Browsing by Author "Lee, Yi-Shian"

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Exploring the Critical Factors Influencing the Quality of Blog Interfaces Using the Decision- Making Trial and Evaluation Laboratory ( DEMATEL) Method
(2014-02-01)
Hsu, Chun-Cheng
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Lee, Yi-Shian
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傳播與科技學系
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Department of Communication and Technology
Many studies have shown the significance of a good user interface in affecting a blogger's decision to select a platform for their blog and a reader's decision to read one, and so it is vital that both developers and bloggers need a greater understanding of how they can improve user experience through perfecting their blog interfaces. The aim of this research is to explore the critical factors influencing the quality of blog interfaces and the causal relationships between these factors, enabling blog interfaces to be designed more effectively. Using an approach combining a focus group and the Decision-Making Trial and Evaluation Laboratory (DEMATEL), this study defined eight factors in four dimensions that influence blog interface quality. The results of the DEMATEL analysis identify the key causal factors and effect factors, as well as the causal relationships between the eight factors via the impact-relations map. The research also indicates the most critical causal factors that bloggers and developers should focus on, in order to most effectively improve the quality and attractiveness of blog interfaces.
Forecasting energy consumption using a grey model improved by incorporating genetic programming
(2011-01-01)
Lee, Yi-Shian
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Tong, Lee-Ing
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工業工程與管理學系
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Department of Industrial Engineering and Management
Energy consumption is an important economic index, which reflects the industrial development of a city or a country. Forecasting energy consumption by conventional statistical methods usually requires the making of assumptions such as the normal distribution of energy consumption data or on a large sample size. However, the data collected on energy consumption are often very few or non-normal. Since a grey forecasting model, based on grey theory, can be constructed for at least four data points or ambiguity data, it can be adopted to forecast energy consumption. In some cases, however, a grey forecasting model may yield large forecasting errors. To minimize such errors, this study develops an improved grey forecasting model, which combines residual modification with genetic programming sign estimation. Finally, a real case of Chinese energy consumption is considered to demonstrate the effectiveness of the proposed forecasting model. (C) 2010 Elsevier Ltd. All rights reserved.
Forecasting nonlinear time series of energy consumption using a hybrid dynamic model
(2012-06-01)
Lee, Yi-Shian
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Tong, Lee-Ing
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Energy consumption is an important index of the economic development of a country. Rapid changes in industry and the economy strongly affect energy consumption. Although traditional statistical approaches yield accurate forecasts of energy consumption, they may suffer from several limitations such as the need for large data sets and the assumption of a linear formula. This work describes a novel hybrid dynamic approach that combines a dynamic grey model with genetic programming to forecast energy consumption. This proposed approach is utilized to forecast energy consumption because of its excellent accuracy, applicability to cases with limited data sets and ease of computability using mathematical software. Two case studies of energy consumption demonstrate the reliability of the proposed model. Computational results indicate that the proposed approach outperforms other models in forecasting energy consumption. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.
Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming
(2011-02-01)
Lee, Yi-Shian
;
Tong, Lee-Ing
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model. (C) 2010 Elsevier B.V. All rights reserved.
Forecasting value of agricultural imports using a novel two-stage hybrid model
(2014-06-01)
Lee, Yi-Shian
;
Liu, Wan-Yu
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工業工程與管理學系
;
Department of Industrial Engineering and Management
Agricultural imports are becoming increasingly important in terms of their impact on economic development. An accurate model must be developed for forecasting the value of agricultural imports since rapid changes in industry and economic policy affect the value of agricultural imports. Conventionally, the ARIMA model has been utilized to forecast the value of agricultural imports, but it generally requires a large sample size and several statistical assumptions. Some studies have applied nonlinear methods such as the GM(1,1) and improved GM(1,1) models, yet neglected the importance of enhancing the accuracy of residual signs and residual series. Therefore, this study develops a novel two-stage forecasting model that combines the GM(1,1) model with genetic programming to accurately forecast the value of agricultural imports. Moreover, accuracy of the proposed model is demonstrated based on two agricultural imports data sets from the Taiwan and USA. (C) 2014 Elsevier B.V. All rights reserved.
Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming
(2012-03-01)
Lee, Yi-Shian
;
Tong, Lee-Ing
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Solar energy has become an important energy source in recent years as it generates less pollution than other energies. A photovoltaic (PV) system, which typically has many components, converts solar energy into electrical energy. With the development of advanced engineering technologies, the transfer efficiency of a PV system has been increased from low to high. The combination of components in a PV system influences its transfer efficiency. Therefore, when predicting the transfer efficiency of a PV system, one must consider the relationship among system components. This work accurately predicts whether transfer efficiency of a PV system is high or low using a novel hybrid model that combines rough set theory (RST), data envelopment analysis (DEA), and genetic programming (GP). Finally, real data-set are utilized to demonstrate the accuracy of the proposed method.