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Prof. Dr. Her-Terng Yau
1. Department of Mechanical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan

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0 Robust Control
0 Signal Processing
0 Non-linear system analysis and control
0 Electrical and mechanical system control
0 Communication security and confidentiality control

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Robust Control
Signal Processing
Communication security and confidentiality control

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Journal article
Published: 29 March 2021 in IEEE Access
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Ball bearings are one of the most common components used in rotating machines. They reduce the rotational friction between the shaft and fixed components and maintain the center line of rotation of the shaft. A damaged bearing will cause abnormal vibration and noise, and often results in machine failure and loss of production. In this study the public database on ball bearings, provided by the Vibration Institute of Machinery Failure Prevention Technology (MFPT), was used for data retrieval and analysis, and a diagnosis model was created according to the data sets of the bearing in the database. Three different approaches were used for the extraction of features and a classifier was used to implement a diagnostic system. The aim of this study was a comparison of three approaches. The first was the Short-time Fourier Transform (STFT) where the time-frequency domain image is extracted as the feature used for status identification. The second and third approaches were based on the Chen-Lee Chaotic and the Lorenz Chaotic Systems and chaotic dynamic error maps were used in analysis and feature status identification. Chaotic systems are particularly sensitive to the slightest changes in input signals, and the time domain signals from bearings in different conditions were mapped onto individual images. The feature images extracted by the three different approaches were then used for training and verification in a Convolutional Neural Network (CNN). From the results of the experiments, it can be seen that all three approaches gave high identification rates. The interactive verification identification rate of the Chen-Lee chaotic system with CNN under three statuses reached 98.33%, and it also had the best computational efficiency in the condition without losing any classification accuracy. This will make a substantial contribution to real-time ball bearing fault diagnosis.

ACS Style

Bo-Lin Jian; Xiao-Yi Su; Her-Terng Yau. Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components. IEEE Access 2021, 9, 53509 -53517.

AMA Style

Bo-Lin Jian, Xiao-Yi Su, Her-Terng Yau. Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components. IEEE Access. 2021; 9 (99):53509-53517.

Chicago/Turabian Style

Bo-Lin Jian; Xiao-Yi Su; Her-Terng Yau. 2021. "Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components." IEEE Access 9, no. 99: 53509-53517.

Journal article
Published: 22 March 2021 in Actuators
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This study discusses a circular trajectory tracking function through a proposed pneumatic artificial muscle (PAM)-actuated robot manipulator. First, a dynamic model between a robot arm and a PAM cylinder is introduced. Then the parameters thereof are identified through a genetic algorithm (GA). Finally, PID is used along with a high-order sliding-mode feedback controller to perform circular trajectory tracking. As the experimental results show, the parameters of sampling time and moment of inertia are set to accomplish the trajectory tracking task in this study. In addition, the maximum error between the objective locus and the following locus was 11.3035 mm when applying theta-axis control to the circular trajectory of the robot arm with zero load or lower load. In an experiment of controller comparison, the results demonstrate that a high-order sliding-mode feedback controller is more robust in resisting external interference and the uncertainty of modeling, making the robot arm have good performance when tracking.

ACS Style

Chih-Jer Lin; Ting-Yi Sie; Wen-Lin Chu; Her-Terng Yau; Chih-Hao Ding. Tracking Control of Pneumatic Artificial Muscle-Activated Robot Arm Based on Sliding-Mode Control. Actuators 2021, 10, 66 .

AMA Style

Chih-Jer Lin, Ting-Yi Sie, Wen-Lin Chu, Her-Terng Yau, Chih-Hao Ding. Tracking Control of Pneumatic Artificial Muscle-Activated Robot Arm Based on Sliding-Mode Control. Actuators. 2021; 10 (3):66.

Chicago/Turabian Style

Chih-Jer Lin; Ting-Yi Sie; Wen-Lin Chu; Her-Terng Yau; Chih-Hao Ding. 2021. "Tracking Control of Pneumatic Artificial Muscle-Activated Robot Arm Based on Sliding-Mode Control." Actuators 10, no. 3: 66.

Journal article
Published: 11 November 2020 in IEEE Access
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The abrasion of milling cutters is an important factor that affects the accuracy of a workpiece. The intervals between cutter changes is based on the burr condition of the edges on the finished products as well as their dimensional precision. Delayed replacement of cutters will result in a degradation of workpiece quality and it is important that the wear of cutters be monitored in a timely manner. In this study the actual vibration signals generated in a milling process were measured using an Automatic Intelligent Diagnosis Mechanism (AIDM) to determine cutter wear. The AIDM included two feature extraction approaches and three classification methods. The first approach used the Finite Impulse Response Filter (FIR) with Approximate Entropy (ApEn) for feature extraction. The second approach was nonlinear feature mapping using a fractional order Chen-Lee chaotic system. This used chaotic dynamic error centroids and chaotic dynamic error mapping for status identification. After feature extraction the results were substituted into a Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and a Convolutional Neural Network (CNN) for identification. The results of the experiments showed that a Chaotic Dynamic Error Map of the fractional order Chen-Lee chaotic system in the AIDM had an identification rate of 96.33% using a convolutional neural network. In addition, it was shown that the AIDM model could automatically select the most suitable feature extraction and classification model from the input signal and could determine the wear level milling cutters.

ACS Style

Bo-Lin Jian; Kuan-Ting Yu; Xiao-Yi Su; Her-Terng Yau. An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear. IEEE Access 2020, 8, 199359 -199368.

AMA Style

Bo-Lin Jian, Kuan-Ting Yu, Xiao-Yi Su, Her-Terng Yau. An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear. IEEE Access. 2020; 8 ():199359-199368.

Chicago/Turabian Style

Bo-Lin Jian; Kuan-Ting Yu; Xiao-Yi Su; Her-Terng Yau. 2020. "An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear." IEEE Access 8, no. : 199359-199368.

Journal article
Published: 08 September 2020 in IEEE Access
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In this paper, the multi-objective Hybrid Taguchi-genetic Algorithm is used to search for the best processing parameters with specified processing accuracy. The experimental cutting parameters used for the L9 orthogonal table process are cutting depth, cutting velocity and feed rate. The surface roughness of the machined workpiece surface was measured according to the standard of centerline average roughness. The Material Removal Rate (MRR) will be calculated by measuring the diameter of the processed workpiece from the formula to give the MRR. A linear regression model is constructed from the processed quality and the processing parameters of the orthogonal table and the reliability of the model is confirmed by analysis of variance (ANOVA). A Hybrid Taguchi Genetic Algorithm (HTGA) was used to calculate the optimal cutting parameters for multi-objective processing. The results of the experiments indicate that HTGA gave better convergence and robustness than the conventional Genetic Algorithm (GA) using the same number of iterations. This process produces multiple combinations of optimal cutting parameters for material removal rate and surface roughness. As the enhancement of material removal rate improved efficiency on the production line, the optimal cutting parameters were based on the tolerance range of Ra 1.6μm ~ 3.2μm according to the international standard of surface roughness. After actual processing with the selected optimum cutting parameters, the quality of processing is even better than the experimental design of the L9 Orthogonal table.

ACS Style

Wen-Lin Chu; Min-Jia Xie; Li-Wei Wu; Yu-Syong Guo; Her-Terng Yau. The Optimization of Lathe Cutting Parameters Using a Hybrid Taguchi-Genetic Algorithm. IEEE Access 2020, 8, 169576 -169584.

AMA Style

Wen-Lin Chu, Min-Jia Xie, Li-Wei Wu, Yu-Syong Guo, Her-Terng Yau. The Optimization of Lathe Cutting Parameters Using a Hybrid Taguchi-Genetic Algorithm. IEEE Access. 2020; 8 (99):169576-169584.

Chicago/Turabian Style

Wen-Lin Chu; Min-Jia Xie; Li-Wei Wu; Yu-Syong Guo; Her-Terng Yau. 2020. "The Optimization of Lathe Cutting Parameters Using a Hybrid Taguchi-Genetic Algorithm." IEEE Access 8, no. 99: 169576-169584.

Journal article
Published: 23 March 2020 in Applied Sciences
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This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator’s night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.

ACS Style

Bo-Lin Jian; Wen-Lin Chu; Yu-Chung Li; Her-Terng Yau. Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle. Applied Sciences 2020, 10, 2178 .

AMA Style

Bo-Lin Jian, Wen-Lin Chu, Yu-Chung Li, Her-Terng Yau. Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle. Applied Sciences. 2020; 10 (6):2178.

Chicago/Turabian Style

Bo-Lin Jian; Wen-Lin Chu; Yu-Chung Li; Her-Terng Yau. 2020. "Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle." Applied Sciences 10, no. 6: 2178.

Journal article
Published: 20 February 2020 in Energies
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Thermal error is one of the main reasons for the loss of accuracy in lathe machining. In this study, a thermal deformation compensation model is presented that can reduce the influence of spindle thermal error on machining accuracy. The method used involves the collection of temperature data from the front and rear spindle bearings by means of embedded sensors in the bearing housings. Room temperature data were also collected as well as the thermal elongation of the main shaft. The data were used in a linear regression model to establish a robust model with strong predictive capability. Three methods were used: (1) Comsol was used for finite element analysis and the results were compared with actual measured temperatures. (2) This method involved the adjustment of the parameters of the linear regression model using the indicators of the coefficient of determination, root mean square error, mean square error, and mean absolute error, to find the best parameters for a spindle thermal displacement model. (3) The third method used system recognition to determine similarity to actual data by dividing the model into rise time and stable time. The rise time was controlled to explore the accuracy of prediction of the model at different intervals. The experimental results show that the actual measured temperatures were very close to those obtained in the Comsol analysis. The traditional model calculates prediction error values within single intervals, and so the model was divided to give rise time and stable time. The experimental results showed two error intervals, 19µm in the rise time and 15µm in the stable time, and these findings allowed the machining accuracy to be enhanced.

ACS Style

Chih-Jer Lin; Xiao-Yi Su; Chi-Hsien Hu; Bo-Lin Jian; Li-Wei Wu; Her-Terng Yau. A Linear Regression Thermal Displacement Lathe Spindle Model. Energies 2020, 13, 949 .

AMA Style

Chih-Jer Lin, Xiao-Yi Su, Chi-Hsien Hu, Bo-Lin Jian, Li-Wei Wu, Her-Terng Yau. A Linear Regression Thermal Displacement Lathe Spindle Model. Energies. 2020; 13 (4):949.

Chicago/Turabian Style

Chih-Jer Lin; Xiao-Yi Su; Chi-Hsien Hu; Bo-Lin Jian; Li-Wei Wu; Her-Terng Yau. 2020. "A Linear Regression Thermal Displacement Lathe Spindle Model." Energies 13, no. 4: 949.

Journal article
Published: 13 February 2020 in IEEE Access
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With the trend of Industry 4.0, the global machine tool industry is developing towards smart manufacturing. The ball bearing is a key component of the rotary axis of machine tool, and its functionality is to bear the external load on the axis as well as maintain the center position of the axis. A damaged bearing will result in abnormal vibration and noise, and thus will lead to the damage of the machine and produced workpieces. Therefore, inspection and identification of ball bearing failures is particularly important. This paper discusses the fault signals of ball bearings published by the Society for Machinery Failure Prevention Technology (MFPT) and creates a recognition model for the ball bearing state based on different fault states, and then we adopt two different approaches for feature extraction. The first approach implements Finite Impulse Response Filter (FIR) and Approximate Entropy (ApEn) to extract the signal features. The second approach utilizes the Chen-Lee chaotic system for analysis and takes its chaotic attractor as the feature of the state recognition. The comparison of model recognition accuracy for Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) was conducted after acquiring the features through the two approaches in this paper. The results of the experiments in this paper show that both of the feature extraction approaches enable the state to be recognized easily. The Chen-Lee chaotic system with BPNN not only reaches 100% identification rate and it has the highest overall efficiency; it takes only 0.054 second to complete the feature extraction for 63 sets of data; this study is able to provide timely and precise solution for the failure of key mechanical components.

ACS Style

Chih-Jer Lin; Xiao-Yi Su; Kuan-Ting Yu; Bo-Lin Jian; Her-Terng Yau. Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System. IEEE Access 2020, 8, 28267 -28275.

AMA Style

Chih-Jer Lin, Xiao-Yi Su, Kuan-Ting Yu, Bo-Lin Jian, Her-Terng Yau. Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System. IEEE Access. 2020; 8 ():28267-28275.

Chicago/Turabian Style

Chih-Jer Lin; Xiao-Yi Su; Kuan-Ting Yu; Bo-Lin Jian; Her-Terng Yau. 2020. "Inspection on Ball Bearing Malfunction by Chen-Lee Chaos System." IEEE Access 8, no. : 28267-28275.

Journal article
Published: 03 February 2020 in IEEE Access
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Weak spectral response and low model accuracy problems occurred in the process of quantitative inversion of low organic matter content of desert soil in arid areas. This study collects soil samples and field spectral data from different human interference regions in Fukang City, Xinjiang, to search the soil hyperspectral response law that based on the fractional Sprott chaotic system, and combined with the gray system theory to estimate organic matter content rapidly and accurately. Simulation shows that for those sampling soil with lower organic matter content, the range of X and Y components of the dynamic error motion curve distribution of the Sprott chaotic system is larger, and the motion curve of the non-integer order 1.9-order dynamic error is the most obvious. Since the chaotic attractors will appear as a linear trend according to different contents of the organic matter, so this thesis establishes a gray prediction model based on the 1.9 fractional order chaotic attractors. The R2, RPD, and RMSE of the low organic matter content in the region without human interruption are 0.995, 14.86, and 0.17, respectively. The R2, RPD, and RMSE of low organic matter content in the region with human interruption are 0.992, 11.95, and 0.11, respectively. This study demonstrates that it is feasible to estimate the low organic matter content of desert soils in arid regions via the gray prediction model that based on fractional chaotic attractors. This study provides a novel method for soil spectra signal analysis and estimation of the organic matter content.

ACS Style

Anhong Tian; Chengbiao Fu; Xiaoyi Su; Her-Terng Yau; Hei Gang Xiong. Estimation of Low Organic Matter Content in Desert Soil of Arid Area Based on Fractional Order Sprott Chaotic Circuit and Gray Theory. IEEE Access 2020, 8, 25001 -25013.

AMA Style

Anhong Tian, Chengbiao Fu, Xiaoyi Su, Her-Terng Yau, Hei Gang Xiong. Estimation of Low Organic Matter Content in Desert Soil of Arid Area Based on Fractional Order Sprott Chaotic Circuit and Gray Theory. IEEE Access. 2020; 8 (99):25001-25013.

Chicago/Turabian Style

Anhong Tian; Chengbiao Fu; Xiaoyi Su; Her-Terng Yau; Hei Gang Xiong. 2020. "Estimation of Low Organic Matter Content in Desert Soil of Arid Area Based on Fractional Order Sprott Chaotic Circuit and Gray Theory." IEEE Access 8, no. 99: 25001-25013.

Journal article
Published: 17 October 2019 in Sensors
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Soil salinization is very complex and its evolution is affected by numerous interacting factors produce strong non-linear characteristics. This is the first time fractional order chaos theory has been applied to soil salinization-level classification to decrease uncertainty in salinization assessment, solve fuzzy problems, and analyze the spectrum chaotic features in soil with different levels of salinization. In this study, typical saline soil spectrum data from different human interference areas in Fukang City (Xinjiang) and salt index test data from an indoor chemical analysis laboratory are used as the base information source. First, we explored the correlation between the spectrum reflectance features of soil with different levels of salinization and chaotic dynamic error and chaotic attractor. We discovered that the chaotic status error in the 0.6 order has the greatest change. The 0.6 order chaotic attractors are used to establish the extension matter-element model. The determination equation is built according to the correspondence between section domain and classic domain range to salinization level. Finally, the salt content from the chemical analysis is substituted into the discriminant equation in the extension matter-element model. Analysis found that the accuracy of the discriminant equation is higher. For areas with no human interference, the extension classification can successfully identify nine out of 10 prediction data, which is a 90% identification accuracy rate. For areas with human interference, the extension classification can successfully identify 10 out of 10 prediction data, which is a success rate of 100%. The innovation in this study is the building of a smart classification model that uses a fractional order chaotic system to inversely calculate soil salinization level. This model can accurately classify salinization level and its predictive results can be used to rapidly calculate the temporal and spatial distribution of salinization in arid area/desert soil.

ACS Style

Anhong Tian; Chengbiao Fu; Xiao-Yi Su; Her-Terng Yau; Heigang Xiong; Tian; Fu; Su; Yau. Classifying and Predicting Salinization Level in Arid Area Soil Using a Combination of Chua’s Circuit and Fractional Order Sprott Chaotic System. Sensors 2019, 19, 4517 .

AMA Style

Anhong Tian, Chengbiao Fu, Xiao-Yi Su, Her-Terng Yau, Heigang Xiong, Tian, Fu, Su, Yau. Classifying and Predicting Salinization Level in Arid Area Soil Using a Combination of Chua’s Circuit and Fractional Order Sprott Chaotic System. Sensors. 2019; 19 (20):4517.

Chicago/Turabian Style

Anhong Tian; Chengbiao Fu; Xiao-Yi Su; Her-Terng Yau; Heigang Xiong; Tian; Fu; Su; Yau. 2019. "Classifying and Predicting Salinization Level in Arid Area Soil Using a Combination of Chua’s Circuit and Fractional Order Sprott Chaotic System." Sensors 19, no. 20: 4517.

Original article
Published: 30 August 2019 in The International Journal of Advanced Manufacturing Technology
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Machine tools may be affected by room temperature, the heat generated by the process, and many other factors. These cause the temperature of the spindle, motor, lead screw, and other parts to rise, and this causes thermal deformation. The main purpose of this study was an exploration of the relationship between the temperature of the spindle and thermal deformation. Measurements were made of the increases in temperature of a CNC lathe spindle, and the related axial displacements involved, at spindle speeds of 1000, 2000, and 3000 rpm. Multiple regression analysis and a general regression neural network were used to establish the relationship between thermal deformation and temperature change individually. The results showed the coefficient of determination of the multiple regression analysis to be 0.9275, while the general regression determined by the neural network was 1. The fitting result of the regression neural network was better than that of multiple regression analysis, and the maximum error was less than 0.1 μm. In addition, this study also used COMSOL simulation analysis software to analyze features of the thermal behavior generated by the spindle structure. A trial and error method was used to adjust the boundary conditions. Results showed that the maximum error in temperature rise determination of simulation and experiment was less than 1 °C.

ACS Style

Bo-Lin Jian; Cheng-Chi Wang; Chin-Tsung Hsieh; Ying-Piao Kuo; Mao-Chin Houng; Her-Terng Yau. Predicting spindle displacement caused by heat using the general regression neural network. The International Journal of Advanced Manufacturing Technology 2019, 104, 4665 -4674.

AMA Style

Bo-Lin Jian, Cheng-Chi Wang, Chin-Tsung Hsieh, Ying-Piao Kuo, Mao-Chin Houng, Her-Terng Yau. Predicting spindle displacement caused by heat using the general regression neural network. The International Journal of Advanced Manufacturing Technology. 2019; 104 (9-12):4665-4674.

Chicago/Turabian Style

Bo-Lin Jian; Cheng-Chi Wang; Chin-Tsung Hsieh; Ying-Piao Kuo; Mao-Chin Houng; Her-Terng Yau. 2019. "Predicting spindle displacement caused by heat using the general regression neural network." The International Journal of Advanced Manufacturing Technology 104, no. 9-12: 4665-4674.

Journal article
Published: 10 July 2019 in IEEE Access
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So far, there have been many types of researches subject to technical requirements due to image registration. Using image registration can lower deviation from sequential images and make it possible to analyze the information variation of the particular area subsequently. This study provides a procedure creating fixed image based on the data of facial IR thermography, where its methods include the visual saliency map by detected image, as well as cluster algorithm. Comparison is also made here to solve the merits and demerits by affine parameter to reach the optimum measure among Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing Algorithm, where there are two control parameters concerning this experiment: one is the calculation of time confined each alignment, while the other one is to use parallel computing toolbox or not. The optimum method will be chosen by the values of the objective function based on the control parameters. Afterward, the optimal internal parameter is to be verified through the Taguchi experiment and the validity of this procedure in this study will be built following the parameter result as above. Therefore, the difference of images before and after alignment can be validated by overlapping the images before and after alignment as well as the image quality measurements, where its results reveal that the alignment procedure of IR thermography in this study is capable of performing human face alignment precisely, and subsequently, do help data statistics and analysis concerning temperature area interdependence.

ACS Style

Bo-Lin Jian; Chieh-Li Chen; Chih-Jer Lin; Her-Terng Yau. Optimization Method of IR Thermography Facial Image Registration. IEEE Access 2019, 7, 93501 -93510.

AMA Style

Bo-Lin Jian, Chieh-Li Chen, Chih-Jer Lin, Her-Terng Yau. Optimization Method of IR Thermography Facial Image Registration. IEEE Access. 2019; 7 ():93501-93510.

Chicago/Turabian Style

Bo-Lin Jian; Chieh-Li Chen; Chih-Jer Lin; Her-Terng Yau. 2019. "Optimization Method of IR Thermography Facial Image Registration." IEEE Access 7, no. : 93501-93510.

Journal article
Published: 10 June 2019 in IEEE Access
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This study primarily discusses the measurement of partial discharge (PD) phenomena and clustering in the defect pattern of a cross-linked polyethylene power cable joint. First, high-speed data acquisition and pretreatment were performed for partial discharge electrical signals at a sampling rate of 20 MS/s. Crucial characteristic signals were reversed to reduce the calculated amount of noise. A characteristic matrix was created according to the resulting dynamic error of chaos synchronization. The characteristic parameters were extracted using fractal theory. Finally, extension theory was used to develop a diagnostic system and anti-interference test. A comparison with the existing Hilbert–Huang transform (HHT) method revealed that the two characteristics extracted from the chaos synchronization results using fractal theory were recognized at a higher pattern recognition rate by employing extension theory. The proposed method can extract crucial information concerning partial discharge as a defect in power cable joints.

ACS Style

Feng-Chang Gu; Her-Terng Yau; Hung-Cheng Chen. Application of Chaos Synchronization Technique and Pattern Clustering for Diagnosis Analysis of Partial Discharge in Power Cables. IEEE Access 2019, 7, 76185 -76193.

AMA Style

Feng-Chang Gu, Her-Terng Yau, Hung-Cheng Chen. Application of Chaos Synchronization Technique and Pattern Clustering for Diagnosis Analysis of Partial Discharge in Power Cables. IEEE Access. 2019; 7 (99):76185-76193.

Chicago/Turabian Style

Feng-Chang Gu; Her-Terng Yau; Hung-Cheng Chen. 2019. "Application of Chaos Synchronization Technique and Pattern Clustering for Diagnosis Analysis of Partial Discharge in Power Cables." IEEE Access 7, no. 99: 76185-76193.

Journal article
Published: 15 May 2019 in IEEE Access
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Chatter, the vibration between the workpiece and cutting tool, is a common phenomenon during the milling process. Traditionally, Fourier transform analysis is mainly used to extract the features and determine whether chatter occurs. In this paper, an innovative and practical chatter identification method combining fractional order chaotic system and extension theory is proposed. A lathe spindle with embedded sensors is used in this study. The boundary of chattering state of the lathe spindle is decided by the center of gravity of phase plane of dynamic errors. The boundaries are then fed into extension model and relational function calculation is performed. In this way, chatter identification can be easily achieved based on the position of chaotic center of gravity. Three chaotic systems, i.e. Lorenz, Chen-Lee and Sprott, of different fractional orders are used and their results are compared. The experiment results indicate that Chen-Lee system (93.5%) exhibits has better chatter diagnosis rate than Lorenz (92.75%) and Sprott (69%) systems Chen-Lee system even reaches a diagnosis rate of 100% for orders 0.5~0.7. Therefore, the method presented in this paper has a very high diagnosis rate and is thus very effective for chatter identification of machine tools.

ACS Style

Bo-Lin Jian; Cheng-Chi Wang; Jin-Yu Chang; Xiao-Yi Su; Her-Terng Yau. Machine Tool Chatter Identification Based on Dynamic Errors of Different Self-Synchronized Chaotic Systems of Various Fractional Orders. IEEE Access 2019, 7, 67278 -67286.

AMA Style

Bo-Lin Jian, Cheng-Chi Wang, Jin-Yu Chang, Xiao-Yi Su, Her-Terng Yau. Machine Tool Chatter Identification Based on Dynamic Errors of Different Self-Synchronized Chaotic Systems of Various Fractional Orders. IEEE Access. 2019; 7 (99):67278-67286.

Chicago/Turabian Style

Bo-Lin Jian; Cheng-Chi Wang; Jin-Yu Chang; Xiao-Yi Su; Her-Terng Yau. 2019. "Machine Tool Chatter Identification Based on Dynamic Errors of Different Self-Synchronized Chaotic Systems of Various Fractional Orders." IEEE Access 7, no. 99: 67278-67286.

Journal article
Published: 02 April 2019 in IEEE Access
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Currently, research on emotion recognition is gaining increasing attention. Inner emotions or thought activity can be determined by analyzing facial expressions, behavioral responses, audio, and physiological signals; facial expressions are one of the forms of non-verbal interactions. We constructed emotion-specific activation maps to establish infrared thermal facial image sequences, which is an alternative approach for determining the correlation between emotional triggers and changes in facial temperature. During the testing process, data stored in The International Affective Picture System were used to create emotional clips that triggered three different types of emotions in the subjects, and their infrared thermal facial image sequences were simultaneously recorded. For processing, an image calibration protocol was first employed to reduce the variance produced by irregular micro-shifts in the faces of the subjects, followed by independent component analysis and statistical analysis protocols to create the facial emotional activation maps. The test results showed that we resolved the problem of selecting local regions when analyzing frame temperature. Emotion-specific facial activation maps provide visualized results that facilitate the observation and understanding of information.

ACS Style

Bo-Lin Jian; Chieh-Li Chen; Min-Wei Huang; Her-Terng Yau. Emotion-Specific Facial Activation Maps Based on Infrared Thermal Image Sequences. IEEE Access 2019, 7, 48046 -48052.

AMA Style

Bo-Lin Jian, Chieh-Li Chen, Min-Wei Huang, Her-Terng Yau. Emotion-Specific Facial Activation Maps Based on Infrared Thermal Image Sequences. IEEE Access. 2019; 7 (99):48046-48052.

Chicago/Turabian Style

Bo-Lin Jian; Chieh-Li Chen; Min-Wei Huang; Her-Terng Yau. 2019. "Emotion-Specific Facial Activation Maps Based on Infrared Thermal Image Sequences." IEEE Access 7, no. 99: 48046-48052.

Journal article
Published: 01 February 2019 in International Journal of Bifurcation and Chaos
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Soil salinization has become a highly significant eco-system issue that is encountered all over the world. Serious soil salinization leads to soil deterioration and has a negative impact on sustainable development of the eco-system and agriculture. However, the spectral reflectance of soils with high overlap and indecipherability makes it difficult to classify the soil salinization degree quickly and accurately. In this paper, an innovative, intelligent methodology using a fractional-order chaotic system to classify the soil salinization degree is proposed. To select a suitable order for the fractional-order chaotic system, the integer-order and noninteger order master-slave Lorenz chaotic systems were used to observe variations in the phase plane distributions. Movement traces of the chaotic system show that severely saline soil will exhibit more active changes, and its distribution status of the Lorenz chaotic system will be more scattered. After analyzing the characteristics of phase plane distributions, a preferred 0.9 fractional-order chaotic system is selected to obtain good analytical characteristics. Finally, extenics theory is used to verify the accuracy of salinization status classified by the coordinate values of the chaotic attractors, and an extenic matter element model is established to analyze the salinization degree. From the results, it was found that 100% analysis accuracy in the judgment of salinization level could be achieved under noninteger order status, and this judgment method is also suitable for soils in different human activity areas. This method has now become a benchmark for testing soil salinization with a chaotic system and is an innovative method that can be used to test the soil salinization degree quickly and accurately.

ACS Style

An-Hong Tian; Cheng-Biao Fu; Hei-Gang Xiong; Her-Terng Yau. Innovative Intelligent Methodology for the Classification of Soil Salinization Degree Using a Fractional-Order Master-Slave Chaotic System. International Journal of Bifurcation and Chaos 2019, 29, 1 .

AMA Style

An-Hong Tian, Cheng-Biao Fu, Hei-Gang Xiong, Her-Terng Yau. Innovative Intelligent Methodology for the Classification of Soil Salinization Degree Using a Fractional-Order Master-Slave Chaotic System. International Journal of Bifurcation and Chaos. 2019; 29 (2):1.

Chicago/Turabian Style

An-Hong Tian; Cheng-Biao Fu; Hei-Gang Xiong; Her-Terng Yau. 2019. "Innovative Intelligent Methodology for the Classification of Soil Salinization Degree Using a Fractional-Order Master-Slave Chaotic System." International Journal of Bifurcation and Chaos 29, no. 2: 1.

Journal article
Published: 24 January 2019 in IEEE Access
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Machining refers to a variety of processes in which a cutting tool is used to remove unwanted material from a workpiece. Tool wear, advertently or inadvertently, occurs after long-time use. It is crucial to monitor tool wear so that the cutting tool can deliver the best performance and meet the technological challenges nowadays. In this paper, through different fractional order chaotic systems, i.e. Chen-Lee, Lorenz and Sprott, and extension theory is proposed to predict tool life. The results of the three chaotic systems are compared. The Centroid of the two-dimensional plane of dynamic errors are used as the characteristics. Four wear states are defined in accordance with different levels of surface roughness, i.e. normal, slight, moderate and severe. The boundaries of the four states are identified according to the locations of the Centroid generated with the systems of different fraction orders. The boundaries are then fed into the extension model and relational function calculation is performed. In this way, the identification of tool state can be easily achieved. The experiment results indicate that Chen-Lee system and Lorenz systems exhibit the same diagnosis rate (97.375%), higher than that of Sprott system (35.75%). It is demonstrated that two chaotic systems are fit for use with the method proposed in this paper. It is also proven that Chen-Lee and Lorenz fractional order master-slave chaotic systems are very effective for tool life monitoring. The robustness of diagnosis is also greatly improved.

ACS Style

Her-Terng Yau; Cheng-Chi Wang; Jin-Yu Chang; Xiao-Yi Su. A Study on the Application of Synchronized Chaotic Systems of Different Fractional Orders for Cutting Tool Wear Diagnosis and Identification. IEEE Access 2019, 7, 15903 -15911.

AMA Style

Her-Terng Yau, Cheng-Chi Wang, Jin-Yu Chang, Xiao-Yi Su. A Study on the Application of Synchronized Chaotic Systems of Different Fractional Orders for Cutting Tool Wear Diagnosis and Identification. IEEE Access. 2019; 7 (99):15903-15911.

Chicago/Turabian Style

Her-Terng Yau; Cheng-Chi Wang; Jin-Yu Chang; Xiao-Yi Su. 2019. "A Study on the Application of Synchronized Chaotic Systems of Different Fractional Orders for Cutting Tool Wear Diagnosis and Identification." IEEE Access 7, no. 99: 15903-15911.

Research article
Published: 13 December 2018 in Journal of Low Frequency Noise, Vibration and Active Control
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As product quality and production capacity requirements for precision processing become higher, making machine tools smart and improving performance is becoming the trend. In terms of machine tool processing quality, tool wear and chatter vibration are the factors that most directly affect processing quality. Traditionally, operators rely on their experience in determining whether the tool is worn or if there is chatter vibration. However, experience is not quantified data. Experience does not have a uniform judgment standard and can easily lead to wrong judgment. In recent years, many scholars have conducted in-depth studies of the two aforementioned phenomenons. These studies mainly use time and frequency domains to search for phenomenon features as the determination basis. Of time and frequency domain, studies on frequency domain are the most common. However, the frequency domain method requires a large amount of calculation, and the analysis process requires an excessive quantity of data dimensions, making this method unsuitable for real-time analysis. Thus, we propose a general regression neural network analysis method based on Chua’s circuit and a fractional-order Lorenz master/slave composite chaotic system for detecting lathe tool turning chatter vibration in this study. We compared the dynamic error features produced by various fractional-order chaotic systems and chose fractional orders with more obvious feature changes. We then substituted general regression neural network categorization. Compared to the frequency domain analysis method, the method proposed in this study requires fewer data dimensions, fewer calculations, and higher efficiency. Our proposed method also has higher precision and a higher discrimination rate. The result of this study shows that our proposed method has a 100% discrimination rate for determining turning chatter vibration.

ACS Style

An-Hong Tian; Cheng-Biao Fu; Xiao-Yi Su; Her-Terng Yau. Lathe tool chatter vibration diagnostic using general regression neural network based on Chua’s circuit and fractional-order Lorenz master/slave chaotic system. Journal of Low Frequency Noise, Vibration and Active Control 2018, 38, 953 -966.

AMA Style

An-Hong Tian, Cheng-Biao Fu, Xiao-Yi Su, Her-Terng Yau. Lathe tool chatter vibration diagnostic using general regression neural network based on Chua’s circuit and fractional-order Lorenz master/slave chaotic system. Journal of Low Frequency Noise, Vibration and Active Control. 2018; 38 (3-4):953-966.

Chicago/Turabian Style

An-Hong Tian; Cheng-Biao Fu; Xiao-Yi Su; Her-Terng Yau. 2018. "Lathe tool chatter vibration diagnostic using general regression neural network based on Chua’s circuit and fractional-order Lorenz master/slave chaotic system." Journal of Low Frequency Noise, Vibration and Active Control 38, no. 3-4: 953-966.

Research article
Published: 08 November 2018 in Mathematical Problems in Engineering
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In this study the nonlinear behavior of a buck converter was simulated and the responses of Phases 1 and 2 and the chaotic phase were investigated using changes of input voltage. After a dynamic system model had been acquired using basic electronic circuit theory, Matlab and Pspice simulations were used to study system inductance, resistance, and capacitance. The characteristic changes of input voltage, and phase plane traces from simulation and experiments showed nonlinear behavior in Phases 1 and 2, as well as a chaotic phase. PID control and Integral Absolute Error (IAE) were used as adaption coefficients to control chaotic behavior, and particle swarm optimization (PSO) and the genetic algorithm were used to find the optimal gain parameters for the PID controller. Simulation results showed that the control of chaotic phenomena could be achieved and errors were close to zero. Fuzzy control was also used effectively to prevent chaos. The experimental results also showed nonlinear behavior from Phases 1 and 2 as well as the chaotic phase. Laboratory experiments conducted using both PID and fuzzy control echoed the simulation results. The fuzzy control results were somewhat better than those obtained with PID.

ACS Style

Cheng-Biao Fu; An-Hong Tian; Kuo-Nan Yu; Yi‐Hung Lin; Her-Terng Yau. Analyses and Control of Chaotic Behavior in DC-DC Converters. Mathematical Problems in Engineering 2018, 2018, 1 -13.

AMA Style

Cheng-Biao Fu, An-Hong Tian, Kuo-Nan Yu, Yi‐Hung Lin, Her-Terng Yau. Analyses and Control of Chaotic Behavior in DC-DC Converters. Mathematical Problems in Engineering. 2018; 2018 ():1-13.

Chicago/Turabian Style

Cheng-Biao Fu; An-Hong Tian; Kuo-Nan Yu; Yi‐Hung Lin; Her-Terng Yau. 2018. "Analyses and Control of Chaotic Behavior in DC-DC Converters." Mathematical Problems in Engineering 2018, no. : 1-13.

Journal article
Published: 12 September 2018 in Sensors
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In this study we used a non-autonomous Chua’s circuit, and the fractional Lorenz chaos system. This was combined with the Extension theory detection method to analyze the voltage signals. The bearing vibration signals, measured using an acceleration sensor, were introduced into the master and slave systems through a Chua’s circuit. In a chaotic system, minor differences can cause significant changes that generate dynamic errors. The matter-element model extension can be used to determine the bearing condition. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from discrete Fourier transform analysis, wavelet analysis and an integer order chaos system. The diagnostic rate of the fractional-order master and slave chaotic system could reach 100% if the fractional-order parameter adjustment was used. This study presents a very efficient and inexpensive method for monitoring the state of ball bearings.

ACS Style

An-Hong Tian; Cheng-Biao Fu; Yu-Chung Li; Her-Terng Yau. Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection. Sensors 2018, 18, 3069 .

AMA Style

An-Hong Tian, Cheng-Biao Fu, Yu-Chung Li, Her-Terng Yau. Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection. Sensors. 2018; 18 (9):3069.

Chicago/Turabian Style

An-Hong Tian; Cheng-Biao Fu; Yu-Chung Li; Her-Terng Yau. 2018. "Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection." Sensors 18, no. 9: 3069.

Journal article
Published: 04 September 2018 in Journal of Low Frequency Noise, Vibration and Active Control
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Subscription and open access journals from SAGE Publishing, the world's leading independent academic publisher.

ACS Style

Cheng-Biao Fu; An-Hong Tian; Yu-Chung Li; Her-Terng Yau. Active controller design for precision computerized numerical control machine tool systems. Journal of Low Frequency Noise, Vibration and Active Control 2018, 38, 1149 -1159.

AMA Style

Cheng-Biao Fu, An-Hong Tian, Yu-Chung Li, Her-Terng Yau. Active controller design for precision computerized numerical control machine tool systems. Journal of Low Frequency Noise, Vibration and Active Control. 2018; 38 (3-4):1149-1159.

Chicago/Turabian Style

Cheng-Biao Fu; An-Hong Tian; Yu-Chung Li; Her-Terng Yau. 2018. "Active controller design for precision computerized numerical control machine tool systems." Journal of Low Frequency Noise, Vibration and Active Control 38, no. 3-4: 1149-1159.