In recent years, the micro-electro-mechanical systems (MEMS)-based inertial sensors fabrication t... more In recent years, the micro-electro-mechanical systems (MEMS)-based inertial sensors fabrication technology became matured. The technology created inertial sensors greatly enhance the stability, miniature the size, and reduce the cost Therefore, smartphones or mobile phones are embed many kinds of sensors. These sensors can provide many useful data, enabling new applications in various domain such as pedestrian navigation system, fall detection, and activity recognition. In this paper, we focus on the MEMS-based magnetometer, which can detect earth's magnetic field and guide directions. However, magnetometer is susceptible to interference resulted from ferrous material. This study presents the implementation of magnetometer's measurements on Android-based platform, observes the interference phenomenon by realistic measurements, and uses the compensation algorithms to calibrate.
Autonomous driving technology has not yet been widely adopted, in part due to the challenge of ac... more Autonomous driving technology has not yet been widely adopted, in part due to the challenge of achieving high-accuracy trajectory tracking in complex and hazardous driving scenarios. To this end, we proposed an adaptive sliding mode controller optimized by an improved particle swarm optimization (PSO) algorithm. Based on the improved PSO, we also proposed an enhanced grey wolf optimization (GWO) algorithm to optimize the controller. Taking the expected trajectory and vehicle speed as inputs, the proposed control scheme calculates the tracking error based on an expanded vector field guidance law and obtains the control values, including the vehicle’s orientation angle and velocity on the basis of sliding mode control (SMC). To improve PSO, we proposed a three-stage update function for the inertial weight and a dynamic update law for the learning rates to avoid the local optimum dilemma. For the improvement in GWO, we were inspired by PSO and added speed and memory mechanisms to the G...
2018 IEEE International Symposium on Circuits and Systems (ISCAS), 2018
Frequency-domain adaptive echo cancellers are a conventional approach for cancelling the line ech... more Frequency-domain adaptive echo cancellers are a conventional approach for cancelling the line echoes in wire-line full-duplex data transmission. However, abrupt changes in sampling instant at the receiver side can cause significant signal-to-noise ratio (SNR) degradation since the equivalent echo path is changed simultaneously. This paper proposes a digital compensation method to mitigate such SNR degradation by predicting the coefficients of echo cancellers (EC) while sampling instants are being changed. We mathematically derive the proposed prediction algorithm and numerically verify that our method is insensitive to the nonlinearity of the phase interpolator in the timing recovery loop. As an example, in a 10GBASE-T system, the simulation results show that our method could reduce SNR degradation by up to 2.25 dB. Without compensation, the adaptive EC would require about 2.4 × 105 symbol times to recover such degradation.
Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power d... more Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power development. To promote the utilization of wind energy and improve the accuracy of wind power prediction, we comprehensively consider the influence of wind farm environmental factors and historical power on wind power generation. This paper presents a short-term wind power prediction model based on time convolution neural network (TCN) and variational mode decomposition (VMD). First, due to the non-smooth characteristics of the wind farm environmental data, this paper uses VMD to decompose the data of each environmental variable to reduce the influence of the random noise of the data on the prediction model. Then, the modal components with rich feature information are extracted according to the Pearson correlation coefficient and Maximal information coefficient (MIC) between each modal component and the power. Thirdly, a prediction model based on TCN is trained according to the preferred ...
Automated inspection has proven to be the most effective approach to maintaining quality in indus... more Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickl...
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2016
This paper proposes a time-varying sliding surface for a second-order sliding mode controller to ... more This paper proposes a time-varying sliding surface for a second-order sliding mode controller to improve the control performance and energy efficiency of a quad-rotor helicopter. The time-varying sliding surface is designed with a nonlinear function to provide varying properties of the closed-loop dynamics in order to reduce energy consumption. It is shown that the second-order sliding mode technique, known as a generalized super twisting algorithm, providing a robust controller and a nonlinear sliding surface is effective in reducing the energy consumption. A Lyapunov stability analysis is described to prove the stability of the proposed method. The effectiveness and reliability of the proposed method are evaluated by performing experiments several times using a quad-rotor helicopter experimental testbed under wind disturbance.
In recent years, the micro-electro-mechanical systems (MEMS)-based inertial sensors fabrication t... more In recent years, the micro-electro-mechanical systems (MEMS)-based inertial sensors fabrication technology became matured. The technology created inertial sensors greatly enhance the stability, miniature the size, and reduce the cost Therefore, smartphones or mobile phones are embed many kinds of sensors. These sensors can provide many useful data, enabling new applications in various domain such as pedestrian navigation system, fall detection, and activity recognition. In this paper, we focus on the MEMS-based magnetometer, which can detect earth's magnetic field and guide directions. However, magnetometer is susceptible to interference resulted from ferrous material. This study presents the implementation of magnetometer's measurements on Android-based platform, observes the interference phenomenon by realistic measurements, and uses the compensation algorithms to calibrate.
Autonomous driving technology has not yet been widely adopted, in part due to the challenge of ac... more Autonomous driving technology has not yet been widely adopted, in part due to the challenge of achieving high-accuracy trajectory tracking in complex and hazardous driving scenarios. To this end, we proposed an adaptive sliding mode controller optimized by an improved particle swarm optimization (PSO) algorithm. Based on the improved PSO, we also proposed an enhanced grey wolf optimization (GWO) algorithm to optimize the controller. Taking the expected trajectory and vehicle speed as inputs, the proposed control scheme calculates the tracking error based on an expanded vector field guidance law and obtains the control values, including the vehicle’s orientation angle and velocity on the basis of sliding mode control (SMC). To improve PSO, we proposed a three-stage update function for the inertial weight and a dynamic update law for the learning rates to avoid the local optimum dilemma. For the improvement in GWO, we were inspired by PSO and added speed and memory mechanisms to the G...
2018 IEEE International Symposium on Circuits and Systems (ISCAS), 2018
Frequency-domain adaptive echo cancellers are a conventional approach for cancelling the line ech... more Frequency-domain adaptive echo cancellers are a conventional approach for cancelling the line echoes in wire-line full-duplex data transmission. However, abrupt changes in sampling instant at the receiver side can cause significant signal-to-noise ratio (SNR) degradation since the equivalent echo path is changed simultaneously. This paper proposes a digital compensation method to mitigate such SNR degradation by predicting the coefficients of echo cancellers (EC) while sampling instants are being changed. We mathematically derive the proposed prediction algorithm and numerically verify that our method is insensitive to the nonlinearity of the phase interpolator in the timing recovery loop. As an example, in a 10GBASE-T system, the simulation results show that our method could reduce SNR degradation by up to 2.25 dB. Without compensation, the adaptive EC would require about 2.4 × 105 symbol times to recover such degradation.
Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power d... more Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power development. To promote the utilization of wind energy and improve the accuracy of wind power prediction, we comprehensively consider the influence of wind farm environmental factors and historical power on wind power generation. This paper presents a short-term wind power prediction model based on time convolution neural network (TCN) and variational mode decomposition (VMD). First, due to the non-smooth characteristics of the wind farm environmental data, this paper uses VMD to decompose the data of each environmental variable to reduce the influence of the random noise of the data on the prediction model. Then, the modal components with rich feature information are extracted according to the Pearson correlation coefficient and Maximal information coefficient (MIC) between each modal component and the power. Thirdly, a prediction model based on TCN is trained according to the preferred ...
Automated inspection has proven to be the most effective approach to maintaining quality in indus... more Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickl...
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2016
This paper proposes a time-varying sliding surface for a second-order sliding mode controller to ... more This paper proposes a time-varying sliding surface for a second-order sliding mode controller to improve the control performance and energy efficiency of a quad-rotor helicopter. The time-varying sliding surface is designed with a nonlinear function to provide varying properties of the closed-loop dynamics in order to reduce energy consumption. It is shown that the second-order sliding mode technique, known as a generalized super twisting algorithm, providing a robust controller and a nonlinear sliding surface is effective in reducing the energy consumption. A Lyapunov stability analysis is described to prove the stability of the proposed method. The effectiveness and reliability of the proposed method are evaluated by performing experiments several times using a quad-rotor helicopter experimental testbed under wind disturbance.
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Papers by Ying-Ren Chien