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This paper analyzes the influence of non-sinusoidal inductance distortion on the saliency-based position estimation for the permanent magnet (PM) machines. Because of the high-power-density design of PM machines, the flux saturation usually causes the inductance self-saturation, inductance cross-saturation, and most importantly, secondary inductance harmonics. This paper fully investigates the saturation effect on saliency-based drives. Analytical model for the position estimation with saturated inductance is developed to understand the stability of saliency-based drive. It is confirmed that the saturation effect deviates the estimated flux position from actual rotor position. These estimation errors are critical for the saliency-based drive especially under load. This paper defines the feasible estimation region for PM machines with saturated and non-sinusoidal inductances. Considering the position estimation errors due to the saturated and non-sinusoidal inductance, the typical maximum torque per amp (MTPA) current trajectory based on the encoder-based control system should be modified especially for the IPM machines at full load. For the machines where the saturation condition is unknown, $q$ -axis current without negative $d$ -axis current is suggested to maintain control stability while the torque reduction is resultant. By contrast, the modified current trajectory could also be designed once the saturation reflected position estimation errors could be obtained. Finite element analysis (FEA) with inverter co-simulation is used to investigate the inductance distortion at various loads. A 6 kW IPM machine prototype with highly non-sinusoidal inductance is tested for the experimental verification.

Electric discharge machining (EDM) is a type of high-precision machining usually applied to hard-material machining for mold manufacturing and in the aerospace industry. Longer process times typically reduce facility efficiency. The use of electrochemistry machining (ECM) can overcome this challenge to efficiently machine large workpieces. Some industries have adopted and combined these two processes for Inconel 718 material machining. However, the use of coordinate-measuring machine times to determine the machining accuracy of these two processes is difficult. This study matched process features by analyzing the electric driving pulses of ECM and EDM. Fitting intelligent sensing signals that respond to dimensional measurements can be used to analyze electrical pulse signals. For analyzing a cross-process model using extracted key features of the process, our feedback-based system determines lower machining measurement errors and improves geometric size. Finally, the processing time of experiments can be reduced by 80%, and our proposed model has a prediction accuracy of approximately 0.01 mm2.

Microelectromechanical system (MEMS)-based gyroscopes have been widely applied to various inertial-sensing-based human-computer interaction (HCI) devices. However, the random drift of MEMS-based gyroscopes limits their applications. Hence, studies 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 the 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.

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 over rotor fsample 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 is 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 cross-coupling are compensated. However, the drive performance strongly depends on the accuracy of the inductance parameter. By contrast, the controller using direct digital design achieves the better FOC performance independent to machine parameters. Different types of controllers have 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.

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%.

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 algorithm with the principal component analysis 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.

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.

This paper presents a wearable inertial measurement system and its associated spatiotemporal gait analysis algorithm to obtain quantitative measurements and explore clinical indicators from the spatiotemporal gait patterns for patients with stroke or Parkinson's disease. The wearable system is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and an RF wireless transmission module. The spatiotemporal gait analysis algorithm, consisting of procedures of inertial signal acquisition, signal preprocessing, gait phase detection, and ankle range of motion estimation, has been developed for extracting gait features from accelerations and angular velocities. In order to estimate accurate ankle range of motion, we have integrated accelerations and angular velocities into a complementary filter for reducing the accumulation of integration error of inertial signals. All 24 participants mounted the system on their foot to walk along a straight line of 10 m at normal speed and their walking recordings were collected to validate the effectiveness of the proposed system and algorithm. Experimental results show that the proposed inertial measurement system with the designed spatiotemporal gait analysis algorithm is a promising tool for automatically analyzing spatiotemporal gait information, serving as clinical indicators for monitoring therapeutic efficacy for diagnosis of stroke or Parkinson's disease.

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.

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.

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.

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.

This paper proposes a new voltage control methodology using the particle swarm optimization (PSO) technique for smart grid loadability enhancement. The goal of this paper is to achieve reliable and efficient voltage profile/stability regulation in power grids. This methodology is based on the decouple power flow equations and the worst-case design technique. Specifically, the secondary voltage control (SVC) problem is formulated as an L-infinity norm minimization problem which considers overall load voltage deviations in electrical power systems as an objective model, and the PSO technique is employed to determine a robust control action which aims to improve voltage profile and to enlarge transmission grid loadability by optimal coordinated control of VAR sources. The methodology was successfully tested on several IEEE benchmark systems.

This paper proposes an optimal control scheme based on a synchronized phasor (synchrophasor) for power system secondary voltage control. The framework covers voltage stability monitoring and control. Specifically, a voltage stability margin estimation algorithm is developed and built in the newly designed adaptive secondary voltage control (ASVC) method to achieve more reliable and efficient voltage regulation in power systems. This new approach is applied to improve voltage profile across the entire power grid by an optimized plan for VAR (reactive power) sources allocation; therefore, voltage stability margin of a power system can be increased to reduce the risk of voltage collapse. An extensive simulation study on the IEEE 30-bus test system is carried out to demonstrate the feasibility and effectiveness of the proposed scheme.