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Reinforced Quantum-behaved Particle Swarm Optimization Based Neural Networks for Image Inspection

JOURNAL OF robotICS NETWORKING AND ARTIFICIAL LIFE 2018年第2期5卷 139-143页

作者： Lai, Li-Chun Ko, Chia-Nan Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Neipu Taiwan Nan Kai Univ Technol Dept Automat Engn Caotun Nantou Taiwan

This paper combines the niche particle concept and quantum-behaved particle swarm optimization (QPSO) method with chaotic mutation to train neural networks for image inspection. When exploring the methodology of reinforced quantum-behaved particle swarm (RQPSO) to train neural networks (RQPSONNs) for image inspection, first, image clustering is adopted to capture feasible information. In this research, the use of support vector regression (SVR) method determines the initial architecture of the neural networks. After initialization, the neural network architecture can be optimized by RQPSO. Then the optimal neural networks can perform image inspection. In this paper, the program of RQPSONNs for image inspection will be built. The values of root mean square error (RMSE) and peak signal to noise ratio (PSNR) are calculated to evaluate the efficiency of the RQPSONNs. Moreover, the experiment results will verify the usability of the proposed RQPSONNs for inspecting image. This research can be used in industrial automation to improve product quality and production efficiency.

关键词： Quantum-behaved particle swarm optimization Niche particle Support vector regression Image inspection

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A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

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ENERGIES 2018年第1期11卷 213页

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

One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

关键词： artificial intelligence convolutional neural network deep neural networks short-term load forecasting

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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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A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

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ENERGIES 2018年第10期11卷 2777-2777页

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

To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

关键词： artificial neural network wind speed forecasting wind energy power system energy management

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Design and Development of Condition Monitoring System for Wind Turbines Based on Generator Output Voltages

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SENSORS AND MATERIALS 2020年第3期32卷 895-907页

作者： Lee, Rong-Mao Hu, Shih-Hsuan Wang, Cheng-Chi Chen, Tsung-Chia Liu, Jui-Hung 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 PhD Program Grad Inst Precis Mfg 57Sec 2Zhongshan Rd Taichung 41170 Taiwan Southern Taiwan Univ Sci & Technol Dept Mech Engn 1 Nan Tai St Tainan 71005 Taiwan

As wind energy assumes greater importance in remote and offshore locations, an effective and reliable condition monitoring system (CMS) has become necessary for wind turbines (WTs). Conventional CMSs used in the power generation industry have been applied to WTs commercially. However, the operating environment of a WT is much different from that of a power plant. Moreover, current CMSs require the deployment of various sensors and computationally intensive analysis techniques. An empirical and low-cost CMS against shaft imbalance faults and generator circuit faults is proposed in this work. The diagnostic process of the CMS is merely based on generator outputs. Since the air gap between the rotor and the stator of a generator is limited by rigid bearings, diagnosis rules for shaft imbalance faults are formulated on the basis of experimental results with the aid of a specially designed WT simulation platform. Once a test at a specified speed has been performed, the error limits of the diagnosis for generator outputs can be determined. The proposed fault recognition procedure is practical and easy to conduct. The efficacy of the proposed CMS against four types of fault, namely, low-speed shaft imbalance, high-speed shaft imbalance, and short circuit and open circuit of a generator, has been validated practically on the WT simulation platform.

关键词： wind turbine condition monitoring rotor imbalance signal processing

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A Deep CNN-LSTM Model for Particulate Matter (PM_2.5) Forecasting in Smart Cities

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SENSORS 2018年第7期18卷 2220-2220页

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

In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

关键词： PM2.5 forecasting deep learning big data analytics CNN-LSTM model

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Solar Daily Irradiation Estimation Algorithm Development and Field Verification 4

Solar Daily Irradiation Estimation Algorithm Development and...

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4th IEEE International Conference on Applied System Invention (IEEE ICASI)

作者： Kuo, Ping-Huan Chen, Hsin-Chuan Huang, Chiou-Jye Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree 51 Min Sheng E Rd Pingtung Taiwan Beijing Inst Technol Sch Informat Technol 6 Jinfeng Rd Zhuhai Guangdong Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat 86 Hongqi Rd Ganzhou City Jiangxi Peoples R China

ISBN: (纸本)9781538643426

In this study, we use the inverse distance weighting algorithm to establish the solar irradiation estimation model. The model was built by obtaining information from 20 solar irradiation monitoring stations in central and southern Taiwan, and field verification was implemented at Yuan Chang Township town hall and Liujia Campus of Tainan. The Mean Absolute Percentage Error (MAPE) values of the solar irradiation estimation model at the two field verifications were 4.30% and 3.71% respectively.

关键词： Inverse distance weighting algorithm solar irradiation estimation model

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Acoustic Signal Analysis by Teager-Huang Transform for Milling Chatter Recognition

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SENSORS AND MATERIALS 2020年第3期32卷 873-884页

作者： Lee, Rong-Mao Yu, Yi-Hsing Wang, Cheng-Chi Chen, Tsung-Chia Nilaphruek, Pongpon Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree 4-18 Minsheng Rd Pingtung 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 57Sec 2Zhongshan Rd Taichung 41170 Taiwan Rajamangala Univ Technol Thanyaburi Dept Math & Comp Sci 39 Moo 1Klong6 Khlong Luang Pathum Than 12110 Thailand

Many types of sensor have been utilized to monitor milling vibration, and many analysis methods are devoted to the investigation of milling vibration or milling dynamics. In this work, a noncontact sensor and a time-frequency domain analysis method were applied to identify the state of milling vibration. A microphone was employed in practical tests to record the milling dynamics. The Teager-Huang transform (THT) was adopted for the acoustic signal analysis owing to its high resolution in the time-frequency domain. The potential frequency range for the analysis of milling dynamics is reported in this work to improve the recognition accuracy of milling vibration limited by the effect of environmental noise. The THT was used to distinguish the chatter state from the normal milling dynamics. In addition, the statistical index called the coefficient of variation was applied to define the threshold of chatter occurrence. Milling experiments (including dry and wet cuttings) were performed to verify the proposed chatter detection method.

关键词： milling vibration milling noise time-frequency domain Teager-Huang transform

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PV System Power Model Application in Smart Cities

PV System Power Model Application in Smart Cities

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IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Chiou-Jye Huang Min-Chan Hwang Hsin-Chuan Chen Ping-Huan Kuo School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou China School of Computer Engineering University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Computer and Intelligent Robot Program for Bachelor Degree National Pingtung University Pingtung Taiwan

ISBN: (数字)9781728101101

ISBN: (纸本)9781728101231

The photovoltaic (PV) system energy generation is closely related to climate conditions, installation site, and system design. In this paper, a simple energy generation model is proposed and established by means of module temperature and solar irradiation, module temperature coefficient, module rated power, number of modules, and three correction coefficients including temperature derating coefficient, inverter conversion loss coefficient, and other loss coefficients. The experiment results show that the estimation model proposed in this paper has high accuracy using the Mean Absolute Percentage Error (MAPE) model as the evaluation index, the estimation model MAPE is 6.75%, while the PV SYST software simulation has MAPE of 11.90%. According to international MAPE evaluation standards, the method proposed in this paper has higher accuracy level than PV SYST software. This model can be used for the estimation of energy generation when installing solar PV systems, and can be used for the reasonable assessment of PV system long-term power generation, to predict system power generation anomalies, and reduce system abnormalities caused by downtime losses, and increase investment efficiency.

关键词：

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A Mutual Blocking Technology Applied to Dual Power Source Switching Control

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ENERGIES 2019年第4期12卷 576页

作者： Chen, Hsin-Chuan Kuo, Ping-Huan Huang, Chiou-Jye Univ Elect Sci & Technol China Zhongshan Inst Sch Comp Engn Zhongshan 528402 Peoples R China Natl Pingtung Univ Comp & Intelligent Robot Program Bachelor Degree Pingtung 90004 Taiwan Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou 341000 Peoples R China

In many industries and medical power system applications, dual power source design is often used to ensure that the equipment runs continuously, even when the main power supply is shut down. However, the voltage feedback between two independent power supplies and low loss output are critical issues for the system energy dissipation. Without using a dedicated chip, a new mutual blocking control technology is proposed in this paper to effectively solve the problem of voltage feedback existing in the conventional dual power system. In addition, without adding much hardware cost, the proposed dual power switch design can completely avoid voltage feedback and achieve a low voltage loss of about 30 mV when the load current is less than 0.5 A.

关键词： dual power source switch mutual blocking control technology low loss output voltage feedback

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