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Yu-Liang Hsu
Department of Automatic Control Engineering, Feng Chia University, Taichung 407, Taiwan

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Journal article
Published: 19 May 2021 in IEEE Sensors Journal
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This paper aims to develop an intelligent autonomous surveillance system for the safety surveillance of indoor environments with the integration of techniques such as sensory information fusion, internet of things (IoT), and artificial intelligence (AI). We have developed the following four subsystems to implement the intelligent autonomous surveillance system: (1) an autonomous surveillance vehicle (ASV) to be placed on the track to detect environmental sensory information in indoor environments (such as environmental temperature, flame, CO concentration, and liquefied petroleum gas (LPG) concentration) and the movement information of the ASV (such as position coordinates and movement speed); (2) an intelligent fire detection algorithm to be used for identification of disaster statuses in indoor environments; (3) a visible human-machine interface to instantaneously display the environmental sensory information and ASV movement information, and show the environmental disaster information (such as disaster statuses, disaster images, image capture times, and disaster positions); (4) a remote server composed of a database server, file servers, a web server, and responsive web pages to store and display the recorded environmental sensory information, ASV movement information, and environmental disaster information. Furthermore, a 3D experimental track testbed was constructed to verify the effectiveness and feasibility of the intelligent autonomous surveillance system. Our experimental results have successfully validated that the intelligent autonomous surveillance system can detect disaster events in a variety of conditions.

ACS Style

Hsing-Cheng Chang; Yu-Liang Hsu; Ching-Yuan Hsiao; Yi-Fan Chen. Design and Implementation of an Intelligent Autonomous Surveillance System for Indoor Environments. IEEE Sensors Journal 2021, 21, 17335 -17349.

AMA Style

Hsing-Cheng Chang, Yu-Liang Hsu, Ching-Yuan Hsiao, Yi-Fan Chen. Design and Implementation of an Intelligent Autonomous Surveillance System for Indoor Environments. IEEE Sensors Journal. 2021; 21 (15):17335-17349.

Chicago/Turabian Style

Hsing-Cheng Chang; Yu-Liang Hsu; Ching-Yuan Hsiao; Yi-Fan Chen. 2021. "Design and Implementation of an Intelligent Autonomous Surveillance System for Indoor Environments." IEEE Sensors Journal 21, no. 15: 17335-17349.

Journal article
Published: 05 February 2021 in Sensors
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Water is one of the most precious resources. However, industrial development has made water pollution a critical problem today and thus water quality monitoring and surface cleaning are essential for water resource protection. In this study, we have used the sensor fusion technology as a basis to develop a multi-function unmanned surface vehicle (MF-USV) for obstacle avoidance, water-quality monitoring, and water surface cleaning. The MF-USV comprises a USV control unit, a locomotion module, a positioning module, an obstacle avoidance module, a water quality monitoring system, a water surface cleaning system, a communication module, a power module, and a remote human–machine interface. We equip the MF-USV with the following functions: (1) autonomous obstacle detection, avoidance, and navigation positioning, (2) water quality monitoring, sampling, and positioning, (3) water surface detection and cleaning, and (4) remote navigation control and real-time information display. The experimental results verified that when the floating garbage located in the visual angle ranged from −30° to 30° on the front of the MF-USV and the distances between the floating garbage and the MF-USV were 40 and 70 cm, the success rates of floating garbage detection are all 100%. When the distance between the floating garbage and the MF-USV was 130 cm and the floating garbage was located on the left side (15°~30°), left front side (0°~15°), front side (0°), right front side (0°~15°), and the right side (15°~30°), the success rates of the floating garbage collection were 70%, 92%, 95%, 95%, and 75%, respectively. Finally, the experimental results also verified that the applications of the MF-USV and relevant algorithms to obstacle avoidance, water quality monitoring, and water surface cleaning were effective.

ACS Style

Hsing-Cheng Chang; Yu-Liang Hsu; San-Shan Hung; Guan-Ru Ou; Jia-Ron Wu; Chuan Hsu. Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle. Sensors 2021, 21, 1102 .

AMA Style

Hsing-Cheng Chang, Yu-Liang Hsu, San-Shan Hung, Guan-Ru Ou, Jia-Ron Wu, Chuan Hsu. Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle. Sensors. 2021; 21 (4):1102.

Chicago/Turabian Style

Hsing-Cheng Chang; Yu-Liang Hsu; San-Shan Hung; Guan-Ru Ou; Jia-Ron Wu; Chuan Hsu. 2021. "Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle." Sensors 21, no. 4: 1102.

Journal article
Published: 18 December 2020 in Journal of Clinical Medicine
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Postural orthostatic tachycardia syndrome (POTS) typically occurs in youths, and early accurate POTS diagnosis is challenging. A recent hypothesis suggests that upright cognitive impairment in POTS occurs because reduced cerebral blood flow velocity (CBFV) and cerebrovascular response to carbon dioxide (CO2) are nonlinear during transient changes in end-tidal CO2 (PETCO2). This novel study aimed to reveal the interaction between cerebral autoregulation and ventilatory control in POTS patients by using tilt table and hyperventilation to alter the CO2 tension between 10 and 30 mmHg. The cerebral blood flow velocity (CBFV), partial pressure of end-tidal carbon dioxide (PETCO2), and other cardiopulmonary signals were recorded for POTS patients and two healthy groups including those aged >45 years (Healthy-Elder) and aged ETCO2 relationship and cerebral vasomotor reactivity (CVMR). Among the estimated parameters, the curve-fitting Model I for CBFV and CVMR responses to CO2 for POTS patients demonstrated an observable dissimilarity in CBFVmax (p = 0.011), mid-PETCO2 (p = 0.013), and PETCO2 range (p = 0.023) compared with those of Healthy-Youth and in CBFVmax (p = 0.015) and CVMRmax compared with those of Healthy-Elder. With curve-fitting Model II for POTS patients, the fit parameters of curvilinear (p = 0.036) and PETCO2 level (p = 0.033) displayed significant difference in comparison with Healthy-Youth parameters; range of change (p = 0.042), PETCO2 level, and CBFVmax also displayed a significant difference in comparison with Healthy-Elder parameters. The results of this study contribute toward developing an early accurate diagnosis of impaired CBFV responses to CO2 for POTS patients.

ACS Style

Shyan-Lung Lin; Shoou-Jeng Yeh; Ching-Kun Chen; Yu-Liang Hsu; Chih-En Kuo; Wei-Yu Chen; Cheng-Pu Hsieh. Comparisons of the Nonlinear Relationship of Cerebral Blood Flow Response and Cerebral Vasomotor Reactivity to Carbon Dioxide under Hyperventilation between Postural Orthostatic Tachycardia Syndrome Patients and Healthy Subjects. Journal of Clinical Medicine 2020, 9, 4088 .

AMA Style

Shyan-Lung Lin, Shoou-Jeng Yeh, Ching-Kun Chen, Yu-Liang Hsu, Chih-En Kuo, Wei-Yu Chen, Cheng-Pu Hsieh. Comparisons of the Nonlinear Relationship of Cerebral Blood Flow Response and Cerebral Vasomotor Reactivity to Carbon Dioxide under Hyperventilation between Postural Orthostatic Tachycardia Syndrome Patients and Healthy Subjects. Journal of Clinical Medicine. 2020; 9 (12):4088.

Chicago/Turabian Style

Shyan-Lung Lin; Shoou-Jeng Yeh; Ching-Kun Chen; Yu-Liang Hsu; Chih-En Kuo; Wei-Yu Chen; Cheng-Pu Hsieh. 2020. "Comparisons of the Nonlinear Relationship of Cerebral Blood Flow Response and Cerebral Vasomotor Reactivity to Carbon Dioxide under Hyperventilation between Postural Orthostatic Tachycardia Syndrome Patients and Healthy Subjects." Journal of Clinical Medicine 9, no. 12: 4088.

Journal article
Published: 08 October 2020 in Applied Sciences
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The human–machine interface with head control can be applied in many domains. This technology has the valuable application of helping people who cannot use their hands, enabling them to use a computer or speak. This study combines several image processing and computer vision technologies, a digital camera, and software to develop the following system: image processing technologies are adopted to capture the features of head motion; the recognized head gestures include forward, upward, downward, leftward, rightward, right-upper, right-lower, left-upper, and left-lower; corresponding sound modules are used so that patients can communicate with others through a phonetic system and numeric tables. Innovative skin color recognition technology can obtain head features in images. The barycenter of pixels in the feature area is then quickly calculated, and the offset of the barycenter is observed to judge the direction of head motion. This architecture can substantially reduce the distraction of non-targeted objects and enhance the accuracy of systematic judgment.

ACS Style

Che-Ming Chang; Chern-Sheng Lin; Wei-Cheng Chen; Chung-Ting Chen; Yu-Liang Hsu. Development and Application of a Human–Machine Interface Using Head Control and Flexible Numeric Tables for the Severely Disabled. Applied Sciences 2020, 10, 7005 .

AMA Style

Che-Ming Chang, Chern-Sheng Lin, Wei-Cheng Chen, Chung-Ting Chen, Yu-Liang Hsu. Development and Application of a Human–Machine Interface Using Head Control and Flexible Numeric Tables for the Severely Disabled. Applied Sciences. 2020; 10 (19):7005.

Chicago/Turabian Style

Che-Ming Chang; Chern-Sheng Lin; Wei-Cheng Chen; Chung-Ting Chen; Yu-Liang Hsu. 2020. "Development and Application of a Human–Machine Interface Using Head Control and Flexible Numeric Tables for the Severely Disabled." Applied Sciences 10, no. 19: 7005.

Journal article
Published: 25 November 2019 in IEEE Access
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This paper develops a wearable sport activity classification system and its associated deep learning-based sport activity classification algorithm for accurately recognizing sport activities. The proposed wearable system used two wearable inertial sensing modules worn on athletes’ wrist and ankle to collect sport motion signals and utilized a deep convolutional neural network (CNN) to extract the inherent features from the spectrograms of the short-term Fourier transform (STFT) of the sport motion signals. The wearable inertial sensing module is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, an RF wireless transmission module, and a power supply circuit. All ten participants wore the two wearable inertial sensing modules on their wrist and ankle to collect motion signals generated by sport activities. Subsequently, we developed a deep learning-based sport activity classification algorithm composed of sport motion signal collection, signal preprocessing, sport motion segmentation, signal normalization, spectrogram generation, image mergence/resizing, and CNN-based classification to recognize ten types of sport activities. The CNN classifier consisting of two convolutional layers, two pooling layers, a fully-connected layer, and a softmax layer can be used to divide the sport activities into table tennis, tennis, badminton, golf, batting baseball, shooting basketball, volleyball, dribbling basketball, running, and bicycling, respectively. Finally, the experimental results show that the proposed wearable sport activity classification system and its deep learning-based sport activity classification algorithm can recognize 10 sport activities with the classification rate of 99.30%.

ACS Style

Yu-Liang Hsu; Hsing-Cheng Chang; Yung-Jung Chiu. Wearable Sport Activity Classification Based on Deep Convolutional Neural Network. IEEE Access 2019, 7, 170199 -170212.

AMA Style

Yu-Liang Hsu, Hsing-Cheng Chang, Yung-Jung Chiu. Wearable Sport Activity Classification Based on Deep Convolutional Neural Network. IEEE Access. 2019; 7 ():170199-170212.

Chicago/Turabian Style

Yu-Liang Hsu; Hsing-Cheng Chang; Yung-Jung Chiu. 2019. "Wearable Sport Activity Classification Based on Deep Convolutional Neural Network." IEEE Access 7, no. : 170199-170212.

Journal article
Published: 06 May 2019 in IEEE Access
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This paper investigates digital implementation issues on high speed permanent magnet (PM) machine drives. The machine operating speed is designed beyond 30krpm where the ratio of controller sample frequency fsample over rotor operating frequency fe is less than 5. Because microcontrollers are used in variable frequency drives to realize the field oriented control (FOC), the discretized effects must be considered to maintain the controller stability under low ratios of fsample/fe. In this paper, the FOC based on different digital controllers are compared specifically at low fsample/fe. It is shown that digital controllers using forward difference and bilinear approximation are able to maintain the drive stability if the voltage delay and inductance crosscoupling are compensated. However, the drive performance strongly depends on the accuracy of inductance parameter. By contrast, the controller using direct digital design achieves the better FOC performance independent to machine parameters. Different types of controllers are experimentally compared to find a suited high speed FOC drive. This paper includes the design guidance of high speed FOC drive for PM machines with different time constants.

ACS Style

Shih-Chin Yang; Yu-Liang Hsu; Po-Huan Chou; Jyun-You Chen; Guan-Ren Chen. Digital Implementation Issues on High Speed Permanent Magnet Machine FOC Drive Under Insufficient Sample Frequency. IEEE Access 2019, 7, 61484 -61493.

AMA Style

Shih-Chin Yang, Yu-Liang Hsu, Po-Huan Chou, Jyun-You Chen, Guan-Ren Chen. Digital Implementation Issues on High Speed Permanent Magnet Machine FOC Drive Under Insufficient Sample Frequency. IEEE Access. 2019; 7 (99):61484-61493.

Chicago/Turabian Style

Shih-Chin Yang; Yu-Liang Hsu; Po-Huan Chou; Jyun-You Chen; Guan-Ren Chen. 2019. "Digital Implementation Issues on High Speed Permanent Magnet Machine FOC Drive Under Insufficient Sample Frequency." IEEE Access 7, no. 99: 61484-61493.

Technical paper
Published: 14 February 2019 in Microsystem Technologies
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Hollow nanofiber multiple-gas microsensors containing a ceramic package in parallel were developed via doping metal oxides in tin oxide (SnO2) and electrospinning and calcination. Different metal oxides including nickel oxide (NiO), copper oxide (CuO), and indium oxide (In2O3) were doped into SnO2 individually as sensing films for the detection of acetone (CH3COCH), ammonia (NH3), and hydrogen sulfide (H2S) with high selectivity. After heating and calcination, the hollow electrospun nanofibers formed with high specific surface area and porosity resulting in increased sensing area and enhancing the rate of surface chemical reaction and responsivity. The experiments show that the optimal local operation temperature of the activated sensors was 330 °C. High responsivity of the packaged gas sensors was obtained with high accuracy and repeatability. Gas sensitivities were 0.071, 0.075, and 0.09 for sensors of acetone, ammonia, and hydrogen sulfide, respectively, at concentrations of 1 ppm. The response and recovery times of the acetone, ammonia, and hydrogen sulfide gas sensors were 4 and 8 s, 5 and 9 s, and 5 and 12 s, respectively.

ACS Style

Hsing-Cheng Chang; Yu-Liang Hsu; Shyan-Lung Lin; Ya-Hui Chen; Tai-Jiun Guo; Fan-Wei Cheng. Parallel-packaged multiple-gas microsensors based on hollowed nanostructures of doped tin oxides. Microsystem Technologies 2019, 27, 1445 -1455.

AMA Style

Hsing-Cheng Chang, Yu-Liang Hsu, Shyan-Lung Lin, Ya-Hui Chen, Tai-Jiun Guo, Fan-Wei Cheng. Parallel-packaged multiple-gas microsensors based on hollowed nanostructures of doped tin oxides. Microsystem Technologies. 2019; 27 (4):1445-1455.

Chicago/Turabian Style

Hsing-Cheng Chang; Yu-Liang Hsu; Shyan-Lung Lin; Ya-Hui Chen; Tai-Jiun Guo; Fan-Wei Cheng. 2019. "Parallel-packaged multiple-gas microsensors based on hollowed nanostructures of doped tin oxides." Microsystem Technologies 27, no. 4: 1445-1455.

Journal article
Published: 29 January 2019 in IEEE Access
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Microelectromechanical system (MEMS)-based gyroscopes have been widely applied to various inertial-sensing-based HCI devices. However, random drift of MEMS-based gyroscopes limit their applications. Hence, literatures pay attention to develop various models to model and compensate the random drift for improving the performance of the MEMS-based gyroscopes. This paper presents a self-constructing Wiener-type recurrent neural network (SCWRNN) with its false nearest neighbors-based self-constructing strategy and recursive recurrent learning algorithm to model the random drift of the MEMS-based gyroscopes and then compensate them from the calibrated gyroscope measurement. Subsequently, the proposed random drift modeling and compensation algorithm is integrated into the handwriting trajectory reconstruction algorithm of the inertial-sensing-based HCI device, called IMUPEN, for accurately obtaining reconstructed handwriting trajectory. Users can hold the IMUPEN, which is composed of an accelerometer, two gyroscopes, a microcontroller, and an RF wireless transmission module, to write numerals at normal speed. The accelerations and angular velocities measured by the accelerometer and gyroscopes are transmitted to a personal computer through the RF module for further reconstructing the handwriting trajectory via the handwriting trajectory reconstruction algorithm. In addition, we have developed the SCWRNN-based random drift modeling and compensation algorithm to eliminate the cumulative errors caused by the random drift of the MEMS-based gyroscopes for further increasing the accuracy of handwriting trajectory reconstruction. Our experimental results have successfully validated the effectiveness of the proposed random drift modeling and compensation algorithm and its application in handwriting trajectory reconstruction.

ACS Style

Yu-Liang Hsu; Jeen-Shing Wang. Random Drift Modeling and Compensation for MEMS-Based Gyroscopes and Its Application in Handwriting Trajectory Reconstruction. IEEE Access 2019, 7, 17551 -17560.

AMA Style

Yu-Liang Hsu, Jeen-Shing Wang. Random Drift Modeling and Compensation for MEMS-Based Gyroscopes and Its Application in Handwriting Trajectory Reconstruction. IEEE Access. 2019; 7 (99):17551-17560.

Chicago/Turabian Style

Yu-Liang Hsu; Jeen-Shing Wang. 2019. "Random Drift Modeling and Compensation for MEMS-Based Gyroscopes and Its Application in Handwriting Trajectory Reconstruction." IEEE Access 7, no. 99: 17551-17560.

Journal article
Published: 07 November 2018 in Sensors and Materials
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ACS Style

Po-Huan Chou; Yu-Liang Hsu; Shih-Chin Yang; Hsing-Cheng Chang; Yu-Chen Kuo; Li-Feng Chiu; Yu-Tai Chen. Vibration Measurement and Suppression for Laser Galvanometers Using a Micro-electromechanical System-based Accelerometer. Sensors and Materials 2018, 30, 2401 .

AMA Style

Po-Huan Chou, Yu-Liang Hsu, Shih-Chin Yang, Hsing-Cheng Chang, Yu-Chen Kuo, Li-Feng Chiu, Yu-Tai Chen. Vibration Measurement and Suppression for Laser Galvanometers Using a Micro-electromechanical System-based Accelerometer. Sensors and Materials. 2018; 30 (11):2401.

Chicago/Turabian Style

Po-Huan Chou; Yu-Liang Hsu; Shih-Chin Yang; Hsing-Cheng Chang; Yu-Chen Kuo; Li-Feng Chiu; Yu-Tai Chen. 2018. "Vibration Measurement and Suppression for Laser Galvanometers Using a Micro-electromechanical System-based Accelerometer." Sensors and Materials 30, no. 11: 2401.

Journal article
Published: 03 September 2018 in Applied Sciences
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In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.

ACS Style

Chern-Sheng Lin; Yu-Chia Huang; Shih-Hua Chen; Yu-Liang Hsu; Yu-Chen Lin. The Application of Deep Learning and Image Processing Technology in Laser Positioning. Applied Sciences 2018, 8, 1542 .

AMA Style

Chern-Sheng Lin, Yu-Chia Huang, Shih-Hua Chen, Yu-Liang Hsu, Yu-Chen Lin. The Application of Deep Learning and Image Processing Technology in Laser Positioning. Applied Sciences. 2018; 8 (9):1542.

Chicago/Turabian Style

Chern-Sheng Lin; Yu-Chia Huang; Shih-Hua Chen; Yu-Liang Hsu; Yu-Chen Lin. 2018. "The Application of Deep Learning and Image Processing Technology in Laser Positioning." Applied Sciences 8, no. 9: 1542.

Conference paper
Published: 01 September 2018 in 2018 IEEE Energy Conversion Congress and Exposition (ECCE)
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This paper proposes a sensorless vibration frequency estimation for a servo control system driven by a permanent magnet (PM) synchronous machine. Traditional vibration estimation relies on the measurement using external sensors. A separated accelerometer is installed in the servo system to obtain the vibration harmonic information. Because the accelerometer must be attached on the visibly vibrational location inside the system, this vibration detection results in both installation and reliability issues. Instead of accelerometer, this paper proposes a disturbance observer to estimate the vibration harmonic using machine drive available current and position signal. A real-time signal process is developed to identify the vibration frequency from estimated harmonic. A servo system with a 400W PM machine drive is built to verify the proposed vibration frequency estimation. This paper includes the comparatively evaluation between the accelerometer-based harmonic measurement and the sensorless harmonic estimation.

ACS Style

Ching-Lon Huang; Shih-Chin Yang; Yu-Liang Hsu. Sensorless Harmonic Estimation for Servo Drive with Vibration Load. 2018 IEEE Energy Conversion Congress and Exposition (ECCE) 2018, 1728 -1732.

AMA Style

Ching-Lon Huang, Shih-Chin Yang, Yu-Liang Hsu. Sensorless Harmonic Estimation for Servo Drive with Vibration Load. 2018 IEEE Energy Conversion Congress and Exposition (ECCE). 2018; ():1728-1732.

Chicago/Turabian Style

Ching-Lon Huang; Shih-Chin Yang; Yu-Liang Hsu. 2018. "Sensorless Harmonic Estimation for Servo Drive with Vibration Load." 2018 IEEE Energy Conversion Congress and Exposition (ECCE) , no. : 1728-1732.

Conference paper
Published: 01 September 2018 in 2018 IEEE Energy Conversion Congress and Exposition (ECCE)
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This paper improves the position sensorless drive of permanent magnet (PM) machine by measuring actual phase voltages with pulse width modulation (PWM). On the basis, the phase voltage is obtained based on the digital integration of PWM voltage using the capture modulator in existing drive microcontrollers (MCU's). Comparing to existing phase voltage measurement, no separated A/D converter and communication hardware are required because PWM pulses are directly measured using MCUs. However for standard machines without neutral points, only line-to-line AC PWM voltages can be measured for the phase voltage reconstruction. Since the capture based on TTL logics receives only digital signals, a preprocess circuit to convert AC PWM line voltages to equivalent digital signals is proposed. This paper clearly explains the hardware implementation using MCU capture modulator. According to experimental results, a 150MHz sampling rate for phase voltage measurement is achieved based on the proposed capture-based voltage measurement.

ACS Style

Guan-Ren Chen; Shih-Chin Yang; Yu-Liang Hsu. Integration of Capture Based Phase Voltage Measurement for Surface Permanent Magnet Machine Position Sensorless Drive. 2018 IEEE Energy Conversion Congress and Exposition (ECCE) 2018, 2389 -2393.

AMA Style

Guan-Ren Chen, Shih-Chin Yang, Yu-Liang Hsu. Integration of Capture Based Phase Voltage Measurement for Surface Permanent Magnet Machine Position Sensorless Drive. 2018 IEEE Energy Conversion Congress and Exposition (ECCE). 2018; ():2389-2393.

Chicago/Turabian Style

Guan-Ren Chen; Shih-Chin Yang; Yu-Liang Hsu. 2018. "Integration of Capture Based Phase Voltage Measurement for Surface Permanent Magnet Machine Position Sensorless Drive." 2018 IEEE Energy Conversion Congress and Exposition (ECCE) , no. : 2389-2393.

Journal article
Published: 23 May 2018 in IEEE Access
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This paper presents a wearable inertial sensor network and its associated activity recognition algorithm for accurately recognizing human daily and sport activities. The proposed wearable inertial sensor network is composed of two wearable inertial sensing devices, which comprise a microcontroller, a triaxial accelerometer, a triaxial gyroscope, an RF wireless transmission module, and a power supply circuit. The activity recognition algorithm, consisting of procedures of motion signal acquisition, signal preprocessing, dynamic human motion detection, signal normalization, feature extraction, feature normalization, feature reduction, and activity recognition, has been developed to recognize human daily and sport activities by using accelerations and angular velocities. In order to reduce the computational complexity and improve the recognition rate simultaneously, we have utilized the nonparametric weighted feature extraction (NWFE) algorithm with the principal component analysis (PCA) method for reducing the feature dimensions of inertial signals. All 23 participants wore the wearable sensor network on their wrist and ankle to execute 10 common domestic activities in human daily lives and 11 sport activities in a laboratory environment, and their activity recordings were collected to validate the effectiveness of the proposed wearable inertial sensor network and activity recognition algorithm. Experimental results showed that our approach could achieve recognition rates for the 10 common domestic activities of 98.23% and 11 sport activities of 99.55% by the 10-fold cross-validation strategy, which have successfully validated the effectiveness of the proposed wearable inertial sensor network and its activity recognition algorithm.

ACS Style

Yu-Liang Hsu; Shih-Chin Yang; Hsing-Cheng Chang; Hung-Che Lai. Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network. IEEE Access 2018, 6, 31715 -31728.

AMA Style

Yu-Liang Hsu, Shih-Chin Yang, Hsing-Cheng Chang, Hung-Che Lai. Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network. IEEE Access. 2018; 6 (99):31715-31728.

Chicago/Turabian Style

Yu-Liang Hsu; Shih-Chin Yang; Hsing-Cheng Chang; Hung-Che Lai. 2018. "Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network." IEEE Access 6, no. 99: 31715-31728.

Journal article
Published: 07 May 2018 in IEEE Transactions on Industry Applications
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This paper analyzes the influence of stator windings turn-to-turn fault on inverter-fed permanent magnet (PM) machines. By adding a secondary neutral point (NP) in stator windings, the turn fault detection and tolerant capability can all improve considering a small turn-to-turn short at early stage. From the perspective of fault detection, the proposed windings with two NPs increase the equipment impedance per NP. Once a turn fault occurs, the fault induced voltage harmonic magnitude increases. It results in a better signal-to-noise ratio for the fault detection. From the perspective of tolerant drive, the fault induced circulating currents can flow across different phase coils in the windings with two NPs. Reduced effect on circulating currents in a faculty machine is achieved due to the current diversion. Turn fault induced additional losses are then decreased comparing to conventional windings with only one NP. A 1-kW PM machine with two different windings is tested to verify the fault detection and tolerant performance.

ACS Style

Shih-Chin Yang; Yu-Liang Hsu; Po-Huan Chou; Cheng-Chung Hsu; Guan-Ren Chen; Kang Li. Fault Detection and Tolerant Capability of Parallel-Connected Permanent Magnet Machines Under Stator Turn Fault. IEEE Transactions on Industry Applications 2018, 54, 4447 -4456.

AMA Style

Shih-Chin Yang, Yu-Liang Hsu, Po-Huan Chou, Cheng-Chung Hsu, Guan-Ren Chen, Kang Li. Fault Detection and Tolerant Capability of Parallel-Connected Permanent Magnet Machines Under Stator Turn Fault. IEEE Transactions on Industry Applications. 2018; 54 (5):4447-4456.

Chicago/Turabian Style

Shih-Chin Yang; Yu-Liang Hsu; Po-Huan Chou; Cheng-Chung Hsu; Guan-Ren Chen; Kang Li. 2018. "Fault Detection and Tolerant Capability of Parallel-Connected Permanent Magnet Machines Under Stator Turn Fault." IEEE Transactions on Industry Applications 54, no. 5: 4447-4456.

Conference paper
Published: 01 April 2018 in 2018 IEEE International Conference on Applied System Invention (ICASI)
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In this paper, with the use of noninvasive TCD, Finapres, and Capnography, we investigated the cerebrovascular response to CO 2 in PD patients and explored the interaction between cerebral autoregulation and ventilatory control by using nonlinear regression model. The results of these patients were compared with those of healthy subjects by examining their CBFV, CVMR (cerebrovascular Vasomotor Reactivity), and CVC i (cerebrovascular conductance index) responses to CO 2 . Statistical analysis of significance values between PD patients and healthy groups was evaluated. The results showed that the PD patients demonstrated a significantly lower level of CBFV max (%) than the healthy elders with nonlinear regression model.

ACS Style

Ching-Kun Chen; Shoou-Jeng Yeh; Shyan-Lung Lin; Yu-Liang Hsu; Hsing-Cheng Chang. Innovated noninvasive assessment of Parkinson's disease based on measurement of cerebral vasomotor reactivity with transcranial Doppler ultrasonography and capnography. 2018 IEEE International Conference on Applied System Invention (ICASI) 2018, 1256 -1259.

AMA Style

Ching-Kun Chen, Shoou-Jeng Yeh, Shyan-Lung Lin, Yu-Liang Hsu, Hsing-Cheng Chang. Innovated noninvasive assessment of Parkinson's disease based on measurement of cerebral vasomotor reactivity with transcranial Doppler ultrasonography and capnography. 2018 IEEE International Conference on Applied System Invention (ICASI). 2018; ():1256-1259.

Chicago/Turabian Style

Ching-Kun Chen; Shoou-Jeng Yeh; Shyan-Lung Lin; Yu-Liang Hsu; Hsing-Cheng Chang. 2018. "Innovated noninvasive assessment of Parkinson's disease based on measurement of cerebral vasomotor reactivity with transcranial Doppler ultrasonography and capnography." 2018 IEEE International Conference on Applied System Invention (ICASI) , no. : 1256-1259.

Journal article
Published: 18 December 2017 in IEEE Transactions on Affective Computing
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This paper presents an automatic ECG-based emotion recognition algorithm for human emotion recognition. First, we adopt a musical induction method to induce participants real emotional states and collect their ECG signals without any deliberate laboratory setting. Afterward, we develop an automatic ECG-based emotion recognition algorithm to recognize human emotions elicited by listening to music. Physiological ECG features extracted from the time-, and frequency-domain, and nonlinear analyses of ECG signals are used to find emotion-relevant features and to correlate them with emotional states. Subsequently, we develop a sequential forward floating selection-kernel-based class separability-based (SFFS-KBCS-based) feature selection algorithm and utilize the generalized discriminant analysis (GDA) to effectively select significant ECG features associated with emotions and to reduce the dimensions of the selected features, respectively. Positive/negative valence, high/low arousal, and four types of emotions (joy, tension, sadness, and peacefulness) are recognized using least squares support vector machine (LS-SVM) recognizers. The results show that the correct classification rates for positive/negative valence, high/low arousal, and four types of emotion classification tasks are 82.78%, 72.91%, and 61.52%, respectively.

ACS Style

Yu-Liang Hsu; Jeen-Shing Wang; Wei-Chun Chiang; Chien-Han Hung. Automatic ECG-Based Emotion Recognition in Music Listening. IEEE Transactions on Affective Computing 2017, 11, 85 -99.

AMA Style

Yu-Liang Hsu, Jeen-Shing Wang, Wei-Chun Chiang, Chien-Han Hung. Automatic ECG-Based Emotion Recognition in Music Listening. IEEE Transactions on Affective Computing. 2017; 11 (1):85-99.

Chicago/Turabian Style

Yu-Liang Hsu; Jeen-Shing Wang; Wei-Chun Chiang; Chien-Han Hung. 2017. "Automatic ECG-Based Emotion Recognition in Music Listening." IEEE Transactions on Affective Computing 11, no. 1: 85-99.

Journal article
Published: 11 October 2017 in Energies
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In conventional position sensorless permanent magnet (PM) machine drives, the rotor position is obtained from the phase-locked loop (PLL) with the regulation of spatial signal in estimated back electromotive force (EMF) voltages. Due to the sinusoidal distribution of back-EMF voltages, a small-signal approximation is assumed in the PLL in order to estimate the position. That is, the estimated position is almost equal to the actual position per sample instant. However, at high speed when the ratio of sampling frequency, fsample, over the rotor operating frequency, fe, is low, this approximation might not be valid during the speed and load transient. To overcome this limitation, a position estimation is proposed specifically for the high-speed operation of a PM machine drive. A discrete-time EMF voltage estimator is developed to obtain the machine spatial signal. In addition, an arctangent calculation is cascaded to the PLL in order to remove this small-signal approximation for better sensorless drive performance. By using the discrete-time EMF estimation and modified PLL, the drive is able to maintain the speed closed-loop at 36 krpm with only 4.2 sampling points per electrical cycle on a PM machine, according to experimental results.

ACS Style

Guan-Ren Chen; Shih-Chin Yang; Yu-Liang Hsu; Kang Li. Position and Speed Estimation of Permanent Magnet Machine Sensorless Drive at High Speed Using an Improved Phase-Locked Loop. Energies 2017, 10, 1571 .

AMA Style

Guan-Ren Chen, Shih-Chin Yang, Yu-Liang Hsu, Kang Li. Position and Speed Estimation of Permanent Magnet Machine Sensorless Drive at High Speed Using an Improved Phase-Locked Loop. Energies. 2017; 10 (10):1571.

Chicago/Turabian Style

Guan-Ren Chen; Shih-Chin Yang; Yu-Liang Hsu; Kang Li. 2017. "Position and Speed Estimation of Permanent Magnet Machine Sensorless Drive at High Speed Using an Improved Phase-Locked Loop." Energies 10, no. 10: 1571.

Journal article
Published: 02 October 2017 in IEEE Transactions on Industrial Electronics
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Conventional permanent magnet (PM) machine open-phase fault detection relies on the estimation of current harmonics using available phase current sensors. Because the magnitudes of current harmonic are proportional to the machine load condition, it is still a challenge to detect a phase fault at light load when fault-induced current harmonics are too small to estimate. This paper investigates the phase fault detection specifically for the conditions under low fault harmonic magnitudes. Both fault-induced harmonics in dq currents and neutral point (NP) voltage are analyzed to find a suited signal for the fault detection. By adding a voltage sensor to measure the NP voltage in a PM machine, a better fault detection performance is achieved because the fault harmonic in NP voltage results in the reduced effect on torque loads and sensor measurement errors. A 50-W PM machine is tested to verify the phase fault detection performance under low fault harmonic magnitudes. This paper includes the experimental evaluation on the detection limitation in terms of load capability using current and voltage fault signals.

ACS Style

Shih-Chin Yang; Yu-Liang Hsu; Po-Huan Chou; Guan-Ren Chen; Da-Ren Jian. Online Open-Phase Fault Detection for Permanent Magnet Machines With Low Fault Harmonic Magnitudes. IEEE Transactions on Industrial Electronics 2017, 65, 4039 -4050.

AMA Style

Shih-Chin Yang, Yu-Liang Hsu, Po-Huan Chou, Guan-Ren Chen, Da-Ren Jian. Online Open-Phase Fault Detection for Permanent Magnet Machines With Low Fault Harmonic Magnitudes. IEEE Transactions on Industrial Electronics. 2017; 65 (5):4039-4050.

Chicago/Turabian Style

Shih-Chin Yang; Yu-Liang Hsu; Po-Huan Chou; Guan-Ren Chen; Da-Ren Jian. 2017. "Online Open-Phase Fault Detection for Permanent Magnet Machines With Low Fault Harmonic Magnitudes." IEEE Transactions on Industrial Electronics 65, no. 5: 4039-4050.

Proceedings article
Published: 01 October 2017 in 2017 IEEE Energy Conversion Congress and Exposition (ECCE)
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This paper analyzes the stator windings turn-to-turn fault tolerant capability on inverter-fed permanent magnet (PM) machines. By adding a secondary neutral point (NP) in stator windings, both the fault detection and tolerant capability can be improved considering a small turn-to-turn short at early stage. From the perspective of fault detection, the proposed two NP's windings increase the equipment impedance per NP. Once a turn fault occurs, the fault induced voltage harmonic magnitude can be increased. It results in a better signal-to-noise ratio for the fault detection. From the perspective of tolerant drive, the fault induced circulating currents can flow across different phase coils in the windings with two NP's. Reduced influence of circulating currents on a machine is achieved due to the current diversion. Turn fault induced copper losses are then decreased comparing to conventional windings with only one NP. By using the proposed two NP's windings, the influence of turn fault on PM machines can be minimized. A 1-kW PM machine with two different windings is experimentally evaluated to verify the proposed fault detection and tolerant performance.

ACS Style

Shih-Chin Yang; Yu-Liang Hsu; Po-Huan Chou; Cheng-Xin Liu; Guan-Ren Chen; Kang Li. Fault detection and tolerant capability of parallel connected permanent magnet machines under stator turn fault. 2017 IEEE Energy Conversion Congress and Exposition (ECCE) 2017, 379 -384.

AMA Style

Shih-Chin Yang, Yu-Liang Hsu, Po-Huan Chou, Cheng-Xin Liu, Guan-Ren Chen, Kang Li. Fault detection and tolerant capability of parallel connected permanent magnet machines under stator turn fault. 2017 IEEE Energy Conversion Congress and Exposition (ECCE). 2017; ():379-384.

Chicago/Turabian Style

Shih-Chin Yang; Yu-Liang Hsu; Po-Huan Chou; Cheng-Xin Liu; Guan-Ren Chen; Kang Li. 2017. "Fault detection and tolerant capability of parallel connected permanent magnet machines under stator turn fault." 2017 IEEE Energy Conversion Congress and Exposition (ECCE) , no. : 379-384.

Journal article
Published: 15 July 2017 in Sensors
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This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

ACS Style

Yu-Liang Hsu; Po-Huan Chou; Hsing-Cheng Chang; Shyan-Lung Lin; Shih-Chin Yang; Heng-Yi Su; Chih-Chien Chang; Yuan-Sheng Cheng; Yu-Chen Kuo. Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology. Sensors 2017, 17, 1631 .

AMA Style

Yu-Liang Hsu, Po-Huan Chou, Hsing-Cheng Chang, Shyan-Lung Lin, Shih-Chin Yang, Heng-Yi Su, Chih-Chien Chang, Yuan-Sheng Cheng, Yu-Chen Kuo. Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology. Sensors. 2017; 17 (7):1631.

Chicago/Turabian Style

Yu-Liang Hsu; Po-Huan Chou; Hsing-Cheng Chang; Shyan-Lung Lin; Shih-Chin Yang; Heng-Yi Su; Chih-Chien Chang; Yuan-Sheng Cheng; Yu-Chen Kuo. 2017. "Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology." Sensors 17, no. 7: 1631.