I/O Automaton Models∶ Basic, Timed, Hybrid, Probab

合集下载

automodel.from_pretrained的参数

automodel.from_pretrained的参数

automodel.from_pretrained的参数automodel.from_pretrained是PaddlePaddle深度学习框架中的一个重要功能,它能够从预训练模型中加载并初始化模型参数,从而快速构建出可用的模型。

在使用automodel.from_pretrained时,有一些参数是必须要了解和设置的。

一、参数概述automodel.from_pretrained函数接受一系列可选参数,用于控制模型的加载和初始化过程。

这些参数包括但不限于模型路径、数据增强、模型保存等。

通过合理设置这些参数,可以更好地适应不同的应用场景。

二、常用参数详解1. model_path:模型路径,指定要加载的预训练模型的路径。

该参数是必需的,必须提供正确的模型路径才能调用automodel.from_pretrained。

2. model_name:模型名称,指定要加载的预训练模型的名称。

该参数用于在指定的模型路径下查找对应的模型文件。

3. pretrained_params:预训练模型参数,指定要从预训练模型中加载的参数。

这些参数可以通过PaddlePaddle提供的API进行设置和调整。

4. data_dir:数据目录,指定要进行模型训练的数据集的路径。

该参数用于将数据集加载到模型中,以便进行模型的训练和验证。

5. save_dir:保存目录,指定要将训练得到的模型保存到的路径。

该参数可用于在训练过程中保存模型,并在需要时进行加载和验证。

6. use_gpu:是否使用GPU进行模型训练。

该参数用于控制是否使用GPU进行模型的计算和训练,可以根据硬件设备情况进行设置。

7. optimizer:优化器,指定用于模型训练的优化器类型。

常见的优化器类型包括Adam、SGD等,可以根据具体任务需求进行选择和设置。

三、使用示例```pythonimport paddlefrom paddle.vision.models import resnet50# 加载预训练模型参数pretrained_params = resnet50(pretrained=True)model = paddle.Model(model_name="resnet50", pretrained_params=pretrained_params)```以上代码中,我们首先使用resnet50函数创建了一个预训练的ResNet-50模型,并将其作为预训练模型参数传递给automodel.from_pretrained函数进行初始化。

Python

Python

Python 实现驱动AI机器⼈1.如果尚未启动WSL-ROS环境,请运⾏Windows“开始”菜单中的WSL-ROS快捷⽅式。

这将打开⼀个终端应⽤程序和⼀个Ubuntu终端实例。

我们将这个终端实例称为终端1。

在终端中输⼊以下命令,在⼀个空世界中启动TurtleBot3华夫饼的模拟:[TERMINAL 1] $ roslaunch turtlebot3_gazebo turtlebot3_empty_unch⼀个露台模拟窗⼝应该打开,在⾥⾯你会看到⼀个TurtleBot3华夫饼⼲2.在新终端实例中,输⼊以下命令:[TERMINAL 2] $ roslaunch turtlebot3_teleop turtlebot3_teleop_unch总结:到⽬前为⽌,您已经使⽤roslaunch命令启动了两个单独的ROS应⽤程序。

roslaunch是启动ROS项⽬的⼀种⽅式。

正如您应该从上述⽰例中观察到的,我们以以下⽅式使⽤此命令:roslaunch{package name}{launch file}该命令将两个参数作为输⼊:{package name}是包含我们想要执⾏的功能的ROS包的名称,{launch file}是该包中的⼀个⽂件,它确切地告诉ROS我们想要启动的包中的功能。

3.roscd是⼀个ROS命令,它允许我们导航到系统上安装的任何ROS包的⽬录,⽽⽆需我们事先知道该包的路径。

打开⼀个新的终端实例(terminal 3),并使⽤roscd命令导航到Linux⽂件系统上的turtlebot3_teleop包⽬录:4.In TERMINAL 3 enter $ cd ~ to go back to your home directory[TERMINAL 3] $ roscd turtlebot3_teleop[TERMINAL 3] $ pwd[TERMINAL 3] $ ls[TERMINAL 3] $ ls -F[TERMINAL 3] $ cat turtlebot3_teleop_unch包的启动⽂件通常位于包⽬录中的启动⽂件夹中。

太阳集团TM T1设备 T1规格说明书

太阳集团TM T1设备 T1规格说明书

SPECIFICATIONSConnectorsBantam jacks (Eq Tx, Eq Rx, Fac Tx, Fac Rx)8-pin mini DIN RS232C serial port, DTEAccessSingle ModeDSX Monitor: 100ΩBridged Monitor: > 1000ΩTerminated: 100ΩTerminated Loop: 100ΩBridged Loop: > 1000ΩDSX Monitor Loop: 100ΩDual ModeThru A/B, Split A/B, Split E/F, Loop E/F, Mon E/FTerminationThru, Split, Loop: 100ΩMon: > 1000ΩTransmitterFraming: SF-D4, ESF, SLC-96, T1DMCoding: AMI, B8ZSLine Build Out (LBO): 0, 7.5, 15 dBDSX pre-equalization: 0 to 655 ft, 133 ft per step Clock: Internal (1.544 MHz ± 5 ppm), looped,externalPulse shape to Telcordia TR-TSY-000499; reference: G.703, CB113, CB119, CB132, CB143, PUB62508, PUB62411 Transmit PatternsRepeating: 3 in 24, 1 in 8 (1:7), all 1s, 1 in 16, 55octet, alt 1010, all 0s, T1-T6, DDS1-DDS6User programmable pattern 1 to 2048 bitsStore up to 10 programmable patterns with alphanu-meric namesPseudo random: QRS, PRBS, n = 6, 7, 9, 11, 15, 20, 23 Test pattern inversionInsert errors: BPV, logic, frame errors; programmableerror burst 1 to 9999 counts, or error rate 2 x 10-3to 1 x 10-9ReceiverInput sensitivityTerminate, Bridge: +6 to -36 dB cable lossDSXMON: -15 to -30 dB, resistiveCoding: AMI, B8ZS, AutoFraming: SF, ESF, SLC-96, T1DM, auto frame Frequency range: 1542 kHz to 1546 kHzAuto pattern synchronizationReceived pattern sync independent of transmitted patternProgrammable loss of frame criteria, error averaging interval Basic MeasurementsSummary MeasurementsElapsed time, remaining time, framing, line coding, transmitted pattern, received pattern, BPV count and rate, bit error count and rate, framing bit error count, pulse level (dB), CRC-6 block error count, line frequency, errored second count and percent, severely errored second count and percent, error free second percent, available second percent, unavailable second count and percentLogical Error MeasurementsBit error count and current rate, average bit error rate since start, bit slips, bit errored seconds and percent, severely bit errored seconds and percent, available seconds and percent, unavailable seconds and percent, degraded minutes count and percent, loss of sync seconds count and percentSignal MeasurementsSignal available seconds count and percent, loss of signal seconds count and percent, low density seconds count, excess 0s seconds count, AIS seconds count, signalunavailable seconds percentSimplex current: 1 to 150 mA, ± 1 mA ± 5%.Receive bit rate: 1542 to 1546 kbps, ± 1 bps, ± clock source accuracy, external or internal clockReceive level (volts and dBdsx)Peak to peak: 60 mV to 15V, ± 10 mV, ± 5%Positive pulse: 30 mV to 7.5V, ± 10 mV, ± 5%Negative pulse: -30 mV to -7.5V, ± 10 mV, ± 5%eLine Error MeasurementsBPV count and rate (current and average), BPV error seconds count and percent, BPV SES count and percent, BPV AS count and percent, BPV UAS count and percent, BPV degraded minutes count and percentPath - Frame MeasurementsFrame bit error count and rate (current and average), frame slip count, OOF second count, COFA count, frame synch loss seconds, yellow alarm second count, frame error second count and percent, frame severely errored second count and rate, frame available second count and percent, frame unavailable second count and percent Path - CRC-6 MeasurementsCRC-6 block error count and rate (current and average), CRC-6 errored second count and percent, CRC-6 severely errored second count and percent, CRC-6 available second count and percent, CRC-6 unavailable second count and percentFrequency MeasurementsMoving bar graph of slip rate, received signal frequency, max frequency, min frequency, clock slips, frame slips, max positive wander, max negative wanderOther MeasurementsView Received DataView T1 data in binary, hex, ASCIIShows data in bytes by time slotShows 8 time slots per display pageCaptures 256 consecutive time slots as test patternPropagation DelayMeasure round trip propagation delay in unit intervals ± 1 UI, with translation to microseconds and one way distance over cable Quick Test I and II2 programmable automated loopback tests that save time when performing standardized acceptance testsBridge TapAutomated transmission and measurement of 21 different patterns to identify possible bridge taps at some point on lineLoopbacksLoopback Control, In-bandCSU, NIU, 10000010 programmable user patterns, 1 to 32 bitsLoopback Control, ESF-Facility Data LinkPayload, Line, Network10 programmable user patterns, 1 to 32 bitsWestell & Teltrend Looping Devices Control (SW1010) Automated looping of Westell and Teltrend line and central office repeaters. Includes SF and ESF modes, arm, loop up/down, loopback query, sequential loopback, power loop query, span power down/up, unblocking.Voice Frequency CapabilityMonitor speaker with volume controlBuilt-in microphone for talkView all 24 channel A, B (C, D) bitsControl A, B (C, D) bits (E&M ground/loop start, FXO, FXS, on/off hook, wink)Generator: 404, 1004, 1804, 2713, 2804 Hz @ 0 dBm and -13 dBm DTMF dialing, 32 digits, 10 sets preprogrammable speed dial number Programmable tone and interdigital periodCompanding law - µ LawHitless drop and insertProgrammable idle channel A, B (C, D) bitsSelectable idle channel code, 7F or FF hexVF Level, Freq & Noise Measurement (SW111) Generator: 50 to 3950 Hz @ 1 Hz step; +3 to -60 dBm @ 1 dBm step Level, Freq measurements: 50 to 3950 Hz +3 dBm to -60 dBm Noise: 3 kHz flat, C-message, C-notch, S/NMF/DTMF/DP Dialing, Decoding and Analysis (SW141) MF/DTMF/DP dialingProgrammable DP %break and interdigital period @ 10 ppsMF/DTMF decode up to 40 received digits. Analyze number, high/low frequencies, high/low levels, twist, tone period, interdigital time. DP decode up to 40 digits. Analyze number, %break, PPS, interdigital time.Signaling AnalysisLive: Graphical display of A, B (C, D) signaling state changesTrigger: Programmable A, B (C, D) trigger state to start analysis on the opposite sideMFR1: Timing analysis of signaling transition states and decoding of dialed digitsMFR1M: Modified MFR1 CO switches signaling analysisMIXTONE: Decode a signaling sequence that has both MF andDTMF digitsFractional T1 (SW105, SW1010)Error measurements, channel configuration verificationNx64 kbps, Nx56 kbps, N=1 to 24Sequential, alternating, or random channelsAuto scan and auto configure to any FT1 orderScan for active channelsRx and Tx do not need to be same channelsHitless drop and insertProgrammable idle channel A, B (C, D) bitsSelectable idle channel code, 7F or FF hexESF Facility Data Link (SW107, SW1010)Read and Send T1.403 message on FDL (PRM and BOM)Automatic HDLC protocol handlingYEL ALM, LLB ACT, LLB DEA, PLB ACT, PLB DEAAT&T 54016, 24 hr performance report retrievalT1.403, 24 hour PRM collection per 15 min intervalSunSet TM T1SLC-96 Data Link (SW107, SW1010)Send and receive messageWP1, WP1B, NOTE formatsAlarms, switch-to-protect, far end loopTo Telcordia TR-TSY-000008 specificationsSLC-96 FEND loopCSU/NI Emulation (SW106, SW1010)Bidirectional (Equipment and Facility Directions)CSU/NI replacement emulationResponds to loopback commands - inband and datalinkGraphic indication of incoming signal status in both directions Simultaneous display of T1 line measurementsAutomatic generation of AISLoopbacksFacility: Line and payload loopbackEquipment: Line loopbackSimultaneous loopbacks in both directionsLocal and remote loopback controlRemote Control (SW100)VT100 emulation with same graphical interface used by test set Circuit status table provides current & historical information on test set LEDs Uses test set's serial port at 9600 baud, 8-pin MINI DINSerial port can not be connected to printer during remote control Westell PM NIU and MSS (SW120)Supports Westell performance monitoring network interface unit and maintenance switch system with rampSet/query NIU time and date. Query performance data by hour or all.Reset performance registers. Read data over ramp line. Perform maintenance switch function for Westell and Teltrend.Pulse Mask Analysis (SW130)Scan Period: 800 nsMeasurements: Pass/Fail, ns Rise time, ns fall time, ns pulse width, %overshoot, %undershootResolution: 1 ns or 1%, as applicableMasks: ANSI T1.102, T1.403, AT&T CB119, Pub 62411Pulse/Mask Display: Test set screen and SS118 printerDDS Basic Package (SW170)Choose receive and transmit time slots independentlyTest rates: 2.4, 4.8, 9.6, 19.2, 56, 64 kbpsPatterns: 2047, 511, 127, 63, all 1s, all 0s, DDS-1, DDS-2, DDS-3, DDS-4, DDS-5, DDS-6, 8-bit userLoopbacks: Latching, interleaved, CSU, DSU, OCU, DSO-DP, 8-bit user Measurements: Bit errors, Bit error rateControl code send/receive: Abnormal, mux out of sync, idleAccess Mode: Loopback tests require intrusive access to T1Teleos & Switched 56 Tests (SW144)Switched 56 call set up: Supervision and dialingSend test patterns: 2047, 511, 127, 63, all 1s, all 0s, FOX, DDS1-6, USERBit error, bit error rate measurementTeleos signaling sequence timing analysis and dial digits decoding GENERALOperating temperature: 0˚C to 50˚COperating humidity: 5% to 90%, noncondensingStorage temperature: -20˚C to 70˚CSize: 2.4" (max) x 4.2" (max) x 10.5"Weight: 2.7 lb [1.2 kg]Battery operation time: 2.5 hr nominalAC operation: 110V/120V @ 60 Hz, or 220V/240V @ 50/60 HzORDERING INFORMATIONTest SetSS100SunSet T1 ChassisIncludes battery charger, User's manual, Instrument stand.Software cartridge must be ordered separately.CLEI: T1TUW04HAACPR: 674488Software OptionsSW1000Software T1Includes basic measurements, loopback control, testpatterns send/rcv, bridge tap, propagation delay, quick test.Also includes VF channel capabilities: Talk/listen, view/control A, B (C ,D), DTMF dialing, send 5 tones at 2 levelsCLEI: T1TUW01HAACPR: 674485SW1010Software FT1Includes all Software T1 features and adds: Fractional T1,Teltrend/Westell looping device control, CSU/NIU emula-tion, ESF/SLC-96 data link controlCLEI: T1TUW02HAACPR: 674486SW100Remote ControlGraphical, menu driven VT100 emulationIncludes SS115 & SS122SW105Fractional T1Purchased with SW1000 onlySW106CSU/NIU EmulationPurchased with SW1000 onlySW107ESF & SLC-96 Data Link Send and ReceivePurchased with SW1000 onlySW111VF Level, Frequency & Noise MeasurementSW120Westell Maintenance Switch, PM NIU, RAMPPurchased with SW1010 onlySW130Pulse Mask AnalysisSW141MF/DTMF/DP Dialing, Decoding, and AnalysisSW144Teleos/Northern Switched 56 testsSW170Basic DDS PackageAccessoriesSS101Carrying CaseSS104Cigarette Lighter Battery ChargerSS105Repeater ExtenderSS106Single Bantam to Single Bantam Cable, 6'SS107Dual Bantam to Dual Bantam Cable, 6'SS108Single Bantam to Single 310 Cable, 6'SS109Single Bantam to Probe Clip Cable, 6'Note: Specifications subject to change without notice.© 2001 Sunrise Telecom Incorporated. All rights reserved.Printed in USA.00SS110Dual Bantam to 15-pin D Connector Cable,Male, 6'SS111Dual Bantam to 15-pin D Connector Cable,Female, 6'SS112Dual Bantam to 8-position Modular Plug Cable, 6'SS113A AC Battery Charger, 120VAC SS113B AC Battery Charger, 110VAC SS114SunSet T1 User's ManualSS115DIN-8 to RS232C Printer Cable SS115B DIN-8 to DB-9 Printer Cable SS116Instrument StandSS117A Printer Paper, 5 rolls, for SS118B/CSS118B High Capacity Thermal Printer with 110 VAC charger. Includes SS115B.SS118C High Capacity Thermal Printer with 220 VAC charger. Includes SS115B.SS121A SunSet AC Charger, 230VAC, 50/60 Cycle European style connectorSS121B SunSet AC Charger, 220VAC, 50/60 Cycle 3-prong IEC connectorSS121C SunSet AC Charger, 240VAC, 50/60 Cycle 3-prong IEC connectorSS122Null Modem Adapter, DB-25SS122A Null Modem Adapter, DB-9SS123A SunSet JacketSS125SunSet T1 Training Tape, EnglishSS130A Removable SunSet Rack Mount - 19"/23"SS130B Permanent SunSet Rack Mount - 19"/23"SS132Two Single Bantams to 4-position Modular Plug Cable。

first order motion model代码

first order motion model代码

first order motion model代码首先,我们需要了解什么是First-Order Motion Model。

First-Order Motion Model是一种机器学习模型,可以通过给定的姿态(pose)和动作(motion)来生成一个跟随该动作的视频。

该模型的基本思想是,在给定的姿态下,通过学习运动系统的运动特征,来推断出接下来的动作信息,并使用该信息来生成视频。

接下来,我们将介绍使用Python编写的First-Order Motion Model代码的主要步骤。

第一步是导入所需的Python包。

在使用First-Order Motion Model时,我们需要的主要Python包是OpenCV、NumPy和PyTorch。

我们还需要安装DensePose,它是Facebook AI Research中一种功能强大的流形预测库,用于估计人体区域的密集映射。

第二步是解析输入数据。

我们需要两个输入文件,一个是目标视频文件,另一个是预测数据文件。

目标视频文件是我们想要生成的视频,预测数据文件包含姿势和运动数据。

第三步是使用OpenCV加载目标视频。

在这一步中,我们需要使用OpenCV库提供的VideoCapture方法来加载视频,并将视频帧作为输入列表存储在变量中。

第四步是使用导入的DensePose库来读取指定静止图像的姿势数据。

在这一步中,我们需要使用DensePose库来读取图片并获取人脸姿势数据。

第五步是将静态图像的姿势数据应用于整个目标视频序列。

在这一步中,我们将解析的姿势数据应用于整个目标视频序列。

我们可以使用OpenCV库中的resize方法来调整解析的姿势数据的大小,并使用NumPy库来对其进行基于时间的线性插值。

第六步是使用预测数据文件中提供的动作数据来编码新视频的运动信息。

在这一步中,我们需要使用预测数据文件和PyTorch库中的LSTM模型来编码提供的动作信息并生成新的运动信息。

automodelforimageclassification.from_pretrained_说明

automodelforimageclassification.from_pretrained_说明

automodelforimageclassification.from_pretrained说明1. 引言1.1 概述在当今数字化时代,图像分类任务变得越来越重要。

图像分类是一个将输入的图像自动归类到预定义分类标签中的任务。

它在许多领域中都有广泛的应用,包括计算机视觉、人工智能、医学影像处理等。

为了解决这个问题,研究者们提出了各种各样的方法和算法。

1.2 文章结构本文将详细介绍automodelforimageclassification.from_pretrained函数及其在图像分类任务中的应用。

文章将分为五个部分进行讨论:第一部分是引言部分,对整篇文章进行概述,并描述文章的结构。

第二部分将介绍自动模型用于图像分类任务时所面临的挑战以及传统方法的局限性。

第三部分将详细解释automodelforimageclassification.from_pretrained函数的功能和使用方法,并通过实例演示其操作过程。

第四部分将对该函数进行优点和局限性分析,评估其在实际应用中的效果和限制。

最后一部分是结论部分,对全文进行总结回顾,并展望未来研究方向。

1.3 目的本文旨在介绍automodelforimageclassification.from_pretrained函数以及其在图像分类任务中的应用。

通过深入分析该函数的功能和使用方法,我们希望读者能够对这一技术有更全面的了解,并对其在实际应用中的优点和局限性有清晰的认识。

同时,我们也希望激发读者对未来相关研究方向的兴趣,并为进一步研究提供参考。

2. 自动模型用于图像分类的介绍2.1 图像分类任务图像分类是计算机视觉中最基础和常见的任务之一。

其目标是将输入的图像分为预定义类别中的一个或多个。

在现实世界中,图像分类应用广泛,例如人脸识别、物体识别和场景分析等领域。

2.2 传统方法的局限性在过去,图像分类主要依赖于手工设计特征和使用传统机器学习算法进行学习和预测。

范畴论

范畴论
A Abstract
Refine
Chess Review, Nov. 21, 2005 6
Hybrid Category Theory: Framework
• One begins with:
– A collection of “non-hybrid” mathematical objects – A notion of how these objects are related to one
"Hybrid System Theory", C. Tomlin
[Ames, Sastry]
Chess Review, Nov. 21, 2005 8
Hybrid Reduction Theorem
"Hybrid System Theory", C. Tomlin
[Ames, Sastry]
Chess Review, Nov. 21, 2005 9
x
3 2
0
Байду номын сангаас
x
3 2
l
0
3
"Hybrid System Theory", C. Tomlin
3 2
x
3
Linear optimization problem !
[Doyen, Henzinger, Raskin]
Chess Review, Nov. 21, 2005 16
Methodology
Original Automaton
Chess Review, Nov. 21, 2005 4
Interchange format for HS: Abstract Semantics (Execution)

paddledetection best_model预训练 -回复

paddledetection best_model预训练 -回复

paddledetection best_model预训练-回复如何使用[paddledetection best_model预训练]模型进行目标检测任务?目标检测是计算机视觉领域中的重要任务之一,它旨在识别图像或视频中的特定物体,并将其标记出来。

随着深度学习技术的发展,目标检测算法的性能不断提升。

PaddleDetection是百度推出的一个开源目标检测工具库,它提供了多种经典的目标检测模型,其中包括了[paddledetection best_model预训练]模型。

本文将详细介绍如何使用[paddledetection best_model预训练]模型进行目标检测任务。

1. 安装PaddleDetection库首先,需要在本地环境中安装PaddleDetection库。

可以通过pip命令来安装最新版本的PaddleDetection:pip install paddlepaddle paddlepaddle-gpupip install paddlepaddle paddlepaddle-gpupip install paddlepaddle paddlepaddle-gpu安装完成后,可以使用下面的命令来验证是否安装成功:python -c "import paddle; print(paddle.__version__)"2. 准备训练数据在进行目标检测任务之前,需要准备相应的训练数据集。

数据集应该包括已标记的图像以及对应的标签信息。

标签信息通常以XML、JSON或CSV 等格式存储。

确保将数据集划分为训练集和验证集,并按照一定的比例进行划分。

3. 配置训练参数在使用[paddledetection best_model预训练]模型进行目标检测任务之前,需要进行一些训练参数的配置。

可以通过修改`configs`目录下的`.yml`文件来进行配置。

例如,要使用[paddledetection best_model预训练]模型进行目标检测任务,可以选择`yolov3_mobilenet_v1_best_model.yml`文件。

基于SMT的PTACTL限界模型检测方法

基于SMT的PTACTL限界模型检测方法

。: … ,其中 SO, S2,… ∈S。对每一 条 路径 和 n∈N,令 (n)表示第 n个 状态 s ,1T“表示
第 n个 后 缀 Sn

… ,对 n m ∈
si 1=EX。Ⅸ当且仅 当 j .(s0=s ^(sl I=I ^ )) si l=EG,po ̄当且仅当 j .(s0=si A Vk≥o.(sk l=I — )) s。l=E(etU 13)当且 仅当 1r.(s0=si A k O.((skl= I:A B)A0 Vj<k.(Si I= )))
probabilistic timed automata against PTACTL properties.This method adds the transition times and tra n sition probabilities into the ACTL proper ties and changes the encodings of models and properties in order to ver ify them ,which is come from the SMT—based bounded model checking.W e alSO give two examples to show that this method is effectiveness and ef iciency. Key words:bounded model checking;probab ilistic timed systems;probabilistic timed automata;PTACTL;SMT
N,令 n..m]表示有 限状态 序列 s _二 s +
例 如 ,Ex。Ot在 状 态 si上 成 立 ,就 意 味 着 有 一 条

transformers automodel用法 -回复

transformers automodel用法 -回复

transformers automodel用法-回复Transformers 是一个开源的自然语言处理库,提供了丰富的模型架构和预训练模型,以帮助开发者更好地处理文本数据。

其中,AutoModel 是Transformers 中的一个模型类别,它是一个高级别的模型类,可以自动推断模型类型并加载对应的预训练模型。

AutoModel 的使用相对简单,但对于新手来说,可能还是有些困惑。

在这篇文章中,我们将一步一步地介绍如何使用Transformers 中的AutoModel。

首先,我们需要安装Transformers 库。

可以通过以下命令使用pip 来安装Transformers:pip install transformers安装完成后,我们可以开始使用AutoModel 类。

1. 导入所需的库和模块在代码中,我们首先需要导入transformers 模块,使其可用于使用AutoModel。

另外,我们可能还需要导入其他的Python 模块,如torch,numpy 等,以便对数据进行处理和模型操作。

下面是一个简单的导入示例:pythonimport torchimport numpy as npfrom transformers import AutoModel2. 创建一个AutoModel 对象接下来,我们需要创建一个AutoModel 对象。

AutoModel 类是一个抽象类,不能直接被实例化,但可以通过`AutoModel.from_pretrained` 方法来创建一个AutoModel 对象,并加载相应的预训练模型。

`from_pretrained` 方法接受一个参数,即预训练模型的名称或路径。

下面是一个简单的示例:pythonmodel_name = "bert-base-uncased"model = AutoModel.from_pretrained(model_name)上述代码将从Hugging Face 模型仓库中下载并加载了一个基于BERT的预训练模型,该模型是大小写不敏感的。

深度学习之自动编码器总结_20160216

深度学习之自动编码器总结_20160216
rand:
生成均匀分布的伪随机数。分布在(0~1)之间
主要语法:rand(m,n)生成m行n列的均匀分布的伪随机数
rand(m,n,'double')生成指定精度的均匀分布的伪随机数,参数还可以是'single'
rand(RandStream,m,n)利用指定的RandStream(我理解为随机种子)生成伪随机数
最后的W1的权值如下所示:
2.
根据代码中的sparseAutoencoderCost模块进行说明。
%将长向量转换成每一层的权值矩阵和偏置向量值;将theta中的数据赋值给w1,w2,b1,b2,这些矩阵的维度与其初始维度相同。
W1 =reshape(theta(1:hiddenSize*visibleSiபைடு நூலகம்e),hiddenSize,visibleSize);
b2 =theta(2*hiddenSize*visibleSize+hiddenSize+1:end);
% Costandgradient variables (your code needstocompute these values).
%Here, we initialize themtozeros.
b1grad = (1/m)*b1grad;%注意b的偏导是一个向量,所以这里应该把每一行的值累加起来
%计算b2grad
b2grad = b2grad+sum(d3,2);
b2grad = (1/m)*b2grad;
3.
3.1
%% CS294A/CS294W Programming Assignment Starter Code
%损失函数的总表达式 ;

Hybrid_Automata

Hybrid_Automata

17
Water Tank Problem: Hybrid Execution
x1 ≥ r1 Λx 2 ≥ r2 x1 ≥ r1 Λx 2 ≥ r2
x 2 ≤ r2
x := x
q2 x1 = −v1 x2 = w − v2 x1 ≥ r1
. .
q1 x1 = w − v1 x2 = −v2 x2 ≥ r2
Introduction to Hybrid Automata
Arijit Mondal Kapil Modi Arnab Sinha
Formal-V Group, IIT KGP 1
Introduction
• A hybrid automaton is a formal model for a mixed discrete continuous system. • Systems with ‘discrete jumps’ & ‘continuous flow’ can be modeled into Hybrid Automata. • Bouncing Ball Example: Here, the following properties hold:
Domain (Fly)
v := −ev
Formal-V Group, IIT KGP
Reset condition
4
An Illustration: Water Tank Problem
Formal-V Group, IIT KGP
5
Water Tank: Properties
• The supplier can supply water at a rate of w to only one reservoir at a time. [Discrete Behavior] • The current levels are x1 and x2 respectively. [Continuous Variables] • The minimum threshold to be maintained are r1 and r2 respectively. [Guard Conditions] • It is assumed that while transition between reservoirs none of the level changes. [Reset Property]

autotextmaster使用教程

autotextmaster使用教程

autotextmaster使用教程
1. 安装依赖库
在使用 autotextmaster 之前,需要安装几个 Python 依赖库。

可以使用 pip 命令安装。

```
pip install nltk numpy click
```
2. 下载预料库
可以从官方网站下载预料库,也可以使用以下命令下载:
```
python -m nltk.downloader all
```
3. 创建新的文章
输入以下命令,创建新的文章:
```
python autotextmaster.py generate -o <output_file> -n <number_of_sentences> [<input_files> ...]
```
其中,`-o` 参数指定输出文件名,`-n` 参数指定生成文章的句子数,`<input_files>` 参数指定输入文件。

4. 使用关键词生成新的文章
输入以下命令,根据给定的关键词生成新的文章:
```
python autotextmaster.py generate -o <output_file> -n <number_of_sentences> -s <seed_text>
```
其中,`-s` 参数指定生成文章的关键词或种子文本。

以上就是使用 autotextmaster 的步骤,当然还有其它更多的选项和参数可以设置,可以使用`python autotextmaster.py -h` 命令查看帮助。

Trio-BASIC运动控制编程语言Motion Perfect翠欧自控技术伺服控制器常用指令使用

Trio-BASIC运动控制编程语言Motion Perfect翠欧自控技术伺服控制器常用指令使用

TRIO 常用指令使用BASIC运动控制编程语言ACC类型:运动控制指令语法:ACC(acc率)注意:加速度率和减速度率可用ACCEL 和DECEL轴参数设定。

说明:同时设定加速度率和减速度率参数:acc率: 参数单位决定于单位轴参数。

例子:ACC(100) ps:则当前轴轴的加减速度为100ADDAX类型:运动控制指令语法:ADDAX(轴)说明:ADDAX指令将叠加轴的目标位置加到运动轴的轨迹上。

ADDAX指令发出,两轴连接。

使用ADDAX(-1)取消轴的连接。

ADDAX允许执行两轴叠加运动。

连接两轴以上,同样可以使用ADDAX。

ADDAX通常在缺省轴,除非使用BASE定义临时基本轴。

注意:注意多条ADDAX指令可能会产生危险。

例如一轴连接到另一轴,反之亦然。

这会造成系统的不稳定。

参数:轴:轴被设置成叠加轴,设置-1取消连接并返回正常操作。

例子:BASE(0)UNITS =10000SPEED =200ACCEL =1000DECEL =1000BASE(1)UNITS =10000SPEED =400ACCEL =2000DECEL =2000TRIGGERFORWARD AXIS(0)ADDAX(1) AXIS(0)WHILE TRUEWA(5000)MOVE(1000) AXIS(1)WA(5000)MOVE(-1000) AXIS(1)WENDAXIS类型:运动控制指令语法:AXIS(轴数)说明:AXIS修改设置单轴运动指令或单轴参数读写。

AXIS参数在命令行或程序行特别有效。

使用BASE指令改变基本轴。

参数:轴数:任何有效的BASIC表达式特定轴数。

例子:speed axis(1)=100 '修改轴1的速度BASE类型:运动控制指令语法:BASE(轴1,轴2,轴3)BASE参数:BA(轴1,轴2,轴3)BA说明:BASE指令用于设置缺省轴或特定轴组。

所有顺序运动指令和轴的参数会应用于基本轴或特定轴组,除非BASE指令定义暂时基本轴。

automodelforcausallm进行指令微调-概述说明以及解释

automodelforcausallm进行指令微调-概述说明以及解释

automodelforcausallm进行指令微调-概述说明以及解释1.引言1.1 概述概述部分的内容可以从automodelforcausallm(自动模型因果语言模型)进行指令微调的背景和意义入手。

可以简单介绍自然语言处理技术的发展和应用广泛性,以及由此带来的挑战和需求。

接着,可以引入automodelforcausallm作为一种新的自动模型因果语言模型,并说明它在指令微调领域的独特性和优点。

该模型结合了因果推理和语言模型的特点,在处理复杂指令任务时具有良好的性能。

通过进行指令微调,模型可以学习并适应特定领域的指令数据,提高对指令的理解和生成能力。

此外,可以提及automodelforcausallm在自动驾驶、智能客服、智能助理等领域的应用前景,并对本文中将要介绍的内容进行总结。

最后,可以概括强调automodelforcausallm进行指令微调的重要性,为读者引起兴趣,带入后续章节的内容。

1.2文章结构1.2 文章结构文章将按照以下结构进行论述:引言部分将提供对本篇文章的概述,包括automodelforcausallm进行指令微调的重要性和应用领域。

同时,还会介绍文章的目的,即探讨指令微调在automodelforcausallm 中的实施方法和效果。

正文部分将详细介绍automodelforcausallm 的基本概念和原理。

首先,会对automodelforcausallm 进行全面的介绍,包括其应用领域和其在因果关系建模中的重要性。

紧接着,会对指令微调的概念进行解释,包括其定义、原理和应用场景。

接下来,我们将探讨指令微调在automodelforcausallm 中的具体实施方法。

这将包括数据收集和准备、模型选择和设置、指令微调的步骤和技巧等内容。

我们将对每个步骤进行详细说明,并提供实际案例来加深理解。

结论部分将对automodelforcausallm 进行指令微调的重要性进行总结。

SHARP PC-1211A-计算机科学类计算器用户手册说明书

SHARP PC-1211A-计算机科学类计算器用户手册说明书

INTRODUCTIONAbout operation examples, please refer to the attached sheet.Refer to the number on the right of each title for use.After reading this manual, store it in a convenient location for future reference.Note:Some of the models described in this manual may notbe available in some countries.Operational NotesTo ensure trouble-free operation, please observe the follow-ing points:1.Do not carry the calculator in the back pocket of slacks or trousers.2.Do not subject the calculator to extreme temperatures.3.Do not drop it or apply excessive force.4.Clean only with a soft, dry cloth.5.Do not use or store the calculator where fluids can splash onto it.o Press the RESET switch only in the following cases:•When using for the first time •After replacing the batteries •To clear all memory contents•When an abnormal condition occurs and all keys are inoperative.If service should be required on this calculator, use only a SHARP servicing dealer, SHARP approved service facility, or SHARP repair service where available.Hard Case(°↔DISPLAY(During actual use not all symbols are displayed at the same time.)If the value of mantissa does not fit within the range ±0.000000001 – ±9999999999, the display changes to scien-tific notation. The display mode can be changed according to the purpose of the calculation./:Appears when the entire equation cannot be dis-played. Press </> to see the remaining (hid-den) section.2ndF :Appears when @ is pressed, indicating that the functions shown in orange are enabled.HYP:Indicates that h has been pressed and the hy-perbolic functions are enabled. If @ H are pressed, the symbols “2ndF HYP ” appear, indicat-ing that inverse hyperbolic functions are enabled.ALPHA:Indicates that @ K or O (R ) has beenpressed, and entry (recall) of memory contents and recall of statistics can be performed.FIX/SCI/ENG: Indicates the notation used to display a valueand changes each time @ f are pressed.DEG/RAD/GRAD: Indicates angular units and changes eachtime G is pressed.STAT:Appears when statistics mode is selected.M:Indicates that a numerical value is stored in the independent memory.DisplayBEFORE USING THE CALCULATOR Key Notation Used in this ManualIn this manual, key operations are described as follows:e x x To specify e x :@e lnTo specify ln :ITo specify x :@KXFunctions that are printed in orange above the key require@ to be pressed first before the key. When you specify the memory, press @K first. Numbers are not shown as keys, but as ordinary numbers.Power On and OffPress ª to turn the calculator on, and @F to turn it off.Clearing MethodsThere are three clearing methods as follows:Clearing Entry M*1A-D, X,Y*2operation (Display)STAT, ANSª××@c ×RESET: Clear × : Retain *1Independent memory M.*2Temporary memories A-D, X and Y, statistical data, and last answer memory.Editing the Equation•Press < or >to move the cursor. You can also return to the equation after getting an answer by pressing > (<). See below for Multi-line playback function.•If you need to delete a number, move the cursor to the number you wish to delete then press d .The number under the cursor will be deleted.•If you need to insert a number, move the cursor to the place immediately after where you wish to insert the number then enter the number.Multi-line Playback function (1)This calculator is equipped with a function to recall previousequations. Equations also include calculation ending instruc-tions such as “=” and a maximum of 142 characters can be stored in memory. When the memory is full, stored equations are deleted in the order of the oldest first. Pressing[ will display the previous equation and the answer. Further press-ing [ will display preceding equations (after returning to the previous equation, press ] to view equations in or-der). In addition,@[ can be used to jump to the oldest equation.(Radians)SCIENTIFIC CALCULATIONS•Press @ m 0 to select the normal mode.•In each example, press ª to clear the display. And if the FIX, SCI, or ENG indicator is displayed, clear the indicator by pressing @ f .Arithmetic Operations (2)•The closing parenthesis ) just before = or ;may be omitted.Constant Calculations (3)•In the constant calculations, the addend becomes a con-stant. Subtraction and division are performed in the same manner. For multiplication, the multiplicand becomes a con-stant.•When performing calculations using constants, constants will be displayed as K.Functions (4)•Refer to the operation examples of each function.•Before starting calculations, specify the angular unit.•The results of inverse trigonometric functions are displayed within the following range:Coordinate Conversions(10)•Before performing a calculation, select the angular unit.Rectangular coord.Polar coord.•The calculation result is automatically stored in memories X and Y.Value of r or x : X memory Value of θ or y : Y memoryModify Function (11)In this calculator, all calculation results are internally obtained in scientific notation with up to 12 digits for the mantissa.However, since calculation results are displayed in the form designated by the display notation and the number of decimal places indicated, the internal calculation result may differ from that shown in the display. By using the modify function, the internal value is converted to match that of the display, so that the displayed value can be used without change in subse-quent operations.STATISTICAL CALCULATIONSPress @ m 1 to select single-variable statistics mode and @ m 2 to select two- variable statistics mode. The following statistics can be obtained for each statis-tical calculation (refer to the table below):Single-variable statistical calculation(12)Statistics of 1Linear regression calculation(13)Statistics of 1 and 2 and, in addition, estimate of y for a given x (estimate y’) and estimate of x for a given y (esti-mate x’)x Mean of samples (x data)sx Sample standard deviation (x data)1σx Population standard deviation (x data)n Number of samplesΣx Sum of samples (x data)Σx 2Sum of squares of samples (x data)y Means of samples (y data)sy Sample standard deviation (y data)σy Population standard deviation (y data)Σy Sum of samples (y data)2Σy 2Sum of squares of samples (y data)Σxy Sum of products of samples (x , y )r Correlation coefficienta Coefficient of regression equation (y =a +bx )bCoefficient of regression equation (y =a +bx )Entered data are kept in memory until @ c or @m 1 (2) are pressed. Before entering new data,clear the memory contents.[Data Entry]Single-variable dataData kData & frequency k (To enter multiples of the same data)Two-variable dataData x & Data y kData x & Data y & frequency k (To enter multiples of the same data x and y .)[Data Correction]Correction prior to pressing k :Delete incorrect data with ª.Correction after pressing k :Press > to confirm the latest entry and press @J to delete it.Statistical Calculation Formulas (14)Refer also to the operation examples sheet.In the statistical calculation formulas, an error will occur when:•the absolute value of the intermediate result or calculation result is equal to or greater than 1 × 10100.•the denominator is zero.•an attempt is made to take the square root of a negative number.ERROR AND CALCULATION RANGES ErrorsAn error will occur if an operation exceeds the calculation ranges, or if a mathematically illegal operation is attempted.When an error occurs, pressing < (or >) automatically moves the cursor back to the place in the equation where the error occurred. Edit the equation or press ª to clear the equation.Error Codes and Error TypesSyntax error (Error 1):•An attempt was made to perform an invalid operation.Ex. 2 @{Calculation error (Error 2):•The absolute value of an intermediate or final calculation result equals or exceeds 10100.•An attempt was made to divide by 0.•The calculation ranges were exceeded while performing calcu-lations.Depth error (Error 3):•The available number of buffers was exceeded. (There are 8buffers* for numeric values and 16 buffers for calculation in-structions). *4 buffers in STAT mode.Equation too long (Error 4):•The equation exceeded its maximum input buffer (142 charac-ters). An equation must be shorter than 142 characters.Calculation Ranges (15)Refer also to the operation examples sheet.•Within the ranges specified, this calculator is accurate to ±1in the least significant digit of the mantissa. When perform-ing continuous calculations (including chain calculations),errors accumulate leading to reduced accuracy.Time, Decimal and Sexagesimal Calculations (9)Conversion between decimal and sexagesimal numbers can be performed. In addition, the four basic arithmetic operations and memory calculations can be carried out using the sexagesimal system.Random NumbersA pseudo-random number with three significant digits can be generated by pressing @ ` =. To generate the next random number, press =. You can perform this func-tion in the normal and statistics modes.•Random numbers use memory Y. Each random number is generated on the basis of the value stored in memory Y (pseudo-random number series).Angular Unit Conversions (5)Each time @ g are pressed, the angular unit changes in sequence.Memory Calculations (6)This calculator has 6 temporary memories (A-D, X and Y),one independent memory (M) and one last answer memory.Independent memory and temporary memories are only avail-able in the normal mode.[Temporary memories (A-D, X and Y)]A stored value can be recalled as a value or variable for the use in equations.•In case you store an infinite decimal in the memory, recall it as a variable to obtain accurate answers.Ex.)1 / 3 O Y (0.3333...is stored to Y)3 * R Y =0.9999999993 * @ K Y =1.[Independent memory (M)]In addition to all the features of temporary memories, a value can be added to or subtracted from an existing memory value.[Last answer memory (ANS)]The calculation result obtained by pressing = or any other calculation ending instruction is automatically stored in the last answer memory.Note:Calculation results from the functions indicated below are automatically stored in memories X or Y. For this reason,when using these functions, be careful with the use of memo-ries X and Y.•Random numbers ..................Y memory•→r θ, →xy ...............................X memory, Y memory even when the same mode is reselected.π2π2θ = sin –1 x , θ = tan –1 xθ = cos –1 xDEG –90 ≤ θ ≤ 900 ≤ θ ≤ 180RAD – — ≤ θ ≤ —0 ≤ θ ≤ πGRAD–100 ≤ θ ≤ 1000 ≤ θ ≤ 200PRINTED IN CHINA / IMPRIM É EN CHINE00KUP (TINSK0424THZZ)•The multi-line memory is cleared by the following opera-tions: @c , @F (including the Automatic Power Off feature), mode change, RESET, @`,@?, constant calculation, angle conversion/change,coordinate conversion, numerical value storage to the tem-porary memories and independent memory, and input/dele-tion of statistical data.Priority Levels in CalculationThis calculator performs operations according to the following priority:1 Functions preceded by their argument (x -1, x 2, n!, etc.) 2Y x , x ¿ 3 Implied multiplication of a memory value (2Y, etc.)4 Functions followed by their argument (sin, cos, etc.) 5Implied multiplication of a function (2sin30, etc.) 6 n C r , n P r 7×, ÷ 8 +, – 9 =, M+, M –, ⇒M, |DEG, |RAD, |GRAD,DATA, CD, →r θ, →xy and other calculation ending instruction •If parentheses are used, parenthesized calculations have precedence over any other calculations.INITIAL SETUP Mode SelectionNormal mode (NORMAL): @m0Used to perform arithmetic operations and function calcula-tions.Single-variable statistics mode (STAT x ): @m1Used to perform 1-variable statistical calculations.Two-variable statistic mode (STAT xy ): @m2Used to perform 2-variable statistical calculations.When executing mode selection, temporary memories, statis-tical data and last answer memory will be cleared even when reselecting the same mode.Selecting the Display Notation and Decimal PlacesThe calculator has four display notation systems for display-ing calculation results. When FIX, SCI, or ENG symbol is displayed, the number of decimal places can be set to any value between 0 and 9. Displayed values will be reduced to the corresponding number of digits.100000÷3=[Floating point]ª100000/3=33333.33333→[Fixed decimal point]@f 33333.33333[TAB set to 2]@i 233333.33→[SCIentific notation]@f 3.33×104→[ENGineering notation]@f 33.33×103→[Floating point]@f33333.33333•If the value for floating point system does not fit in the following range, the calculator will display the result using scientific notation system:0.000000001 ≤ | x | ≤ 9999999999Determination of the Angular UnitIn this calculator, the following three angular units can be specified.Chain Calculations (7)This calculator allows the previous calculation result to be used in the following calculation.For example, you can calculate by ⁄= and s=.The previous calculation result will not be recalled after enter-ing multiple instructions.Fraction Calculations (8)This calculator performs arithmetic operations and memory calculations using a fraction, and conversion between a deci-mal number and a fraction.•In all cases, a total of up to 10 digits including integer,numerator, denominator and the symbol (l ) can be entered.•If the number of digits to be displayed is greater than 10, the number is converted to and displayed as a decimal number.• A decimal number, variable, or exponent cannot be used in a fraction.BATTERY REPLACEMENT Notes on Battery ReplacementImproper handling of batteries can cause electrolyte leakage or explosion. Be sure to observe the following handling rules:•Replace both batteries at the same time.•Do not mix new and old batteries.•Make sure the new batteries are the correct type.•When installing, orient each battery properly as indicated in the calculator.•Batteries are factory-installed before shipment, and may be exhausted before they reach the service life stated in the specifications.When to Replace the BatteriesIf the display has poor contrast, the batteries require replace-ment.Caution•Keep batteries out of the reach of children.•Exhausted batteries left in the calculator may leak and damage the calculator.•Explosion risk may be caused by incorrect handling.•Batteries must be replaced only with others of the same type.•Do not throw batteries into a fire as they may explode.Replacement Procedure1.Turn the power off by pressing @ F .2.Remove two screws. (Fig. 1)3.Slide the battery cover slightly and lift it to remove.4.[EL-509V/EL-531V] Remove the used batteries by prying them with a ball-point pen or other similar pointed device.(Fig. 2)[EL-509VH/EL-531VH] Remove used batteries.5.Install two new batteries.[EL-509V/EL-531V] Make sure the “+” side facing up.[EL-509VH/EL-531VH] First insert the “ – ” side to the spring. (Fig. 3)6.Replace the back cover and screws.7.Press the RESET switch (on the back).•Make sure that the display appears as shown below. If the display does not appear as shown, remove the batteries reinstall them and check the display once again.(Fig. 2)(Fig. 3)Automatic Power Off FunctionThis calculator will turn itself off to save battery power if no key is pressed for approximately 10 minutes.SPECIFICATIONSCalculations:Scientific calculations, statistical cal-culations, etc.Internal calculations:Mantissas of up to 12 digits Pending operations:16 calculations 8 numeric values(4 numeric values in STAT mode)Power source:3V ¶ (DC):[EL-509V/EL-531V]Alkaline batteries (LR44) × 2[EL-509VH/EL-531VH]Heavy duty manganese batteries (size AA or R6) × 2Power consumption:0.0006 W Operating time:[EL-509V/EL-531V]Approx. 2500 hours [EL-509VH/EL-531VH]Approx. 15000 hourswhen continuously displaying 55555.at 25°C (77°F).Varies according to use and other factors.Operating temperature:0°C – 40°C (32°F – 104°F)External dimensions:[EL-509V/EL-531V]78.6 mm (W) × 152 mm (D) × 10.5mm (H)3-3/32” (W) × 5-31/32” (D) × 13/32”(H)[EL-509VH/EL-531VH]78.6 mm (W) × 166 mm (D) × 19.5mm (H)3-3/32” (W) × 6-17/32” (D) × 25/32” (H)Weight:[EL-509V/EL-531V]Approx. 75 g (0.166 lb)(Including batteries)[EL-509VH/EL-531VH]Approx. 115 g (0.254 lb)(Including batteries)Accessories:Batteries × 2 (installed), operationmanual, operation examples sheet,quick reference card and hard case•Calculation ranges±10-99 ~ ±9.999999999×1099 and 0.If the absolute value of an entry or a final or intermediate result of a calculation is less than 10–99, the value is consid-ered to be 0 in calculations and in the display.FOR MORE INFORMATION ABOUT THIS CALCULATORVisit our Web site./calculator/。

自适应、完全非递归的模态分解算法

自适应、完全非递归的模态分解算法

自适应、完全非递归的模态分解算法下载提示:该文档是本店铺精心编制而成的,希望大家下载后,能够帮助大家解决实际问题。

文档下载后可定制修改,请根据实际需要进行调整和使用,谢谢!本店铺为大家提供各种类型的实用资料,如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等,想了解不同资料格式和写法,敬请关注!Download tips: This document is carefully compiled by this editor. I hope that after you download it, it can help you solve practical problems. The document can be customized and modified after downloading, please adjust and use it according to actual needs, thank you! In addition, this shop provides you with various types of practical materials, such as educational essays, diary appreciation, sentence excerpts, ancient poems, classic articles, topic composition, work summary, word parsing, copy excerpts, other materials and so on, want to know different data formats and writing methods, please pay attention!自适应、完全非递归的模态分解算法概述在信号处理和数据分析领域,模态分解算法是一种有效的工具,用于提取信号或数据中的模态特征。

ldamodel函数

ldamodel函数

ldamodel函数如何使用Python中的LDA模型进行主题建模在自然语言处理和机器学习领域,主题建模是一项重要的任务,用于从大量的文本数据中提取出隐含的主题和语义信息。

其中,Latent Dirichlet Allocation(LDA)模型是一种常用的主题建模方法之一。

本文将介绍如何使用Python中的LDA模型进行主题建模,并逐步回答您的问题。

首先,我们需要安装Python中的gensim库,该库提供了LDA模型的实现。

您可以通过以下命令安装gensim库:pip install gensim一旦安装完成,我们就可以开始使用LDA模型进行主题建模了。

第一步:加载数据集在使用LDA模型之前,我们首先需要准备一份文本数据集。

您可以使用任何您感兴趣的文本数据集,如新闻文章、论文摘要、社交媒体评论等等。

在本文中,我们假设您已经有一份名为"corpus.txt"的文本数据集。

接下来,我们可以使用Python中内置的open函数和readlines方法来加载数据集:pythoncorpus_path = "corpus.txt"with open(corpus_path, "r", encoding="utf-8") as f:corpus = [line.strip() for line in f.readlines()]这里,我们使用了一个列表推导式,将读取到的每一行文本去除换行符,并将其添加到一个名为corpus的列表中。

第二步:数据预处理在进行主题建模之前,我们需要对文本数据进行一些预处理工作,如分词、去除停用词、词形还原等。

这些预处理步骤可帮助我们更好地提取主题信息。

下面是数据预处理的一些常见操作,您可以根据自己的需求选择性地进行使用:1. 分词:将文本划分为词语的序列。

在Python中,可以使用nltk库或jieba库等进行中文和英文分词。

I-DEAS CAE问题总结Verson01

I-DEAS CAE问题总结Verson01

一、软件安装1. ideas v5安装问题/forum/viewthread.php?tid=810036&highlight=ideas%2B%B0%B2%D7% B02. i-deas10 NX安装方法/forum/viewthread.php?tid=232824&highli ght=ideas%2B%B0%B2%D7%B03.i-deas11 NX安装方法/forum/viewthread.php?tid=298208&highli ght=ideas%2B%B0%B2%D7%B04.i-deas12安装的过程说明/forum/viewthread.php?tid=746023&page=1 &extra=page%3D45.i-deas12的视频安装教程,录制的/forum/thread-752975-1-8.html二.网格划分1.如何在ideas中给一个圆柱体划分六面体网格/forum/viewthread.php?tid=113931&highli ght=%CD%F8%B8%F12.如何将此模型的两边网格画得一样!/forum/viewthread.php?tid=245930&highli ght=%CD%F8%B8%F13.怎样在一个圆面上画同心网格/forum/viewthread.php?tid=298391&highlight=%CD%F8%B8%F14.关于网格划分的问题/forum/viewthread.php?tid=328127&highli ght=%CD%F8%B8%F1/forum/viewthread.php?tid=462114&highli ght=%CD%F8%B8%F15.网格划分问题/forum/viewthread.php?tid=496814&extra= &highlight=%CD%F8%B8%F1&page=16.一个复杂的网格划分问题/forum/viewthread.php?tid=569410&highli ght=%CD%F8%B8%F17.用 I-DEAS 对大模型划分网格/forum/viewthread.php?tid=570307/forum/viewthread.php?tid=768147&highli ght=%CD%F8%B8%F18.有限元网格划分的基本原则/forum/viewthread.php?tid=505531&highli ght=%CD%F8%B8%F19.那一种网格的精度较高?/forum/viewthread.php?tid=723019&highli ght=%CD%F8%B8%F110.前处理之网格划分/forum/viewthread.php?tid=635394&highlight=%CD%F8%B8%F111.网格数目与温度计算结果有多大联系/forum/viewthread.php?tid=725707&highli ght=%CD%F8%B8%F112.几个ideas六面体网格模型/forum/viewthread.php?tid=539885&highli ght=%CD%F8%B8%F113.网格划分大讨论/forum/viewthread.php?tid=653572&highli ght=%CD%F8%B8%F114.如何画网格/forum/viewthread.php?tid=439411&highli ght=%CD%F8%B8%F115.网格划分技巧/forum/viewthread.php?tid=800324&highli ght=%CD%F8%B8%F116.I-DEAS 自由网格划分的free option的参数设置方法/forum/viewthread.php?tid=807861&highli ght=%CD%F8%B8%F117.I-DEAS 自由mapped meshing option的参数设置/forum/redirect.php?fid=33&tid=807861&g oto=nextoldset18.轮毂弯曲的问题/forum/thread-803497-1-4.html三.热分析1.I-DEAS热分析实用教程/forum/viewthread.php?tid=613369&highli ght=%C8%C8%B7%D6%CE%F6/forum/viewthread.php?tid=803898&highli ght=%C8%C8%B7%D6%CE%F62.请教版主/forum/thread-810346-1-2.html四.TMG/ESC1.TMG中的卫星轨道问题/forum/viewthread.php?tid=748802&highli ght=TMG2.请问用TMG做热分析的各位,怎么确定热偶合的偶合系数?/forum/viewthread.php?tid=730417&highli ght=TMG3.TMG 中的 TEMPERATAVE MAPPING 的使用方法一个视频/forum/viewthread.php?tid=809080&highli ght=TMG4.分享 TMG 的一些英文教程/forum/viewthread.php?tid=784001&hig hlight=TMG5.关于 TMG 热耦合的一点资料/forum/viewthread.php?tid=784040&highli ght=TMG6.发个繁体的ESC的介绍/forum/viewthread.php?tid=615307&highli ght=ESC7.ESC里面设置矢量路径的方法/forum/viewthread.php?tid=712912&highli ght=ESC8.I-DEAS ESC 散熱設計-使用Assembly Method的步驟/forum/viewthread.php?tid=750259&highli ght=ESC9.我做一个ESC模型的几点收获/forum/viewthread.php?tid=702900&highli ght=ESC10.[原創]I-DEAS/ESC PC散熱設計之流程與報告- 使用Assembly FE Model/forum/viewthread.php?tid=750399&highli ght=ESC五.模态、响应分析1.【原创】某底座支架约束模态分析/forum/viewthread.php?tid=156310&highli ght=%C4%A3%CC%AC2.关于I-DEAS的模态分析的几个问题/forum/viewthread.php?tid=252035&highli ght=%C4%A3%CC%AC3.模态问题求教/forum/viewthread.php?tid=498901&highli ght=%C4%A3%CC%AC4.模态是个什么概念/forum/viewthread.php?tid=457094&highli ght=%C4%A3%CC%AC5.装配体做模态分析,连接出怎样处理/forum/viewthread.php?tid=613165&highli ght=%C4%A3%CC%AC6. 关于简正模态和正则模态/forum/viewthread.php?tid=731293&highli ght=%C4%A3%CC%AC7. 如何解释I-DEAS模态分析结果中的位移和应力??/forum/viewthread.php?tid=454373&highli ght=%C4%A3%CC%AC8. 刚体模态(讨论)/forum/viewthread.php?tid=788335&highli ght=%C4%A3%CC%AC9.模态分析使用COUPLED DOF问题/forum/viewthread.php?tid=794921&highli ght=%C4%A3%CC%AC10.关于模态和振动的几个问题/forum/viewthread.php?tid=785983&highli ght=%C4%A3%CC%AC11.响应分析边界条件的定义/forum/viewthread.php?tid=501566&highli ght=%CF%EC%D3%A612.响应分析中激励函数的创建/forum/viewthread.php?tid=511231&highli ght=%CF%EC%D3%A613.响应分析中的加载问题/forum/viewthread.php?tid=513555&highli ght=%CF%EC%D3%A614.响应分析问题(常见问题)/forum/viewthread.php?tid=531755&highli ght=%CF%EC%D3%A615.请教一个关于I-DEAS响应结果显示问题/forum/viewthread.php?tid=725282&highli ght=%CF%EC%D3%A616.随机振动的动态响应问题/forum/viewthread.php?tid=735681&highli ght=%CF%EC%D3%A617.如何在IDEAS用直接法做响应分析,因为我的模型要考虑接触! /forum/viewthread.php?tid=778587&highli ght=%CF%EC%D3%A618.频率响应分析定义的CONNECTION DOF太多,计算时间超长/forum/viewthread.php?tid=738660&highli ght=%CF%EC%D3%A619.动力响应分析如何将结构重力考虑进去/forum/viewthread.php?tid=807550&highli ght=%CF%EC%D3%A620.关于I-DEAS 动力分析培训幻灯片/forum/viewthread.php?tid=588431&highli ght=%B6%AF%C1%A621. 正确理解动力学分析中的阻尼/forum/viewthread.php?tid=746899&highli ght=%B6%AF%C1%A622.【原创】一个response analysis的小例子/forum/viewthread.php?tid=146417&highli ght=response23.请教htbbzzg 兄/forum/thread-763050-1-15.html六.疲劳耐久分析1.应力集中对疲劳分析的影响问题/forum/viewthread.php?tid=738156&highli ght=%C6%A3%C0%CD2.I-DEAS疲劳练习/forum/viewthread.php?tid=57581&highlig ht=%C6%A3%C0%CD/forum/viewthread.php?tid=803839&highli ght=%C6%A3%C0%CD3.难得的原版疲劳分析一手资料/forum/viewthread.php?tid=792732&highli ght=%C6%A3%C0%CD4.I-DEAS 持久性分析的幻灯片,Unit 1~9/forum/thread-809136-1-1.html七.接触分析1.做接触有限元分析的请进/forum/viewthread.php?tid=392029&highlight=%BD%D3%B4%A52.如何建立实体的面接触单元/forum/viewthread.php?tid=431254&highli ght=%BD%D3%B4%A53.哪位兄弟介绍一下用GAP单元处理轴与孔的接触分析?/forum/viewthread.php?tid=189665&highli ght=%BD%D3%B4%A54.请帮忙分析装配体接触错误在哪/forum/viewthread.php?tid=636227&highli ght=%BD%D3%B4%A55.求教一个接触计算的问题/forum/viewthread.php?tid=617336&highli ght=%BD%D3%B4%A56.请高手指点,铲斗和土接触约束如何加载?/forum/viewthread.php?tid=735226&highli ght=%BD%D3%B4%A57.如何在IDEAS用直接法做响应分析,因为我的模型要考虑接触! /forum/viewthread.php?tid=778587&highli ght=%BD%D3%B4%A58. I-DEAS assembly contac t/forum/viewthread.php?tid=628711&highli ght=contact八.其他1.梁单元的后处理显示问题/forum/thread-812512-1-1.html2.扭转常数、翘曲常数和翘曲约束因子 - 一篇翻译资料/forum/thread-813826-1-1.html3. 梁单元的后处理显示问题/forum/thread-812512-1-1.html4.restraint 和 constraint 有什么不同/forum/thread-812852-1-2.html5.有限元分析中看结果的问题/forum/thread-800559-1-5.html6.难得的原版bolt_connection资料/forum/thread-722056-1-7.html7.在模拟铰接的结构时,用rigid还是用constraint?/forum/thread-788350-1-8.html8.I-DEAS元素过多,内存不够,怎么办?/forum/thread-549280-1-9.html9.请问I-DEAS中求解运算时的横坐标的supernode是什么意思?/forum/thread-773816-1-12.html 10. 弹簧元的设置/forum/thread-769933-1-12.html 11.如何提取中面?/forum/thread-525860-1-13.html 12.如何仿真加扭矩的光轴/forum/thread-757351-1-13.html 13.分析难题/forum/thread-762508-1-15.html 14. 8个问题请教htbbzzg 大哥/forum/thread-757855-1-16.html 15. GAP element 例子/forum/thread-565022-1-16.html 16. 试问高手几个问题/forum/thread-752457-1-17.html 17.请问如图所示的销钉如何模拟,请htbbzzg兄帮忙/forum/thread-745663-1-20.html 18. GM CAE Criteria(Original Edition)/forum/thread-625467-1-21.html。

APPLICATION OF NEURAL NETWORKS TO PREDICT

APPLICATION OF NEURAL NETWORKS TO PREDICT

NAVALPOSTGRADUATESCHOOLMONTEREY, CALIFORNIATHESISAPPLICATION OF NEURAL NETWORKS TO PREDICT UH-60L ELECTRICAL GENERATOR CONDITION USING(IMD-HUMS) DATAbyEvangelos TourvalisDecember 2006Thesis Advisor: Lyn R. Whitaker Second Reader: Samuel E. Buttrey Approved for public release; distribution is unlimitedTHIS PAGE INTENTIONALLY LEFT BLANKi REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503.1. AGENCY USE ONLY (Leave blank)2. REPORT DATE December 20063. REPORT TYPE AND DATES COVERED Master’s Thesis4. TITLE AND SUBTITLE : Application of Neural Networkss to Predict UH-60L Electrical Generator Condition using (IMD-HUMS) data6. AUTHOR(S) Tourvalis Evangelos5. FUNDING NUMBERS7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMINGORGANIZATION REPORTNUMBER9. SPONSORING /MONITORING AGENCY NAME(S) AND ADDRESS(ES) N/A 10. SPONSORING/MONITORINGAGENCY REPORT NUMBER11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government.12a. DISTRIBUTION / AVAILABILITY STATEMENT Approved for public release; distribution is unlimited12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words)In 2003, the US Army began using the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS), an integrated airborne and ground-based system developed by Goodrich Corporation, to support maintenance of the UH-60L. IMD-HUMS is responsible for collecting, processing, analyzing, and storing an enormous amount of vibratory and flight regime data obtained from sensors located throughout the aircraft.The purpose of this research is to predict failures of the UH-60L’s electrical generators, applying Artificial Neural Networks (ANN) on the IMD-HUMS-produced data. Artificial NNs are data based vice rule based, thereby possessing the potential capability to operate where analytical solutions are inadequate. They are reputed to be robust and highly tolerant of noisy data. Software tools such as Clementine 10.0, S-Plus 7.0, and Excel are used to establish these predictions.This research has verified that ANNs have a position in machinery condition monitoring and diagnostics. However, the limited nature of these results indicates that ANNs will not solve all machinery condition monitoring and diagnostics problems by themselves. They certainly will not completely replace conventional rule-based expert systems. Ultimately, it is anticipated that a symbiotic combination of these two technologies will provide the optimal solution to the machinery condition monitoring and diagnostics problem.15. NUMBER OF PAGES9914. SUBJECT TERMS Condition Based Maintenance, IMD-HUMS, ANNs, Backpropagation, Learning Process 16. PRICE CODE17. SECURITYCLASSIFICATION OFREPORTUnclassified18. SECURITY CLASSIFICATION OF THIS PAGE Unclassified 19. SECURITY CLASSIFICATION OF ABSTRACT Unclassified 20. LIMITATION OF ABSTRACT UL NSN 7540-01-280-5500S tandard Form 298 (Rev. 2-89) Prescribed by ANSI Std. 239-18THIS PAGE INTENTIONALLY LEFT BLANKiiApproved for public release; distribution is unlimited bold APPLICATION OF NEURAL NETWORKS TO PREDICT UH-60L ELECTRICAL GENERATOR CONDITION USING (IMD-HUMS) DATAEvangelos TourvalisMajor, Hellenic Air ForceSubmitted in partial fulfillment of therequirements for the degree ofMASTER OF SCIENCE IN OPERATIONS RESEARCHfrom theNAVAL POSTGRADUATE SCHOOLDecember 2006Author: Evangelos TourvalisApproved by: Lyn R. WhitakerThesis AdvisorSamuel E. ButtreySecond ReaderJames N. EagleChairman, Department of Operations ResearchiiiTHIS PAGE INTENTIONALLY LEFT BLANKivABSTRACTIn 2003, the US Army began using the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS), an integrated airborne and ground-based system developed by Goodrich Corporation, to support maintenance of the UH-60L. IMD-HUMS is responsible for collecting, processing, analyzing, and storing an enormous amount of vibratory and flight regime data obtained from sensors located throughout the aircraft.The purpose of this research is to predict failures of the UH-60L’s electrical generators, applying Artificial Neural Networks (ANN) on the IMD-HUMS-produced data. Artificial NNs are data based vice rule based, thereby possessing the potential capability to operate where analytical solutions are inadequate. They are reputed to be robust and highly tolerant of noisy data. Software tools such as Clementine 10.0, S-Plus 7.0, and Excel are used to establish these predictions.This research has verified that ANNs have a position in machinery condition monitoring and diagnostics. However, the limited nature of these results indicates that ANNs will not solve all machinery condition monitoring and diagnostics problems by themselves. They certainly will not completely replace conventional rule-based expert systems. Ultimately, it is anticipated that a symbiotic combination of these two technologies will provide the optimal solution to the machinery condition monitoring and diagnostics problem.vTHIS PAGE INTENTIONALLY LEFT BLANKviTABLE OF CONTENTSI.INTRODUCTION (1)A.CONDITION BASED MAINTENANCE (1)B.IMD-HUMS (3)1.On-Board System (OBS) (3)2.Ground Station System (GSS) (4)C.PREVIOUS WORK (6)D.AREA OF RESEARCH AND APPROACH (7)E.STATISTICAL TOOLS (8)ANIZATION OF STUDY (8)II.ARTIFICIAL NEURAL NETWORKS OVERVIEW (9)A.HISTORY (9)B.BIOLOGICAL NEURON (10)C.ARTIFICIAL NEURON (11)D.ARCHITECTURE OF NEURAL NETWORKS (12)1.Single Layer Networks (SLN) (12)2.Multi Layer Networks (MLN) (12)3.Feed -Forward Networks (FFN) (13)4.Radial Basis Function Networks (RBFN) (13)E.LEARNING PROCESS (14)1.Supervised Learning (14)a.Hebbian Learning (14)b.Delta Rule Learning (15)petitive Learning (15)2.Unsupervised Learning (15)3.Activation Functions (16)4. Gradient Descent (18)5.Back propagation Algorithm (19)a.First Case (22)b.Second Case (22)6.Efficient Algorithms (24)7. Batch Vs Incremental Learning (28)a.Advantages of Incremental Learning (IL) (28)b.Advantages of Batch Learning (BL) (28)III.DATA DESCRIPTION AND METHODOLOGY (31)A.SOURCES OF VIBRATION (31)1.Gear Vibration (31)2.Bearings (32)3.Shafts (32)B.DATA COLLECTION (32)C.SELECTING VARIABLES (33)1.Input Vector (33)viia. Torque (33)b. SO_1 (Shaft Order 1) (33)c. SO_2 (Shaft Order 2) (33)d. SO_3 (34)e. Signal Average RMS (34)f. Residual Kurtosis (34)g. Residual RMS (34)h. Side Band Modulation_1 (34)i. Gear Distributed Fault (34)j. G2_1 (34)k. Residual Peak to Peak (34)l. Gear Misalignment_1 (35)m. Ball Energy (35)n. Cage Energy (35)o. Inner Race Energy (35)p. Outer Race Energy (35)q. Envelope RMS (35)2.Output Vector (36)D. DATA PREPROCESSING (37)E.DATA SETS (40)1.Training Set (40)2.Test Sets (40)3.Validation Set (41)WORK ARCHITECTURE AND EVALUATION CRITERIA (41)IV.RESULTS AND DISCUSSION (43)A.MODEL WITH ALL PREDICTORS (43)B.ARTIFICIAL TRAINING SETS (49)C.STEPWISE PREDICTORS USAGE (52)V.CONCLUSIONS AND RECOMMENDATIONS (55)APPENDIX A (59)APPENDIX B (61)APPENDIX C (69)APPENDIX D (71)LIST OF REFERENCES (75)INITIAL DISTRIBUTION LIST (77)v iiiLIST OF FIGURESFigure 1. Overview of Maintenance Terminology (2)Figure 2. OBS & GSS (From: IMD-HUMS User Manual, 2005) (5)Figure 3. A Biological Neuron (From: Lawrence, J., 1993) (10)Figure 4. An Artificial Neuron (11)Figure 5. Single Layer Network (12)Figure 6. Multi Layer Network (13)Figure 7. Feed Forward and RBF Network Representation (14)Figure 8. Identity Function (17)Figure 9. Sigmoid Function (17)Figure 10. Hyperbolic Tangent Function (18)Figure 11. Gradient Descent (18)Figure 12. Sigmoid Function (19)Figure 13. Gradient Descent Using One Weight (20)Figure 14. Learning Rate effect on Gradient Descent (From: Fausett, L., 1994) (25)Figure 15. Ill Conditioning. (From: Bishop, C., 1995) (27)Figure 16. Clementine Preprocessing Data (39)Figure 17. Model Architecture GUI (40)Figure 18. Clementine Prediction Table (42)Figure 19. Ggobi screen for “bad” generators (44)Figure 20. ˆp from “bad” Generator 9 (54)Figure 21. ˆp from “good” Generator 66 (54)Figure 22. Model 1 (Predict Bad 09 and Good 4, 26, 42, 66) (61)Figure 23. Model 2 (Predict Bad 22 and Good 4, 26, 42, 66) (61)Figure 24. Model 3 (Predict Bad 31 and Good 4, 26, 42, 66) (62)Figure 25. Model 4 (Predict Bad 33 and Good 4, 26, 42, 66) (62)Figure 26. Model 5 (Predict Bad 53 and Good 4, 26, 42, 66) (63)Figure 27. Model 6 (Predict Bad 56 and Good 4, 26, 42, 66) (63)Figure 28. Model 7 (Predict Bad 53, 9 and Good 4, 26, 42, 66) (64)Figure 29. Model 8 (Predict Bad 53, 22 and Good 4, 26, 42, 66) (64)Figure 30. Model 9 (Predict Bad 53, 31 and Good 4, 26, 42, 66) (65)Figure 31. Model 10 (Predict Bad 53, 33 and Good 4, 26, 42, 66) (65)Figure 32. Model 11 (Predict Bad 53, 56 and Good 4, 26, 42, 66) (66)Figure 33. Model 12 (Predict Bad 31, 9 and Good 4 ,26, 42, 66) (66)Figure 34. Model 13 (Predict Bad 31, 22 and Good 4, 26, 42, 66) (67)Figure 35. Model 14 (Predict Bad 31, 33 and Good 4, 26, 42, 66) (67)Figure 36. Model 15 (Predict Bad 31, 53 and Good 4, 26, 42, 66) (68)Figure 37. Model 16 (Predict Bad 31, 56 and Good 4, 26, 42, 66) (68)Figure 38. Model 17 (Predict Bad 9 Using Artificial Sets) (69)Figure 39. Model 18 (Predict Bad 33 Using Artificial Sets) (70)Figure 40. Stepwise Model Using 5 Predictors (Predict Bad 9) (71)Figure 41. Stepwise Model Using 5 Predictors (Predict Bad 22) (71)Figure 42. Stepwise Model Using 5 Predictors (Predict Bad 31) (72)ixFigure 43. Stepwise Model Using 5 Predictors (Predict Bad 33) (72)Figure 44. Stepwise Model Using 5 Predictors (Predict Bad 53) (73)Figure 45. Stepwise Model Using 5 Predictors (Predict Bad 56) (73)xLIST OF TABLESTable 1. Potential Model Predictors (35)Table 2. Bad Generators—Reasons for Replacement (37)Table 3. Training Set using only Original Data (38)Table 4. Data Multiplication of Bad Observations (39)Table 5. Models Predicting Single Generator (46)Table 6. Models Predicting Pair of Generators Including 53 (47)Table 7. Models Predicting Pair of Generators Including 31 (48)Table 8. Summary Statistics for each Predictor variable including the Minimum, Maximum, Average and Standard Deviation (50)Table 9. Artificial Training Set to Predict Gen 9 (51)Table 10. Artificial Training Set to Predict Gen 33 (51)Table 11. Stepwise Good Generated Model (53)xiTHIS PAGE INTENTIONALLY LEFT BLANKxiiLIST OF ACRONYMS AND ABBREVIATIONSANN(s) Artificial Neural Network(s)PropagationBP BackLearningBL BatchCBM Condition Based MaintenanceGSS Ground Station SystemFFN Feed Forward NetworksLearningIL IncrementalIMD-HUMS Integrated Mechanical Diagnostics Health and Usage Maintenance System LMS Least-Mean-SquaresLayerNetworksMLN MultiOBS On Board SystemMaintenancePM PredeterminedRBFN Radial Basis Function NetworksSLN Single Layer Networksx iiiTHIS PAGE INTENTIONALLY LEFT BLANKxivACKNOWLEDGMENTSI would like to acknowledge the help of my excellent thesis advisor, Professor Lyn R. Whitaker, and my second reader, Samuel E. Buttrey, for their direction and assistance as this research was developed.Also, I would like to mention that this thesis could not have been completed without the presence, support and encouragement of my wife Elena and my son Vasilis.xvTHIS PAGE INTENTIONALLY LEFT BLANKxviEXECUTIVE SUMMARYReadiness is a key factor for military forces to stay effective and reliable in a continuously growing and demanding environment. Increased readiness can be achieved by increasing availability through performing efficient maintenance, performing less corrective maintenance actions, and identifying more accurate preventive maintenance periods. Today, the United States and allied forces spend billions of dollars for time or phased maintenance periods that overlook several facts and realities of operational use. Important savings can be gained by using hardware and software to evaluate component health and the conditions of systems based on operational usage and performing maintenance in relation to statistical and engineering analyses that predict availability and readiness.Nowadays, the majority of maintenance processes are accomplished by either the predetermined preventive or the corrective approach. The former approach has fixed maintenance intervals; the latter is performed after the fault of the component. Because both approaches are costly, some industries have started to perform maintenance action in a predictive manner, Condition Based Maintenance (CBM), where the condition is the key parameter to set the maintenance intervals and appropriate maintenance tasks.Condition Based Maintenance (CBM) is a technology weapon that tries hard to recognize initial faults before they develop into critical failures, which permits more precise scheduling of the preventive maintenance. The causes that have motivated a boost in the action of CBM include the need for reduced maintenance and logistics costs, protection against failure of mission-important equipment, and upgraded equipment availability.In 2003, the US Army began using the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS), an integrated airborne and ground-based system developed by Goodrich Corporation, to support maintenance of the UH-60L. IMD-HUMS is responsible for collecting, processing, analyzing, and storing an enormous amount of data obtained from sensors located throughout the aircraft. The IMD-HUMS improves aircraft availability for operators by identifying potentialx viiproblems early so that maintenance can be performed before it becomes an issue that could impact flight operations. The system also provides operators with accurate flight parameter data, monitored automatically on each flight, allowing them to better schedule routine maintenance and, in some cases, avoid unnecessary early repair and overhaul.Neural networks are used in numerous fields, including medical diagnostics. In this thesis neural networks are used for machinery diagnostics and specifically for diagnosing the UH-60L helicopter’s electrical generator. In order to accomplish this, a database collected from IMD-HUMS is used. The emphasis in this thesis is to develop a neural network that would utilize the collected data from IMD-HUMS, manufactured by Goodrich Corporation, in order to discover patterns that would predict a potential failure of a UH-60L helicopter generator. Many different neural networks are evaluated for their success rate for this faulting diagnosis.As in any prediction/forecasting model, the selection of appropriate model inputs is extremely important. However, in most ANN Artifiacial Neural Network) applications, less attention is given to this task. The main reason for this is that ANNs belong to the class of data-driven approaches, whereas conventional statistical methods are model driven. In the latter, the structure of the model has to be determined first, which is done with the aid of empirical or analytical approaches, before the unknown model parameters can be estimated. Data-driven approaches, on the other hand, have the ability to determine which model inputs are critical, so there is less need for “...a priori rationalization about relationships between variables...” However, presenting a large number of inputs to ANN models and relying on the network to determine the critical model inputs usually increases network size. This has a number of disadvantages, such as decreasing processing speed, increasing the amount of data required to estimate the connection weights efficiently and degrading performance of the AAN. This is particularly true for complex problems, where the number of potential inputs is large and where no a priori knowledge is available to suggest which inputs to include.Clementine which is the software used in this research, incorporates several features to avoid some of the common pitfalls of ANNs, including sensitivity analysis, network accuracy, and feedback graph. With these options selected, a sensitivity analysisxviiiwill provide information on which input fields are most important in predicting the output field, a network accuracy will provide the percentage of records for which the prediction of the model matches the observed value in the data, and the feedback graph will depict the accuracy of the network over time as it learns.In practice, building an ANN forecasting model involves a lot of trial and error. Consequently, the objective of this thesis is to provide a practical, non-technical introduction to structure an ANN forecasting model using real operating data of UH-60L helicopters. The success of ANN applications for an individual researcher depends on three key factors. First, the researcher must have the time, patience, and resources to experiment. Second, the ANN software must allow automated routines, such as walk-forward testing, optimization of hidden neurons, and testing of input variable combinations—either through direct programming or the use of batch/script files. Third, the researcher must maintain a good set of records that lists all parameters for each network tested.This research has verified that ANNs have a position in machinery condition monitoring and diagnostics. However, the limited nature of these results indicates that ANNs will not solve all machinery condition monitoring and diagnostics problems by themselves. They certainly will not completely replace conventional rule-based expert systems. Ultimately, it is anticipated that a symbiotic combination of these two technologies will provide the optimal solution to the machinery condition monitoring and diagnostics problem.xixTHIS PAGE INTENTIONALLY LEFT BLANKxxI. INTRODUCTIONReadiness is a key factor in enabling military forces to stay effective and reliable in a continuously growing and demanding environment. Increased readiness can be achieved by increasing availability through performing efficient maintenance, performing fewer corrective maintenance actions, and identifying more accurate preventive maintenance periods. Today, the United States and allied forces spend billions of dollars on time or phased-maintenance approaches that overlook several facts and realities of operational use. Important savings can be gained by using hardware and software to evaluate component health and the conditions of systems based on operational usage and performing maintenance in relation to statistical and engineering analyses that predict availability and readiness.The emphasis in this thesis is to develop a neural network that utilizes data collected from IMD-HUMS, manufactured by Goodrich Corporation, in order to discover patterns that can predict a failure of a UH-60L helicopter generator. Many different neural networks will be evaluated for their success rate on this faulting diagnosis.A. CONDITION BASED MAINTENANCEMaintenance is usually carried out in either time-based scheduled periods (so-called preventive maintenance) or by corrective maintenance. Preventive maintenance aims to avoid system or component failure by performing repair, service, or replacement within the fixed time intervals. On the other hand, corrective maintenance is performed after the failure or when an apparent fault has taken place (Davis, A., 1998). For several types of equipment or systems the maintenance action must be done without delay, but for many others it can be delayed depending on the equipment’s function. In many cases the preventive maintenance can be divided into two groups: Condition-Based Maintenance (CBM) and Predetermined Maintenance (PM). PM is scheduled in time, while CBM mostly has dynamic or on-request intervals (Figure 1).Figure 1. Overview of Maintenance TerminologyEntire CBM schemes involve a number of efficient capabilities, like sensing and data acquisition, signal processing, condition and health estimation, prognostics, and decision assistance. Moreover, in order for the user to have access to the system, a Human System Interface (HSI) development is necessary. Generally, the integration of various hardware and software components is needed to implement a CBM system.A complete architecture for CBM systems should cover the range of functions from data collection through the recommendation of specific maintenance actions. The major tasks that assist CBM consist of ():•Sensing and data acquisition•Signal processing and feature extraction•Production of alarms or alerts•Fault or failure diagnosis and health evaluation•Prognostics: projection of health profiles to future health or estimation of remaining useful life•Decision aiding: maintenance recommendations, or evaluation of asset readiness for a particular operational setting•Management and control of data flows or test sequences•Management of historical data storage and historical data access•System configuration management•Human system interface.CBM makes use of information collected on equipment through monitoring devices. As equipment becomes more complex, more manufacturers are providing these monitoring devices to assist companies or organizations handle and maintain their equipment (Tsang, A., 1995). CBM uses this online data to compare equipment conditions to predefined operating thresholds. Data that happen to fall outside these thresholds generates a maintenance alert by the software that signals a problem or area of concern.B. IMD-HUMSIn 2003, the US Army began using the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS), an integrated airborne and ground-based system developed by Goodrich Corporation, to support maintenance of the UH-60L. IMD-HUMS is responsible for collecting, processing, analyzing, and storing an enormous amount of data obtained from sensors located throughout the aircraft. The IMD-HUMS improves aircraft availability for operators by identifying potential problems early so that maintenance can be performed before it becomes an issue that could impact flight operations. The system also provides operators with accurate flight parameter data, monitored automatically on each flight, allowing them to better schedule routine maintenance and, in some cases, avoid unnecessary early repair and overhaul. The IMD-HUMS consists of two main subsystems: the On-Board System (OBS) and the Ground Station System (GSS) (System Users Manual For IMD-HUMS, 1995).1. On-Board System (OBS)The OBS is comprised of the following components (Figure 2):•Cockpit display unit (CDU)•Data transfer unit (DTU)•Remote data concentrator (RDC)•Main processor unit (MPU)• 2 junction boxes (JB1/JB2)•20 drive train and gearbox accelerometers• 4 engine accelerometers• 5 trim and balance accelerometers• 1 4g body accelerometer for regime recognition•Main and tail rotor magnetic RPM sensors•Main rotor blade tracker•Engine output shaft optical tachometers.The heart of the IMD-HUMS OBS is the Main Processing Unit (MPU). The MPU collects the data from the accelerometers, analyzes the inputs, and records the data, seeking for vibration exceedances and events. It calculates time spent in various flight regimes, performs various diagnostic algorithms, and stores the data to an onboard data cartridge. The OBS also provides for crew interaction through a Cockpit Display Unit (CDU) in order to support prompted procedure actions related to power assurance checks, power train analyses, and rotor track and balance data acquisitions. Besides prompted actions, the OBS uses regimes information to automatically store power train and rotor vibration data.2. Ground Station System (GSS)The GSS is the major user interface for the IMD-HUMS. It performs after-flight debrief and is designed to analyze, process, and compile flight data into useful information for the maintenance crew, logistics teams, the operations department, and engineering support. The IMD-HUMS GSS functions include:Figure 2. OBS & GSS (From: IMD-HUMS User Manual, 2005)•Rotor Time and Balance•Strip Charts of Aircraft Data•Engine Performance•Trending•Usage Computation and Tracking•Regime Identification and Processing•Flight Operations Management•Fault/BIT Display•Maintenance ManagementWORKC. PREVIOUSWillard and Klesch in their 2005 thesis, used 36,742 observations from monitored components of 30 UH-60L helicopter’s generators. The data was collected during the two-year period where the IMD-HUMS were installed. Each IMD-HUMS acquisition concerning the shaft, spur gear, and bearing of generators results in 170 variables. Each generator is assigned a binary value 1 or 0 to classify its known state. The value of one was given to the generators that were removed for fault, hence referred as bad generators. The value of zero was given to the generators that were not removed, referred to as good generators. To accomplish this generator classification, maintenance records and photographs from the 101st AVN Division were used.Principal components and other techniques were applied to reduce the 170 initial predictors to only 10. A logistic regression model and random forest classifiers were used on each generator, and the plotted probabilities of being bad were smoothed and used to predict the current functional condition of generators in the test set. Only Condition Indicators (CI) computed in the last 20 observations of each generator were used in the predictive models because generators classified as bad were not necessary bad through their entire two-year history. Due to the highly variable nature of the predictor values, the model had lower success predicting states with just one acquisition. One the other hand, some surprising cases of generators which were wrongly presumed to be bad and, conversely, another generator which was wrongly assumed to be good, were classified correctly by this study’s approach.D. AREA OF RESEARCH AND APPROACHANNs have a number of traits that make them an attractive alternative to conventionally configured expert systems. First, many are capable of discriminating non-linear relationships. Second, they are capable of functioning with a certain degree of background noise and erroneous information with minimal degradation of their pattern recognition abilities. Third, they have the ability to generalize, having the ability to classify previously unseen vector patterns into existing and, in some cases, new output categories. They are also capable of identifying multiple faults. These are all areas where traditional expert systems typically fall short. Moreover, ANNs are data-based rather than rule-based. This means that they may be capable of correctly discriminating relationships previously hidden from the best of “experts”.ANNs are not without their disadvantages. They, like all computer algorithms, are capable only of manipulating numbers and require an engineer to discern the intelligence of their output. Their success is largely limited to the quality of the data that they are provided. If the input vectors provided are inadequate to describe the decision space fully, then their likelihood for success is small. Again, they require an engineer to provide the proper inputs. Finally, they may be able to distinguish new relationships, but the relationships themselves remain hidden; all that is seen external to the network are the input and the output vectors. It is generally believed that the relationships are somehow hidden in the connection weights and the hidden layers but meaningful extraction of this information has yet to occur.ANNs appeared to have potential in numerous fields, including machinery diagnostics. The question might be asked whether an ANN should theoretically be capable of recognizing patterns in vibration signatures. It is the scope of this research to determine whether this potential can be realized in the region of machinery diagnostics and specifically for the UH-60L helicopter’s electrical generator. In order to accomplish this, a database collected from IMD-HUMS is be used. Pattern recognition is an essential component of rotating machinery condition forecasting; therefore, examining and training different model structures and shapes in trying to identify patterns that are “storing” the “weights” of the networks is being researched.。

keras实现自动编码器(autoencoder)实现图像除噪音(denoisy)

keras实现自动编码器(autoencoder)实现图像除噪音(denoisy)

keras实现⾃动编码器(autoencoder)实现图像除噪⾳(denoisy)//20201109> 写在前⾯:今天实现了⼀个⾃动解码器,后⾯有⼀个除噪的应⽤,在这⾥做个summary> 核⼼思想:设置对等⽹络(⼊⼝和出⼝size⼀样),然后训练对等图⽚#### 零、导包- 代码如下:import tensorflow as tffrom tensorflow import kerasimport numpy as npimport matplotlib.pyplot as plt#### ⼀、数据准备- 此处使⽤keras mnist数据集,或许训练量不是很⼤,但是主要理解逻辑- 代码如下:- 获取数据:(x_train,_),(x_test,_) = keras.datasets.mnist.load_data()x_train = x_train.astype('float32')/255.x_test = x_test.astype('float32')/255.x_train = np.reshape(x_train,(-1,28,28,1))x_test = np.reshape(x_test,(-1,28,28,1))- 给初始数据加上噪⾳(noisy):noise_factor = 0.5x_train_noisy = x_train + noise_factor * np.random.normal(loc = 0.0,scale = 1.0,size = x_train.shape)x_test_noisy = x_test + noise_factor * np.random.normal(loc = 0.0,scale = 1.0,size = x_test.shape)x_train_noisy = np.clip(x_train_noisy,0.,1.)x_test_noisy = np.clip(x_test_noisy,0.,1.)#### ⼆、展⽰加上噪⾳之后的图⽚n = 10plt.figure(figsize = (20,2))for i in range(n):ax = plt.subplot(1,n,i+1)plt.imshow(x_test_noisy[i].reshape(28,28))plt.gray()ax.get_xaxis().set_visible(False)ax.get_yaxis().set_visible(False)plt.show()#### 三、构建⽹络模型- 此处使⽤⼀个卷积层作为输⼊,多个卷积层、池化层、上采样层来进⾏训练,最后使⽤⼀个卷积层作为输出(注意:最后⼀个卷积层只有⼀个卷积核,因为输出的是图像,mnist中图像只有⼀个轨道)img_input = yers.Input(shape=(28,28,1))x = yers.Conv2D(32,(3,3),activation='relu',padding='same')(img_input)x = yers.MaxPool2D((2,2),padding='same')(x)x = yers.Conv2D(32,(3,3),activation='relu',padding='same')(x)encoder = yers.MaxPool2D((2,2),padding = 'same')(x)x = yers.Conv2D(32,(3,3),activation='relu',padding='same')(encoder)x = yers.UpSampling2D((2,2))(x)x = yers.Conv2D(32,(3,3),activation = 'relu',padding='same')(x)x = yers.UpSampling2D((2,2))(x)decoder = yers.Conv2D(1,(3,3),activation='sigmoid',padding = 'same')(x)autoencoder_2 = keras.models.Model(inputs = img_input,outputs = decoder)#### 四、开始训练(这⾥使⽤adam优化器,⼆元交叉熵函数作为损失函数)autoencoder_pile(optimizer='adam',loss = 'binary_crossentropy') # print(autoencoder_2.summary())print(x_train_noisy.shape)print(x_train.shape)autoencoder_2.fit(x_train_noisy,x_train,epochs = 100,batch_size = 128,shuffle = True,validation_data = (x_test_noisy,x_test))#### 五、使⽤训练好的⽹络模型进⾏预测predict_pic_denoisy = autoencoder_2.predict(x_test_noisy)#### 六、展⽰输⼊输出图像n = 10 # 我們想展⽰圖像的數量plt.figure(figsize=(20, 4))for i in range(n):# 秀出原圖像ax = plt.subplot(2, n, i + 1)plt.imshow(x_test_noisy[i].reshape(28, 28))plt.gray()ax.get_xaxis().set_visible(False)ax.get_yaxis().set_visible(False)# 秀出重建圖像ax = plt.subplot(2, n, i + 1 + n)plt.imshow(predict_pic_denoisy[i].reshape(28, 28))plt.gray()ax.get_xaxis().set_visible(False)ax.get_yaxis().set_visible(False)plt.show()图像实例如下:以上希望对⼤家有所帮助。

  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。

• Hybrid (continuous/discrete): HIOA
– Systems with real world + computer components
– Vehicle control: ground, air, space
– Embedded systems
• Probabilistic: PIOA, PTIOA, PHIOA
9
Timed I/O Automata (TIOA)
• Add special time-passage actions, pass(t), to IOA model.
• Example: Reliable FIFO channel that always delivers messages within time d.
– Inductive proofs.
10
Example Applications
• Theoretical distributed algorithms:
– Mutual exclusion, consensus,…
• Timeout-based communication protocols:
• Transitions:
– send(m)
• Effect: Add m to end of queue
– receive(m)
• Precondition: m is first on queue
• Effect: remove first element of queue
3
Levels of Abstraction
Based on work with Roberto Segala, Frits Vaandrager
1
I/O Automata
• Mathematical, infinite-state, automaton models. • Describe states, transitions. • Describe system modularity:
compositional methods 12
Example Applications
• Ground transportation:
• Operations for building automata:
– Parallel composition, identifying inputs and outputs.
– Action hiding.
• Reasoning methods:
– Invariant assertions: Property holds in all reachable states.
– pass(t)
• Precondition: for all (m,u) in queue, now + t u
• Effect: now := now + t
• Can use standard automaton-based reasoning methods:
– Invariant: for all (m,u) in queue, now u now + d.
– Randomized distributed algorithms
– Security protocols
– Safety-critical systems
5
Talk Outline
1. Brief overview of the models 2. HIOA model, in more detail (Lynch) 3. TIOA model (Kirli) 4. PIOA model (Lynch) 5. Future work on models 6. Future work on applications
– send(m)
• Effect: Add (m, now + d) to end of queue
– receive(m)
• Precondition: (m,u) is first on queue (for some u)
• Effect: remove first element of queue
– RR crossing, steam boiler controller – Stretched TIOA capabilities; motivated HIOA.
11
Hybrid I/O Automata (HIOA)
• TIOA plus facilities for representing continuous behavior. • Statiystem development by successive refinement.
• Top level: Specification for allowed behaviors.
• Can write in same automaton style.
• Refine through many levels, to code-like, detailed description.
• Dynamic description:
– Execution 0 a1 1 a2 2 … – Trace: Project on external variables, external actions. – A implements B if traces(A) traces(B).
• Operations: Composition, hiding • Reasoning methods: Invariants, simulation relations,
– Simulation relations: Imply one automaton implements another.
– Compositional methods
8
Example Applications
• Theoretical distributed algorithms:
– Mutual exclusion, Byzantine agreement, atomic object implementation, resource allocation, data management…
• Dynamic description:
– Execution: s0 a1 s1 a2 s2 … – Trace: Sequence of input and output actions; externally visible behavior.
– A implements B: traces(A) traces(B).
6
1. Brief Overview of the Models
7
I/O Automata (IOA)
• Static description:
– Actions a (input, output, internal)
– States s, start states
– Transitions (s, a, s'); input actions enabled in all states.
• Example: Group communication:
– Automata used to represent totally-ordered reliable broadcast service, group communication service, and algorithm.
– Composition of algorithm and GCS automata implements TO-Bcast automaton.
automaton.
2
Reliable FIFO Channel Model
• Signature:
– Inputs: • send(m), m in M
send(m)
receive(m)
Channel(M)
– Outputs:
• receive(m), m in M
• States:
– queue, a finite sequence of elements of M, initially empty
– TCP,…
• Group communication systems:
– Using GCS to build TO-Bcast: Conditional performance analysis.
– Scalable GCS: Performance analysis.
• RAMBO: Performance analysis. • Hybrid (continuous/discrete) systems:
• Distributed systems:
– Orca DSM system: Two-layer model, following the implementation. Found, fixed logical error. Proofs.
– Transis group communication system: Models for key layers. Proofs. Algorithmic improvements.
– Discrete events (not continuous behavior).
• Timing: TIOA
– For describing timeout-based algorithms.
– Local clocks, clock synchronization.
– Timing/performance analysis.
I/O Automaton Models: Basic, Timed, Hybrid, Probabilistic, Etc.
相关文档
最新文档