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Shih-Wei Tan
Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan

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Journal article
Published: 22 July 2021 in Energies
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This study uses deep learning to model the discharge characteristic curve of the lithium-ion battery. The battery measurement instrument was used to charge and discharge the battery to establish the discharge characteristic curve. The parameter method tries to find the discharge characteristic curve and was improved by MLP (multilayer perceptron), RNN (recurrent neural network), LSTM (long short-term memory), and GRU (gated recurrent unit). The results obtained by these methods were graphs. We used genetic algorithm (GA) to obtain the parameters of the discharge characteristic curve equation.

ACS Style

Shih-Wei Tan; Sheng-Wei Huang; Yi-Zeng Hsieh; Shih-Syun Lin. The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm. Energies 2021, 14, 4423 .

AMA Style

Shih-Wei Tan, Sheng-Wei Huang, Yi-Zeng Hsieh, Shih-Syun Lin. The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm. Energies. 2021; 14 (15):4423.

Chicago/Turabian Style

Shih-Wei Tan; Sheng-Wei Huang; Yi-Zeng Hsieh; Shih-Syun Lin. 2021. "The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm." Energies 14, no. 15: 4423.

Journal article
Published: 08 July 2021 in Energies
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The focus of this study is under the auspices of China Steel Corporation, Taiwan, in carrying out the national energy policy of 2025 Non-Nuclear Home. Under this policy, an estimated 600 offshore wind turbines will be installed by 2025. In order to carry out the wind energy project effectively, a preliminary study must be conducted. In this article, we investigated the influence of the wake effect on the efficiency of the turbines’ layout in a windfarm. A distributed genetic algorithm is deployed to study the wind turbines’ layout in order to alleviate the detrimental wake effect. In the current stage of this research, the historical weather data of weather stations near the site of the 29th windfarm, Taiwan, were collected by Academia Sinica. Our wake effect resilient optimized windfarm showed superior performance over that of the conventional windfarm. Additionally, an operation cost minimization process is also demonstrated and implemented using an ant colony optimization algorithm to optimize the total length of the power-carrying interconnecting cables for the turbines inside the optimized windfarm.

ACS Style

Yi-Zeng Hsieh; Shih-Syun Lin; En-Yu Chang; Kwong-Kau Tiong; Shih-Wei Tan; Chiou-Yi Hor; Shyi-Chy Cheng; Yu-Shiuan Tsai; Chao-Rong Chen. Wind Technologies for Wake Effect Performance in Windfarm Layout Based on Population-Based Optimization Algorithm. Energies 2021, 14, 4125 .

AMA Style

Yi-Zeng Hsieh, Shih-Syun Lin, En-Yu Chang, Kwong-Kau Tiong, Shih-Wei Tan, Chiou-Yi Hor, Shyi-Chy Cheng, Yu-Shiuan Tsai, Chao-Rong Chen. Wind Technologies for Wake Effect Performance in Windfarm Layout Based on Population-Based Optimization Algorithm. Energies. 2021; 14 (14):4125.

Chicago/Turabian Style

Yi-Zeng Hsieh; Shih-Syun Lin; En-Yu Chang; Kwong-Kau Tiong; Shih-Wei Tan; Chiou-Yi Hor; Shyi-Chy Cheng; Yu-Shiuan Tsai; Chao-Rong Chen. 2021. "Wind Technologies for Wake Effect Performance in Windfarm Layout Based on Population-Based Optimization Algorithm." Energies 14, no. 14: 4125.

Journal article
Published: 12 July 2020 in Sustainability
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Under the vigorous development of global anticipatory computing in recent years, there have been numerous applications of artificial intelligence (AI) in people’s daily lives. Learning analytics of big data can assist students, teachers, and school administrators to gain new knowledge and estimate learning information; in turn, the enhanced education contributes to the rapid development of science and technology. Education is sustainable life learning, as well as the most important promoter of science and technology worldwide. In recent years, a large number of anticipatory computing applications based on AI have promoted the training professional AI talent. As a result, this study aims to design a set of interactive robot-assisted teaching for classroom setting to help students overcoming academic difficulties. Teachers, students, and robots in the classroom can interact with each other through the ARCS motivation model in programming. The proposed method can help students to develop the motivation, relevance, and confidence in learning, thus enhancing their learning effectiveness. The robot, like a teaching assistant, can help students solving problems in the classroom by answering questions and evaluating students’ answers in natural and responsive interactions. The natural interactive responses of the robot are achieved through the use of a database of emotional big data (Google facial expression comparison dataset). The robot is loaded with an emotion recognition system to assess the moods of the students through their expressions and sounds, and then offer corresponding emotional responses. The robot is able to communicate naturally with the students, thereby attracting their attention, triggering their learning motivation, and improving their learning effectiveness.

ACS Style

Yi-Zeng Hsieh; Shih-Syun Lin; Yu-Cin Luo; Yu-Lin Jeng; Shih-Wei Tan; Chao-Rong Chen; Pei-Ying Chiang. ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation. Sustainability 2020, 12, 5605 .

AMA Style

Yi-Zeng Hsieh, Shih-Syun Lin, Yu-Cin Luo, Yu-Lin Jeng, Shih-Wei Tan, Chao-Rong Chen, Pei-Ying Chiang. ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation. Sustainability. 2020; 12 (14):5605.

Chicago/Turabian Style

Yi-Zeng Hsieh; Shih-Syun Lin; Yu-Cin Luo; Yu-Lin Jeng; Shih-Wei Tan; Chao-Rong Chen; Pei-Ying Chiang. 2020. "ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation." Sustainability 12, no. 14: 5605.