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114 results on '"Chang, Jieh-Ren"'

6. Advanced Study: Improving the Quality of Cooling Water Towers' Conductivity Using a Fuzzy PID Control Model.

Author

Chen, You-Shyang, Hung, Ying-Hsun, Lee, Mike Yau-Jung, Chang, Jieh-Ren, Lin, Chien-Ku, and Wang, Tai-Wen

Subjects
COOLING towers, ELECTRIC conductivity, WATER quality, COMMUNICATIONS software, INDUSTRIAL sites
Abstract

Cooling water towers are commonly used in industrial and commercial applications. Industrial sites frequently have harsh environments, with certain characteristics such as poor air quality, close proximity to the ocean, large quantities of dust, or water supplies with a high mineral content. In such environments, the quality of electrical conductivity in the cooling water towers can be significantly negatively affected. Once minerals (e.g., calcium and magnesium) form in the water, conductivity becomes too high, and cooling water towers can become easily clogged in a short time; this leads to a situation in which the cooling water host cannot be cooled, causing it to crash. This is a serious situation because manufacturing processes are then completely shut down, and production yield is thus severely reduced. To solve these problems, in this study, we develop a practical designation for a photovoltaic industry company called Company-L. Three control methods are proposed: the motor control method, the PID control method, and the fuzzy PID control method. These approaches are proposed as solutions for successfully controlling the forced replenishment and drainage of cooling water towers and controlling the opening of proportional control valves for water release; this will further dilute the electrical conductivity and control it, bringing it to 300 µS/cm. In the experimental processes, we first used practical data from Company-L for our case study. Second, from the experimental results of the proposed model for the motor control method, we can see that if electrical conductivity is out of control and the conductivity value exceeds 1000 µS/cm, the communication software LINE v8.5.0 (accessible via smartphone) displays a notification that the water quality of the cooling water towers requires attention. Third, although the PID control method is shown to have errors within an acceptable range, the proportional (P) controller must be precisely controlled; this control method has not yet reached this precise control in the present study. Finally, the fuzzy PID control method was found to have the greatest effect, with the lowest level of errors and the most accurate control. In conclusion, the present study proposes solutions to reduce the risk of ice-water host machines crashing; the solutions use fuzzy logic and can be used to ensure the smooth operation of manufacturing processes in industries. Practically, this study contributes an applicable technical innovation: the use of the fuzzy PID control model to control cooling water towers in industrial applications. Concurrently, we present a three-tier monitoring checkpoint that contributes to the PID control method. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Vehicle Detection and Tracking in Night Times Using Vision and Rear Features with an Intelligent Methodology

Author

Chang, Jieh-Ren, Loh, Wai-Leong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hung, Jason C., editor, Yen, Neil Y., editor, and Chang, Jia-Wei, editor

Published
2020
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10. Real Time Human Fall Detection Using Accelerometer and IoT

Author

Johari, Kritika, Liu, Jing-Wei, Perumal, Thinagaran, Sharma, Abhishek, Chaturvedi, Tanmay, Chang, Jieh-Ren, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hung, Jason C., editor, Yen, Neil Y., editor, and Hui, Lin, editor

Published
2019
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11. IOT Based Smart Water Monitoring Using Image Processing

Author

Chang, Jieh-Ren, Agarwal, Nakul, Bao, Yipeng, Sharma, Abhishek, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hung, Jason C., editor, Yen, Neil Y., editor, and Hui, Lin, editor

Published
2019
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15. Real-Time Traffic Light Signal Recognition System for a Self-driving Car

Author

Agarwal, Nakul, Sharma, Abhishek, Chang, Jieh Ren, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Thampi, Sabu M., editor, Krishnan, Sri, editor, Corchado Rodriguez, Juan Manuel, editor, Das, Swagatam, editor, Wozniak, Michal, editor, and Al-Jumeily, Dhiya, editor

Published
2018
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28. Prevention of PID Phenomenon for Solar Panel Based on Mathematical Data Analysis Models.

Author

Chen, You-Shyang, Hung, Ying-Hsun, Lin, Yu-Sheng, Chang, Jieh-Ren, Lo, Chi-Hsiang, and You, Hong-Kai

Subjects
SOLAR panels, MATHEMATICAL analysis, DATA analysis, SOLAR energy, SOLAR cells, PHOTOVOLTAIC power systems, SOLAR power plants
Abstract

In recent years, the problem of potential-induced degradation (PID) phenomenon has been deeply associated with solar power issues because it causes serious power attenuation of solar panels and results in lowering its power generation efficiency. Thus, effectively identifying the PID problem from insights of industry data analysis to reduce production costs and increase the performance of power generation is an interesting and important subject for the solar power industry. Moreover, by the traditional standard rule (IEC62804) and the condition of a 96 h testing time, the costs of testing time and assembling materials against PID are very high and must be improved. Given the above reasons, this study proposes a hybrid procedure to organizes four mathematical methods: the mini-module testing, solar cell testing, a settling time, and a neural network, which are named as Method-1–Method-4, respectively, to efficiently solve the PID problem. Consequently, there are four key outcomes from the empirical results for solar power application: (1) In Method-1 with a 96 h testing time, it was found that the large module with higher costs and the mini module with lower costs have a positive correlation; thus, we can replace the large-module testing by the effective mini module for lower cost on module materials. (2) In Method-2 with a 24 h testing time, it was also found that the mini module and the solar cell are positively correlated; this result provides evidence that we can conduct the PID test by the easier solar cell to lower the costs. (3) In Method-3, the settling time achieves an average accuracy of 94% for PID prediction with a 14 h testing time. (4) In Method-4, the experimental result provides an accuracy of 80% when identifying the PID problem with the mathematical neural network model and are obtained within a 2 h testing time. From the above results, these methods succeed in reducing cost of materials and testing time during the manufacturing process; thus, this study has an industrial application value. Concurrently, Method-3 and Method-4 are rarely seen in the limited literature review for identifying PID problem; therefore, this study also offers a novel contribution for technical application innovation. [ABSTRACT FROM AUTHOR]

Published
2023
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47. Multi-resident activity recognition using multi-label classification in ambient sensing smart homes

Author

Jethanandani, Manan, Perumal, Thinagaran, Chang, Jieh Ren, Sharma, Abhishek, Bao, Yipeng, Jethanandani, Manan, Perumal, Thinagaran, Chang, Jieh Ren, Sharma, Abhishek, and Bao, Yipeng

Abstract

Activity recognition in smart home environment using wireless ambient sensing is a well-known problem that is being researched very actively. Rapid development in the sensing technologies has made human activity recognition very important for various fields such as health care, home monitoring, surveillance, etc. In this paper, we describe the use of Classifier Chain method of the Multi-Label Classification approach to tackle the task of multi-resident activity recognition. We evaluate the developed model of Classifier Chain with K-Nearest Neighbor as base classifier on real world ARAS dataset which consists of two smart homes with evaluation metrics such as accuracy, precision and hamming loss. Through results, it can be inferred that Classifier Chain method successfully caters the problem of multi-resident activity recognition taking into consideration underlying label dependencies.

Published
2019

48. Binary relevance model for activity recognition in home environment using ambient sensors

Author

Jethanandani, Manan, Perumal, Thinagaran, Liaw, Yuh Ching, Chang, Jieh Ren, Sharma, Abhishek, Bao, Yipeng, Jethanandani, Manan, Perumal, Thinagaran, Liaw, Yuh Ching, Chang, Jieh Ren, Sharma, Abhishek, and Bao, Yipeng

Abstract

One of the most important applications of the smart home environment is health monitoring and assistance by analysing activities of daily living and here Human Activity Recognition (HAR) plays a major role. The HAR problem, basically a temporal classification problem has been modelled in the past with various methods such as Bayesian Networks, Hidden Markov Model, Conditional Random Field, etc. Here, we propose the Binary Relevance Method of the multi- label classification to tackle the multi-resident activity recognition problem on real world dataset. Through the results obtained by the evaluation metrics namely accuracy, precision and hamming loss, it can be inferred that the model not only computes competitive results to previous works but also signifies the importance of the baseline Binary Relevance method to solve multi-label problems.

Published
2019

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