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Статті в журналах з теми "CONTROL SIGNAL"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Shellenberger, Richard O., and Paul Lewis. "Signal Control by Six Signals." Psychological Reports 63, no. 1 (August 1988): 311–18. http://dx.doi.org/10.2466/pr0.1988.63.1.311.

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In previous signal-control experiments, several types of stimuli elicited pecking when paired with peck-contingent grain. Here, we compared the effectiveness of an auditory stimulus and five visual stimuli. For 12 pigeons, the first keypeck to follow the offset of a 4-sec. signal was reinforced with grain. We examined the following signals: a tone, a white keylight, a dark keylight, a keylight that changed from white to red, houselight onset, and houselight offset. All signals acquired strong control over responding. According to one measure, percent of signals with a peck, houselight offset showed less control than the others; according to another measure, pecking rate, the white keylight showed greater control than the others. In this experiment, we found that a wide variety of stimuli can elicit strong pecking in the signal-control procedure. The present findings increase the chances that in past conditioning experiments, some keypecks thought to be due to contingencies of reinforcement were in fact elicited.

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2

Logan, Gordon D., Russell J. Schachar, and Rosemary Tannock. "Impulsivity and Inhibitory Control." Psychological Science 8, no. 1 (January 1997): 60–64. http://dx.doi.org/10.1111/j.1467-9280.1997.tb00545.x.

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We report an experiment testing the hypothesis that impulsive behavior reflects a deficit in the ability to inhibit prepotent responses Specifically, we examined whether impulsive people respond more slowly to signals to inhibit (stop signals) than non-impulsive people In this experiment, 136 undergraduate students completed an impulsivity questionnaire and then participated in a stop-signal experiment, in which they performed a choice reaction time (go) task and were asked to inhibit their responses to the go task when they heard a stop signal The delay between the go signal and the stop signal was determined by a tracking procedure designed to allow subjects to inhibit on 50% of the stop-signal trials Reaction time to the go signal did not vary with impulsivity, but estimated stop-signal reaction time was longer in more impulsive subjects, consistent with the hypothesis and consistent with results from populations with pathological problems with impulse control

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3

Lacefield, Soni, and Nicholas Ingolia. "Signal Transduction: External Signals Influence Spore-Number Control." Current Biology 16, no. 4 (February 2006): R125—R127. http://dx.doi.org/10.1016/j.cub.2006.02.005.

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4

Mulung, Bibi Rawiyah, and Andino Maseleno. "Proposed SMART Traffic Control Signal in Brunei Darussalam." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 277. http://dx.doi.org/10.11591/tijee.v15i2.1540.

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This paper presents proposed SMART (Systematic Monitoring of Arterial Road Traffic Signals) traffic control signal in Brunei Darussalam. Traffic congestion due to stops and delays at traffic light signals has much been complained about in Brunei Darussalam as well as across the world during the recent years. There are primarily two types of traffic signal controls in Brunei Darussalam. The most common one is the fixed or pre-timed signal operation traffic light and the other one is the actuated signal operation traffic light. Although the actuated signal control is more efficient than the fixed or pre-fixed signal control in the sense that it provides fewer stops and delays to traffic on the major arteries, the best option for Brunei Darussalam would be to introduce smart traffic control signal. This type of traffic signal uses artificial intelligence to take the appropriate action by adjusting the times in real time to minimise the delay in the intersection while also coordinating with intersections in the neighbourhood. SMART Signal simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time signal performance measures, including arterial travel time, number of stops, queue length, intersection delay, and level of service. In Brunei Darussalam, where we have numerous intersections where several arterial roads are linked to one another, The SMART signal traffic control method should be implemented.

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5

Polushin, P. A., O. R. Nikitin, and I. R. Dubov. "Quasioptimal control in diversed signal transmission." IOP Conference Series: Materials Science and Engineering 1227, no. 1 (February 1, 2022): 012003. http://dx.doi.org/10.1088/1757-899x/1227/1/012003.

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Abstract To increase the noise immunity of signal transmission, diversity methods are now widely used, consisting in obtaining and combining several copies of the transmitted signal. In this case, it is possible to perform a combination either before the detection procedure or after it. If you do not take into account the possible use of non-linear types of modulation, then the pre-detector combination always has advantages over the post-detector combination. However, taking into account the nonlinear properties of the transmitted signals, new possibilities appear for increasing the noise immunity in combination and simplifying the processing. In the case of using analog signals, in particular frequency modulation, at certain points in time, the pre-detection combination can lose to the post-detection combination. At the same time, by combining pre-detector and post-detector combining circuits, it is possible to lower the threshold level during demodulation and increase noise immunity. In the case of using digital modes of modulation, it is possible to process only the signals after demodulation without reducing the noise immunity and to eliminate the need for preliminary phasing of the diversity signals before detection.

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6

Schlessinger, J. "SIGNAL TRANSDUCTION: Autoinhibition Control." Science 300, no. 5620 (May 2, 2003): 750–52. http://dx.doi.org/10.1126/science.1082024.

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7

Azhar, Al-Mudhaffar, and Berg Svante. "Signal Control of Roundabouts." Procedia - Social and Behavioral Sciences 16 (2011): 729–38. http://dx.doi.org/10.1016/j.sbspro.2011.04.492.

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8

Zhao, Lin. "Numerical Control Lathe Cutting Force Signal On-Line Monitoring Design." Applied Mechanics and Materials 711 (December 2014): 329–32. http://dx.doi.org/10.4028/www.scientific.net/amm.711.329.

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The main research direction of Numerical control lathe cutting force signal on-line monitoring is to process real-time monitoring, using the sensor, charge amplifier, video acquisition card and computer to collect data and signal. Signal acquisition makes use of the piezoelectric sensor signals and send them to the computer in order to acquire the real-time data and display the dynamic signal so that monitor the process. Signal processing is the course that data will be collected for subsequent processing and analyzing. It includes display, filtering, correlation analysis, spectral analysis, etc. We can conclude the signal’s characteristics after the time domain and frequency domain analysis of signals.

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9

Huang, Kun, Wei Cai Peng, Zhi Xiong Huang, Min Xian Shi, and Guang Bing Wan. "Research of the Piezoelectric Composite Active Control." Advanced Materials Research 1030-1032 (September 2014): 1513–16. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1513.

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Instantaneous signal and alternating signals are as driven signals, using PID control, change control coefficient P's size, The stopping time of impact force under instantaneous signal with the piezoelectric ceramic content increases, the stopping time is reduced; after applying active control, the stopping is shorter than that is not applied. After applying the alternating signal to force the cantilever beam, the driving effect of the ceramic sheet is located in a cantilever position A, B, C, the effect of C under controlling is at best. The effect of two driven ceramic sheets is better than that of three driven ceramic sheets, the best control position is B+C.

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10

Pan, Fuquan, Lixia Zhang, Changxi Ma, Haiyuan Li, Jinshun Yang, Tao Liu, Fengyuan Wang, and Shushan Chai. "Impact of Vehicular Countdown Signals on Driving Psychologies and Behaviors: Taking China as an Example." Journal of Advanced Transportation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5838520.

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Countdown signal control is a relatively new control mode that can inform a driver in advance about the remaining time to pass through intersections or the time needed to wait for other drivers and pedestrians. At present, few countries apply vehicular countdown signals. However, in China, some cities have applied vehicular countdown signals for years, though it is unclear how and how much such signals influence driving psychologies and behaviors compared with non-countdown signal controls. The present work aims to clarify the impact of vehicular countdown signals on driving psychologies and behaviors on the cognitive level. A questionnaire survey with 32 questions about driving psychologies and behaviors was designed, and an online survey was conducted. A total of 1051 valid questionnaires were received. The survey data were analyzed, and the main results indicate that most of the surveyed drivers prefer countdown signal controls and think that such controls can improve not only traffic safety but also traffic operational efficiency. The surveyed drivers also think that countdown signal controls have an impact on driving psychologies and behaviors and the survey results have demonstrated that the driving behaviors of female drivers surveyed are not conservative under the clear conditions of green countdown signal control. Further studies and methods concerning the effects of countdown signals on driving psychologies and behaviors are discussed.

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11

Genders, Wade, and Saiedeh Razavi. "Evaluating Reinforcement Learning State Representations for Adaptive Traffic Signal Control." International Journal of Traffic and Transportation Management 1, no. 1 (June 10, 2019): 19–26. http://dx.doi.org/10.5383/jttm.01.01.003.

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12

Prof. C.Y. Patil, Pallavi Mandhare, Dr Jyoti Yadav, Prof Vilas Kharat,. "Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 1, 2021): 373–79. http://dx.doi.org/10.17762/itii.v9i1.141.

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The most observable obstacle to sustainable mobility is traffic congestions. These congestions cannot effectively be fixed by traditional control of traffic signals. Safe and smooth movement of traffic is ensured by a self-controlled traffic signal. As such, to coordinate the traffic flow it is necessary to implement dynamic traffic signal subsequences. Primarily, Traffic Signal Controllers (TSC) provides sophisticated control and coordination of vehicles. The control and coordination of traffic signal control systems can be effectively achieved by implementing the Deep Reinforcement Learning (DRL) approaches. The decision-making capabilities at intersections are improved by having variations of traffic signal timing using an adaptive TSC. Alternatively, the actual traffic demand is nothing but managing the traffic systems. It analyses the incoming number and type of vehicles and gives a real-time response at intersection geometrics and controls the traffic signals accordingly. The proposed DRL algorithm observes traffic data and operates optimum management plans for the regulation of the traffic flow. Furthermore, an existing traffic simulator is used to help provide a realistic environment to support the proposed algorithm.

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13

Табаков, Yu Tabakov, Лавлинский, and V. Lavlinskiy. "TYPOLOGY LF SIGNAL ON ADAPTATION CONTROL SIGNALS FOR INTELLECTUAL SIMULATORS." Modeling of systems and processes 7, no. 4 (February 24, 2015): 38–41. http://dx.doi.org/10.12737/7906.

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The article considers the variety and amplitude-frequency characteristics of low-frequency signals. Considered experimental removal of the low-frequency signals from the cerebral cortex of a few healthy people to identify the control signals.

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14

Iwaki, Satoshi. "Acoustic signal transmission with insertion signal for machine control." Journal of the Acoustical Society of America 119, no. 6 (2006): 3524. http://dx.doi.org/10.1121/1.2212609.

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15

Yamamoto, Yutaka, Kaoru Yamamoto, Masaaki Nagahara, and Pramod P. Khargonekar. "Signal processing via sampled-data control theory." Impact 2020, no. 2 (April 15, 2020): 6–8. http://dx.doi.org/10.21820/23987073.2020.2.6.

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Digital sounds and images are used everywhere today, and they are all generated originally by analogue signals. On the other hand, in digital signal processing, the storage or transmission of digital data, such as music, videos or image files, necessitates converting such analogue signals into digital signals via sampling. When these data are sampled, the values from the discrete, sampled points are kept while the information between the sampled points is lost. Various techniques have been developed over the years to recover this lost data, but the results remain incomplete. Professor Yutaka Yamamoto's research is focused on improving how we can recover or reconstruct the original analogue data.

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16

Chen, Songhang, Chunlin Shang, and Fenghua Zhu. "A Flexible Traffic Signal Coordinated Control Approach and System on Complicated Transportation Control Infrastructure." Sensors 23, no. 13 (June 21, 2023): 5796. http://dx.doi.org/10.3390/s23135796.

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The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper proposes a novel approach to coordinated traffic signal control that emphasizes flexibility. To achieve this flexibility, we combine the flexible model of complex networks with robust fuzzy control methods. This approach enables us to overcome the complexity of the transportation control infrastructure and ensure efficient management of traffic signals. Additionally, to ensure long-term operational ease, we develop a regional traffic signal control system using steam computing technology, which provides high scalability and compatibility. Finally, computational experiments are performed to validate adaptability and performance of our proposed approach.

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17

Bagye, Wire. "Implementasi Jalur Komunikasi Global System Mobile (GSM) Untuk Kontrol Robot Jarak Jauh Berbasis Mikrikontroller Atmel89S52." Jurnal Informatika dan Rekayasa Elektronik 1, no. 1 (May 3, 2018): 1. http://dx.doi.org/10.36595/jire.v1i1.24.

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Some of the previous research builds controlled robots using devices with limited distance. The remote controlled robot controls the control distance along the rays of the sender's Infrared (IR) ray is not blocked by a 20 meters long Infrared (IR) receiver. The blue tooth controlled robot has a control distance of 40 meters. The controlled Robot Wireless Fidelity controls the maximum range of range specifications of the Access point range used. Maximum coverage ranges for outdoor access points as far as 15 Kilometers. The next research have to do with a longer distance control than previous research. In this research is Built a robot controlled by utilizes of the Global System Mobile (GSM) communication line. This project uses two Hand phones as a sender and the other one to become receiver. Hand phone sender function is sends control signal to receiver phone. Signals sent are DTMF signals (Dual Tone Multiple Frequency). There are 9 signals control with difference function. Those functions are (1) Turn left (2) Right turn (3) Left rewind (4) Right rewind (5) Rear left (6) Rear Right (7) Stop (8) Move forward (9) Move backward. On the mobile phone receiver signal DTMF in the form of analog signal is converted by Integrated Circuit (IC) MT8870p become Signal Digital. Digital signal from IC MT 8870p is used to be input of Atmel89s52 microcontroller. Atmel89s52 output has not been able to be used for DC motor drive signal so that used transistors amplifier to control the relays. Relay as an electronic switch to connect and disconnect the DC voltage to the motor so that the DC motor can be two-way. DC motors rotating clockwise and counterclockwise. This rotation is used as a robot movement. The research results shown that 9 DTMF key tones can be used to control the robot through the GSM line. Long of distance control as far as location that is accessible GSM signal from BTS (Base Transceiver Station). Further research needs to be done to transmit video signals so that it can be done long distance visual monitor on the movement of the robot.

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18

Bang, Yung-Jue. "Control of Signal Transduction Pathway." Journal of the Korean Medical Association 41, no. 8 (1998): 824. http://dx.doi.org/10.5124/jkma.1998.41.8.824.

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19

Inui, Masafumi, Graziano Martello, and Stefano Piccolo. "MicroRNA control of signal transduction." Nature Reviews Molecular Cell Biology 11, no. 4 (March 10, 2010): 252–63. http://dx.doi.org/10.1038/nrm2868.

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20

Hodgson, W. S. "Motorway Signal and Control Systems." Measurement and Control 26, no. 1 (February 1993): 6–9. http://dx.doi.org/10.1177/002029409302600102.

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21

Hartong, Mark W., and Olga K. Cataldi. "Microprocessor Signal and Train Control." Transportation Research Record: Journal of the Transportation Research Board 1943, no. 1 (January 2006): 25–30. http://dx.doi.org/10.1177/0361198106194300104.

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22

Guglielmi, Michel. "signal synthesis with precision control." Signal Processing 86, no. 10 (October 2006): 2548–53. http://dx.doi.org/10.1016/j.sigpro.2006.02.012.

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23

Niittymäki, Jarkko, and Matti Pursula. "Signal control using fuzzy logic." Fuzzy Sets and Systems 116, no. 1 (November 2000): 11–22. http://dx.doi.org/10.1016/s0165-0114(99)00034-2.

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24

Mei, Fei, Zhihong Man, and Xinghuo Yu. "Sliding mode control signal analysis." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4118–22. http://dx.doi.org/10.1016/s1474-6670(17)56702-6.

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25

Karin, Michael. "Signal transduction and gene control." Current Opinion in Cell Biology 3, no. 3 (June 1991): 467–73. http://dx.doi.org/10.1016/0955-0674(91)90075-a.

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26

Ottersten, Björn, Torsten Söderström, and Bo Wahlberg. "Statistical signal processing and control." Automatica 30, no. 1 (January 1994): 9. http://dx.doi.org/10.1016/0005-1098(94)90224-0.

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27

Fanuc Ltd. "Video signal phase control circuit." Displays 15, no. 3 (July 1994): 194. http://dx.doi.org/10.1016/0141-9382(94)90015-9.

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28

Hägglund, Tore. "Signal Filtering in PID Control." IFAC Proceedings Volumes 45, no. 3 (2012): 1–10. http://dx.doi.org/10.3182/20120328-3-it-3014.00002.

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29

Gao, Hongliang, Xiaoling Li, Chao Gao, and Jie Wu. "Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control." Discrete Dynamics in Nature and Society 2021 (March 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/5536573.

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This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method.

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30

Hirsch, Scott M., and J. Q. Sun. "Control signal scheduling for active noise control systems." Smart Materials and Structures 8, no. 3 (June 1, 1999): 315–23. http://dx.doi.org/10.1088/0964-1726/8/3/304.

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31

Qu, Feng Zhen, and Bao Cheng Lu. "Comparative Study of the Auto Electronic Control System." Applied Mechanics and Materials 539 (July 2014): 587–91. http://dx.doi.org/10.4028/www.scientific.net/amm.539.587.

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Based on mathematical principle of function interpolation we design dominant and recessive auto electronic control system. In order to compare the performance of the two systems, we design a control experiment of automobile electronic signals for these two systems. The system adopts the CAN bus control, the wheel speed and door signal use the 0001-0100 signal channel to control, oil pressure and fault signal use the 0100-1111 signal channel to control. From the testing results of two systems, the dominant auto electronic control system occupies 8 channels, which is far from the theoretical value; and the recessive control occupies only 2 channels, which is accord with the theoretical value. It provides the technical support for the design of automotive electronic control system

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32

Yu, Sen Bin, Yong Ju Hu, Xin Sheng He, Si Wei Yang, and Xi Wen Chen. "Single-Line Traffic Lights Wireless Signal Control Device." Applied Mechanics and Materials 178-181 (May 2012): 2721–24. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.2721.

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In the light of tunnel construction, traffic accidents, sudden geological disasters cause of single-line road conditions, designing a single-line road traffic lights wireless signal control device to solve the traffic congestion and the problem of low efficiency of traffic. The device uses wireless transceiver module to sent the signal what was got from the photoelectric sensor to the single-chip microcomputer, single chip microcomputer (SCM) processes switch signals, the last step is to control the traffic light state through the driver circuit. This paper expounds the wireless signal control device trickle structure, working principle, and technical index of the various components, and it puts forward the operation under a feasible solution.

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33

WANG, XINGYUAN, and MINGJUN WANG. "TRACKING CONTROL FOR A CLASS OF CHAOTIC SYSTEMS." International Journal of Modern Physics B 22, no. 12 (May 10, 2008): 1977–84. http://dx.doi.org/10.1142/s0217979208039186.

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This paper presents a tracking control strategy for a class of chaotic systems. A general tracking controller is designed. It is proved theoretically that the error signal can exponentially converge to zero. Numerical simulations show that the controller can make the output signal track all kinds of reference signals. Besides, a better effect can be obtained via changing the control parameters properly.

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34

Ramos Fuentes, Germán Andrés, Josep Olm Miras, and Ramón Costa Castelló. "A survey of repetitive control in varying frequency conditions." Ingeniería e Investigación 31, no. 2 (May 1, 2011): 29–37. http://dx.doi.org/10.15446/ing.investig.v31n2.23462.

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Digital repetitive control is a strategy that allows tracking/ rejecting periodic signals. Repetitive controllers are designed assuming that the exogenous signal period is constant and known, its main drawback being the dramatic loss of performance when signal frequency varies. This paper reviews the most relevant proposals advanced for overcoming this problem.

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35

Zhu, Yu Tian, Hao Wu, and Zhao Liu. "Development of Online Oil Control Valve Detection System." Applied Mechanics and Materials 380-384 (August 2013): 833–36. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.833.

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An online detecting system is developed to check the function of oil control valve. This system consists of hydraulic detecting platform, signal collecting, signal processing and upper computer. It uses a domestic controller as the main control unit and the control program is developed. The signal conditioning board and the software of upper computer within LabVIEW environment are developed. By using this system, the signals of all sensors can be collected, conditioned, displayed and recorded quickly. The application shows that this system has been proved with the characteristics of high speed, high automation and detection precision.

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36

Gartner, Nathan H., and Mohammed Al-Malik. "Combined Model for Signal Control and Route Choice in Urban Traffic Networks." Transportation Research Record: Journal of the Transportation Research Board 1554, no. 1 (January 1996): 27–35. http://dx.doi.org/10.1177/0361198196155400104.

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Traffic signals have a significant effect on the choice of routes by motorists in urban areas. They are of primary importance in the development of advanced traffic management strategies that involve dynamic rerouting of traffic flows through signal-controlled street networks. A combined network model that simultaneously accounts for both the route choices made by motorists and the desired signal controls to match these choices is presented. Given origin-destination travel demand information, the model generates signal controls to optimize network performance and calculates the resulting traffic volumes in the network. This optimization model inherently reflects the mutual consistency between traffic flows and signal controls. The model is applicable to both fixed-time and demand-responsive signals. Computational procedures and sample network solutions are presented.

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37

Sheng, Zhoubin, Chunbiao Li, Yikai Gao, Zhinan Li, and Lin Chai. "A Switchable Chaotic Oscillator with Multiscale Amplitude/Frequency Control." Mathematics 11, no. 3 (January 26, 2023): 618. http://dx.doi.org/10.3390/math11030618.

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For the wide frequency spectrum of chaotic signals, it is difficult to realize chaotic signal conditioning. Therefore, researchers turn to the exploration of chaotic systems with independent non-bifurcation control for easy chaos modification. In this paper, a system with only one non-quadratic term is modified for providing multiscale amplitude/frequency control. By adjusting the feedback with an odd higher degree term, a switchable chaotic oscillator is obtained, which provides the different scales of amplitude/frequency control with the chaotic signal. Multisim-based circuit simulation shows the efficiency and convenience of chaotic signal control.

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38

Mu, Haibo, Linzhong Liu, Yubo Song, and Na Wang. "Control Strategy of Signal Transition after Emergency Vehicle Signal Preemption." Discrete Dynamics in Nature and Society 2020 (January 27, 2020): 1–11. http://dx.doi.org/10.1155/2020/1382415.

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Emergency vehicle (EV) plays an important role in evacuations or rescues when emergencies occur. To insure that an EV can transfer people in danger to emergency shelters or medical assistance organizations as soon as possible, EV signal preemption (EVSP) strategy is usually adopted. After EV has passed through the intersection, traffic signal has to transfer back to normal signal timing scheme. This paper focuses on the control strategy of EV signal transitioning from EVSP back to normal operation. Considering both efficiency and fairness, the maximum vehicles passing through in per unit time during the transition period and the minimum difference between the maximum and the minimum queue length after transition are selected as objectives, and a multi-objective optimization model is presented. A nondominated sorting genetic algorithm II (NSGA-II) is designed to solve the optimization model and unique encoding and decoding methods are presented. The established model and designed algorithm are verified and the control effect is analyzed. Simulation results indicate that by adopting the control strategy obtained by the presented model, the number of vehicles passing through in per unit time during the transition period is increased and the difference of vehicle length in different directions is reduced significantly, from which we can conclude that the control method proposed in this paper has good performance.

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39

Zhang, T., C. Chen, and M. Nakamura. "Reliable EOG signal-based control approach with EEG signal judgment." Artificial Life and Robotics 14, no. 2 (November 2009): 195–98. http://dx.doi.org/10.1007/s10015-009-0652-7.

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40

Zhang, Nannan. "Intelligent Control Optimization of Remote Transmission Quality of Communication Signals Based on 5G Network." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012031. http://dx.doi.org/10.1088/1742-6596/2066/1/012031.

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Abstract With the rapid development of the information age, mobile communication networks have gradually entered people’s lives. The emergence of 5G networks has accelerated the convenience of people’s communication and contact, and has become an advanced information technology that has been widely used in various fields. Although the emergence of 5G networks is beneficial, the remote transmission quality of communication signals has also caused many problems. The optimization of remote transmission quality can effectively improve the operational stability of the communication network. There are many ways to optimize the transmission quality of communication signals. For example, when signal interference needs to be reduced, calculate the average value of signals in various places, set the interference reduction threshold function, and complete the remote transmission quality optimization of communication signals. The existing method is to use microblogs with hard and soft thresholds to decompose the signal quality through multiple scales to provide interference reduction thresholds, but ignore the calculation of the average value of the subband signals, so the signal transmission quality is optimized The effect is not satisfactory. It is recommended to reduce the interference in the network communication process based on the anti-window. First, the input communication signal is Pulley converted, the frequency domain interference detection is performed on the communication signal, a low-pass prototype filter is formed, the average value of the signal in each place is calculated, the interference reduction threshold is set, and the network communication process is completed. The weakening of interference realizes the optimization of transmission quality. This article conducts research and discussion on the optimization of the long-distance transmission quality of the communication signal of the 5G network. Experimental research results show that by demonstrating the optimization of the quality of the 5G communication signal transmission process, these demonstrations can ensure the quality of the communication signal, and people’s demand for the network is already indispensable.

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41

Oh, H. E., W. B. Jeong, and C. Hong. "Implementation of a single-channel active noise control system with multiple reference sensors." Noise Control Engineering Journal 68, no. 5 (September 1, 2020): 358–66. http://dx.doi.org/10.3397/1/376830.

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When multiple sources contribute competitively to the noise level, multi-channel control architecture is needed, leading to more cost and time for control computation. We, hence, are concerned with a single-channel control method with a single-reference signal obtained from a linear combination of the multiple source signals. First, we selected 3 source signal sensors for the reference signals and the error sensor, selected a proper actuator and designed the controllers: 3 cases of single-channel feedforward controllers with a single-reference signal respectively from the source signals, a multi-channel feedforward controller with the reference signals from the source signals, and the proposed controller with the reference signal from weighted sum of the source signals. The weighting factors and the filter coefficients of the controller were determined by the FxLMS algorithm. An experiment was then performed to confirm the effectiveness of the proposed method comparing the control performance with other methods for a tower air conditioner. The overall sound pressure level (SPL) detected by the error sensor is compared to evaluate their performance. The reduction in the overall SPL was obtained by 4.74 dB, 1.96 dB and 6.62 dB, respectively, when using each of the 3 reference signals. Also, the overall SPL was reduced by 7.12 dB when using the multi-reference controller and by 7.66 dB when using the proposed controller. Conclusively, under the multiple source contribution, a single-channel feed forward controller with the reference signal from a weighted sum of the source signals works well with lower cost than multi-channel feedforward controller.

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42

Daniel, TO, and HE Ives. "Endothelial Control of Vascular Function." Physiology 4, no. 4 (August 1, 1989): 139–42. http://dx.doi.org/10.1152/physiologyonline.1989.4.4.139.

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Endothelial cells are exposed to stimuli within the lumen of blood vessels. Interpreting these signals, endothelial cells regulate production and release of mediators that signal underlying smooth muscle to contract, relax, or proliferate. Specialization of endothelial cells in different vascular beds may determine regional responses.

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43

Vosvrda, Miloslav S. "Discrete random signals and statistical signal processing." Automatica 29, no. 6 (November 1993): 1617. http://dx.doi.org/10.1016/0005-1098(93)90033-p.

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44

Jia, Libin, Jeffrey D. Naber, and Jason R. Blough. "Review of Sensing Methodologies for Estimation of Combustion Metrics." Journal of Combustion 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8593523.

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For reduction of engine-out emissions and improvement of fuel economy, closed-loop control of the combustion process has been explored and documented by many researchers. In the closed-loop control, the engine control parameters are optimized according to the estimated instantaneous combustion metrics provided by the combustion sensing process. Combustion sensing process is primarily composed of two aspects: combustion response signal acquisition and response signal processing. As a number of different signals have been employed as the response signal and the signal processing techniques can be different, this paper did a review work concerning the two aspects: combustion response signals and signal processing techniques. In-cylinder pressure signal was not investigated as one of the response signals in this paper since it has been studied and documented in many publications and also due to its high cost and inconvenience in the application.

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45

Xu, Bingyu, Yaowei Wang, Zhaozhi Wang, Huizhu Jia, and Zongqing Lu. "Hierarchically and Cooperatively Learning Traffic Signal Control." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 669–77. http://dx.doi.org/10.1609/aaai.v35i1.16147.

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Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated superior performance to conventional control methods. However, there are still several challenges we have to address before fully applying deep RL to traffic signal control. Firstly, the objective of traffic signal control is to optimize average travel time, which is a delayed reward in a long time horizon in the context of RL. However, existing work simplifies the optimization by using queue length, waiting time, delay, etc., as immediate reward and presumes these short-term targets are always aligned with the objective. Nevertheless, these targets may deviate from the objective in different road networks with various traffic patterns. Secondly, it remains unsolved how to cooperatively control traffic signals to directly optimize average travel time. To address these challenges, we propose a hierarchical and cooperative reinforcement learning method-HiLight. HiLight enables each agent to learn a high-level policy that optimizes the objective locally by selecting among the sub-policies that respectively optimize short-term targets. Moreover, the high-level policy additionally considers the objective in the neighborhood with adaptive weighting to encourage agents to cooperate on the objective in the road network. Empirically, we demonstrate that HiLight outperforms state-of-the-art RL methods for traffic signal control in real road networks with real traffic.

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46

Pan, Qi, Yanli Zhou, and Juntao Fei. "Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control." Mathematics 11, no. 6 (March 18, 2023): 1495. http://dx.doi.org/10.3390/math11061495.

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This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller.

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47

Wang, Yiming, and Yangfan Liu. "A directional active noise control algorithm based on all-pass/minimum phase decomposition." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A99. http://dx.doi.org/10.1121/10.0015672.

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In this paper, we propose the design of a directional active noise control (ANC) system, which can suppress the noise coming from one specific direction without affecting the noise coming from other directions. The proposed directional ANC system integrates the sound extraction feature of a beamforming system with the active noise canceling feature of an ANC system. Instead of using the signals collected by the reference microphones as the reference signals directly, the directional ANC system uses the signal filtered by carefully designed minimum phase beamforming filters as the new reference signal, in which the noise signal coming from the look-direction of the beamformer is enhanced and at the same time noise signals coming from other directions are suppressed. Usually, the filtered signal of a beamforming system cannot be used as the reference signal of an ANC system because the delays introduced by the beamforming process degrade the performance of the ANC system. To resolve the problem, a spectral factorization technique is introduced and used to extract the minimum phase component of the beamforming filters which decreases the required delays of the beamforming system.

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48

Glowinski, Sebastian, Sebastian Pecolt, Andrzej Błażejewski, and Bartłomiej Młyński. "Control of Brushless Direct-Current Motors Using Bioelectric EMG Signals." Sensors 22, no. 18 (September 9, 2022): 6829. http://dx.doi.org/10.3390/s22186829.

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(1) Background: The purpose of this study was to evaluate the analysis of measurements of bioelectric signals obtained from electromyographic sensors. A system that controls the speed and direction of rotation of a brushless DC motor (BLDC) was developed; (2) Methods: The system was designed and constructed for the acquisition and processing of differential muscle signals. Basic information for the development of the EMG signal processing system was also provided. A controller system implementing the algorithm necessary to control the speed and direction of rotation of the drive rotor was proposed; (3) Results: Using two muscle groups (biceps brachii and triceps), it was possible to control the direction and speed of rotation of the drive unit. The control system changed the rotational speed of the brushless motor with a delay of about 0.5 s in relation to the registered EMG signal amplitude change; (4) Conclusions: The prepared system meets all the design assumptions. In addition, it is scalable and allows users to adjust the signal level. Our designed system can be implemented for rehabilitation, and in exoskeletons or prostheses.

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49

LI, XIAOLI, and R. DU. "MONITORING MACHINING PROCESSES BASED ON DISCRETE WAVELET TRANSFORM AND STATISTICAL PROCESS CONTROL." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 03 (September 2004): 299–311. http://dx.doi.org/10.1142/s0219691304000548.

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This paper presents a new method to monitor machining processes based on a combination of discrete wavelet transform (DWT) and statistical process control (SPC), called a multi-scale statistical approach. First, DWT is applied to decompose the sensor signal onto different scales. Next, the detection limits are formed for each decomposed signal components, called the sub-signals, using Shewhart control charts. Finally, by inverse wavelet transform of the threshold crossing points of the sub-signals, malfunctions can be detected. Based on a test on the tool condition monitoring in turning using acoustic emission (AE) signal, it is shown that the new method is effective and robust.

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50

He, Juan, Guang Yi Luo, and Ji Qiong Chen. "Overview of Repetitive Control System." Applied Mechanics and Materials 536-537 (April 2014): 1174–77. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.1174.

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The repetitive control theory is a theory of control system design, the purpose is to design a controller , which can make the system steady-state error be zero under tracking arbitrary periodicity reference signal, the repetitive control theory has been widely applied in tracking control system with periodic reference signals.

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