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4. An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture.

Author

Wiryasaputra, Rita, Huang, Chin-Yin, Lin, Yu-Ju, and Yang, Chao-Tung

Subjects
*WATER quality monitoring, *DRINKING water, *FECAL contamination, *ARTIFICIAL intelligence, *DATABASES, *PREDICTION models, *CLOUD computing
Abstract

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks. [ABSTRACT FROM AUTHOR]

Published
2024
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15. The BASDAI Cut-Off for Disease Activity Corresponding to the ASDAS Scores in a Taiwanese Cohort of Ankylosing Spondylitis

Author

Chen, Yi-Hsing, primary, Huang, Wen-Nan, additional, Chen, Yi-Ming, additional, Lai, Kuo-Lung, additional, Hsieh, Tsu-Yi, additional, Hung, Wei-Ting, additional, Lin, Ching-Tsai, additional, Tseng, Chih-Wei, additional, Tang, Kuo-Tung, additional, Chou, Yin-Yi, additional, Wu, Yi-Da, additional, Huang, Chin-Yin, additional, Hsieh, Chia-Wei, additional, Chen, Yen-Ju, additional, Liao, Yu-Wan, additional, and Chen, Hsin-Hua, additional

Published
2022
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21. International Dual-Degree Programs: Learning Experience in Student's Perspective

Author

Wei, Shih-Hsuan, Tan, Chih-Fan, Koomsap, Pisut, and Huang, Chin-Yin

Subjects
learning experience, ComputingMilieux_COMPUTERSANDEDUCATION, master of industrial engineering, International dual-degree programs, GeneralLiterature_MISCELLANEOUS, dual- master's degree program
Abstract

International dual-degree programs have been attractive and well accepted by students and parents, as earning anadditional degree from another country is perceived as having better job opportunities. The program developers have triedtheir best to collaborate to put together attractive and useful content for the programs to meet the expectation. However,what the students experience is far beyond fulfill the program credits at both universities to graduate. They encounter andmay have to adjust themselves back and forth for different styles of teaching, different learning environment, andunavoidable cultural difference. Presented in this paper are the viewpoints of the students who have graduated from anInternational dual- master's degree program in Industrial Engineering. The students' perspective on their two-year dualdegree learning journey is described based on the two different educational environments and the process of self-recognition experiences.

Published
2020
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25. Gender difference in ASAS HI among patients with ankylosing spondylitis

Author

Chen, Hsin-Hua, primary, Chen, Yi-Ming, additional, Lai, Kuo-Lung, additional, Hsieh, Tsu-Yi, additional, Hung, Wei-Ting, additional, Lin, Ching-Tsai, additional, Tseng, Chih-Wei, additional, Tang, Kuo-Tung, additional, Chou, Yin-Yi, additional, Wu, Yi-Da, additional, Huang, Chin-Yin, additional, Hsieh, Chia-Wei, additional, Huang, Wen-Nan, additional, and Chen, Yi-Hsing, additional

Published
2020
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28. Evaluation of agent-based manufacturing systems based on a parallel simulator

Author

Huang, Chin-Yin and Nof, Shimon Y.

Subjects
Shop floor control software, Numerical control software, Manufacturing software, Bills of material (BOM) software, Work groups
Abstract

Recent research in artificial autonomous agents has paved a way to fulfill the requirements of autonomous operations in manufacturing systems in terms of planning, scheduling, control, and processing. Those manufacturing systems are also coined agent-based manufacturing systems (ABMS). However, it still lacks an evaluation tool to judge the performance of a designed ABMS. Hence, an ABMS is difficult to be accepted by users and managers, and is difficult to be realized in the real world. Based on a developed ABMS model, this research develops a simulator on a parallel computer system. The simulator is designed to imitate the changes of the environment by the changes of incoming tasks, and to imitate the internal changes by the resource breakdowns. Besides, the autonomous, communicative, and cooperative behaviors of agents are also developed in the simulators. Cases and scenarios for ABMS are developed to apply in the simulator. The analytical results show that through the simulators, decision-makers can clearly observe the viability of each agent as well as the whole ABMS. Thus, adjustments on the ABMS design can be made accordingly and the final ABMS can successfully survive in the real world. Keywords: Autonomy; Message passing interface; Task administration protocol; Teamwork integration evaluation; Viability

Published
2002

36. Short-Term Prediction of PM 2.5 Using LSTM Deep Learning Methods.

Author

Kristiani, Endah, Lin, Hao, Lin, Jwu-Rong, Chuang, Yen-Hsun, Huang, Chin-Yin, and Yang, Chao-Tung

Abstract

This paper implements deep learning methods of recurrent neural networks and short-term memory models. Two kinds of time-series data were used: air pollutant factors, such as O3, SO2, and CO2 from 2017 to 2019, and meteorological factors such as temperature, humidity, wind direction, and wind speed. A trained model was used to predict air pollution within an eight-hour period. Correlation analysis was applied using Pearson and Spearman correlation coefficients. The KNN method was used to fill in the missing values to improve the generated model's accuracy. The average absolute error percentage value was used in the experiments to evaluate the model's performance. LSTM had the lowest RMSE value at 1.9 than the other models from the experiments. CNN had a significant RMSE value at 3.5, followed by Bi-LSTM at 2.5 and Bi-GRU at 2.7. In comparison, the RNN was slightly higher than LSTM at a 2.4 value. [ABSTRACT FROM AUTHOR]

Published
2022
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