, distance, and noise environments. Digital multiplexing is operable to thousands of miles, with the proper transmission equipment, for digital transmission systems can offer the powerful noise-rejection characteristics that are required for29 Data Acquisition Systems long-distance transmission.Digital MultiplexingFor systems with small numbers of channels, medium-scale integrated digital multiplexers are available in TTL and MOS logic families. The 74151 is a typical example. Eight of these integrated circuits can be used to multiplex eight A/D con-verters of 8-bit resolution onto a common data bus.This digital multiplexing example offers little advantages in wiring economy, but it is lowest in cost, and the high switching speed allows operation at sampling rates much faster than analog multiplexers. The A/D converters are required only to keep up with the channel sample rate, and not with the commutating rate. When large numbers of A/D converters are multiplexed, the data-bus technique reduces system interconnections. This alone may in many cases justify multiple A/D converters. Data can be bussed onto the lines in bit-parallel or bit-serial format, as many converters have both serial and parallel outputs. A variety of devices can be used to drive the bus, from open collector and tristate TTL gates to line drivers and optoelectronic isolators. Channel-selection decoders can be built from 1-of-16 decoders to the required size. This technique also allows additional reliability in that a failure of one A/D does not affect the other channels. An important requirement is that the multiplexer operate without introducing unacceptable errors at the sample-rate speed. For a digital MUX system, one can determine the speed from propagation delays and the time required to charge the bus capacitance.Analog multiplexers can be more difficult to characterize. Their speed is a func-tion not only of internal parameters but also external parameters such as channel, source impedance, stray capacitance and the number of channels, and the circuit lay-out. The user must be aware of the limiting parameters in the system to judge their ef-fect on performance.The nonideal transmission and open-circuit characteristics of analog multiplexers can introduce static and dynamic errors into the signal path. These errors include leakage through switches, coupling of control signals into the analog path, and inter-actions with sources and following amplifiers. Moreover, the circuit layout can com-pound these effects.Since analog multiplexers may be connected directly to sources which may have little overload capacity or poor settling after overloads, the switches should have a break-before-make action to prevent the possibility of shorting channels together. It may be necessary to avoid shorted channels when power is removed and a chan-nels-off with power-down characteristic is desirable. In addition to the chan-nel-addressing lines, which are normally binary-coded, it is useful to have inhibited or enable lines to turn all switches off regardless of the channel being addressed. This simplifies the external logic necessary to cascade multiplexers and can also be useful in certain modes of channeladdressing. Another requirement for both analog and digital multiplexers is the tolerance of line transients and overload conditions, and the ability to absorb the transient energy and recover without damage.数据采集系统数据采集系统是用来获取数据处理和存储在二级存储设备，为后来的分析。
文献收集法 英文
文献收集法英文
文献收集法英文为：Literature collection method
例句
1.本文采用文献收集法、比较分析法、田野调查法、归纳演绎法等研究方法，对旅游目的地型森林公园经营管理问题进行了深入、系统的研究。
The paper adopts methods of literature collection, comparison and analysis, interview and field investigation, in addition to induction and deductive method, making deep and systematic study to the operation and administration issues of tourism destination-type forest park.
2.全文撰写当中主要应用了文献法、数据收集法、求平均法、对比分析法等研究方法。
This paper utilized methods of documentary analysis, data collection, averaging and comparative analysis.
3在研究过程中，收集和整理资料的方法主要是深度访谈法、观察法、文献法以及实物收集法。
The method of collecting and collating information include depth interviews, observation, literature, law and in-kind collection method.。
关于大数据的学术英文文献
关于大数据的学术英文文献Big Data: Challenges and Opportunities in the Digital Age.Introduction.In the contemporary digital era, the advent of big data has revolutionized various aspects of human society. Big data refers to vast and complex datasets generated at an unprecedented rate from diverse sources, including social media platforms, sensor networks, and scientific research. While big data holds immense potential for transformative insights, it also poses significant challenges and opportunities that require thoughtful consideration. This article aims to elucidate the key challenges and opportunities associated with big data, providing a comprehensive overview of its impact and future implications.Challenges of Big Data.1. Data Volume and Variety: Big data datasets are characterized by their enormous size and heterogeneity. Dealing with such immense volumes and diverse types of data requires specialized infrastructure, computational capabilities, and data management techniques.2. Data Velocity: The continuous influx of data from various sources necessitates real-time analysis and decision-making. The rapid pace at which data is generated poses challenges for data processing, storage, andefficient access.3. Data Veracity: The credibility and accuracy of big data can be a concern due to the potential for noise, biases, and inconsistencies in data sources. Ensuring data quality and reliability is crucial for meaningful analysis and decision-making.4. Data Privacy and Security: The vast amounts of data collected and processed raise concerns about privacy and security. Sensitive data must be protected fromunauthorized access, misuse, or breaches. Balancing data utility with privacy considerations is a key challenge.5. Skills Gap: The analysis and interpretation of big data require specialized skills and expertise in data science, statistics, and machine learning. There is a growing need for skilled professionals who can effectively harness big data for valuable insights.Opportunities of Big Data.1. Improved Decision-Making: Big data analytics enables organizations to make informed decisions based on comprehensive data-driven insights. Data analysis can reveal patterns, trends, and correlations that would be difficult to identify manually.2. Personalized Experiences: Big data allows companies to tailor products, services, and marketing strategies to individual customer needs. By understanding customer preferences and behaviors through data analysis, businesses can provide personalized experiences that enhancesatisfaction and loyalty.3. Scientific Discovery and Innovation: Big data enables advancements in various scientific fields,including medicine, genomics, and climate modeling. The vast datasets facilitate the identification of complex relationships, patterns, and anomalies that can lead to breakthroughs and new discoveries.4. Economic Growth and Productivity: Big data-driven insights can improve operational efficiency, optimize supply chains, and create new economic opportunities. By leveraging data to streamline processes, reduce costs, and identify growth areas, businesses can enhance their competitiveness and contribute to economic development.5. Societal Benefits: Big data has the potential to address societal challenges such as crime prevention, disease control, and disaster management. Data analysis can empower governments and organizations to make evidence-based decisions that benefit society.Conclusion.Big data presents both challenges and opportunities in the digital age. The challenges of data volume, velocity, veracity, privacy, and skills gap must be addressed to harness the full potential of big data. However, the opportunities for improved decision-making, personalized experiences, scientific discoveries, economic growth, and societal benefits are significant. By investing in infrastructure, developing expertise, and establishing robust data governance frameworks, organizations and individuals can effectively navigate the challenges and realize the transformative power of big data. As thedigital landscape continues to evolve, big data will undoubtedly play an increasingly important role in shaping the future of human society and technological advancement.。
数据库英文参考文献(最新推荐120个)
由于我国经济的高速发展,计算机科学技术在当前各个科技领域中迅速发展,成为了应用最广泛的技术之一.其中数据库又是计算机科学技术中发展最快,应用最广泛的重要分支之一.它已成为计算机信息系统和计算机应用系统的重要技术基础和支柱。
下面是数据库英文参考文献的分享，希望对你有所帮助。
数据库英文参考文献一：[1]Nú?ez Matías,Weht Ruben,Nú?ez Regueiro Manuel. Searching for electronically two dimensional metals in high-throughput ab initio databases[J]. Computational Materials Science,2020,182.[2]Izabela Karsznia,Marta Przychodzeń,Karolina Sielicka. Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland[J]. Geocarto International,2020,35(7).[3]Alankrit Chaturvedi. Secure Cloud Migration Challenges and Solutions[J]. Journal of Research in Science and Engineering,2020,2(4).[4]Ivana Nin?evi? Pa?ali?,Maja ?uku?i?,Mario Jadri?. Smart city research advances in Southeast Europe[J]. International Journal of Information Management,2020.[5]Jongseong Kim,Unil Yun,Eunchul Yoon,Jerry Chun-Wei Lin,Philippe Fournier-Viger. One scan based high average-utility pattern mining in static and dynamic databases[J]. Future Generation Computer Systems,2020.[6]Jo?o Peixoto Martins,António Andrade-Campos,Sandrine Thuillier. Calibration of Johnson-Cook Model Using Heterogeneous Thermo-Mechanical Tests[J]. Procedia Manufacturing,2020,47.[7]Anna Soriani,Roberto Gemignani,Matteo Strano. A Metamodel for the Management of Large Databases: Toward Industry 4.0 in Metal Forming[J]. Procedia Manufacturing,2020,47.[8]Ayman Elbadawi,Karim Mahmoud,Islam Y. Elgendy,Mohammed Elzeneini,Michael Megaly,Gbolahan Ogunbayo,Mohamed A. Omer,Michelle Albert,Samir Kapadia,Hani Jneid. Racial disparities in the utilization and outcomes of transcatheter mitral valve repair: Insights from a national database[J]. Cardiovascular Revascularization Medicine,2020.[9]Maurizio Boccia,Antonio Sforza,Claudio Sterle. Simple Pattern Minimality Problems: Integer Linear Programming Formulations and Covering-Based Heuristic Solving Approaches[J]. INFORMS Journal on Computing,2020.[10]. Inc.; Patent Issued for Systems And User Interfaces For Dynamic Access Of Multiple Remote Databases And Synchronization Of Data Based On User Rules (USPTO 10,628,448)[J]. Computer Technology Journal,2020.[11]. Bank of America Corporation; Patent Issued for System For Electronic Data Verification, Storage, And Transfer (USPTO 10,628,058)[J]. Computer Technology Journal,2020.[12]. Information Technology - Database Management; Data from Technical University Munich (TU Munich) Advance Knowledge in Database Management (Make the most out of your SIMD investments: counter control flow divergence in compiled query pipelines)[J]. Computer Technology Journal,2020.[13]. Information Technology - Database Management; Studies from Pontifical Catholic University Update Current Data on Database Management (General dynamic Yannakakis: conjunctive queries with theta joins under updates)[J]. Computer Technology Journal,2020.[14]Kimothi Dhananjay,Biyani Pravesh,Hogan James M,Soni Akshay,Kelly Wayne. Learning supervised embeddings for large scale sequence comparisons.[J]. PloS one,2020,15(3).[15]. Information Technology; Studies from University of California San Diego (UCSD) Reveal New Findings on Information Technology (A Physics-constrained Data-driven Approach Based On Locally Convex Reconstruction for Noisy Database)[J]. Information Technology Newsweekly,2020.[16]. Information Technology; Researchers from National Institute of Information and Communications Technology Describe Findings in Information Technology (Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases)[J]. Information Technology Newsweekly,2020.[17]. Information Technology; Investigators at Gdansk University of Technology Report Findings in Information Technology (A Framework for Accelerated Optimization of Antennas Using Design Database and Initial Parameter Set Estimation)[J]. Information Technology Newsweekly,2020.[18]. Information Technology; Study Results from Palacky University Update Understanding of Information Technology (Evaluation of Replication Mechanisms on Selected Database Systems)[J]. Information Technology Newsweekly,2020.[19]Runfola Daniel,Anderson Austin,Baier Heather,Crittenden Matt,Dowker Elizabeth,Fuhrig Sydney,Goodman Seth,Grimsley Grace,Layko Rachel,MelvilleGraham,Mulder Maddy,Oberman Rachel,Panganiban Joshua,Peck Andrew,Seitz Leigh,Shea Sylvia,Slevin Hannah,Youngerman Rebecca,Hobbs Lauren. geoBoundaries: A global database of political administrative boundaries.[J]. PloS one,2020,15(4).[20]Dupré Damien,Krumhuber Eva G,Küster Dennis,McKeown Gary J. A performance comparison of eight commercially available automatic classifiers for facial affect recognition.[J]. PloS one,2020,15(4).[21]Partha Pratim Banik,Rappy Saha,Ki-Doo Kim. An Automatic Nucleus Segmentation and CNN Model based Classification Method of White Blood Cell[J]. Expert Systems With Applications,2020,149.[22]Hang Dong,Wei Wang,Frans Coenen,Kaizhu Huang. Knowledge base enrichment by relation learning from social tagging data[J]. Information Sciences,2020,526.[23]Xiaodong Zhao,Dechang Pi,Junfu Chen. Novel trajectory privacy-preserving method based on clustering using differential privacy[J]. Expert Systems With Applications,2020,149.[24]. Information Technology; Researchers at Beijing University of Posts and Telecommunications Have Reported New Data on Information Technology (Mining top-k sequential patterns in transaction database graphs)[J]. Internet Weekly News,2020.[25]Sunil Kumar Sharma. An empirical model (EM: CCO) for clustering, convergence and center optimization in distributive databases[J]. Journal of Ambient Intelligence and Humanized Computing,2020(prepublish).[26]Naryzhny Stanislav,Klopov Nikolay,Ronzhina Natalia,Zorina Elena,Zgoda Victor,Kleyst Olga,Belyakova Natalia,Legina Olga. A database for inventory of proteoform profiles: "2DE-pattern".[J]. Electrophoresis,2020.[27]Noel Varela,Jesus Silva,Fredy Marin Gonzalez,Pablo Palencia,Hugo Hernandez Palma,Omar Bonerge Pineda. Method for the Recovery of Images in Databases of Rice Grains from Visual Content[J]. Procedia Computer Science,2020,170.[28]Ahmad Rabanimotlagh,Prabhu Janakaraj,Pu Wang. Optimal Crowd-Augmented Spectrum Mapping via an Iterative Bayesian Decision Framework[J]. Ad Hoc Networks,2020.[29]Ismail Boucherit,Mohamed Ould Zmirli,Hamza Hentabli,Bakhtiar Affendi Rosdi. Finger vein identification using deeply-fused Convolutional Neural Network[J]. Journal of King Saud University - Computer and Information Sciences,2020.[30]Sachin P. Patel,S.H. Upadhyay. Euclidean Distance based Feature Ranking andSubset Selection for Bearing Fault Diagnosis[J]. Expert Systems With Applications,2020.[31]Julia Fomina,Denis Safikanov,Alexey Artamonov,Evgeniy Tretyakov. Parametric and semantic analytical search indexes in hieroglyphic languages[J]. Procedia Computer Science,2020,169.[32]Selvine G. Mathias,Sebastian Schmied,Daniel Grossmann. An Investigation on Database Connections in OPC UA Applications[J]. Procedia Computer Science,2020,170.[33]Abdourrahmane Mahamane Atto,Alexandre Benoit,Patrick Lambert. Timed-image based deep learning for action recognition in video sequences[J]. Pattern Recognition,2020.[34]Yonis Gulzar,Ali A. Alwan,Abedallah Zaid Abualkishik,Abid Mehmood. A Model for Computing Skyline Data Items in Cloud Incomplete Databases[J]. Procedia Computer Science,2020,170.[35]Xiaohan Yang,Fan Li,Hantao Liu. Deep feature importance awareness based no-reference image quality prediction[J]. Neurocomputing,2020.[36]Dilana Hazer-Rau,Sascha Meudt,Andreas Daucher,Jennifer Spohrs,Holger Hoffmann,Friedhelm Schwenker,Harald C. Traue. The uulmMAC Database—A Multimodal Affective Corpus for Affective Computing in Human-Computer Interaction[J]. Sensors,2020,20(8).[37]Tomá? Pohanka,Vilém Pechanec. Evaluation of Replication Mechanisms on Selected Database Systems[J]. ISPRS International Journal of Geo-Information,2020,9(4).[38]Verheggen Kenneth,Raeder Helge,Berven Frode S,Martens Lennart,Barsnes Harald,Vaudel Marc. Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows.[J]. Mass spectrometry reviews,2020,39(3).[39]Moscona Leon,Casta?eda Pablo,Masrouha Karim. Citation analysis of the highest-cited articles on developmental dysplasia of the hip.[J]. Journal of pediatric orthopedics. Part B,2020,29(3).[40]Nasseh Daniel,Schneiderbauer Sophie,Lange Michael,Schweizer Diana,Heinemann Volker,Belka Claus,Cadenovic Ranko,Buysse Laurence,Erickson Nicole,Mueller Michael,Kortuem Karsten,Niyazi Maximilian,Marschner Sebastian,Fey Theres. Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich OnlineComprehensive Cancer Analysis Platform.[J]. Journal of medical Internet research,2020,22(4).数据库英文参考文献二：[41]Meiling Chai,Changgeng Li,Hui Huang. A New Indoor Positioning Algorithm of Cellular and Wi-Fi Networks[J]. Journal of Navigation,2020,73(3).[42]Mandy Watson. How to undertake a literature search: a step-by-step guide[J]. British Journal of Nursing,2020,29(7).[43]. Patent Application; "Memorial Facility With Memorabilia, Meeting Room, Secure Memorial Database, And Data Needed For An Interactive Computer Conversation With The Deceased" in Patent Application Approval Process (USPTO 20200089455)[J]. Computer Technology Journal,2020.[44]. Information Technology; Data on Information Technology Detailed by Researchers at Complutense University Madrid (Hr-sql: Extending Sql With Hypothetical Reasoning and Improved Recursion for Current Database Systems)[J]. Computer Technology Journal,2020.[45]. Science - Metabolomics; Study Data from Wake Forest University School of Medicine Update Knowledge of Metabolomics (Software tools, databases and resources in metabolomics: updates from 2018 to 2019)[J]. Computer Technology Journal,2020.[46]. Sigma Computing Inc.; Researchers Submit Patent Application, "GeneratingA Database Query To Dynamically Aggregate Rows Of A Data Set", for Approval (USPTO 20200089796)[J]. Computer Technology Journal,2020.[47]. Machine Learning; Findings on Machine Learning Reported by Investigators at Tongji University (Comparing Machine Learning Algorithms In Predicting Thermal Sensation Using Ashrae Comfort Database Ii)[J]. Computer Technology Journal,2020.[48]. Sigma Computing Inc.; "Generating A Database Query Using A Dimensional Hierarchy Within A Graphical User Interface" in Patent Application Approval Process (USPTO 20200089794)[J]. Computer Technology Journal,2020.[49]Qizhi He,Jiun-Shyan Chen. A physics-constrained data-driven approach based on locally convex reconstruction for noisy database[J]. Computer Methods in Applied Mechanics and Engineering,2020,363.[50]José A. Delgado-Osuna,Carlos García-Martínez,JoséGómez-Barbadillo,Sebastián Ventura. Heuristics for interesting class association rule mining a colorectal cancer database[J]. Information Processing andManagement,2020,57(3).[51]Edival Lima,Thales Vieira,Evandro de Barros Costa. Evaluating deep models for absenteeism prediction of public security agents[J]. Applied Soft Computing Journal,2020,91.[52]S. Fareri,G. Fantoni,F. Chiarello,E. Coli,A. Binda. Estimating Industry 4.0 impact on job profiles and skills using text mining[J]. Computers in Industry,2020,118.[53]Estrela Carlos,Pécora Jesus Djalma,Dami?o Sousa-Neto Manoel. The Contribution of the Brazilian Dental Journal to the Brazilian Scientific Research over 30 Years.[J]. Brazilian dental journal,2020,31(1).[54]van den Oever L B,Vonder M,van Assen M,van Ooijen P M A,de Bock G H,Xie X Q,Vliegenthart R. Application of artificial intelligence in cardiac CT: From basics to clinical practice.[J]. European journal of radiology,2020,128.[55]Li Liu,Deborah Silver,Karen Bemis. Visualizing events in time-varying scientific data[J]. Journal of Visualization,2020,23(2–3).[56]. Information Technology - Database Management; Data on Database Management Discussed by Researchers at Arizona State University (Architecture of a Distributed Storage That Combines File System, Memory and Computation In a Single Layer)[J]. Information Technology Newsweekly,2020.[57]. Information Technology - Database Management; New Findings from Guangzhou Medical University Update Understanding of Database Management (GREG-studying transcriptional regulation using integrative graph databases)[J]. Information Technology Newsweekly,2020.[58]. Technology - Laser Research; Reports from Nicolaus Copernicus University in Torun Add New Data to Findings in Laser Research (Nonlinear optical study of Schiff bases using Z-scan technique)[J]. Journal of Technology,2020.[59]Loeffler Caitlin,Karlsberg Aaron,Martin Lana S,Eskin Eleazar,Koslicki David,Mangul Serghei. Improving the usability and comprehensiveness of microbial databases.[J]. BMC biology,2020,18(1).[60]Caitlin Loeffler,Aaron Karlsberg,Lana S. Martin,Eleazar Eskin,David Koslicki,Serghei Mangul. Improving the usability and comprehensiveness of microbial databases[J]. BMC Biology,2020,18(1).[61]Dean H. Barrett,Aderemi Haruna. Artificial intelligence and machine learningfor targeted energy storage solutions[J]. Current Opinion in Electrochemistry,2020,21.[62]Chenghao Sun. Research on investment decision-making model from the perspective of “Internet of Things + Big data”[J]. Future Generation Computer Systems,2020,107.[63]Sa?a Adamovi?,Vladislav Mi?kovic,Nemanja Ma?ek,Milan Milosavljevi?,Marko ?arac,Muzafer Sara?evi?,Milan Gnjatovi?. An efficient novel approach for iris recognition based on stylometric features and machine learning techniques[J]. Future Generation Computer Systems,2020,107.[64]Olivier Pivert,Etienne Scholly,Grégory Smits,Virginie Thion. Fuzzy quality-Aware queries to graph databases[J]. Information Sciences,2020,521.[65]Javier Fernando Botía Valderrama,Diego José Luis Botía Valderrama. Two cluster validity indices for the LAMDA clustering method[J]. Applied Soft Computing Journal,2020,89.[66]Amer N. Kadri,Marie Bernardo,Steven W. Werns,Amr E. Abbas. TAVR VS. SAVR IN PATIENTS WITH CANCER AND AORTIC STENOSIS: A NATIONWIDE READMISSION DATABASE REGISTRY STUDY[J]. Journal of the American College of Cardiology,2020,75(11).[67]. Information Technology; Findings from P. Sjolund and Co-Authors Update Knowledge of Information Technology (Whole-genome sequencing of human remains to enable genealogy DNA database searches - A case report)[J]. Information Technology Newsweekly,2020.[68]. Information Technology; New Findings from P. Yan and Co-Researchers in the Area of Information Technology Described (BrainEXP: a database featuring with spatiotemporal expression variations and co-expression organizations in human brains)[J]. Information Technology Newsweekly,2020.[69]. IDERA; IDERA Database Tools Expand Support for Cloud-Hosted Databases[J]. Information Technology Newsweekly,2020.[70]Adrienne Warner,David A. Hurley,Jonathan Wheeler,Todd Quinn. Proactive chat in research databases: Inviting new and different questions[J]. The Journal of Academic Librarianship,2020,46(2).[71]Chidentree Treesatayapun. Discrete-time adaptive controller based on IF-THEN rules database for novel architecture of ABB IRB-1400[J]. Journal of the Franklin Institute,2020.[72]Tian Fang,Tan Han,Cheng Zhang,Ya Juan Yao. Research and Construction of the Online Pesticide Information Center and Discovery Platform Based on Web Crawler[J]. Procedia Computer Science,2020,166.[73]Dinusha Vatsalan,Peter Christen,Erhard Rahm. Incremental clustering techniques for multi-party Privacy-Preserving Record Linkage[J]. Data & Knowledge Engineering,2020.[74]Ying Xin Liu,Xi Yuan Li. Design and Implementation of a Business Platform System Based on Java[J]. Procedia Computer Science,2020,166.[75]Akhilesh Kumar Bajpai,Sravanthi Davuluri,Kriti Tiwary,Sithalechumi Narayanan,Sailaja Oguru,Kavyashree Basavaraju,Deena Dayalan,Kavitha Thirumurugan,Kshitish K. Acharya. Systematic comparison of the protein-protein interaction databases from a user's perspective[J]. Journal of Biomedical Informatics,2020,103.[76]P. Raveendra,V. Siva Reddy,G.V. Subbaiah. Vision based weed recognition using LabVIEW environment for agricultural applications[J]. Materials Today: Proceedings,2020,23(Pt 3).[77]Christine Rosati,Emily Bakinowski. Preparing for the Implementation of an Agnis Enabled Data Reporting System and Comprehensive Research Level Data Repository for All Cellular Therapy Patients[J]. Biology of Blood and Marrow Transplantation,2020,26(3).[78]Zeiser Felipe André,da Costa Cristiano André,Zonta Tiago,Marques Nuno M C,Roehe Adriana Vial,Moreno Marcelo,da Rosa Righi Rodrigo. Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning.[J]. Journal of digital imaging,2020.[79]Dhaked Devendra K,Guasch Laura,Nicklaus Marc C. Tautomer Database: A Comprehensive Resource for Tautomerism Analyses.[J]. Journal of chemical information and modeling,2020,60(3).[80]Pian Cong,Zhang Guangle,Gao Libin,Fan Xiaodan,Li Fei. miR+Pathway: the integration and visualization of miRNA and KEGG pathways.[J]. Briefings in bioinformatics,2020,21(2).数据库英文参考文献三：[81]Marcello W. M. Ribeiro,Alexandre A. B. Lima,Daniel Oliveira. OLAP parallel query processing in clouds with C‐ParGRES[J]. Concurrency and Computation: Practice and Experience,2020,32(7).[82]Li Gao,Peng Lin,Peng Chen,Rui‐Zhi Gao,Hong Yang,Yun He,Jia‐Bo Chen,Yi ‐Ge Luo,Qiong‐Qian Xu,Song‐Wu Liang,Jin‐Han Gu,Zhi‐Guang Huang,Yi‐Wu Dang,Gang Chen. A novel risk signature that combines 10 long noncoding RNAs to predict neuroblastoma prognosis[J]. Journal of Cellular Physiology,2020,235(4).[83]Julia Krzykalla,Axel Benner,Annette Kopp‐Schneider. Exploratory identification of predictive biomarkers in randomized trials with normal endpoints[J]. Statistics in Medicine,2020,39(7).[84]Jianye Ching,Kok-Kwang Phoon. Measuring Similarity between Site-Specific Data and Records from Other Sites[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering,2020,6(2).[85]Anne Kelly Knowles,Justus Hillebrand,Paul B. Jaskot,Anika Walke. Integrative, Interdisciplinary Database Design for the Spatial Humanities: the Case of the Holocaust Ghettos Project[J]. International Journal of Humanities and Arts Computing,2020,14(1-2).[86]Sheng-Feng Sung,Pei-Ju Lee,Cheng-Yang Hsieh,Wan-Lun Zheng. Medication Use and the Risk of Newly Diagnosed Diabetes in Patients with Epilepsy: A Data Mining Application on a Healthcare Database[J]. Journal of Organizational and End User Computing (JOEUC),2020,32(2).[87]Rashkovits Rami,Lavy Ilana. Students' Difficulties in Identifying the Use of Ternary Relationships in Data Modeling[J]. International Journal of Information and Communication Technology Education (IJICTE,2020,16(2).[88]Yusuf Akhtar,Dipti Prasad Mukherjee. Context-based ensemble classification for the detection of architectural distortion in a digitised mammogram[J]. IET Image Processing,2020,14(4).[89]Gurpreet Kaur,Sukhwinder Singh,Renu Vig. Medical fusion framework using discrete fractional wavelets and non-subsampled directional filter banks[J]. IET Image Processing,2020,14(4).[90]Qian Liu,Bo Jiang,Jia-lei Zhang,Peng Gao,Zhi-jian Xia. Semi-supervised uncorrelated dictionary learning for colour face recognition[J]. IET Computer Vision,2020,14(3).[91]Yipo Huang,Leida Li,Yu Zhou,Bo Hu. No-reference quality assessment for live broadcasting videos in temporal and spatial domains[J]. IET Image Processing,2020,14(4).[92]Panetta Karen,Wan Qianwen,Agaian Sos,Rajeev Srijith,Kamath Shreyas,Rajendran Rahul,Rao Shishir Paramathma,Kaszowska Aleksandra,Taylor Holly A,Samani Arash,Yuan Xin. A Comprehensive Database for Benchmarking Imaging Systems.[J]. IEEE transactions on pattern analysis and machine intelligence,2020,42(3).[93]Rahnev Dobromir,Desender Kobe,Lee Alan L F,Adler William T,Aguilar-Lleyda David,Akdo?an Ba?ak,Arbuzova Polina,Atlas Lauren Y,Balc? Fuat,Bang Ji Won,Bègue Indrit,Birney Damian P,Brady Timothy F,Calder-Travis Joshua,Chetverikov Andrey,Clark Torin K,Davranche Karen,Denison Rachel N,Dildine Troy C,Double Kit S,Duyan Yaln A,Faivre Nathan,Fallow Kaitlyn,Filevich Elisa,Gajdos Thibault,Gallagher Regan M,de Gardelle Vincent,Gherman Sabina,Haddara Nadia,Hainguerlot Marine,Hsu Tzu-Yu,Hu Xiao,Iturrate I?aki,Jaquiery Matt,Kantner Justin,Koculak Marcin,Konishi Mahiko,Ko? Christina,Kvam Peter D,Kwok Sze Chai,Lebreton Ma?l,Lempert Karolina M,Ming Lo Chien,Luo Liang,Maniscalco Brian,Martin Antonio,Massoni Sébastien,Matthews Julian,Mazancieux Audrey,Merfeld Daniel M,O'Hora Denis,Palser Eleanor R,Paulewicz Borys?aw,Pereira Michael,Peters Caroline,Philiastides Marios G,Pfuhl Gerit,Prieto Fernanda,Rausch Manuel,Recht Samuel,Reyes Gabriel,Rouault Marion,Sackur Jér?me,Sadeghi Saeedeh,Samaha Jason,Seow Tricia X F,Shekhar Medha,Sherman Maxine T,Siedlecka Marta,Skóra Zuzanna,Song Chen,Soto David,Sun Sai,van Boxtel Jeroen J A,Wang Shuo,Weidemann Christoph T,Weindel Gabriel,WierzchońMicha?,Xu Xinming,Ye Qun,Yeon Jiwon,Zou Futing,Zylberberg Ariel. The Confidence Database.[J]. Nature human behaviour,2020,4(3).[94]Taipalus Toni. The Effects of Database Complexity on SQL Query Formulation[J]. Journal of Systems and Software,2020(prepublish).[95]. Information Technology; Investigators from Deakin University Target Information Technology (Conjunctive query pattern structures: A relational database model for Formal Concept Analysis)[J]. Computer Technology Journal,2020.[96]. Machine Learning; Findings from Rensselaer Polytechnic Institute Broaden Understanding of Machine Learning (Self Healing Databases for Predictive Risk Analytics In Safety-critical Systems)[J]. Computer Technology Journal,2020.[97]. Science - Library Science; Investigators from Cumhuriyet University Release New Data on Library Science (Scholarly databases under scrutiny)[J]. Computer Technology Journal,2020.[98]. Information Technology; Investigators from Faculty of Computer Science and Engineering Release New Data on Information Technology (FGSA for optimal quality of service based transaction in real-time database systems under different workload condition)[J]. Computer Technology Journal,2020.[99]Muhammad Aqib Javed,M.A. Naveed,Azam Hussain,S. Hussain. Integrated data acquisition, storage and retrieval for glass spherical tokamak (GLAST)[J]. Fusion Engineering and Design,2020,152.[100]Vinay M.S.,Jayant R. Haritsa. Operator implementation of Result Set Dependent KWS scoring functions[J]. Information Systems,2020,89.[101]. Capital One Services LLC; Patent Issued for Computer-Based Systems Configured For Managing Authentication Challenge Questions In A Database And Methods Of Use (USPTO 10,572,653)[J]. Journal of Robotics & Machine Learning,2020.[102]Ikawa Fusao,Michihata Nobuaki. In Reply to Letter to the Editor Regarding "Treatment Risk for Elderly Patients with Unruptured Cerebral Aneurysm from a Nationwide Database in Japan".[J]. World neurosurgery,2020,135.[103]Chen Wei,You Chao. Letter to the Editor Regarding "Treatment Risk for Elderly Patients with Unruptured Cerebral Aneurysm from a Nationwide Database in Japan".[J]. World neurosurgery,2020,135.[104]Zhitao Xiao,Lei Pei,Lei Geng,Ying Sun,Fang Zhang,Jun Wu. Surface Parameter Measurement of Braided Composite Preform Based on Faster R-CNN[J]. Fibers and Polymers,2020,21(3).[105]Xiaoyu Cui,Ruifan Cai,Xiangjun Tang,Zhigang Deng,Xiaogang Jin. Sketch‐based shape‐constrained fireworks simulation in head‐mounted virtual reality[J]. Computer Animation and Virtual Worlds,2020,31(2).[106]Klaus B?hm,Tibor Kubjatko,Daniel Paula,Hans-Georg Schweiger. New developments on EDR (Event Data Recorder) for automated vehicles[J]. Open Engineering,2020,10(1).[107]Ming Li,Ruizhi Chen,Xuan Liao,Bingxuan Guo,Weilong Zhang,Ge Guo. A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras[J]. Remote Sensing,2020,12(5).[108]Matthew Grewe,Phillip Sexton,David Dellenbach. Use Risk‐Based Asset Prioritization to Develop Accurate Capital Budgets[J]. Opflow,2020,46(3).[109]Jose R. Salvador,D. Mu?oz de la Pe?a,D.R. Ramirez,T. Alamo. Predictive control of a water distribution system based on process historian data[J]. Optimal Control Applications and Methods,2020,41(2).[110]Esmaeil Nourani,Vahideh Reshadat. Association extraction from biomedicalliterature based on representation and transfer learning[J]. Journal of Theoretical Biology,2020,488.[111]Ikram Saima,Ahmad Jamshaid,Durdagi Serdar. Screening of FDA approved drugs for finding potential inhibitors against Granzyme B as a potent drug-repurposing target.[J]. Journal of molecular graphics & modelling,2020,95.[112]Keiron O’Shea,Biswapriya B. Misra. Software tools, databases and resources in metabolomics: updates from 2018 to 2019[J]. Metabolomics,2020,16(D1).[113]. Information Technology; Researchers from Virginia Polytechnic Institute and State University (Virginia Tech) Describe Findings in Information Technology (A database for global soil health assessment)[J]. Energy & Ecology,2020.[114]Moosa Johra Muhammad,Guan Shenheng,Moran Michael F,Ma Bin. Repeat-Preserving Decoy Database for False Discovery Rate Estimation in Peptide Identification.[J]. Journal of proteome research,2020,19(3).[115]Huttunen Janne M J,K?rkk?inen Leo,Honkala Mikko,Lindholm Harri. Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach.[J]. International journal for numerical methods in biomedical engineering,2020,36(3).[116]Kunxia Wang,Guoxin Su,Li Liu,Shu Wang. Wavelet packet analysis for speaker-independent emotion recognition[J]. Neurocomputing,2020.[117]Fusao Ikawa,Nobuaki Michihata. In Reply to Letter to the Editor Regarding “Treatment Risk for Elderly Patients with Unruptured Cerebral Aneurysm from a Nationwide Database in Japan”[J]. World Neurosurgery,2020,135.[118]Wei Chen,Chao You. Letter to the Editor Regarding “Treatment Risk for Elderly Patients with Unruptured Cerebral Aneurysm from a Nationwide Database in Japan”[J]. World Neurosurgery,2020,135.[119]Lindsey A. Parsons,Jonathan A. Jenks,Andrew J. Gregory. Accuracy Assessment of National Land Cover Database Shrubland Products on the Sagebrush Steppe Fringe[J]. Rangeland Ecology & Management,2020,73(2).[120]Jing Hua,Yilu Xu,Jianjun Tang,Jizhong Liu,Jihao Zhang. ECG heartbeat classification in compressive domain for wearable devices[J]. Journal of Systems Architecture,2020,104.以上就是关于数据库英文参考文献的全部内容，希望看完后对你有所启发。
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数据采集数据采集是对现实世界抽样产生出可以由计算机操纵的数据，有时也把它缩写为DAS或者DAQ，数据采集和信号通常涉及到的信号波形采集和处理，以获得所需的信息。
数据采集系统的组成部分包括的任何测量参数转换为电信号，然后调节电信号，然后再通过数据采集硬件获取相应数据的传感器。
使用厂商提供的软件，或自定义显示和控制，开发利用如BASIC，C，Fortran，Java，Lisp，Pascal各种通用编程语言把获得的数据显示，分析和存储在计算机中。
为了构建大规模数据采集系统，使用了包括EPICS等专业的编程语言进行的数据采集。
LabVIEW，内置了图形化工具和数据的采集和分析，它提供了图形化编程环境数据采集优化，并使用MATLAB作为其编程语言。
数据是如何取得(1)来源根据调查，数据采集是和物理现象或物体的物理性质一起开始的。
这物理性质或现象，可能是根据温度或房间温度，强度或光源的强度变化而变化，内部的压力，迫使应用到一个对象，或许多其他事情。
一个有效的数据采集系统可以测量这些不同性质或现象。
换能器是一种可以将电压，电流，电阻或电容值的变化等转换成相应的可测量的电信号的装置，数据采集系统衡量不同的物理现象的能力，取决于换能器把数据采集硬件采集到的可测量的物理现象转换成可测量信号。
在DAQ系统中，传感器是感应器的代名词。
不同的传感器有许多不同的应用，如测量温度，压力，或液体流动。
数据采集还进行各种信号调理技术，将充分修改各种不同的电压，使之变为可以使用ADC测量的数字化电信号。
(2)信号信号可能是数字信号（有时也称为逻辑信号）或使用不同的传感器进行模拟分析的结果。
如果从传感器得到的信号与数据采集硬件不兼容，信号调理就是非常必要的了。
该信号可以被放大，或者可能需要过滤，或锁定放大器解调列入执行。
模拟信号容忍几乎没有串音等转换为数字数据，然后才接近一台PC或之前沿长电缆。
对于模拟数据，具有很高的信噪比，信号需要非常高，同时派遣一个50欧姆的终端快速信号路径+ -10伏特，需要强大的驱动程序。
(3)数据采集硬件数据采集硬件通常是与信号和PC接口。
它可以从母板连接到计算机的端口（并行，串行，USB等..）或连接到插槽卡（PCI，ISA和PCI - E等..）。
通常在一个PCI卡背面的空间太小，不能满足所有需要的连接的血药，所以外部的盒式是必需的。
这之间的电缆盒和PC是昂贵的原因是许多的电线需屏蔽。
数据采集卡通常包含复用器，模数转换，数模转换，与TTL印务局，高速定时器，RAM等多个组件。
这些都可以通过由一个可以运行小程序的总线的微控制器进行控制。
该控制器比硬布线逻辑灵活，但比CPU便宜，所以用它阻止它用简单的投票循环是没有问题。
例如：等待一个触发，启动ADC时，查找的时间，等待完成的ADC，移动值到RAM，切换多路，得到TTL输入，让数模转换器进行电压斜坡。
由于16位模数转换器，数模转换器，运算放大器和样品，并作为2007年只有1兆赫运行等精度认为，即使像成本低为AVR32数字控制器有簿记之间约100个时钟周期。
可重构计算可提供高速数字信号。
数字信号处理器算法花费大量的硅，并允许严格控制回路或过滤器。
与个人电脑连接允许舒适固定编制和调试。
使用外部住房在1总线插槽模块化设计，可以增加与用户的需求。
高速二进制数据需要特殊用途的硬件要求时向数字转换器和高速8位ADC称为数字存储示波器示波器＃，这是典型的未连接到DAQ硬件，而是直接到PC。
另外值得注意的是，并非所有的数据采集硬件的运行永久连接到电脑上，例如智能独立伐木者和控制器，可以从电脑操作，但他们可以经营完全独立的个人电脑。
(4)数据采集软件数据采集软件，是为了对数据采集硬件与PC的工作。
这样可能会至少在三个方面：应用程序直接从硬件寄存器，低层次的软件驱动程序（通常包装与数据采集硬件），让开发更高级别的应用程序注册资料从硬件和越野的现成应用程序来驱动软件，通常与数据采集硬件或其他厂商来了，让操作系统识别的数据采集硬件和程序访问的信号正由数据采集硬件阅读。
一个优秀的车手提供了高，低级别的访问。
所以，一开始时会与高级别提供的解决办法，提高到组装说明在时间关键的或外来的申请。
场外的现成应用程序编程接口的手段包括记录，分析和显示所获得的数据。
这种软件的例子是MATLAB和LabVIEW中，既提供一个高层次的图形化编程语言。
Data acquisitionData acquisition is the sampling of the real world to generate data that can be manipulated by a computer. Sometimes abbreviated DAQ or DAS, data acquisition typically involves acquisition of signals and waveforms and processing the signals to obtain desired information. The components of data acquisition systems include appropriate sensors that convert any measurement parameter to an electrical signal, then conditioning the electrical signal which can then be acquired by data acquisition hardware.Acquired data are displayed, analyzed, and stored on a computer, either using vendor supplied software, or custom displays and control can be developed using various general purpose programming languages such as BASIC, C, Fortran, Java, Lisp, Pascal. Specialized programming languages used for data acquisition include EPICS, used to build large scale dataacquisition systems, LabVIEW, which offers a graphical programming environment optimized for data acquisition, and MATLAB which provides a programming language, and also built-in graphical tools and libraries for data acquisition and analysis.How data is acquiredSourceData acquisition begins with the physical phenomenon or physical property of an object (under investigation) to be measured. This physical property or phenomenon could be the temperature or temperature change of a room, the intensity or intensity change of a light source, the pressure inside a chamber, the force applied to an object, or many other things. An effective data acquisition system can measure all of these different properties or phenomena.A transducer is a device that converts a physical property or phenomenon into a corresponding measurable electrical signal, such as voltage, current, change in resistance or capacitor values, etc. The ability of a data acquisition system to measure different phenomena depends on the transducers to convert the physical phenomena into signals measurable by the data acquisition hardware. Transducers are synonymous with sensors in DAQ systems. There are specific transducers for many different applications, such as measuring temperature, pressure, or fluid flow. DAQ also deploy various signal conditioning techniques to adequately modify various different electrical signals into voltage that can then be digitized using ADCs.SignalsSignals may be digital (also called logic signals sometimes) or analog depending on the transducer used.Signal conditioning may be necessary if the signal from the transducer is not suitable for the DAQ hardware to be used. The signal may be amplified, or may require filtering, or a lock-in amplifier is included to perform demodulation.Analog signals tolerate almost no cross talk and so are converted to digital data, before coming close to a PC or before traveling along long cables. For analog data to have a high signal to noise ratio, the signal needs to be very high, and sending +-10 Volts along a fast signal path with a 50 Ohm termination requires powerful drivers.DAQ hardwareDAQ hardware is what usually interfaces between the signal and a PC. Itcould be in the form of modules that can be connected to the computer's ports (parallel, serial, USB, etc...) or cards connected to slots (PCI, ISA, PCI-E, etc...) in the mother board. Usually the space on the back of a PCI card is too small for all the connections needed, so an external breakout box is required. The cable between this Box and the PC is expensive due to the many wires and the required shielding and because it is exotic.DAQ-cards often contain multiple components (multiplexer, ADC, DAC, TTL-IO, high speed timers, RAM). These are accessible via a bus by a micro controller, which can run small programs. The controller is more flexible than a hard wired logic, yet cheaper than a CPU so that it is alright to block it with simple polling loops. For example: Waiting for a trigger, starting the ADC, looking up the time, waiting for the ADC to finish, move value to RAM, switch multiplexer, get TTL input, let DAC proceed with voltage ramp. As 16 bit ADCs, DACs, OpAmps and sample and holds with equal precision as of 2007 only run at 1 MHz, even low cost digital controllers like the AVR32 have about 100 clock cycles for bookkeeping in between.Reconfigurable computing may deliver high speed for digital signals. Digital signal processors spend a lot of silicon on arithmetic and allow tight control loops or filters. The fixed connection with the PC allows for comfortable compilation and debugging. Using an external housing a modular design with slots in a bus can grow with the needs of the user. High speed binary data needs special purpose hardware called Time to digital converter and high speed 8 bit ADCs are called oscilloscope#Digital storage oscilloscope, which are typically not connected to DAQ hardware, but directly to the PC.Also notable is that not all DAQ hardware has to run permanently connected to a PC, for example intelligent stand-alone loggers and controllers, which can be operated from a PC, yet they can operate completely independent of the PC.DAQ softwareDAQ software is needed in order to the DAQ Hardware to work with a PC. This can come in at least three flavors: applications that register directly from the hardware, low-level software driver (usually packaged with the DAQ hardware) to allow developing higher level applications to register data coming from the hardware and off-the-shelf applicationsDriver software that usually comes with the DAQ hardware or from other vendors, allows the operating system to recognize the DAQ hardware and programs to access the signals being read by the DAQ hardware. A good driver offers high and low level access. So one would start out with the highlevel solutions offered and improves down to assembly instructions in time critical or exotic applications.Off-the-shelf applications include interface for programming means to log, analyze and display the acquired data. Examples of this kind of software are MATLAB and LabVIEW, both providing a high level graphical programming language.。