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3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chang, Yu-Cheng Tsai, Hua-Wen Huang, Chao-Yi Wu, Zong-Rong Department of Computer Information and Network Engineer Lunghwa University of Science and Technology Taiwan Department of Bachelor's Program in Intelligent Robot National Yunlin University of Science and Technology Taiwan

ISBN: (纸本)9798350333862

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the Bus Stop Passenger Detection System. Taking the object detection of Wheelchairs into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient. © 2023 IEEE.

关键词： Object detection

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Based-on computer Vision Applications for Bus Stop Passenger Detection System

Based-on Computer Vision Applications for Bus Stop Passenger...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Yu-Cheng Chang Hua-Wen Tsai Chao-Yi Huang Zong-Rong Wu Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taiwan Department of Bachelor's Program in Intelligent Robot National Yunlin University of Science and Technology Taiwan Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Taiwan

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the "Bus Stop Passenger Detection System". Taking the object detection of "Wheelchairs" into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient.

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DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020年第5期16卷 1-13页

作者： Kuo, Ping-Huan Lin, Ssu-Ting Hu, Jun Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Pingtung 90004 Taiwan

Linear predictive coding is an extremely effective voice generation method that operates through simple process. However, linear predictive coding-generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model that combines a denoise autoencoder with generative adversarial networks. This model generates voices with similar semantics through the random input from the latent space of generator. The experimental results indicate that voices generated exclusively by generative adversarial networks exhibit excessive noise. To solve this problem, a denoise autoencoder was connected to the generator for denoising. The experimental results prove the feasibility of the proposed voice generation method. In the future, this method can be applied in robots and voice generation applications to increase the humanistic language expression ability of robots and enable robots to demonstrate more humanistic and natural speaking performance.

关键词： Generative adversarial network autoencoder acoustic signal generator deep learning machine learning

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intelligent Control Strategy for robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization

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IEEE ACCESS 2019年 7卷 126929-126940页

作者： Li, Tzuu-Hseng S. Kuo, Ping-Huan Ho, Ya-Fang Liou, Guan-Hong Natl Cheng Kung Univ Dept Elect Engn aiRobots Lab Tainan 701 Taiwan Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Pingtung 90004 Taiwan

This paper proposes an intelligent control strategy for enabling a robotic arm to grasp and place water-filled bottles without spilling any of the water. First, the system architecture of a five-degree-of-freedom robotic arm and its mechanical design are introduced. Second, both the forward and inverse kinematics of the robotic arm are derived. The study conducted an experiment in which the designed and implemented robotic arm could grasp a bottle of water and move it to another place. However, if the acceleration or the orientation of the robotic arm were inappropriate, the water in the bottle may be spilled during the movement. Therefore, the proposed strategy applies an inertial measurement unit for obtaining relevant information. According to the obtained information, the velocity curves of each joint could be optimized by adaptive inertia weight and acceleration coefficients particle swarm optimization. Finally, the experimental results demonstrated the feasibility and effectiveness of the proposed method.

关键词： AIWCPSO intelligent control robotic arm velocity control

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Deep Belief Network-Based Learning Algorithm for Humanoid robot in a Pitching Game

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IEEE ACCESS 2019年 7卷 165659-165670页

作者： Li, Tzuu-Hseng S. Kuo, Ping-Huan Chang, Chien-Yu Hsu, Hao-Ping Chen, Yuan-Chin Chang, Chien-Hsin Natl Cheng Kung Univ Dept Elect Engn AiRobots Lab Tainan 701 Taiwan Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Pingtung 90004 Taiwan

A cognition learning algorithm based on a deep belief network and inertia weight Particle Swarm Optimization (PSO) is presented and examined in a humanoid robot. The psychology concepts were adopted from Thinking, Fast and Slow by Daniel Kahneman. The human brain comprises two systems, System 1 and System 2. Based on their characteristics, System 1 and System 2 handle different tasks during cerebration. In this study, Deep Belief Network (DBN) is trained to construct the function of System 1 for the rapid reaction. On the other hand, PSO is applied to build System 2 for the slow and complicated brain behavior. Through the cooperation of System 1 and System 2, the proposed cognition learning algorithm can apply the psychology theories to allow the humanoid robot for learning the suitable pitching postures autonomously. In the experiments conducted in this study, the robot was trained for only five selected points and was then asked to throw precisely to nine points. The proposed algorithm provided 100% accuracy in the robot pitching game. The feasibility of the proposed algorithm was thus verified.

关键词： Deep belief network humanoid robot machine learning particle swarm optimization

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Multi-hybrid Active Magnetic Bearing Design for Milling Spindle Applications

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SENSORS AND MATERIALS 2020年第1期32卷 375-385页

作者： Lee, Rong-Mao Wu, Zhi-Bin Wang, Cheng-Chi Chen, Tsung-Chia Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree 4-18 Minsheng Rd Pingtung 90003 Pingtung County Taiwan Natl Chin Yi Univ Technol Dept Mech Engn 57Sec 2Zhongshan Rd Taichung 41170 Taiwan Natl Chin Yi Univ Technol Grad Inst Precis Mfg PhD Program 57Sec 2Zhongshan Rd Taichung 41170 Taiwan

Active magnetic bearings (AMBs) have been gradually developed and used in industrial applications. For a milling spindle system with full magnetic support, at least one axial AMB and two radial AMBs should be included. As a result, the milling spindle dimensions will probably be too large for the application of machine tools. A multi-hybrid AMB design, in which one axial AMB, one radial AMB, and a permanent magnet (PM) are combined, is reported in this work. The PM is embedded for axial suspension for the rotor weight so that the force requirement and the dimensions of the axial AMB can be reduced. On the other hand, magnetic channels were specially designed to couple the three magnetic loops generated by the AMBs and PM instead of isolating these magnetic fluxes individually. The proposed design is also suitable for applications of robot arm joints, such as waist and shoulder joints, to improve the bearing lifetime under heavy loads and to actively regulate the joint position deviation. The proposed multi-hybrid AMB design was practically manufactured after intensive analyses and verified by practical tests.

关键词： active magnetic bearing magnetic loop analysis coupled magnetic flux milling spindle

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Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting

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IEEE ACCESS 2019年 7卷 74822-74834页

作者： Huang, Chiou-Jye Kuo, Ping-Huan Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Gauzhou 341000 Peoples R China Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Pingtung 90004 Taiwan

With the fast expansion of renewable energy system installed capacity in recent years, the availability, stability, and quality of smart grids have become increasingly important. The renewable energy output forecasting applications have also been developing rapidly in recent years, and such techniques have particularly been applied in the fields of wind and solar photovoltaic (PV). In the case of solar PV output forecasting, many applications have been performed with machine learning and hybrid techniques. In this paper, we propose a high-precision deep neural network model named PVPNet to forecast PV system output power. The methodology behind the proposed model is based on deep neural networks, and the model is able to generate a 24-h probabilistic and deterministic forecasting of PV power output based on meteorological information, such as temperature, solar radiation, and historical PV system output data. The forecasting accuracy of PVPNet is determined by the mean absolute error (MAE) and root mean square error (RMSE) values. The results from the experiments show that the MAE and RMSE of the proposed algorithm are 109.4845 and 163.1513, respectively. The results prove that the prediction accuracy of the PVPNet outperforms other benchmark models, and the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity.

关键词： Deep neural network photovoltaic output power forecasting photovoltaic system renewable energy sources

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Research on the nonlinear dynamic characteristics of opposed high-speed gas bearing systems

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2020年第3期39卷 502-522页

作者： Wang, Cheng-Chi Lee, Rong-Mao Lin, Chih-Jer Huang, Chih-Yung Lee, Tsui-Er Natl Chin Yi Univ Technol Grad Inst Precis Mfg Taichung Taiwan Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Pingtung Taiwan Natl Taipei Univ Technol Grad Inst Automat Technol Taipei Taiwan Natl Chin Yi Univ Technol Dept Mech Engn Taichung Taiwan Asia Univ Off Phys Educ Taichung Taiwan

In recent years, the opposed high-speed gas bearing system has been gradually valued and used in the field of precision machinery, especially for precision instruments and mechanisms requiring high speed, high precision, and high rigidity. Although the bearing capacity is not as good as the oil film bearings, it can provide a working environment where the rotor can generate high speed and low heat without deformation of the shaft, and the gas pressure distribution of clearance in bearing also has better stability. Due to the strong nonlinearity of the gas film pressure function of gas bearings and the fact that the actual shaft system possesses dynamic problems including critical speed, spindle imbalance or improper bearing design, it will cause the rotation process of the shaft to produce a nonperiodic motion and instability, and even chaotic motion under certain parameters. And these irregular movements can even cause machine damage or process delays when serious, so in order to understand the process of working under the conditions where the system will have a nonperiodic phenomenon and to avoid the occurrence of irregular vibration especially chaos. In this paper, the opposed high-speed gas bearing system feature will be discussed in detail with three different numerical analysis methods, i.e. the finite difference method, perturbation method, and mixing method. The relevant theories include dynamic trajectories, spectrum analysis, bifurcation diagram, Poincare map, and the maximum Lyapunov exponents. From the results of nonlinear dynamic behavior of the rotor center, periodic and nonperiodic motions occur at different rotor masses and bearing parameters, respectively. Especially, for the chaos of shaft exists at specific intervals and can be distinguished efficiently. Meanwhile, it is found to ensure that the bearing system can suppress the phenomena of chaos actively by adjusting the bearing parameters, and reduce the system loss caused by irregular vibration.

关键词： Opposed high-speed gas bearing nonperiodic chaos

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CNN and LSTM Based Facial Expression Analysis Model for a Humanoid robot

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IEEE ACCESS 2019年 7卷 93998-94011页

作者： Li, Tzuu-Hseng S. Kuo, Ping-Huan Tsai, Ting-Nan Luan, Po-Chien Natl Cheng Kung Univ Dept Elect Engn AiRobots Lab Tainan 701 Taiwan Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Pingtung 90004 Taiwan

robots must be able to recognize human emotions to improve the human-robot interaction (HRI). This study proposes an emotion recognition system for a humanoid robot. The robot is equipped with a camera to capture users' facial images, and it uses this system to recognize users' emotions and responds appropriately. The emotion recognition system, based on a deep neural network, learns six basic emotions: happiness, anger, disgust, fear, sadness, and surprise. First, a convolutional neural network (CNN) is used to extract visual features by learning on a large number of static images. Second, a long short-term memory (LSTM) recurrent neural network is used to determine the relationship between the transformation of facial expressions in image sequences and the six basic emotions. Third, CNN and LSTM are combined to exploit their advantages in the proposed model. Finally, the performance of the emotion recognition system is improved by using transfer learning, that is, by transferring knowledge of related but different problems. The performance of the proposed system is verified through leave-one-out cross-validation and compared with that of other models. The system is applied to a humanoid robot to demonstrate its practicability for improving the HRI.

关键词： Convolutional neural network long short-term memory transfer learning facial expression analysis

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Deep convolutional neural network model for short-term electricity price forecasting

arXiv

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arXiv 2020年

作者： Cheng, Hsu-Yung Kuo, Ping-Huan Shen, Yamin Huang, Chiou-Jye Department of Computer Science and Information Engineering National Central University Computer and Intelligent Robot Program for Bachelor Degree National Pingtung University School of Electrical Engineering and Automation Jiangxi University of Science and Technology

In the modern power market, electricity trading is an extremely competitive industry. More accurate price forecast is crucial to help electricity producers and traders make better decisions. In this paper, a novel method of convolutional neural network (CNN) is proposed to rapidly provide hourly forecasting in the energy market. To improve prediction accuracy, we divide the annual electricity price data into four categories by seasons and conduct training and forecasting for each category respectively. By comparing the proposed method with other existing methods, we find that the proposed model has achieved outstanding results, the mean absolute percentage error (MAPE) and root mean square error (RMSE) for each category are about 5.5% and 3, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Power markets

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