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Journal articles on the topic 'Electronic Performance Tracking Systems (EPTS)'

Author: Grafiati

Published: 15 September 2021

Last updated: 18 February 2022

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1

Linke, Daniel, Daniel Link, and Martin Lames. "Validation of electronic performance and tracking systems EPTS under field conditions." PLOS ONE 13, no. 7 (July 23, 2018): e0199519. http://dx.doi.org/10.1371/journal.pone.0199519.

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2

Rico-González, Markel, Asier Los Arcos, Daniel Rojas-Valverde, Filipe M. Clemente, and José Pino-Ortega. "A Survey to Assess the Quality of the Data Obtained by Radio-Frequency Technologies and Microelectromechanical Systems to Measure External Workload and Collective Behavior Variables in Team Sports." Sensors 20, no. 8 (April 16, 2020): 2271. http://dx.doi.org/10.3390/s20082271.

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Electronic performance and tracking systems (EPTS) and microelectromechanical systems (MEMS) allow the measurement of training load (TL) and collective behavior in team sports so that match performance can be optimized. Despite the frequent use of radio-frequency (RF) technology (i.e., global positioning navigation systems (GNSS/global positioning systems (GPS)) and, local position systems (LPS)) and MEMS in sports research, there is no protocol that must be followed, nor are there any set guidelines for evaluating the quality of the data collection process in studies. Thus, this study aims to suggest a survey based on previously used protocols to evaluate the quality of data recorded by RF technology and MEMS in team sports. A quality check sheet was proposed considering 13 general criteria items. Four additional items for GNSS/GPS, eight additional items for LPS, and five items for MEMS were suggested. This information for evaluating the quality of the data collection process should be reported in the methods sections of future studies.

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3

Kim, Hyunsung, Jaehee Kim, Young-Seok Kim, Mijung Kim, and Youngjoo Lee. "Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification." Sensors 20, no. 21 (October 22, 2020): 6004. http://dx.doi.org/10.3390/s20216004.

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This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic performance, the proposed wearable EPTS device utilizes the GNSS-based positioning solution, the IMU-based movement sensing system, and the real-time data acquisition protocol. As the life-time of the EPTS device is in general limited due to the energy-hungry GNSS sensing operations, for the energy-efficient solution extending the operating time, in this work, we newly develop the advanced optimization methods that can reduce the number of GNSS accesses without degrading the data quality. The proposed method basically identifies football activities during the match time, and the sampling rate of the GNSS module is dynamically relaxed when the player performs static movements. A novel deep convolution neural network (DCNN) is newly developed to provide the accurate classification of human activities, and various compression techniques are applied to reduce the model size of the DCNN algorithm, allowing the on-device DCNN processing even at the memory-limited EPTS device. Experimental results show that the proposed DCNN-assisted sensing control can reduce the active power by 28%, consequently extending the life-time of the EPTS device more than 1.3 times.

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4

Potsaid, Benjamin, John Ting-Yung Wen, Mark Unrath, David Watt, and Mehmet Alpay. "High Performance Motion Tracking Control for Electronic Manufacturing." Journal of Dynamic Systems, Measurement, and Control 129, no. 6 (March 2, 2007): 767–76. http://dx.doi.org/10.1115/1.2789467.

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Motion control requirements in electronic manufacturing demand both higher speeds and greater precision to accommodate continuously shrinking part/feature sizes and higher densities. However, improving both performance criteria simultaneously is difficult because of resonances that are inherent to the underlying positioning systems. This paper presents an experimental study of a feedforward controller that was designed for a point-to-point motion control system on a modern and state of the art laser processing system for electronics manufacturing. We systematically apply model identification, inverse dynamics control, iterative refinement (to address modeling inaccuracies), and adaptive least mean square to achieve high speed trajectory tracking. The key innovations lie in using the identified model to generate the gradient descent used in the iterative learning control, encoding the result from the learning control in a finite impulse response filter and adapting the finite impulse response coefficients during operation using the least-mean-square update based on position, velocity, and acceleration feedforward signals. Experimental results are provided to show the efficacy of the proposed approach, a variation of which has been implemented on the production machine.

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5

Fujioka, Hisaya, Chung-Yao Kao, Stefan Almér, and Ulf Jönsson. "Robust tracking with H∞ performance for PWM systems." Automatica 45, no. 8 (August 2009): 1808–18. http://dx.doi.org/10.1016/j.automatica.2009.03.026.

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6

Kim, K. S. "Analysis of Optical Data Storage Systems—Tracking Performance With Eccentricity." IEEE Transactions on Industrial Electronics 52, no. 4 (August 2005): 1056–62. http://dx.doi.org/10.1109/tie.2005.851685.

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7

Berger, Thomas. "Tracking with prescribed performance for linear non-minimum phase systems." Automatica 115 (May 2020): 108909. http://dx.doi.org/10.1016/j.automatica.2020.108909.

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8

Sun, Xin-xiang, Jie Wu, Xi-Sheng Zhan, and Tao Han. "Optimal modified tracking performance for MIMO systems under bandwidth constraint." ISA Transactions 62 (May 2016): 145–53. http://dx.doi.org/10.1016/j.isatra.2016.01.018.

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9

Woodyatt, A. R., M. M. Seron, J. S. Freudenberg, and R. H. Middleton. "Cheap control tracking performance for non-right-invertible systems." International Journal of Robust and Nonlinear Control 12, no. 15 (October 16, 2002): 1253–73. http://dx.doi.org/10.1002/rnc.671.

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10

Tosi, Daniele, Massimo Olivero, and Guido Perrone. "Performance analysis of peak tracking techniques for fiber Bragg grating interrogation systems." Journal of Microwaves, Optoelectronics and Electromagnetic Applications 11, no. 2 (December 2012): 252–62. http://dx.doi.org/10.1590/s2179-10742012000200003.

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11

Grasselli, Osvaldo Maria, Sauro Longhi, and Antonio Tornambè. "Robust tracking and performance for multivariable systems under physical parameter uncertainties." Automatica 29, no. 1 (January 1993): 169–79. http://dx.doi.org/10.1016/0005-1098(93)90180-2.

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12

Guan, Zhi-Hong, Baoxian Wang, and Li Ding. "Modified tracking performance limitations of unstable linear SIMO feedback control systems." Automatica 50, no. 1 (January 2014): 262–67. http://dx.doi.org/10.1016/j.automatica.2013.10.008.

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13

Welch, Greg, Gary Bishop, Leandra Vicci, Stephen Brumback, Kurtis Keller, and D'nardo Colucci. "High-Performance Wide-Area Optical Tracking: The HiBall Tracking System." Presence: Teleoperators and Virtual Environments 10, no. 1 (February 2001): 1–21. http://dx.doi.org/10.1162/105474601750182289.

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Since the early 1980s, the Tracker Project at the University of North Carolina at Chapel Hill has been working on wide-area head tracking for virtual and augmented environments. Our long-term goal has been to achieve the high performance required for accurate visual simulation throughout our entire laboratory, beyond into the hallways, and eventually even outdoors. In this article, we present results and a complete description of our most recent electro-optical system, the HiBall Tracking System. In particular, we discuss motivation for the geometric configuration and describe the novel optical, mechanical, electronic, and algorithmic aspects that enable unprecedented speed, resolution, accuracy, robustness, and flexibility.

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14

Zhan, Xi-Sheng, Xin-Xiang Sun, Jie Wu, and Tao Han. "Optimal modified tracking performance for networked control systems with QoS constraint." ISA Transactions 65 (November 2016): 109–15. http://dx.doi.org/10.1016/j.isatra.2016.07.006.

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15

Chen, Jie, Shinji Hara, Li Qiu, and Richard H. Middleton. "Best Achievable Tracking Performance in Sampled-Data Systems via LTI Controllers." IEEE Transactions on Automatic Control 53, no. 11 (December 2008): 2467–79. http://dx.doi.org/10.1109/tac.2008.2006924.

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16

Mamun, A. A., L. F. Wang, K. C. Tan, H. M. Heng, and P. C. Ho. "An Automated Methodology for the Tracking of Electrical Performance for Memory Test Systems." IEEE Transactions on Instrumentation and Measurement 55, no. 3 (June 2006): 881–91. http://dx.doi.org/10.1109/tim.2006.873816.

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17

Weizhou Su, Li Qiu, and Jie Chen. "Fundamental performance limitations in tracking sinusoidal signals." IEEE Transactions on Automatic Control 48, no. 8 (August 2003): 1371–80. http://dx.doi.org/10.1109/tac.2003.815019.

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18

Su, W., L. Qiu, and J. Chen. "On Performance Limitation in Tracking a Sinusoid." IEEE Transactions on Automatic Control 51, no. 8 (August 2006): 1320–25. http://dx.doi.org/10.1109/tac.2006.878726.

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19

Arasu, A. Valan, and T. Sornakumar. "DESIGN, DEVELOPMENT AND PERFORMANCE STUDIES OF EMBEDDED ELECTRONIC CONTROLLED ONE AXIS SOLAR TRACKING SYSTEM." Asian Journal of Control 9, no. 2 (October 22, 2008): 163–69. http://dx.doi.org/10.1111/j.1934-6093.2007.tb00319.x.

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20

Guan, Zhi-Hong, Xi-Sheng Zhan, and Gang Feng. "Optimal tracking performance of MIMO discrete-time systems with communication constraints." International Journal of Robust and Nonlinear Control 22, no. 13 (May 25, 2011): 1429–39. http://dx.doi.org/10.1002/rnc.1755.

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21

Yang, Haidou, and Wei Li. "PERFORMANCE MEASUREMENT OF PHOTOELECTRIC DETECTION AND TARGET TRACKING ALGORITHM." International Journal on Smart Sensing and Intelligent Systems 8, no. 3 (2015): 1554–75. http://dx.doi.org/10.21307/ijssis-2017-819.

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22

Hu, Yabo, Yunhai Geng, Baolin Wu, and Danwei Wang. "Model-Free Prescribed Performance Control for Spacecraft Attitude Tracking." IEEE Transactions on Control Systems Technology 29, no. 1 (January 2021): 165–79. http://dx.doi.org/10.1109/tcst.2020.2968868.

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23

Song, Shengli, Rui Jiang, Xieda Song, Yuxuan Cao, and Zhidan Sun. "Driving Performance Assessment Based on Accurate Position Tracking." Wireless Communications and Mobile Computing 2019 (August 8, 2019): 1–8. http://dx.doi.org/10.1155/2019/5256180.

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A driving performance assessment (DPA) system has been proposed in this paper to evaluate drivers’ skills in training. The system is based on centimeter-level localization of the vehicles, thanks to differential BeiDou Navigation Satellite System (BDS). Given a vehicle’s dimensions, its envelopment has been discretized both temporally and spatially as binary images, while the training area is modeled as a grayscale image where the intensity denotes the penalty of the certain area in unit time. The performance index can be obtained from the summation of images along with time. Experiments have been conducted to demonstrate the accuracy of vehicle tracking and the effectiveness of the proposed assessment system.

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24

De Jager, B. "Improving the tracking performance of mechanical systems by adaptive extended friction compensation." Control Engineering Practice 1, no. 6 (December 1993): 1009–18. http://dx.doi.org/10.1016/0967-0661(93)90011-f.

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25

Wang, Xuerao, Qingling Wang, and Changyin Sun. "Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance." Frontiers of Information Technology & Electronic Engineering 22, no. 7 (July 2021): 986–1001. http://dx.doi.org/10.1631/fitee.2000145.

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26

Zhou, Lan, Jinhua She, Xian-Ming Zhang, Zhenwei Cao, and Zhu Zhang. "Performance Enhancement of RCS and Application to Tracking Control of Chuck-Workpiece Systems." IEEE Transactions on Industrial Electronics 67, no. 5 (May 2020): 4056–65. http://dx.doi.org/10.1109/tie.2019.2921272.

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27

Youngjin Choi, Wan Kyun Chung, and Il Hong Suh. "Performance and H/sub ∞/ optimality of PID trajectory tracking controller for Lagrangian systems." IEEE Transactions on Robotics and Automation 17, no. 6 (2001): 857–69. http://dx.doi.org/10.1109/70.976011.

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28

Achkari, O., and A. El Fadar. "Thermal performance comparison of different sun tracking configurations." European Physical Journal Applied Physics 88, no. 2 (November 2019): 20902. http://dx.doi.org/10.1051/epjap/2019190048.

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Parabolic trough collector (PTC) is one of the most widespread solar concentration technologies and represents the biggest share of the CSP market; it is currently used in various applications, such as electricity generation, heat production for industrial processes, water desalination in arid regions and industrial cooling. The current paper provides a synopsis of the commonly used sun trackers and investigates the impact of various sun tracking modes on thermal performance of a parabolic trough collector. Two sun-tracking configurations, full automatic and semi-automatic, and a stationary one have numerically been investigated. The simulation results have shown that, under the system conditions (design, operating and weather), the PTC's performance depends strongly on the kind of sun tracking technique and on how this technique is exploited. Furthermore, the current study has proven that there are some optimal semi-automatic configurations that are more efficient than one-axis sun tracking systems. The comparison of the mathematical model used in this paper with the thermal profile of some experimental data available in the literature has shown a good agreement with a remarkably low relative error (2.93%).

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29

Behera, L., and K. K. Anand. "Guaranteed tracking and regulatory performance of nonlinear dynamic systems using fuzzy neural networks." IEE Proceedings - Control Theory and Applications 146, no. 5 (September 1, 1999): 484–91. http://dx.doi.org/10.1049/ip-cta:19990499.

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30

Shin, Jae-Min, Yu-Sin Kim, Tae-Won Ban, Suna Choi, Kyu-Min Kang, and Jong-Yeol Ryu. "Position Tracking Techniques Using Multiple Receivers for Anti-Drone Systems." Sensors 21, no. 1 (December 23, 2020): 35. http://dx.doi.org/10.3390/s21010035.

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The need for drone traffic control management has emerged as the demand for drones increased. Particularly, in order to control unauthorized drones, the systems to detect and track drones have to be developed. In this paper, we propose the drone position tracking system using multiple Bluetooth low energy (BLE) receivers. The proposed system first estimates the target’s location, which consists of the distance and angle, while using the received signal strength indication (RSSI) signals at four BLE receivers and gradually tracks the target based on the estimated distance and angle. We propose two tracking algorithms, depending on the estimation method and also apply the memory process, improving the tracking performance by using stored previous movement information. We evaluate the proposed system’s performance in terms of the average number of movements that are required to track and the tracking success rate.

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31

Jiang, X. W., Z. H. Guan, G. Feng, Y. Wu, and F. S. Yuan. "Optimal tracking performance of networked control systems with channel input power constraint." IET Control Theory & Applications 6, no. 11 (2012): 1690. http://dx.doi.org/10.1049/iet-cta.2011.0329.

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32

Chen, Chao-Yang, Weihua Gui, Lianghong Wu, Zhaohua Liu, and Huaicheng Yan. "Tracking Performance Limitations of MIMO Networked Control Systems With Multiple Communication Constraints." IEEE Transactions on Cybernetics 50, no. 7 (July 2020): 2982–95. http://dx.doi.org/10.1109/tcyb.2019.2912973.

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33

Dimanidis, Ioannis S., Charalampos P. Bechlioulis, and George A. Rovithakis. "Output Feedback Approximation-Free Prescribed Performance Tracking Control for Uncertain MIMO Nonlinear Systems." IEEE Transactions on Automatic Control 65, no. 12 (December 2020): 5058–69. http://dx.doi.org/10.1109/tac.2020.2970003.

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34

de Ruiter, Anton H. J. "Spacecraft Attitude Tracking with Guaranteed Performance Bounds." Journal of Guidance, Control, and Dynamics 36, no. 4 (July 2013): 1214–21. http://dx.doi.org/10.2514/1.56264.

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35

Lu, Zhi-hua, Meng-yao Zhu, Qing-wei Ye, and Yu Zhou. "Performance analysis of two EM-based measurement bias estimation processes for tracking systems." Frontiers of Information Technology & Electronic Engineering 19, no. 9 (September 2018): 1151–65. http://dx.doi.org/10.1631/fitee.1800214.

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36

Ilchmann, Achim, Eugene P. Ryan, and Stephan Trenn. "Tracking control: Performance funnels and prescribed transient behaviour." Systems & Control Letters 54, no. 7 (July 2005): 655–70. http://dx.doi.org/10.1016/j.sysconle.2004.11.005.

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37

Deng, Chenwei, Yuqi Han, and Baojun Zhao. "High-Performance Visual Tracking With Extreme Learning Machine Framework." IEEE Transactions on Cybernetics 50, no. 6 (June 2020): 2781–92. http://dx.doi.org/10.1109/tcyb.2018.2886580.

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38

Kar, Anuradha, and Peter Corcoran. "GazeVisual: A Practical Software Tool and Web Application for Performance Evaluation of Eye Tracking Systems." IEEE Transactions on Consumer Electronics 65, no. 3 (August 2019): 293–302. http://dx.doi.org/10.1109/tce.2019.2912802.

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39

Jie Chen, S. Hara, and Gang Chen. "Best tracking and regulation performance under control energy constraint." IEEE Transactions on Automatic Control 48, no. 8 (August 2003): 1320–36. http://dx.doi.org/10.1109/tac.2003.815012.

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40

Guo, J., X. Hu, and B. Vucetic. "Performance of a digital code tracking loop for DSSS systems in the presence of Doppler shift." IEE Proceedings - Communications 150, no. 3 (2003): 202. http://dx.doi.org/10.1049/ip-com:20030355.

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41

Gao, Shigen, Yuhan Hou, Hairong Dong, Yixiang Yue, and Shaoyuan Li. "Global Nested PID Control of Strict-Feedback Nonlinear Systems With Prescribed Output and Virtual Tracking Performance." IEEE Transactions on Circuits and Systems II: Express Briefs 67, no. 2 (February 2020): 325–29. http://dx.doi.org/10.1109/tcsii.2019.2907141.

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42

Jiang, Xiao-Wei, Chao-Yang Chen, Qing-Sheng Yang, Xiu-Jun Qu, and Huai-Cheng Yan. "Optimal tracking performance for SIMO systems with packet dropouts and control energy constraints." IET Control Theory & Applications 12, no. 12 (August 14, 2018): 1714–21. http://dx.doi.org/10.1049/iet-cta.2017.1164.

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43

Khan, A. A., J. R. Moyne, and D. M. Tilbury. "Favorable effect of time delays on tracking performance of type-I control systems." IET Control Theory & Applications 2, no. 3 (March 1, 2008): 210–18. http://dx.doi.org/10.1049/iet-cta:20070093.

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44

Wang, Wanli, Chi K. Tse, and Shiyuan Wang. "Performance Comparison of Nonlinear Kalman Filters in Epidemic Tracking on Networks." IEEE Systems Journal 14, no. 4 (December 2020): 5475–85. http://dx.doi.org/10.1109/jsyst.2020.2979221.

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45

Rijnen, Mark, Alessandro Saccon, and Henk Nijmeijer. "Reference Spreading: Tracking Performance for Impact Trajectories of a 1DoF Setup." IEEE Transactions on Control Systems Technology 28, no. 3 (May 2020): 1124–31. http://dx.doi.org/10.1109/tcst.2019.2898953.

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46

Zhang, Hehong, Gaoxi Xiao, Xinghuo Yu, and Yunde Xie. "On Convergence Performance of Discrete-Time Optimal Control Based Tracking Differentiator." IEEE Transactions on Industrial Electronics 68, no. 4 (April 2021): 3359–69. http://dx.doi.org/10.1109/tie.2020.2979530.

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47

Bandyopadhyay, B., and D. Fulwani. "High-Performance Tracking Controller for Discrete Plant Using Nonlinear Sliding Surface." IEEE Transactions on Industrial Electronics 56, no. 9 (September 2009): 3628–37. http://dx.doi.org/10.1109/tie.2008.2007984.

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48

Graettinger, Timothy J., and Bruce H. Krogh. "On the computation of reference signal constraints for guaranteed tracking performance." Automatica 28, no. 6 (November 1992): 1125–41. http://dx.doi.org/10.1016/0005-1098(92)90055-k.

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49

Thornhill, N. F., B. Huang, and S. L. Shah. "Controller performance assessment in set point tracking and regulatory control." International Journal of Adaptive Control and Signal Processing 17, no. 7-9 (2003): 709–27. http://dx.doi.org/10.1002/acs.773.

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50

Peresada, Sergei, and Alberto Tonielli. "High-performance robust speed-flux tracking controller for induction motor." International Journal of Adaptive Control and Signal Processing 14, no. 2-3 (March 2000): 177–200. http://dx.doi.org/10.1002/(sici)1099-1115(200003/05)14:2/3<177::aid-acs579>3.0.co;2-2.

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