Influence of a Single Frequency Electromagnetic Wave on Energy Spectrum of Nonpolariton System i

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峰值系数英文

峰值系数英文The Importance of Peak Factor in Electrical SystemsElectrical systems are designed to efficiently transmit and distribute power to various loads, ensuring a reliable and consistent supply of electricity. One critical parameter in the design and analysis of these systems is the peak factor, which plays a crucial role in determining the system's performance and capacity. In this essay, we will delve into the significance of the peak factor, its implications, and its importance in the context of electrical systems.The peak factor, also known as the crest factor, is a measure of the ratio between the peak value and the root-mean-square (RMS) value of an electrical signal or waveform. It is a dimensionless quantity that provides insight into the shape and characteristics of the waveform. In an ideal scenario, where the waveform is a perfect sine wave, the peak factor is equal to the square root of 2, or approximately 1.414. However, in real-world electrical systems, the waveforms often deviate from the ideal sine wave due to various factors, such as harmonics, distortion, and transient events.The importance of the peak factor lies in its impact on the designand operation of electrical systems. Firstly, it affects the sizing and rating of electrical components, such as transformers, generators, and conductors. These components must be capable of handling the peak values of the electrical signals, which can be significantly higher than the RMS values. Underestimating the peak factor can lead to the selection of components that are not adequately sized, resulting in overloading, reduced efficiency, and potential equipment failures.Secondly, the peak factor influences the power quality and energy efficiency of the electrical system. A high peak factor can indicate the presence of harmonics or other distortions in the waveform, which can have detrimental effects on the performance of sensitive electronic equipment and cause increased power losses in the system. These power quality issues can lead to increased energy consumption, reduced system efficiency, and potential damage to connected devices.Furthermore, the peak factor is crucial in the design and operation of power electronic converters, such as those used in renewable energy systems, motor drives, and power supplies. These converters rely on the accurate control and management of electrical waveforms, and the peak factor plays a crucial role in determining the appropriate sizing and ratings of the power electronic components.In the context of electrical power transmission and distribution, thepeak factor also impacts the design and operation of the grid infrastructure. Transformers, transmission lines, and other grid components must be designed to accommodate the peak values of the electrical signals, ensuring the safe and reliable delivery of power to consumers. Underestimating the peak factor can result in increased system losses, reduced capacity, and potential overloading of the grid components.To address the challenges posed by the peak factor, electrical engineers and system designers employ various techniques and strategies. These include the use of power factor correction devices, harmonic filters, and advanced control algorithms to mitigate the effects of waveform distortions and maintain a low peak factor. Additionally, the monitoring and analysis of the peak factor in electrical systems can provide valuable insights into the system's performance, allowing for proactive maintenance and optimization.In conclusion, the peak factor is a critical parameter in the design, analysis, and operation of electrical systems. It directly impacts the sizing, rating, and performance of electrical components, as well as the overall power quality and energy efficiency of the system. Understanding and effectively managing the peak factor is essential for ensuring the reliable, safe, and efficient operation of electrical systems in a wide range of applications, from industrial facilities to smart grids and renewable energy systems.。

反激式开关电源外文翻译

Measurement of the Source Impedance of Conducted Emission Using Mode Separable LISN: Conducted Emission of a Switching Power SupplyJUNICHI MIY ASHITA,1 MASAYUKI MITSUZAW A,1 TOSHIYUKI KARUBE,1KIYOHITO Y AMASAW A,2 and TOSHIRO SA TO21Precision Technology Research Institute of Nagano Prefecture, Japan2Shinshu University, JapanSUMMARYIn the procedure for reducing conducted emissions, it is helpful to know the noise source impedance. This paper presents a method of measuring noise source complex impedances of common and differential mode separately. We propose a line impedance stabilization network (LISN) to measure common and differential mode noise separately without changing LISN impedances of each mode. With this LISN, conducted emissions of each mode are measured inserting appropriate impedances at the equipment under test (EUT) terminal of the LISN. Noise source complex impedances of switching power supply are well calculated from measured results. © 2002 Scripta Technica, Electr Eng Jpn, 139(2): 72 78, 2002; DOI 10.1002/eej.1154Key words:Conducted emission; noise terminal voltage; noise source impedance; line impedance stabiliza-tion network (LISN); EMI.1. IntroductionSwitching power supplies are employed widely in various devices. High-speed on/off operation is accompa-nied by harmonic noise that may cause electromagnetic interference (EMI) with communication devices and other equipment. To prevent the interference, methods of meas-urement and limit values have been set for conducted noise (~30 MHz) and radiated noise (30 to 1000 MHz). Much time and effort are required to contain the noise within the limit values; hence, the efficiency of noise removal tech-niques is an urgent social problem. Understanding of the mechanism behind noise generation and propagation is necessary in order to develop efficient measures. In particu-lar, the propagation of conducted noise must be investi-gated.Modeling and analysis of equivalent circuits have been carried out in order to investigate conducted noise caused by switching [1, 2]. However, the stray capacitance and other circuit parameters of each device must be known in order to develop an equivalent circuit, which is not practicable in the field of noise removal. On the other hand, noise filters and other noise-removal devices do not actually provide the expected effect [3, 4], which is explained by the difference between the static characteristics measured at an impedance of 50 Ω, and the actual impedance. Thus, it is necessary to know the noise source impedance in order to analyze the conducted noise.Regulations on the measurement of noise terminal voltage [5] suggest using LISN; in particular, the vector sum (absolute voltage) of two propagation modes, namely, common mode and differential mode, is measured in terms of the frequency spectrum. Such a measurement, however, does not provide phase data, and propagation modes cannot be separated; therefore, the noise source impedance cannot be derived easily. There are publications dealing with the calculation of the noise source impedance; for example, common mode is only considered as the principal mode, and the absolute value of the noise source impedance for the common mode is found from the ground wire current and ungrounded voltage [6], or mode-separated measure-ment is performed by discrimination between grounded and ungrounded devices [7]. However, measurement of the ground wire current is impossible in the case of domestic single-phase two-line devices. The complex impedance can be found using an impedance analyzer in the nonoperating state, but its value may be different for the operating state. Thus, there is no simple and accurate method of measuring source noise impedance as a complex impedance.© 2002 Scripta TechnicaElectrical Engineering in Japan, V ol. 139, No. 2, 2002Translated from Denki Gakkai Ronbunshi, V ol. 120-D, No. 11, November 2000, pp. 1376 1381The authors assumed that the noise source impedance could be found easily using only a spectrum analyzer, provided that the noise could be measured separately for each mode, and the LISN impedance could be varied. For this purpose, a LISN with a balun transformer was devel-oped to ensure noise measurement, with the common mode and differential mode strictly separated. An appropriate known impedance is inserted at the EUT (equipment under test) terminals, and the noise source impedance is found from the variation of the noise level. This method was used to measure the conducted noise of a switching power sup-ply, and it was confirmed that the noise source impedance could be measured as a complex impedance independently for each mode. Thus, significant information for noiseremoval and propagation mode analysis was acquired.This paper presents a new method of measuring the noise source impedance of conducted emission using mode-separable LISN.2. Separate Measurement for Common Mode andDifferential ModeThe conventional single-phase LISN circuit for measurement of the noise terminal voltage is shown in Fig.1. The power supply is provided with high impedance by a 50-µH reactor, and a meter with an input impedance of 50Ω is connected between one line and the ground via a high-pass capacitor, and another line is terminated by 50 Ω. Thus, the LISN impedance as seen at the EUT is 100 Ω in the differential mode, and 25 Ω in the common mode. The measured value is the vector sum of both modes, and the noise must be found separately in order to find the noise source impedance for each mode. There is LISN with Y-to-delta switching to provide mode separation [8], but its impedance is 150 Ω, giving rise to a problem of data compatibility with 50-Ω LISN. Thus, a new mode-separa-ble LISN was developed as shown in Fig.2. The circuit is identical to that in Fig. 1 from the power supply through the high-pass capacitor. Switching of the connection pattern ensures measurement with one line of the balun transformer terminated by 50 Ω, and another line connected to the meter.In Fig. 2, the secondary side of the 2:1 balun trans-former is terminated by 50 Ω, while the primary side has 200 Ω; in the differential mode, the impedance (line-to-line) is 100 Ω since 200 Ω at the high-pass capacitor is connected in parallel. With the switch set at D, the meter is connected to the secondary side of the balun transformer. The voltage is one-half that of the line-to-line voltage, and measurement is performed in the standard way.The common mode current flows from both sides of the balun transformer via the middle tap to the 50-Ω termi-nal. The currents in the windings are antiphase, and no voltage is generated at the secondary side. Therefore, the impedance of the primary side is the terminal resistance of the tap. Since this impedance is connected in parallel to 50Ω (two 100 Ω in parallel) at the high-pass capacitor, the impedance between the common line and ground is 25 Ω. With the switch set at C, the meter is connected to the middle tap of the balun transformer, and the common-mode voltage is the line-to-ground voltage.3. Measurement of Noise Source Impedance3.1 Measurement circuit and calculationThough the propagation routes are different in the two modes, propagation from the noise source to the LISN can be represented in a simplified way as shown in Fig. 3. In the initial measurement, the load impedance Z L is the LISN impedance. Z L can be varied by inserting a knownimpedance at the EUT terminals. Consider three load im-Fig. 1. Standard 50-Ω/50-µH LISN.Fig. 2.Mode-separable LISN.Fig. 3. Schematic circuit of noise propagation.pedances, namely, LISN only and LISN with two different impedances inserted, Z L 1(R 1 + jX 1), Z L 2(R 2 + jX 2), andZ L 3(R 3+ jX 3). Using the values I 1, I 2, I 3 (scalars) measured in the three cases, Z 0(R 0 + jX 0) is found. Since V 0 = |Z L | × I ,the following expressions can be derived:From the above,Here a , b , and c are as follows:Substituting Eq. (2) into Eq. (1), the following quadratic equation for R 0 is obtained:Thus, R 0 and X 0 have two solutions each. The series of frequency points with positive R 0 is taken as the noise source impedance.3.2 Method of measurementAn impedance is inserted at the EUT terminals in order to measure the noise source impedance in the LISN as seen at the EUT. As shown in Fig. 4, the impedance is inserted so as to vary only the impedance in the mode under consideration, thus preventing an influence on the imped-ance in the other mode. In the diagram, V m is the voltage at the meter connected to the LISN, while the input impedance of the meter (50 Ω) is represented by the parallel resistance.Since parameters of both the LISN and the inserted imped-ance are known, the noise current I can be calculated from V m . Now Z 0 is calculated for each mode from the measured data obtained while varying Z L , by using Eqs. (2) and (3).With the differential mode shown in Fig. 4(a), CR is inserted between the two lines, thus varying the load im-pedance Z L . In the differential mode, Z 0 is assumed to be a low impedance, and hence the inserted impedance exerts a significant effect on the measured value. For this reason, 1Ω/0.47 µF and 0 Ω/0.1 µF were inserted, which are rather small compared to the LISN impedance.The measurement of the common mode shown in Fig.4(b) employs common-mode chokes that basically have no impedance in the differential mode. The common-mode chokes are provided with a secondary winding (ratio 1:1),so that the impedance at the secondary side can be varied.In the common mode, Z 0 is assumed to have a particularly high impedance in the low-frequency band. For this reason,5.1 k Ω and 100 pF were used as the secondary load for the common-mode choke to obtain a high inserted impedance.The measured data for the inserted impedance in the case of resistive and capacitive loads are presented in Fig. 5. The impedance of the common-mode choke includes its own inductance and the secondary load. In the case of a capaci-tive load, the resonance point is around 200 kHz; at higher frequencies, the impedance becomes capacitive.A single-phase two-line switching power supply (an ac adapter for a PC with an input of ac 100 V , a rated power of 45 W, and PWM switching at 73 kHz) was used as the EUT, and the rated load resistance was connected at the dcside. Filters were used for both the common and differential(1)modes, except for the case in which one common-mode choke was removed, in order to obtain the high noise level required for analysis. Both the EUT and the loads had conventional commercial ratings, and were placed 40 cm above a metal ground plate; the power cord was fixed.4. Measurement Results and Discussion The results of conventional measurement as well as common-mode and differential-mode measurement for the LISN without inserted impedance are shown in Fig. 6. The measurements were performed in the range of 150 kHz through 30 MHz, divided into three bands, using a spectrum analyzer with frequency linear sweep. Time-variable data were measured at their highest levels using the Max Hold function of the spectrum analyzer, and only the peak values were employed for calculation of Z 0. For this purpose, the values measured in every frequency band were subjected to the FFT, and all harmonics higher than the fundamental frequency were removed. The data were smoothed, and about 10 peak points were detected in every frequency band. In addition, only those peaks that were stronger than the meter s background noise by at least 6 dB were consid-ered.The results in Figs. 6(b) and 6(c) pertain to the LISN only; the level would vary with inserted impedance. The noise source impedance for both modes calculated from the measured data (using triple measurement) is given in Figs.7 and 9, respectively. The bold and dashed lines pertain to data acquired with the impedance analyzer at the EUT power plug, with the EUT not in operation. With the differ-ential mode, there were no high-frequency components, as shown in Fig. 6(b), and hence the impedance is calculated only for significant low-frequency peaks.The noise source impedance in differential mode can be represented schematically as in Fig. 8. The noise sourceimpedance is equal to the impedance between the LISNFig. 5.Inserted impedance in common mode.Fig. 6. Measured results of standard, differential-mode,and common-mode.Fig. 7. Noise source impedance for differential mode.terminals when the noise source is short-circuited. With switching power supplies, filtering is usually performed by a capacitor of 0.1 to 1 µF inserted between the lines. Since the impedance of the power cord is small in the measured frequency range, one may assume that the impedance as seen at the LISN is low, and that the phase changes from capacitive toward inductive as with the measured static characteristics. However, in the case of the given EUT, a nonlinear resistor was inserted between the power cord and the filter as shown in Fig. 8, and hence the impedance is rather high in the nonoperating state. In addition, there are rectifying diodes on the propagation route, but they do not conduct at the measurement voltage of the impedance ana-lyzer. The noise levels show considerable variation at 120Hz, which corresponds to the on/off frequency of the recti-fying diodes; however, only the peak values are measured and then used for calculation, and hence the impedance obtained by the proposed method is considered to pertain to the conductive state. For this reason, the results do not agree well with static characteristics. Thus, the impedance in the operating state cannot be measured in the differential mode.On the other hand, the measured data for |Z 0| in common mode agree well with the static characteristics, as shown in Fig. 9. The phase, too, exhibits a similar variation,although the scatter is rather large. The resistive part of three load impedances and Z 0 may be presented in a simplified way as in Fig. 10. From Eq. (1), the following is true for R 2,R 3, and Z 0:The distance ratio from Z 0 to R 3 and R 2 on the R X plane that satisfies this equation is I 2:I 3, which corresponds to a circle with radius r as in Eq. (4), with the center lying on the line R 3R 2:Similar circles for R 1 and R 2 are also shown in the diagram.When Z 0 and the load impedances lie on one line, the twocircles have a common point. Equation (4) indicates that if I 3 increases slightly, the outer circle becomes bigger, and the two circles do not adjoin. On the other hand, when the outer circle becomes smaller, the two circles intersect at two points, and X 0 varies more strongly than R 0. In practice, the difference in noise level due to the inserted impedance may drop below 1 dB at some frequencies, so that the solution for Z 0 becomes unavailable because of the scatter, or the phase scatters too much. The measurement accuracy is governed by the difference in noise level, and thus the inserted impedance should have a large enough variation compared to the measurement scatter; in addition, there should be a phase difference so that the two circles are not aligned, as in Fig. 10.Figures 7 and 9 pertain to one of the solutions of Eq.(3) with larger R 0. Here R 0 is not necessarily positive and the other solution is not necessarily negative. The two solutions may be basically discriminated from the fre-quency response and other characteristics, but other inser-tion data are employed for the sake of accuracy.Fig. 8. Equivalent circuit of differential-mode noisesource impedance.(4)Fig. 9.Noise source impedance for common mode.Fig. 10. Load impedances and Z 0 on R X plane.Figure 11 compares the measured data and calculated data for the variation of noise level due to insertion of a commercially available common-mode choke, with the cal-culation based on the results of Fig. 9 and the impedance of the common-mode choke. As is evident, the calculation agrees well with the measured values. On the other hand, a considerable discrepancy was confirmed for the other solu-tion. The noise source impedance found as explained above is accurate enough to predict the filtering effect.The noise source resistance in the common mode can be represented as in Fig. 12. Here Z 1 is the stray capacitance between the internal circuit and the case, and Z 2 is the stray capacitance between the case and the ground plate (or in the case of the ground wire, the impedance of the wire). The common-mode noise source impedance for a single-phase two-line EUT is primarily Z 2, becoming capacitive at low frequencies. Since the EUT is equipped with a filter, the influence of the primary rectifying diodes is not related to common-mode, and hence the data measured by the pro-posed method are very close to the static characteristics.However, this is not necessarily true in the case of a grounded line (Z 2 short-circuited) with no filter installed.In addition, here the full impedance as seen at the LISN is found; in practice, however, a filter or Z 1 is employed to suppress noise. Therefore, the impedance of the power cord is required as well as Z 1 and Z 2 in order to analyze the filtering effect. The impedance of the power cord or grounded wire can be easily determined by measurement or calculation. In our experiments without ground, the impedance is very close to Z 2; on the other hand, Z 1 might be measured by grounding the case and removing the filter (Fig. 12), and then used to analyze the filtering effect between the case and the lines. However, noise propagation in the inner circuit must be further investigated in order to estimate the noise-suppressing efficiency of Z 1.5. ConclusionsA new mode-separable LISN is proposed that sup-ports noise measurement without changing the impedance depending on the mode. The proposed LISN ensures accu-rate measurement for each mode, thus supporting imped-ance analysis.With the proposed LISN, an appropriate impedance is inserted at the EUT terminals, and the noise impedance can be found as a complex impedance, just as simply as with conventional measurement of the noise terminal voltage.The value of the inserted impedance must be chosen prop-erly in order to determine the phase accurately. The pro-posed method ensures sufficient accuracy not only to investigate noise propagation and design efficient counter-measures, but also to predict the filtering effect. The pro-posed technique can supply important data for future analysis of noise generation and propagation in switching power supplies.REFERENCES1.Matsuda H et al. Analysis of common-mode noise in switching power supplies. NEC Tech Rep 1998;51:60 65.2.Ogasawara S et al. Modeling and analysis of high-frequency leak currents generated by voltage-fed PWM inverter. Trans IEE Japan 1995;115-D:77 83.3.Iwasaki M, Ikeda T. Evaluation of noise filters for power supply. Tech Rep IEICE EMCJ 1999;90:1 6.4.Kamita M, Toyama K. A study on attenuation char-acteristics of power filters. Tech Rep IEICE EMCJ 1996;96:45 50.rmation technology equipment Radio distur-bance characteristics Limits and method of meas-urement. CISPR 22, 1997.Fig. 11. V ariation of noise level due to insertion ofanother impedance (measured and calculated data).Fig. 12. Equivalent circuit of common-mode noisesource impedance.6.K amita M, Oka N. Calculation of common-mode noise output impedance during operation. Tech Rep IEICE EMCJ 1998;98:59 65.7.Ran L, Clare C, Bradley K J, Chriistoopoulos C.Measurement of conducted electromagnetic emis-sions in PWM motor drive without the need for an LISN. IEEE Trans EMC 1999;41:50 55.8.Specification for radio disturbance and immunity measuring apparatus and method Part 1: Radio dis-turbance and immunity measuring apparatus. CISPR 16-1, 1993.AUTHORS (from left to right)Junichi Miyashita (member) graduated from Tohoku University in 1981 and joined the Precision Technology Research Institute of Nagano Prefecture. His research interests are EMC measurement and prevention. He is a member of IEICE.Masayuki Mitsuzawa (nonmember) graduated from Nagoya University in 1984 and joined the Precision Technology Research Institute of Nagano Prefecture. His research interests are EMC measurement and prevention. He is a member of JIEP .Toshiyuki Karube (nonmember) graduated from Waseda University in 1991 and joined the Precision Technology Research Institute of Nagano Prefecture. His research interests are EMC measurement and prevention. He is a member of IEICE and JIEP .Kiyohito Yamasawa (member) completed the M.E. program at Tohoku University in 1970. He has been a professor at Shinshu University since 1993. His research interests are magnetic device integration, microswitching power units, and microwave sensors. He holds a D.Eng. degree and is a member of IEICE, SICE, the Magnetics Society of Japan, the Japan AEM Society, and IEEE.Toshiro Sato (member) completed his doctorate at Chiba University in 1989 and joined Toshiba Research Institute. He has been an associate professor at Shinshu University since 1996. His research interests are magnetic thin-film devices. He received a 1994 IEE Japan Paper Award and a 1999 Japan Society of Applied Magnetism Paper Award. He holds a D.Sc. degree,and is a member of IEE Japan, IEICE, and the Magnetics Society of Japan.。

绝缘纸板干燥过程微水分检测方法

绝缘纸板干燥过程微水分检测方法李万全1,高长银2(1.重庆三峡学院,重庆404000;2.郑州航空工业管理学院,郑州450015)摘要:采用单一频率介电检测技术测量绝缘纸板微水分,通过分析频率及温度对电容传感器检测信号的影响,研究绝缘纸板的介电极化和介电损耗原理,提出频率和温度对电信号影响的理论模型,利用绝缘纸板介电常数与水分含量的函数关系实现绝缘纸板微水分含量的在线检测。

关键词:绝缘纸板;微水分;检测;介电中图分类号:T M 215;T M 201.3;T M 206文献标志码:A文章编号:1009-9239(2010)05-0071-04Measurement of Trace Moisture Contentof Transformer Pressboard in Its Dr y in g ProcessLI Wan-q uan 1,Gao Chan g -y in 2(1.Chon gq i n g T hr ee Gor g es Univ ersit y ,Chon gq in g 404000,China;2.Zhengz hou I nsti tute o f A er onaut ical I ndust ry M anagent ,Zhengz hou 450015,Chi na )Abstract :A single frequency dielectric spectr um detectio n technology was applied to measure the tr ace moisture content of pressboard.Through analyzing the influence of frequency and tempera -ture on the detectin g si g nals of ca p acito r sensor,the p r inci p le of dielectric p olarization and loss of p ressboard was studied.Then a theo r etical model was p ro p osed to re p resent the effect of fre q uenc y and tem p erature on electr ic si g nals.An ex p ressio n relatin g p ermittivit y and moistur e content was su gg ested to calculate the moisture concentration in the dr y in g p rocess o f p ressbo ard.Key words :pressboard;trace moisture;detection;dielectr ic收稿日期:2009-12-05修回日期:2010-05-20作者简介:李万全(1968-),男,内蒙古人,副教授,博士,主要研究方向为传感测控技术,(电子信箱)g in g kotree@ 。

四级阅读手机信号影响飞机

四级阅读手机信号影响飞机Every time you fly,there will be a gentle voice telling you to turn off all electronic devices.A common rumor is that turning off mobile phones and other electronic devices is because the signals they send will interfere with the flight of the aircraft,and there will even be a risk of crash.So,what is the truth?In China,the 118mhz frequency band is used for aviation signals,while the 900MHz frequency band is used for domestic GSM mobile phones.The WiFi signal frequency is higher,and they differ greatly.Theoretically,they cannot interfere with each other,and the impact on flight safety is also extremely low.In addition,due to the different modulation methods,even if there is little possibility of interference,the probability of affecting flight safety is very low.Moreover,the current aircraft communication equipment and flight navigation equipment have strong anti-interference ability and will not be easily affected by the signals sent by mobile phones.As early as 1992,the Federal AviationAdministration(FAA)requested the independent industrial standards organization RTCA(Aeronautical Radio Technical Committee)to conduct research and investigation on the safetyof electronic products used in aircraft.RTCA finally did not find that the electromagnetic radiation of mobile phones and other electronic products can directly interfere with the on-board equipment.。

电子科技英语试题及答案

电子科技英语试题及答案一、选择题（每题2分，共20分）1. Which of the following is not a feature of electronic components?A. High precisionB. High reliabilityC. Low power consumptionD. Large size答案：D2. What is the basic unit of digital information?A. BitB. ByteC. KilobitD. Megabyte答案：A3. In electronics, what does the term "analog" refer to?A. Continuous signalsB. Discrete signalsC. Digital signalsD. Binary signals答案：A4. Which of the following is not a type of semiconductormaterial?A. SiliconB. GermaniumC. PlasticD. Gallium arsenide答案：C5. What is the function of a transistor in an electronic circuit?A. To amplify signalsB. To store dataC. To convert light into electricityD. To filter signals答案：A6. What is the primary function of a capacitor in an electronic circuit?A. To block DC and allow ACB. To block AC and allow DCC. To store electrical energyD. To convert voltage into current答案：C7. What does the abbreviation "LED" stand for in electronics?A. Light Emitting DiodeB. Large Emitting DiodeC. Limited Emitting DiodeD. Low Emitting Diode答案：A8. What is the purpose of a resistor in an electronic circuit?A. To control voltageB. To control currentC. To store energyD. To amplify signals答案：B9. Which of the following is a type of passive component in electronics?A. TransistorB. DiodeC. RelayD. All of the above答案：D10. What is the term used to describe the flow of electric charge?A. VoltageB. CurrentC. ResistanceD. Capacitance答案：B二、填空题（每题2分，共20分）1. The smallest unit of electric charge is called an ________.答案：electron2. A ________ is a type of electronic component that can store energy in an electric field.答案：capacitor3. The process of converting sound into electrical signals is known as ________.答案：modulation4. In digital electronics, a ________ is a single digit number, either 0 or 1.答案：bit5. A ________ is a semiconductor device that can amplify or switch electronic signals and electrical power.答案：transistor6. The unit of electrical resistance is the ________.答案：ohm7. An ________ is a semiconductor device that allows current to flow primarily in one direction.答案：diode8. The ________ is a passive component that opposes the flow of alternating current.答案：inductor9. A ________ is a type of display device that uses liquid crystals to produce images.答案：LCD10. The ________ is a type of electronic component that can store data.答案：memory三、简答题（每题10分，共20分）1. Explain the difference between an analog and a digital signal.答案：Analog signals are continuous and can represent a wide range of values, while digital signals are discrete and can only represent specific values, typically as a series of ones and zeros.2. Describe the role of a microprocessor in a computer system. 答案：A microprocessor is the central processing unit of a computer system, responsible for executing instructions, performing calculations, and controlling other system components to perform various tasks.四、翻译题（每题15分，共30分）1. Translate the following sentence into English: “在电子设备中，晶体管通常用作放大器或开关。

一种非谐振式MEMS电磁能量收集器

第58卷第3期 2021年3月徵鈉电子技术Micronanoelectronic TechnologyVol. 58 No. 3March 2021D O,: 10.13250/,c nk,w nd, 202,03.005一种非谐振式MEMS电磁能量收集器武绍宽a’b，李孟委a’b，金丽a’b，罗戴钟a’b(中北大学a.仪器与电子学院；b.前沿交叉学科研究院，太原030051)摘要：介绍了一种新型非谐振式微电子机械系统（M E M S)电磁振动能量采集器的设计、微加工和表征测试。

该能量收集器由M E M S结构、线圈、小型化N d F eB磁体和陶瓷基板组成。

建立结构模型对结构固有频率、位移和应力进行仿真。

利用M E M S技术制备能量收集器结构和A1线圈等关键部件，并结合嵌有永磁体的陶瓷基板进行组装，在组装过程中使用C u/S n倒装焊键合技术将陶瓷与芯片互连，成功制备出能量收集器原理样机。

利用振动台对样机性能进行测试，测试结果表明，实际加工能量收集器的谐振频率为5 241 H z,在1m/s2固定加速度以及7 H z振动频率条件下，经电路100倍放大测得该能量收集器最大输出电压为257 m V。

关键词：微电子机械系统（M E M S);能量采集器；电磁发电；陶瓷基板；微能源中图分类号：TM919; TH703 文献标识码：A文章编号：1671-4776 (2021) 03-0214-05 Non-Resonant MEMS Electromagnetic Energy HarvesterWu Shaokuana,b, Li Mengweia'b, Jin Lia-b, Luo Daizhonga'b(a. School o f In stru m en t a n d E lectronics；b. A ca d em y fo r A d v a n c e d In te r d is c ip lin a r y Research ,N o rth U n iv ersity o f C hina , T a iy u a n030051, C h in a)Abstract：The design, micro-machining and characterization test of a new type non-resonant micro-electromechanical system (M EM S) electromagnetic vibration energy harvester were introduced. The energy harvester was composed of MEMS structure, coil, miniaturized NdFeB magnet and ceramic substrate. A structural model was established to simulate the natural frequency, displacement and stress of the structure. And the energy harvester structure and A1 coils and other key components were prepared by MEMS technology, and the package was completed by combining with the ceramic substrate embedded with permanent magnets. The Cu/Sn flip-chip bonding technology was used for interconnection of ceramic substrate and chip in the assembly process, and the prototype of the energy harvester was successfully prepared. Finally, a vibration table was used to test the performance of the prototype. The test results show that the resonance frequency of the actual processed energy harvester is 5 241 Hz. Under the conditions ofa fixed acceleration of 1m/s2and a vibration frequency of 7 Hz, the maximum output voltage ofthe energy harvester is 257 mV after 100 times amplification of the circuit.Key words：micro-electromechanical system (MEMS) ；energy harvester；electromagnetic power generation；ceramic su b strate；micro energyEEACC：8460收稿日期：2020-09-02基金项目：国家自然科学基金资助项目（61571405，61573323)通信作者：李孟委，E-mail: ***************214武绍宽等：一种非谐振式M E M S电磁能量收集器0引百近年来，随着微电子机械系统（M EM S)技术和集成电路的迅速发展，传感器尺寸越来越小，功耗越来越低，极大地促进了物联网和智能产品的发展，改善了人们的生活水平〜2]。

单色光频率与折射率的关系英语

单色光频率与折射率的关系英语Refractive Index and Frequency of Monochromatic Light.In the realm of optics, the relationship between the frequency of monochromatic light and the refractive index of a medium plays a pivotal role in understanding the phenomena of refraction and dispersion. This intricate relationship governs the bending of light as it traverses the boundary between two dissimilar media and the subsequent separation of light into its constituent colors.Refractive Index: A Measure of Light's Velocity.The refractive index (n) of a medium is a dimensionless quantity that characterizes the medium's ability to impede the propagation of light. It is defined as the ratio of the speed of light in vacuum (c) to the speed of light in the medium (v):n = c / v.A higher refractive index indicates that light travels slower in that medium. The refractive index is dependent on the wavelength of light, exhibiting a phenomenon known as dispersion.Dispersion: Wavelength Dependence of Refractive Index.Dispersion arises due to the fact that different wavelengths of light interact with the charged particles within a medium to varying degrees. As a result, the refractive index of a medium changes with the wavelength of light. This variation is typically observed as a gradual increase in the refractive index with decreasing wavelength.For a given medium, the refractive index is generally higher for shorter wavelengths (higher frequencies) oflight compared to longer wavelengths (lower frequencies). This means that blue light, with its shorter wavelength, experiences a greater refractive index than red light,which has a longer wavelength.Refraction: Bending of Light at an Interface.When light encounters the boundary between two media with different refractive indices, it undergoes refraction. This phenomenon is characterized by a change in the direction of light as it crosses the interface. The angle of refraction (r) is related to the angle of incidence (i) and the refractive indices of the two media (n1 and n2) by Snell's law:n1 sin(i) = n2 sin(r)。

Automatic cross-talk removal from multi-channel data

Automatic cross-talk removal from multi-channel data
Bruce Allen, Wensheng Hua, ∗ Adrian C. Ottewill February 7, 2008
†
arXiv:gr-qc/9909083v1 27 Sep 1999
B. Allen and W. Hua are at the Department of Physics, University of Wisconsin – Milwaukee, PO Box 413, Milwaukee, WI 53211, USA. E-mail: ballen@. † A. Ottewill is at the Department of Mathematical Physics, University College Dublin, Belﬁeld, Dublin 4, Ireland. E-mail: ottewill@relativity.ucd.ie.
∗
1
as the shaking of the optical tables (seismic noise) and forces due to ambient environmental magnetic ﬁelds. Particularly at low frequencies, these types of ambient environmental noise are the fundamental eﬀects limiting the sensitivity of the instrument [2]. The key point here is that the gravitational waves are not correlated with any of these environmental artifacts. In many such situations, it is possible to monitor the environment, oﬀering the hope of removing from the signal of interest the contaminating eﬀects of the environment. For the prototype gravitational wave detector used as an example in this paper [3], about a dozen of these environmental signals were monitored, including components of the magnetic ﬁeld, acoustic pressure, acceleration of the optical suspension, and so on [4]. It is not hard to see that in many cases, these environmental ﬁelds add directly into the signal of interest, after convolution with some (unknown) response function. For example the suspension of the optical elements of the interferometer may be physically modeled by a coupled set of masses, springs, and frictional elements (dashpots), and thus acts as a mechanical ﬁltering device. The displacement of the ground is ﬁltered through this suspension and the resulting displacement is added into the one arising from any gravitational waves. Thus if the ground displacement were monitored, and if we knew the exact transfer function of the suspension, we could remove from the diﬀerential displacement signal the part due to ground motion. The diﬃculty here is that these transfer functions are not known, and can not be accurately calculated from ﬁrst principles. For example the mechanical ﬁlters which isolate the suspension from the ground contain non-ideal springs, damping elements whose restoring forces are not proportional to velocity, and so on. It might in principle be possible to measure these transfer functions (for example by shaking the ground in a controlled way) but in many cases this is not practical.

电子设备对大学生的影响英语作文

电子设备对大学生的影响英语作文The Impact of Electronic Devices on University StudentsThe digital age has revolutionized the way we live, work, and learn. University students, in particular, have been at the forefront of this technological revolution, with electronic devices becoming an integral part of their academic and personal lives. From laptops and smartphones to tablets and e-readers, these devices have profoundly impacted the way students engage with their studies, communicate with their peers, and manage their daily routines.One of the most significant ways electronic devices have influenced university students is in the realm of academic performance. The availability of laptops and tablets has transformed the traditional classroom experience, allowing students to take notes more efficiently, access course materials instantaneously, and collaborate with their peers in real-time. The ability to quickly search for information, reference online resources, and participate in online discussions has enhanced the learning process, enabling students to delve deeper into course content and engage more actively with their studies.Moreover, the proliferation of e-books and digital learning platforms has provided students with a more flexible and accessible approach to acquiring knowledge. Instead of lugging around heavy textbooks, students can now access their course materials on their electronic devices, allowing them to study and review content at their own pace and in the comfort of their own environment. This convenience has the potential to improve comprehension and retention, as students can easily highlight, annotate, and reference key concepts within their digital resources.However, the widespread use of electronic devices in academic settings has also given rise to some concerns. The temptation to multitask or engage in non-academic activities during class can lead to decreased attention and focus, potentially hindering a student's ability to absorb and retain important information. Additionally, the constant accessibility of social media and entertainment platforms on these devices can distract students from their studies, leading to decreased productivity and academic performance.Another area where electronic devices have had a significant impact on university students is in the realm of communication and social interaction. The ubiquity of smartphones has revolutionized the way students stay connected with their friends, family, and academic community. Instant messaging, video calling, and social media platforms have made it easier for students to maintain relationships,share experiences, and collaborate on projects, even when physically separated.This increased connectivity, however, has also given rise to concerns about the potential negative effects of excessive screen time and social media usage. Studies have shown that prolonged exposure to electronic devices can lead to issues such as sleep deprivation, anxiety, and decreased face-to-face interaction, all of which can impact a student's overall well-being and academic success.Furthermore, the widespread use of electronic devices has also raised concerns about privacy and cybersecurity. As students store sensitive personal and academic information on their devices, they become vulnerable to data breaches, identity theft, and other forms of digital exploitation. This heightened risk has led to the need for greater awareness and education on digital safety and responsible technology use among the university community.Despite these challenges, it is undeniable that electronic devices have also brought about numerous benefits for university students. The ability to access a wealth of information, collaborate with peers, and manage their schedules and tasks more efficiently has the potential to enhance their overall academic and personal experiences.In conclusion, the impact of electronic devices on university studentsis a complex and multifaceted issue. While these technologies have brought about numerous advantages, they have also introduced new challenges that require careful consideration and management. As universities continue to adapt to the digital age, it is crucial that students, faculty, and administrators work together to strike a balance between the benefits and drawbacks of electronic devices, ensuring that they are utilized in a way that supports academic success, personal well-being, and responsible digital citizenship.。

找准频率的作文素材

找准频率的作文素材英文回答：Finding suitable essay material is crucial for writinga compelling piece. It requires careful consideration of various factors such as relevance, interest, and uniqueness. In my experience, I have found that the best way to findthe right frequency of essay material is by exploring awide range of sources and topics.One effective method is to read extensively. By reading books, articles, and essays on different subjects, I am exposed to various ideas and perspectives. This not only broadens my knowledge but also helps me discover potential essay topics. For example, while reading a novel, I might come across a thought-provoking theme or a character's dilemma that I can analyze and discuss in my essay.Another approach I find useful is to observe the world around me. Everyday experiences can be a great source ofinspiration for essay material. For instance, aconversation with a friend, a news article, or a personal encounter might spark an idea that I can develop into an engaging essay. These real-life examples add authenticity and relatability to my writing.Furthermore, I make use of online platforms and social media to stay updated on current events and trending topics. This allows me to tap into discussions and debates that are happening in real-time. For instance, I might find a controversial news story or a viral video that I cananalyze and provide my own perspective on. This not only makes my essay timely but also adds a fresh perspective to the topic.To summarize, finding the right frequency of essay material involves exploring a wide range of sources, observing the world around us, and staying updated oncurrent events. By doing so, we can discover unique and interesting topics that will captivate our readers.中文回答：找准合适的作文素材对于写一篇引人入胜的文章至关重要。

电子流行的影响英语作文

电子流行的影响英语作文Electronic music has become increasingly popular in recent years, and its influence can be seen in many aspects of modern culture. From fashion to advertising, electronic music has left its mark on the world.One area where electronic music has had a significant impact is in the club scene. With its pulsing beats and infectious rhythms, electronic music has become the soundtrack of choice for many club-goers. DJs and producers have become celebrities in their own right, and electronic dance music festivals have become some of the biggest events in the music calendar.Another area where electronic music has made its markis in fashion. The futuristic, often avant-garde style of electronic music has inspired many designers, and elements of the genre can be seen in everything from streetwear to haute couture. The use of bright colors, metallic fabrics, and bold patterns are all hallmarks of the electronic musicaesthetic.The influence of electronic music can also be seen in advertising. Brands looking to appeal to a younger, more fashion-conscious audience often use electronic music in their commercials. The high-energy beats and catchy hooks of electronic music are perfect for creating a sense of excitement and urgency, making it an ideal choice for marketing campaigns.In addition to its influence on popular culture, electronic music has also had a significant impact on the music industry itself. With the rise of digital music production tools, more and more people are able to create and distribute their own electronic music. This has led to a proliferation of new artists and styles, and has helped to democratize the music industry in many ways.In conclusion, electronic music has become a dominant force in modern culture, influencing everything from fashion to advertising to the music industry itself. Its popularity shows no signs of slowing down, and it will beinteresting to see how its influence continues to evolve in the years to come.。

直升机旋翼试验塔电力拖动系统特性分析与仿真计算

Ana lysis and Si m ula tion of Electr ic Power D r ive System i n Hel icopter Rotor Test Bed
L IN C ong 2z h i , M U X in 2hua , ZH U H u i2f ang , H UA N G J ian 2p ing
2 磁场定向的矢量控制技术
电力传动系统要实现高控制精度及良好的动态特性, 就必须对电机的瞬时转矩进行有效的控制[ 5 ]。异步电动机电磁转矩表达式为 T e = K T5 I′ 2 co sΥ 2
( 1)
电机通过齿轮减速箱与旋翼负载相连, 相应的电机工作特性见表2。
表2 电机工作特性 ( 减速比4178)
( r m in 208 305 350 386
- 1
)
(kg m 2 ) 3 152 1 750 1 370 1 047
3 300 1 700 1 000 500
151 503 53 404 27 286 12 371
图1 不同负载要求对应的电机外特性曲线
注: P e 为额定功率; T r 为旋翼扭矩; J r 为转动惯量。
U 1 = R 1 i1 + p Ω1 U 2 = R 2 i2 + p Ω2 + jΞ2 Ω2
注: T L 为负载扭矩; J er 为等效转动惯量。
由交流电机调速理论知道, 对于一般的异步电动机, 基频以下工作在恒转矩方式, 随着转速上升, 功率成比例增加; 基频以上工作在恒功率方式, 功率基本不变。如果要求电机拖动如表1所示的4种机型负载, 即其外特性要分别与4种负载特性相匹配, 这就要求电机随不同负载具有4种不同的外特性曲线 ( 见图1) 。图中曲线1 ～ 4 分别对应常规情况下电机带机型～旋翼负载时所应有的外特性。可见曲线1 将其余三者包含在内, 据此选择额定电压为

出版社通信英语课件IOT物联网

initially 最初，首先 mechanism 机制，原理，结构 virtual 虚拟的参考译文：
物联网开始于1999年，其开始的设想是要连接虚拟世界与物理世界。
被动句翻译： •译成主动句 •译成无主句 •译成被动句：被、遭到，受到、给、叫、让---
长句的翻译：短句合并的翻译：定语从句的翻译：语态转换的翻译：名词从句的翻译：短语扩为句子的翻译：
achieve 获取，获得 not only but also recognition 识别，承认，认出 localization 地方化，定位 identify 识别，认出，确定
The RFID technology is the “speaking technology” for these things, therefore the RFID technology possessed an outstanding status in these key technologies.
definition 定义 refer to 涉及，指的是 object 物体，目标，对象 intelligent 智能的 recognition 识别，承认，认出 orientation trace 定向跟踪 peripheral 外围的，次要的；外围设备 infrared 红外线的；红外线 sensor 传感器 scanner 扫描器，扫描设备
significantly 意味深长的，重大的 manifestation 表示，显示，示威 incorporate 包含，吸收，合并 numerous 为数众多的，许多的 contemporary 同时代的，当代的

参考译文：然而，从早期一出现其发展就引人注目，现在
更是加入了跨行业的许多不同的应用内容，已经成为当下社会日常生活中非常重要的一部分。

Sejnowski Influence of ionic conductances on spike timing reliability of cortical neurons f

Inﬂuence of Ionic Conductances on Spike Timing Reliability of Cortical Neurons for Suprathreshold Rhythmic InputsSusanne Schreiber,1,5Jean-Marc Fellous,2Paul Tiesinga,1,3and Terrence J.Sejnowski1,2,41Sloan-Swartz Center for Theoretical Neurobiology,2Howard Hughes Medical Institute,and Computational Neurobiology Lab,Salk Institute,La Jolla,California92037;3Department of Physics and Astronomy,University of North Carolina,Chapel Hill,North Carolina 27599;4Department of Biology,University of California San Diego,La Jolla,California92037;and5Institute for Theoretical Biology, Humboldt-University Berlin,D-10115Berlin,GermanySubmitted9June2003;accepted inﬁnal form15September2003Schreiber,Susanne,Jean-Marc Fellous,Paul Tiesinga,and Ter-rence J.Sejnowski.Inﬂuence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs.J Neurophysiol91:194–205,2004.First published September24, 2003;10.1152/jn.00556.2003.Spike timing reliability of neuronal responses depends on the frequency content of the input.We inves-tigate how intrinsic properties of cortical neurons affect spike timing reliability in response to rhythmic inputs of suprathreshold mean. Analyzing reliability of conductance-based cortical model neurons on the basis of a correlation measure,we show two aspects of how ionic conductances inﬂuence spike timing reliability.First,they set the preferred frequency for spike timing reliability,which in accordance with the resonance effect of spike timing reliability is well approxi-mated by theﬁring rate of a neuron in response to the DC component in the input.We demonstrate that a slow potassium current can modulate the spike timing frequency preference over a broad range of frequencies.This result is conﬁrmed experimentally by dynamic-clamp recordings from rat prefrontal cortical neurons in vitro.Second, we provide evidence that ionic conductances also inﬂuence spike timing beyond changes in preferred frequency.Cells with the same DCﬁring rate exhibit more reliable spike timing at the preferred frequency and its harmonics if the slow potassium current is larger and its kinetics are faster,whereas a larger persistent sodium current impairs reliability.We predict that potassium channels are an efﬁcient target for neuromodulators that can tune spike timing reliability to a given rhythmic input.I N T R O D U C T I O NIntrinsic neuronal properties,such as their biochemistry,the distribution of ion channels,and cell morphology contribute to the electrical responses of cells(see e.g.Goldman et al.2001; Magee2002;Mainen and Sejnowski1996;Marder et al.1996; Turrigiano et al.1994).In this study we explore the inﬂuence of ionic conductances on the reliability of the timing of spikes of cortical cells.Robustness of spike timing to physiological noise is the prerequisite for a spike timing–based code,and has recently been investigated(Beierholm et al.2001;Brette and Guigon2003;Fellous et al.2001;Fricker and Miles2000; Gutkin et al.2003;Mainen and Sejnowski1995;Reinagel and Reid2002;Tiesinga et al.2002).It has been found experimen-tally that different types of neurons are tuned to different stimuli with respect to spike timing reliability.For example, cortical interneurons show maximum reliability in response to higher-frequency sinusoidal stimuli,whereas pyramidal cells respond more reliably to lower-frequency sinusoidal inputs (Fellous et al.2001).An important difference between those types of neurons is the composition of their ion channels. Taking into account that effective numbers of ion channels can be adjusted on short time scales through neuromodulation, changes in ion channels may also provide a useful way for a neuron to dynamically maximize spike timing reliability ac-cording to the properties of the input.Spike timing reliability is enhanced with increasing stimulus amplitude(Mainen and Sejnowski1995).In the intermediate amplitude regime,the frequency content of the stimulus is an important factor determining reliability(Fellous et al.2001; Haas and White2002;Hunter and Milton2003;Hunter et al. 1998;Jensen1998;Nowak et al.1997;Tiesinga2002).Spike timing reliability of a neuron is maximal for those stimuli that contain frequencies matching the intrinsic frequency of a neu-ron(Hunter et al.1998).The intrinsic(or preferred)frequency is given by theﬁring rate of a neuron in response to the DC component of the stimulus.Because of the relation to the DC ﬁring rate of a neuron,both the DC value(whether the stimulus mean or additional synaptic input)and the conductances of a cell can be expected to inﬂuence the spike timing frequency preference.The former was recently shown by Hunter and Milton(2003).The inﬂuence of conductances(rather than injected current)on spike timing reliability through changes in the neuronal activity according to the resonance effect is the focus of theﬁrst part of this paper,see RESULTS(Inﬂuence of conductances on the frequency preference).In this part we speciﬁcally seek to understand which ionic conductances of cortical neurons can mediate changes of the preferred frequency(with respect to spike timing reliability) over a broad range of frequencies.Reliability is assessed on the basis of the robustness of spike timing to noise(of amplitude smaller than the stimulus amplitude).Injecting sinusoidal cur-rents on top of a DC current into conductance-based model neurons,we conﬁrm that spike timing reliability is frequency-dependent as predicted by the resonance effect.We show that reliability can be regulated at the level of ion channel popula-tions,and identify the slow potassium channels as powerful to inﬂuence the preferred frequency.Our simulations support that the inﬂuence of ion channels on spike timing reliability also holds for more realistic rhythmic stimulus waveforms.Dy-namic-clamp experiments in slices of rat prefrontal cortexAddress for reprint requests and other correspondence:T.J.Sejnowski, Computational Neurobiology Laboratory,The Salk Institute,10010N.Torrey Pines Road,La Jolla,CA92037(E-mail:terry@).The costs of publication of this article were defrayed in part by the payment of page charges.The article must therefore be hereby marked‘‘advertisement’’in accordance with18U.S.C.Section1734solely to indicate this fact.J Neurophysiol91:194–205,2004.First published September24,2003;10.1152/jn.00556.2003.con ﬁrm the theoretical prediction that slow potassium channels can mediate a change in spike timing reliability,dependent on the frequency of the input.In the second part of the RESULTS section (In ﬂuence of con-ductances on spike timing reliability at the preferred fre-quency )we explore the in ﬂuence of ion channels beyond changes in preferred frequency attributed to the resonance effect.Different neurons may have the same preferred fre-quency (i.e.,the same DC ﬁring rate)but different composition of ion channels.We analyze the in ﬂuence of slow potassium channels and persistent sodium channels on spike timing reli-ability for neurons with the same preferred frequency.We ﬁnd that both channel types signi ﬁcantly in ﬂuence spike timing reliability.Slow potassium channels increase reliability,whereas persistent sodium channels lower it.M E T H O D SModel cellsThe single-compartment conductance-based model neurons were implemented in NEURON.In the basic implementation,the neurons contained fast sodium channels (Na),delayed-recti ﬁer potassium channels (K dr ),leak channels (leak),slow potassium channels (K s ),and persistent sodium channels (Na P ).The time resolution of the numerical simulation was 0.1ms.The kinetic parameters of the 5basic channel types and reversal potentials were taken from a model of a cortical pyramidal cell Golomb and Amitai (1997),apart from the reversal potential of the leak channels,which was set to Ϫ80mV (to avoid spikes in the absence of input and noise).The conductances of the cell we will refer to as the reference cell were (in mS /cm 2):g Na ϭ24,g Kdr ϭ3,g leak ϭ0.02,g Ks ϭ1,and g NaP ϭ0.07(Golomb and Amitai 1997).Its input resistance was 186M ⍀.The slow potassium conductance represented potassium channels with an activation time on the order of several tens to hundreds of milliseconds (here 75ms).In the model it is responsible for a spike frequency adaptation to a current step,which is experimentally observed in cortical pyramidal neurons (Connors and Gutnick 1990;McCormick et al.1985).We also investigated cells where the K s channels were replaced by mus-carinic potassium channels (K M )and by calcium-dependent potassium channels (K Ca ).The muscarinic channel K M was a slow noninacti-vating potassium channel with Hodgkin –Huxley style kinetics (Barkai et al.1994;Storm 1990).The calcium-dependent conductance K Ca was based on ﬁrst-order kinetics and was responsible for a slow afterhyperpolarization (Tanabe et al.1998).This channel was acti-vated by intracellular calcium and did not depend on voltage.Because of the dependency of K Ca on calcium,we also inserted an L-type calcium channel as well as a simple Ca-ATPase pump and internal buffering of calcium.For the parameters of these additional currents see APPENDIX .Kinetic parameters of all channels used were set to 36°C.Stimulus waveformsThe stimuli used to characterize spike timing reliability of individ-ual cells consisted of 2components.The ﬁrst component was a constant depolarizing current I DC ,which was the same for all model cells (apart from the simulations designed to study of the in ﬂuence of the DC),and which also remained ﬁxed throughout experimental recording of a cell.The second component was a sine wave with frequency fi ͑t ͒ϭC sin ͓2␲f t ͔ϩI DCThe amplitude of the sine wave C was always smaller than I DC .Examples of stimuli are shown in Fig.1,B and C .To characterize spike timing reliability in model cells,we applied a set of such stimuli with 70different frequencies (1–70Hz in 1-Hz increments)and 3different amplitudes of the sine wave component (C ϭ0.05,0.1,and 0.15nA).I DC ϭ0.3nA in all cases.For each frequency and amplitude combination,spikes for n ϭ20repeated trials of the same stimulus (duration 2.0s)were recorded.Reliability was calculated based on spiking responses 500ms after the onset of the stimulus,discarding the initial transient.To simulate intrinsic noise,we also injected a different random zero-mean noise of small amplitude [SD ␴ϭ20pA]on each individual trial.For the reference cell the noise resulted in voltage ﬂuctuations of about 1.3mV SD at rest.The noise was generated from a Gaussian distribution and ﬁltered with an alpha function with a time constant of ␶ϭ3ms.Although overall reliability systematically decreased with the size of the noise,neither the frequency content of the noise nor the absolute size of the noise (in that range)signi ﬁcantly changed the results.Spike times were determined as the time when the voltage crossed Ϫ20mV from below.The input resistance for the model cells was estimated by application of a depolarizing DC current step suf ﬁciently large to depolarize the cell by Ն10mV.Model neurons were also tested with a stimulus where power was distributed around one dominant frequency.These more realistic stimuli were constructed to have a peak in the power spectrum in either the theta-or the gamma-frequency range.These waveforms mimic theta-and gamma-type inputs and were created by inverse Fourier transform of the power spectrum (with random phases).For the theta-rich wave,the power spectrum consisted of a large peak at 8Hz (Gaussian,␴ϭ1Hz)and a small peak at 50Hz (Gaussian,␴ϭ6Hz).For gamma-rich waves,the power spectrum had a large peak at 30or 50Hz (with ␴ϭ3and 6Hz,respectively),and a small peak at 8Hz (␴ϭ1Hz).These waveforms were ﬁrst normalized to have a root-mean-square (RMS)value of 1and were then used with different scaling factors (yielding different RMS values).The DC component was added after scaling.These stimuli were presented for 10s and when evaluating reliability,the ﬁrst 500ms after stimulus onset were discarded.The reliability measureSpike timing reliability was calculated from the neuronal responses to repeated presentations of the same stimulus.For the model studies this implied the same initial conditions,but different noise for each trial.Reliability was quanti ﬁed by a correlation-based measure,which relies on the structure of individual trials and does not require the de ﬁnition of a priori events.For a more detailed discussion of the method see Schreiber et al.(2003).The spike trains obtained from N repeated presentations of the same stimulus were smoothed with a Gaussian ﬁlter of width 2␴t ,and then pairwise correlated.The nor-malized value of the correlation was averaged over all pairs.The correlation measure R corr ,based on the smoothed spike trains,s ជi (i ϭ1,...,N ),isR corr ϭ2N ͑N Ϫ1͒͸i ϭ1N͸j ϭi ϩ1Ns ជi ⅐s ជj ͉s ជi ͉͉s ជj ͉The normalization guarantees that R corr ʦ[0;1].R corr ϭ1indicates the highest reliability and R corr ϭ0the lowest.For all model cell studies,␴t ϭ1.8ms and for the experimental data ␴t ϭ3ms.The value of ␴t for model cells was chosen such that,given the noise level,reliability values R corr exploited the possible range of its values [0;1],allowing for better discrimination between reliable and unreliable spike timing.The experimental data proved more noisy and therefore a larger ␴t was chosen to yield a good distinction between reliable and unreliable states.All evaluation of model and experimental data (beyond obtaining spike times)was performed in Matlab.195INFLUENCE OF IONIC CONDUCTANCES ON SPIKE TIMINGFiring rate analysisFor the ﬁring rate analysis,the full parameter space of Na,Na P ,K dr ,K s ,and leak conductances was analyzed.DC ﬁring rates were ob-tained for all possible parameter combinations within the parameter space of the 5conductances considered (see APPENDIX ).The maximum change in ﬁring rate achievable by one ion channel type was charac-terized (for each combination of the other 4conductances)as the difference between the maximum and minimum (nonzero)ﬁring rates achievable by variation of the ion channel conductance of interest,keeping the other 4conductances ﬁxed.If a cell never ﬁred despite variation in one conductance,it was excluded from the parameter space (Ͻ5%of the total 4-dimensional conductance space for any channel type tested).The distribution of maximum changes in ﬁring rate achievable by variation of the density of one ion channel type over all combinations of the other 4densities is presented in the paper.Experimental protocolsCoronal slices of rat prelimbic and infra limbic areas of prefrontal cortex were obtained from 2-to 4-wk-old Sprague-Dawley rats.Rats were anesthetized with Iso ﬂurane (Abbott Laboratories,North Chi-cago,IL)and decapitated.Brains were removed and cut into 350-␮m-thick slices using standard techniques.Patch-clamp was performed under visual control at 30–32°C.In most experiments Lucifer yellow (RBI,0.4%)or Biocytin (Sigma,0.5%)was added to the internal solution.In all experiments,synaptic transmission was blocked by D -2-amino-5-phosphonovaleric acid (D-APV;50␮M),6,7-dinitroqui-noxaline-2,3,dione (DNQX;10␮M),and biccuculine methiodide (Bicc;20␮M).All drugs were obtained from RBI or Sigma,freshly prepared in arti ﬁcial cerebrospinal ﬂuid,and bath applied.Whole cellpatch-clamp recordings were achieved using glass electrodes (4–10M ⍀)containing (in mM):KMeSO 4,140;Hepes,10;NaCl,4;EGTA,0.1;MgATP,4;MgGTP,0.3;phosphocreatine,14.Data were ac-quired in current-clamp mode using an Axoclamp 2A ampli ﬁer (Axon Instruments,Foster City,CA).Data were acquired using 2computers.The ﬁrst computer was used for standard data acquisition and current injection.Programs were written using Labview 6.1(National Instrument,Austin,TX)and data were acquired with a PCI16E1data acquisition board (National In-strument).Data acquisition rate was either 10or 20kHz.The second computer was dedicated to dynamic clamp.Programs were written using either a Labview RT 5.1(National Instrument)or a Dapview (Microstar Laboratory,Bellevue,WA)frontend and a C language backend.Dynamic clamp (Hughes et al.1998;Jaeger and Bower 1999;Sharp et al.1993)was implemented using a DAP5216a board (Microstar Laboratory)at a rate of 10kHz.A dynamic clamp was achieved by implementing a rapid (0.1-ms)acquisition/injection loop in current-clamp mode.All experiments were carried in accordance with animal protocols approved by the N.I.H.Stimuli consisted of sine waves of 30different frequencies (1–30Hz)presented for 2s.Only one amplitude was tested.No additional noise was injected.The ﬁrst 500ms were discarded for analysis of reliability.R E S U L T SSpike timing reliability of conductance-based model neu-rons was characterized using a sine wave stimulation protocol for model cells with different amounts of sodium,potassium,and leak conductances.The voltage response of thereferenceFIG .1.Reliability analysis.A :voltage response of the reference cell to a current step (I DC ϭ0.3nA).B and C :examples of stimuli (f ϭ9Hz,C ϭ0.05nA;f ϭ11Hz,C ϭ0.05nA,respectively).D and E :rastergrams of the spiking responses to the stimuli presented above.Reliability in D is low (R corr ϭ0.10);reliability in E is higher (R corr ϭ0.64).F :reliability as a function of frequency f and amplitude C ,of the sine component in the input (Arnold plot,in contrast to all following data calculated with 0.25-Hz resolution,based on 50trials each).Tongue-shaped regions of increased reliability are visible.Strongest tongue marks the resonant (or preferred)frequency of a cell.Rastergrams underlying reliability at positions D and E are those shown in panels D and E .G :DC ﬁring rate (I DC ϭ0.3nA)vs.the preferred frequency for all model cells derived from the reference cell.DC ﬁring rate is a good predictor of the preferred frequency.196SCHREIBER,FELLOUS,TIESINGA,AND SEJNOWSKImodel cell(see METHODS)to stimulation with a DC step current (I DCϭ0.3nA)is shown in Fig.1A.Model cells were stimulated with a set of sine waves on top of aﬁxed DC.Reliability values for each individual stimulus and cell,based on correlation of responses to repeated presen-tation of a stimulus each with an independent realization of the noise,were derived as a function of the frequency f and the amplitude of the sine component C.Figure1,B–E show examples of2stimuli used and responses to those stimuli obtained from the reference cell.Figure1F shows the complete set of reliability values as a function of frequency and ampli-tude of the sine component of the input.Distinct,tongue-shaped regions of high reliability,so-called Arnold tongues(Beierholm et al.2001),arising from the res-onance effect of spike timing reliability,are visible.Figure1F also shows that the degree of reliability depended on the power of the input at the resonant frequency of a neuron.The higher the amplitude at the resonant frequency,the more pronounced was the reliability.At high amplitudes,frequencies close to the resonant frequency also showed enhanced reliability.The Ar-nold tongues were approximately vertical,so that the fre-quency of maximum reliability showed only a weak depen-dency on the amplitude of the sine component.The difference in input frequency for maximal reliability,as the amplitude C varied from0.05to0.15nA,was usuallyϽ2Hz.In most examples presented in this study,the strongest resonance was found at a1:1locking to the stimulus,where one spike per cycle of the sine wave was elicited.Additional regions of enhanced reliability could be observed at harmonics of the main resonant frequency(1:2,1:3,and1:4phase locking,in order of decreasing strength),and at the1st subharmonic(2:1 phase locking).The location of the strongest Arnold tongue in frequency space revealed the preferred frequency of a neuron,which was well approximated by theﬁring rate of the neuron in response to the DC component alone.Figure1G shows a strong corre-lation between the preferred frequency(i.e.,position of the strongest Arnold tongue on the frequency axis determined by the frequency of highest reliability for a given amplitude,C) and the DCﬁring rate of a cell for a wide range of conductance values in the model(see APPENDIX).In all(but2)cases the resonant frequency was close to the DCﬁring ually,the resonant frequency at the lowest amplitude of the sine compo-nent was closest to the DCﬁring frequency.For the2outliers the highest value of reliability was achieved at the subhar-monic,or the1st harmonic of the DCﬁring frequency.The importance of the DCﬁring rate in generating phase-locked ﬁring patterns was previously emphasized(see e.g.Coombes and Bressloff1999;Hunter et al.1998;Keener et al.1981; Knight1972;Rescigno et al.1970).The resonant frequency is referred to as preferred frequency throughout the paper.Inﬂuence of conductances on the frequency preference Because ionic conductances are known to inﬂuence neuronal activity levels,we investigated the ability of ion channels to modulate the preferred frequency in theﬁrst part of this study. SIMULATION RESULTS FOR A CORTICAL SINGLE-COMPARTMENT MODEL CELL.We started from the model of a cortical neuron (the reference cell).First,we varied one channel density at a time,keeping the densities of the other channelsﬁxed.The Arnold plots of cells whose leak density and slow potassiumdensity were varied respectively are shown in Fig.2.Examplespike shapes(at DC stimulation)are shown next to the Arnoldplots.All cells showed a pronounced resonance—that is,a pro-nounced preferred frequency.For variation of the leak chan-nels,the preferred frequency shifted to slightly lower frequen-cies with increasing density of leak channels.In contrast tovariation in leak channels,variation of K s conductance showeda large shift in preferred frequency(see Fig.2B).We alsoexplored changes in preferred frequency induced by the otherconductances(i.e.,Na,K dr,and Na P).Preferred frequenciesyielding maximum reliability(at Cϭ0.1nA)as a function ofnormalized channel density are shown in Fig.3for all chan-nels,including leak and K s.Because each channel type operated in a different range ofdensities,some of which differed by orders of magnitude,wenormalized(for parameter range criteria see APPENDIX)thedensities to the range[0;1]for each channel type,respectively.For Na,K dr,and Na P large changes in densities were necessaryto shift the preferred frequency.The overall observed changefor these channel types was in the range of5to15Hz.Thusstarting from the reference cell,only variation in the K s densitycould shift the preferred frequency by several tens of Hertz,fromϽ10toϾ60Hz.In all cases studied,for a given channel density the reliabil-ity at the preferred frequency was also higher than it wouldhave been at this stimulus frequency for most other values ofchannel parably high values were achieved onlyfor channel densities where the frequency at the1st harmonicor the subharmonic Arnold tongue coincided with the stimulusfrequency.We also analyzed the inﬂuence of2other potassium chan-nels with slower kinetics on frequency preference of the ref-erence cell—a muscarinic potassium channel K M and a cal-cium-dependent potassium channel K Ca(for details see APPEN-DIX).For both cases,we substituted K s by the new potassium conductance,K M or K Ca,respectively.The results of the Ar-nold plot analysis are shown in Fig.3B.For both channel types,an increase of their conductance shifted the preferred fre-quency over a broad range of frequencies.The lowest achiev-able frequency at a given DC depended on the time constant ofthe slow potassium conductance.If2or more slow potassiumconductances were present at high densities,the broad tuningeffect was diminished and eventually suppressed at high con-ductance levels(data not shown).Figure3C presents the pre-ferred frequency as a function of K s conductance for different ␶Ks.The slower the kinetics of the K s channel,the lower the minimum achievable frequency and the broader the frequency range accessible through variation of the slow potassium con-ductance.For completeness we analyzed all combinations of Na,Na P,K dr,K s,and leak conductances.In this case,we relied on theDCﬁring rate as an estimate of the preferred frequency.Thedistribution of maximum changes inﬁring rate(i.e.,preferredfrequency)achievable by variation of the density of one ionchannel type over all combinations of the other4densities ispresented in Fig.4,which shows one curve for each ionchannel type.For a more detailed description of this analysissee METHODS.Variation of K s had a signiﬁcant effect on theﬁring frequency in almost all parameter regimes.Its inﬂuence197INFLUENCE OF IONIC CONDUCTANCES ON SPIKE TIMINGwas weakest when another potassium channel,K dr in this case,was present at high density.The mean change achieved with K s was around 20Hz.The mean change achieved by the other ion channels was Ͻ10Hz.The analysis also showed that,in principle,all ion channel types could achieve ﬁring rate changes of Ն20Hz.Within the parameter space investigated,this was true for only a minority of values of the other 4conductances.Figure 4B shows 4examples of parameter regimes where these channels signi ﬁ-cantly changed the preferred frequency.For example,this occurred for K dr when K s was not present or present only in small amounts.Na P could cause a large frequency shift when both potassium conductances,K dr and K s ,were low.Na was potent in changing the frequency when both potassium con-ductances and Na P were low.Its in ﬂuence in these cases weakened further with a higher density of leak channels.Leak channel variation also gave rise to higher frequency shifts when both potassium conductances were low and thesodiumFIG .2.In ﬂuence of leak and slow potassium conductances on spike timing reliability.A :right column of left panel shows Arnold reliability plots for 7different model cells,systematically varying in the amount of leak channels present (0,0.005,0.01,0.015,0.02,0.03,and 0.04mS/cm 2,top to bottom ).Left column :spikes of the corresponding cells in response to pure DC stimulation without intrinsic noise.Input resistance changed signi ﬁcantly with leak conductance over several hundreds of M ⍀.B :Arnold plots and spikes in response to DC stimulation for 7different model cells with increasing amounts of K s (0.05,0.15,0.3,0.6,1.0,1.5,and 2.0mS/cm 2,top to bottom ).Input resistance changed from about 230to 150M ⍀.For both panels the 3rd plot from the bottom (*)represented the reference cell (as in Fig.1F).FIG .3.Dependency of preferred frequency of the reference cell on individual channel densities.A :preferred frequency as a function of normalized channel density (see text for de ﬁnition),for 5different conductances.Variation in K s achieves the broadest shift in preferred frequency.B :preferred frequency for variation in a muscarinic potassium channel (K M )and a calcium-dependent potassium channel (K Ca )as a function of normalized channel density (based on sine wave reliability analysis).K M and K Ca ,respectively,replaced K s in the reference cell.C :DC ﬁring rate (an estimate of the preferred frequency)for K s channels of different time constants (␶Ks )as a function of K s peak conductance.Densities are not normalized in this panel.Lowest achievable frequency (at I DC ϭ0.3nA)depended on ␶Ks .198SCHREIBER,FELLOUS,TIESINGA,AND SEJNOWSKIconductances were not too large.In general,higher densities of leak channels tended to lower the minimum achievable fre-quency.To illustrate that regulation of ionic conductances on spiketiming reliability frequency preference would allow a cell to dynamically adjust its spike timing reliability,the effect of a temporary increase in K s conductance on spike timing reliabil-ity is presented in Fig.5.The conductance step was chosen such that the preferred frequency of the cell after the conduc-tance increase matched the stimulus frequency.Spike timing reliability during elevation of the K s conductance was signif-icantly enhanced.RELIABILITY OF INPUTS WITH MORE THAN ONE FREQUENCY.Many biologically relevant periodic inputs,such as inputs to neurons that participate in rhythms,exhibit a broad distribution of frequencies in their power spectrum.We therefore stimu-lated model neurons with quasi-random stimuli whose power spectrum contained 2peaks,one in the theta-range (about 8Hz)and one in the gamma range (30–70Hz).The 3rhythm-like stimuli tested are depicted in Fig.6.The reliability of a response to one of those stimuli depended on the amount of K s present in the neuron.For the theta-dominated input (Fig.6A )cells with higher K s conductances responded more reliably,whereas cells with lower K s conduc-tance (therefore tuned to higher frequencies)responded with lower reliability.For the gamma-dominated input,only cells with an optimally low K s conductance achieved a high reli-ability.A high K s conductance made the cell more unreliable.For all stimuli,the cell with preferred frequency (adjusted by K s )closest to the dominant frequency in the input yielded the highest spike timing reliability (as illustrated by the lower panels in Fig.6).Interestingly,the second (smaller)peak in the power spectra of the inputs was also re ﬂected by a small increase of reliability at corresponding densities of K s .Not surprisingly,reliability also tended to increase with the vari-ance (or RMS value),of the stimuli,across all stimuli and cells.EXPERIMENTAL RESULTS.To test the effects of slow potassiumchannels on preferred frequency physiologically,we per-formed patch-clamp recordings in slices of rat prefrontal cor-tex.We used the dynamic-clamp technique,which allows time-dependent currents to be injected that experimentally simulate conductances through on-line feedback.Thus we were able to arti ﬁcially introduce K s currents (with the same dynamics as the K s reference channel used in the modelsim-FIG .4.In ﬂuence of parameter variation on the preferred frequency.A :normalized distribution of frequency shifts (maximum changes in ﬁring rate)achievable by one ion channel type (measured over all combinations of the other 4channel types).Cells in conductance space that did not ﬁre were discarded.Different curves correspond to different ion channel types.B :4examples of cells where Na,Na P ,K leak ,and K dr could mediate large changes in preferred frequency (for parameters see APPENDIX ).Circles and solid lines indicate the preferred frequency derived with the sine wave protocol (C ϭ0.05nA);crosses indicate DC ﬁringrate.FIG .5.Dynamic changes in spike timing reliability attributed to conductance steps.A :superimposed voltage traces (n ϭ20)in response to a sine wave (f ϭ9Hz,C ϭ0.05nA),which is shown in D .K s conductance was temporarily increased,as indicated in B .C :rastergram of the responses.Parameters of the cell were those of the reference cell;g Ks values were 0.9and 1.4mS/cm 2;noise SD ␴ϭ0.03nA.Reliability (here estimated with ␴t ϭ3ms)changed from 0.18to 0.57at the conductance step and back to 0.17.199INFLUENCE OF IONIC CONDUCTANCES ON SPIKE TIMING。

九年级音乐发展英语阅读理解25题

九年级音乐发展英语阅读理解25题1<背景文章>Classical music has a long and rich history. It originated in Europe during the Middle Ages and Renaissance. Classical music is known for its complex harmonies, beautiful melodies, and strict musical forms.Some of the most famous classical composers include Wolfgang Amadeus Mozart, Ludwig van Beethoven, and Johann Sebastian Bach. Mozart was a child prodigy who composed some of the most beautiful and memorable music in history. Beethoven, on the other hand, is known for his powerful and emotional music. Bach is considered one of the greatest composers of all time, known for his complex fugues and chorales.Classical music has had a profound influence on later music genres. It has inspired many composers and musicians over the centuries. The beauty and complexity of classical music continue to attract audiences around the world.The main styles of classical music include symphonies, concertos, sonatas, and operas. Symphonies are large-scale works for orchestra, while concertos feature a solo instrument accompanied by an orchestra. Sonatas are usually for solo piano or a small ensemble, and operas combine music, drama, and singing.Classical music has also had a significant impact on culture and society. It has been used in movies, television shows, and advertisements. It is often performed in concert halls and festivals, attracting audiences of all ages.In conclusion, classical music is a treasure of human civilization. Its beauty and complexity continue to inspire and delight people around the world.1. Mozart is known as a ___.A. great singerB. child prodigyC. famous painterD. talented actor答案：B。

等位基因的频率的英文

等位基因的频率的英文Allele frequencies are like the popularity contest in the gene world. Some alleles are all the rage, while others are more of a niche thing.You know, it's kind of like fashion trends. One year, one allele is in, and the next year, it's out. But some alleles just never go out of style.Imagine if we could vote for our favorite alleles! Well, nature kind of does that through natural selection. The alleles that get picked more often are the ones that stick around.And it's not just random chance, either. There's a reason why certain alleles become more common. They might give an organism an edge, like being better suited to its environment or having a higher chance of survival.But it's also interesting to see how allele frequenciescan change over time. Like, maybe a new mutation comes along and suddenly an allele that was once rare becomes really popular. Or maybe environmental changes cause certain alleles to become less favorable.It's all part of the game of life, really. Allele frequencies are just one way we can see how nature is constantly playing with the rules, trying out new combinations, and seeing what sticks.。

电磁场英语作文

电磁场英语作文Electromagnetic Field。

Introduction。

The electromagnetic field is a fundamental concept in physics that describes the interaction between electrically charged particles. It encompasses both electric fields and magnetic fields, which are closely related and intertwined. Understanding the electromagnetic field is crucial in various scientific and technological applications, ranging from electricity and magnetism to telecommunications and medical imaging.Electric Fields。

An electric field is a region in which an electric charge experiences a force. It is generated by electric charges, either stationary or in motion. Electric fields are characterized by their strength and direction. The strength of an electric field is determined by the magnitude of the charges and their separation distance. The direction of the electric field is defined as the direction in which a positive test charge would move if placed in the field. Electric fields can be visualized using electric field lines, which represent the direction and intensity of the field.Magnetic Fields。

皮带线速度高精度测量系统的设计

皮带线速度高精度测量系统的设计D esi gn of H igh PreciseM easuri ng Syste m for Li near V el ocity of Conveyor Belt李自成江卫华(武汉工程大学电气信息学院,湖北武汉 430073)摘要:传输皮带线速度的在线测量对皮带运输系统的安全运行意义重大。

以光电式传感器为基础,介绍了一种新颖的皮带线速度测量方法。

该方法采用差分信号来传输高频脉冲,使用单片机来捕获脉冲并实时计算线速度。

最后通过RS 485总线与上位机进行数据传送,实现皮带线速度的远程高精度测量。

运行结果表明,该系统测量精度高、稳定性好,具有一定的实用价值。

关键词:线速度光电式传感器差分信号传输高精度测量 RS 485总线高频脉冲中图分类号:TP216 文献标志码:AAbstract :On line measure ment of L i near vel oc i ty of conveyor belt i s very m i port ant t o run conveyor safely .O n the basi s of photoelectr i c sen sor ,a new m easur i ng me t hod for L i near vel oc i ty of t he be l t i s i ntroduced .W i th thismethod ,the h i gh frequency pulses are transferred by adop ti ng differenti a l si gna;l and t he pulses are capt ured i n si ngle chi p co mputer ,t hus t he L inear velocit y is ca l cul ated i n real tm i e .The m easured data are trans m itted to host co mputer t hrough RS 485bus to rea liz e re mote h i gh prec i se m easure ment f or L i near velocity .The operati ng results i ndicate that t he syste m feat ures hi gh accuracy ,good stab ilit y and off ers practica l applicab l e va l ue .K ey words :L i near ve l ocit y Phot oelectric sens or D i ff erenti al si gnal trans m is si on H i gh precis e m eas ure ment RS 485bus H i gh frequency pul se修改稿收到日期:2007-10-31。

频率的英语作文

频率的英语作文Title: Understanding Frequency: Its Importance and Applications。

Frequency is a fundamental concept in various fields of study, ranging from physics and engineering to music and communication. It refers to the rate at which a particular event occurs within a given time frame. In this essay, wewill delve into the significance of frequency, its applications, and its implications across different domains.Firstly, let's explore the significance of frequency in physics and engineering. In physics, frequency is often associated with wave phenomena. For instance, in thecontext of sound waves, frequency determines the pitch ofthe sound. Higher frequencies correspond to higher pitches, while lower frequencies result in lower pitches. In electromagnetic waves, frequency determines the type of radiation, such as radio waves, microwaves, infrared,visible light, ultraviolet, X-rays, and gamma rays.Engineers utilize frequency in various applications, including signal processing, telecommunications, and electronics. Understanding frequency is crucial for designing efficient communication systems, optimizing circuit performance, and analyzing dynamic systems' behavior.Moreover, frequency plays a crucial role in music and acoustics. Musicians use frequency to tune instruments and create harmonious sounds. Different musical notes are characterized by specific frequencies, and manipulating frequency allows musicians to produce melodies and chords. Additionally, frequency analysis is essential in audio engineering for tasks like equalization, compression, and filtering, ensuring high-quality sound reproduction in recordings and live performances.In communication systems, frequency is a cornerstone of wireless technologies such as radio, television, and cellular networks. Each communication channel operates within a specific frequency range allocated by regulatory bodies. By modulating signals onto carrier frequencies,information can be transmitted efficiently over long distances. The concept of frequency reuse enables multiple users to share the limited available bandwidth without significant interference, a principle vital for modern telecommunications infrastructure.Frequency also holds significance in the field of biology and medicine. In neuroscience, brain activity is often measured in terms of neural oscillations, which represent the rhythmic electrical patterns generated by neuronal networks. Different brain states and cognitive functions are associated with specific frequency bands, such as delta, theta, alpha, beta, and gamma waves. Understanding these frequency patterns aids in studying brain disorders, cognitive processes, and consciousness.Furthermore, frequency is crucial in environmental science and climate studies. Scientists analyze the frequency of natural phenomena like rainfall, temperature fluctuations, and seismic activity to understand long-term trends and predict future changes. Climate scientists use frequency analysis to study patterns such as El Niño andLa Niña events, oceanic oscillations, and atmospheric circulation patterns, contributing to climate modeling and forecasting efforts.In conclusion, frequency is a fundamental concept with widespread applications across various disciplines. Its understanding is essential for advancing scientific knowledge, technological innovation, and societal progress. By comprehending the principles of frequency and harnessing its potential, we can further explore the mysteries of the universe, improve communication systems, enhance medical diagnostics, and address pressing environmental challenges. Thus, frequency serves as a cornerstone of modern civilization, shaping our understanding of the world and driving innovation in diverse fields of study.。

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