ABSTRACT Issues such as energy security, sustainable development, and environmental protection ha... more ABSTRACT Issues such as energy security, sustainable development, and environmental protection have been a major topic of international discussions in recent years. Developed countries worldwide are investing substantial sums to develop renewable energy systems. In addition to this trend, wind power generation has revealed potential as a major energy source in Taiwan. However, an accident occurred just as the government and private enterprises began heavily promoting the construction of wind power generators. On September 28, 2008, five large wind turbines (WTs) located in the Changhua Coastal Industrial Park in Taichung sustained blade damage from fierce winds and heavy rainfall brought by Typhoon Jangmi. To examine the causes of this damage, specifically, delamination and cracking in the WT blades, this study first reviewed and analyzed data in related engineering documents. Similar overseas cases were also reviewed to identify the common causes of turbine blade failure incidents. The structural mechanics of WT blades were then analyzed with behavioral models to identify the mechanisms of the damage. Hopefully, the analytical results of this study can help prevent similar engineering incidents in the future and provide a reference for stakeholders devising strategies for improving risk management and disaster prevention in wind power plants.
International Journal of Disaster Risk Reduction, 2015
ABSTRACT As global climate change exacerbates the potential damage of natural disasters, the need... more ABSTRACT As global climate change exacerbates the potential damage of natural disasters, the need for sustained investment in comprehensive disaster prevention training increases. Taiwan is an island located in a seismically-active area and is regularly subject to natural disasters such as floods, landslides, and earthquakes. Therefore, disaster prevention education must be expanded. This study investigates current practices and suggests future disaster prevention training directions in Taipei, first by conducting a review of practical implementation experience and the literature on learning theory. A questionnaire survey was performed to solicit input from community leaders who had completed the training program. Structural equation modeling is used to determine the learning satisfaction index and the impact of construct interaction on learning outcomes. Finally, a two-dimensional pattern is developed as an important performance evaluation indicator, which can then be fed-back into the long-term disaster prevention strategy formulation process to ensure that the improvements in learning effectiveness are sustainable.
ABSTRACT A smart meter is an energy metering device with advanced features that allow consumers t... more ABSTRACT A smart meter is an energy metering device with advanced features that allow consumers to track their energy consumption. Smart meters are widely considered a solution for achieving energy efficiency and sustainable development. In addition to understanding consumer perceptions, expectations and intentions, a clear understanding of the influence of how national values and norms affect smart meter adoption behavior is needed by policy makers and investors in smart grid deployment. The aim of this study was to examine similarities and differences in consumer adoption of smart meters across Taiwan, Korean, Indonesia, and Vietnam. Data obtained from surveys in the four countries were analyzed by structural equation modeling to determine the interacting factors in consumer acceptance of smart meters. Consumer perceptions, expectations, and intentions regarding the potential use of smart meters across the four countries were analyzed and compared. The findings of this study improve understanding of regional differences in consumer adoption of smart grid systems. The findings can also help investors and policy makers involved in smart grid investment decision making. Finally, suggestions are given for maximizing the success of smart grid development in the researched countries.
Governmental Debt Guarantees (GDGs) are often used to encourage involvement by promoters and fina... more Governmental Debt Guarantees (GDGs) are often used to encourage involvement by promoters and financial institutions in Public-Private Partnerships (PPP) projects. However, even after demonstrating the bankability of a project and reducing debt cost, the success of the project may be prevented by the lack of long-term commitment from shareholders. Equity contributions by promoters in the project company may be recovered from earnings on short-term construction activities. Based on lesson learned from early PPP projects with GDG, the hold-up problem for government in the view of transaction cost economic (TCE) theory may worsen if the designed contractual structure does not adequately manage opportunistic behaviours from promoters. This study empirically examined the effects of a structured GDG mechanism with particular complementary measures applied in joint projects to develop the Taipei Mass Rapid Transit (MRT) stations. A GDG game model was then applied to bridge the theoretical g...
ABSTRACT The shear strength of reinforced concrete (RC) deep beams is a dynamic phenomenon that v... more ABSTRACT The shear strength of reinforced concrete (RC) deep beams is a dynamic phenomenon that varies with many mechanical and geometrical factors. Accurately estimating shear strength in RC deep beams is a vital issue in engineering design and management. However, prediction accuracy is still poor. This study presents a nature-inspired metaheuristic least squares support vector regression (LS-SVR) method that combines a novel smart artificial firefly colony algorithm (SFA) and LS-SVR for accurately predicting shear strength in RC deep beams. The SFA integrates firefly algorithm (FA), chaotic map (CM), adaptive inertia weight (AIW), and Lévy flight (LF). Firstly, adaptive approach and randomization methods (i.e., CM, AIW, and LF) were incorporated in FA to construct an effective meta-heuristic algorithm for global optimization. The SFA was then used to optimize the tuning parameters of the LS-SVR model. The proposed model was constructed by using a dataset for 214 RC deep beams, which was derived from the literature. The model performance was evaluated by comparing its results with those of a baseline SVR model and with previous methods via cross-validation algorithm. Analytical results show that the novel optimized prediction model was superior to others in predicting shear strength of RC deep beams. The resulting model can facilitate civil engineers in designing RC deep beam structures.
2010 2nd International Conference on Computer Engineering and Applications, ICCEA 2010, 2010
This study assesses the predictability of neural networks to estimate the cost of thin-film trans... more This study assesses the predictability of neural networks to estimate the cost of thin-film transistor liquid-crystal display (TFT-LCD) equipment. Newly completed equipment-development projects are provided by departments in a Taiwanese high-tech company. Cross-fold validation method is applied to measure model performance and reliability. Analytical results show the generalized regression neural net outperforms multi-layer feed-forward net when used for cost estimation
INEC 2010 - 2010 3rd International Nanoelectronics Conference, Proceedings, 2010
... made with different SAMs as: 'device a' for Si-MU SAM, 'device b&a... more ... made with different SAMs as: 'device a' for Si-MU SAM, 'device b' for Si-MP SAM, 'device c' for Si-PP SAM and Ag-NPs ... mA cm-2). In summary, we have presented a systematic study on the effect of monolayer properties and localized surface plasmon of silver nanoparticles on ...
IEEE International Conference on Industrial Engineering and Engineering Management, 2011
Bankruptcy prediction has been approached by data mining techniques. However, since data pre-proc... more Bankruptcy prediction has been approached by data mining techniques. However, since data pre-processing including feature selection or dimensionality reduction and data reduction is a very important stage for successful data mining, very few consider performing both tasks to examine the impact of data pre-processing on prediction performance. This paper applies genetic algorithms, which have been widely used for the data
ABSTRACT Issues such as energy security, sustainable development, and environmental protection ha... more ABSTRACT Issues such as energy security, sustainable development, and environmental protection have been a major topic of international discussions in recent years. Developed countries worldwide are investing substantial sums to develop renewable energy systems. In addition to this trend, wind power generation has revealed potential as a major energy source in Taiwan. However, an accident occurred just as the government and private enterprises began heavily promoting the construction of wind power generators. On September 28, 2008, five large wind turbines (WTs) located in the Changhua Coastal Industrial Park in Taichung sustained blade damage from fierce winds and heavy rainfall brought by Typhoon Jangmi. To examine the causes of this damage, specifically, delamination and cracking in the WT blades, this study first reviewed and analyzed data in related engineering documents. Similar overseas cases were also reviewed to identify the common causes of turbine blade failure incidents. The structural mechanics of WT blades were then analyzed with behavioral models to identify the mechanisms of the damage. Hopefully, the analytical results of this study can help prevent similar engineering incidents in the future and provide a reference for stakeholders devising strategies for improving risk management and disaster prevention in wind power plants.
International Journal of Disaster Risk Reduction, 2015
ABSTRACT As global climate change exacerbates the potential damage of natural disasters, the need... more ABSTRACT As global climate change exacerbates the potential damage of natural disasters, the need for sustained investment in comprehensive disaster prevention training increases. Taiwan is an island located in a seismically-active area and is regularly subject to natural disasters such as floods, landslides, and earthquakes. Therefore, disaster prevention education must be expanded. This study investigates current practices and suggests future disaster prevention training directions in Taipei, first by conducting a review of practical implementation experience and the literature on learning theory. A questionnaire survey was performed to solicit input from community leaders who had completed the training program. Structural equation modeling is used to determine the learning satisfaction index and the impact of construct interaction on learning outcomes. Finally, a two-dimensional pattern is developed as an important performance evaluation indicator, which can then be fed-back into the long-term disaster prevention strategy formulation process to ensure that the improvements in learning effectiveness are sustainable.
ABSTRACT A smart meter is an energy metering device with advanced features that allow consumers t... more ABSTRACT A smart meter is an energy metering device with advanced features that allow consumers to track their energy consumption. Smart meters are widely considered a solution for achieving energy efficiency and sustainable development. In addition to understanding consumer perceptions, expectations and intentions, a clear understanding of the influence of how national values and norms affect smart meter adoption behavior is needed by policy makers and investors in smart grid deployment. The aim of this study was to examine similarities and differences in consumer adoption of smart meters across Taiwan, Korean, Indonesia, and Vietnam. Data obtained from surveys in the four countries were analyzed by structural equation modeling to determine the interacting factors in consumer acceptance of smart meters. Consumer perceptions, expectations, and intentions regarding the potential use of smart meters across the four countries were analyzed and compared. The findings of this study improve understanding of regional differences in consumer adoption of smart grid systems. The findings can also help investors and policy makers involved in smart grid investment decision making. Finally, suggestions are given for maximizing the success of smart grid development in the researched countries.
Governmental Debt Guarantees (GDGs) are often used to encourage involvement by promoters and fina... more Governmental Debt Guarantees (GDGs) are often used to encourage involvement by promoters and financial institutions in Public-Private Partnerships (PPP) projects. However, even after demonstrating the bankability of a project and reducing debt cost, the success of the project may be prevented by the lack of long-term commitment from shareholders. Equity contributions by promoters in the project company may be recovered from earnings on short-term construction activities. Based on lesson learned from early PPP projects with GDG, the hold-up problem for government in the view of transaction cost economic (TCE) theory may worsen if the designed contractual structure does not adequately manage opportunistic behaviours from promoters. This study empirically examined the effects of a structured GDG mechanism with particular complementary measures applied in joint projects to develop the Taipei Mass Rapid Transit (MRT) stations. A GDG game model was then applied to bridge the theoretical g...
ABSTRACT The shear strength of reinforced concrete (RC) deep beams is a dynamic phenomenon that v... more ABSTRACT The shear strength of reinforced concrete (RC) deep beams is a dynamic phenomenon that varies with many mechanical and geometrical factors. Accurately estimating shear strength in RC deep beams is a vital issue in engineering design and management. However, prediction accuracy is still poor. This study presents a nature-inspired metaheuristic least squares support vector regression (LS-SVR) method that combines a novel smart artificial firefly colony algorithm (SFA) and LS-SVR for accurately predicting shear strength in RC deep beams. The SFA integrates firefly algorithm (FA), chaotic map (CM), adaptive inertia weight (AIW), and Lévy flight (LF). Firstly, adaptive approach and randomization methods (i.e., CM, AIW, and LF) were incorporated in FA to construct an effective meta-heuristic algorithm for global optimization. The SFA was then used to optimize the tuning parameters of the LS-SVR model. The proposed model was constructed by using a dataset for 214 RC deep beams, which was derived from the literature. The model performance was evaluated by comparing its results with those of a baseline SVR model and with previous methods via cross-validation algorithm. Analytical results show that the novel optimized prediction model was superior to others in predicting shear strength of RC deep beams. The resulting model can facilitate civil engineers in designing RC deep beam structures.
2010 2nd International Conference on Computer Engineering and Applications, ICCEA 2010, 2010
This study assesses the predictability of neural networks to estimate the cost of thin-film trans... more This study assesses the predictability of neural networks to estimate the cost of thin-film transistor liquid-crystal display (TFT-LCD) equipment. Newly completed equipment-development projects are provided by departments in a Taiwanese high-tech company. Cross-fold validation method is applied to measure model performance and reliability. Analytical results show the generalized regression neural net outperforms multi-layer feed-forward net when used for cost estimation
INEC 2010 - 2010 3rd International Nanoelectronics Conference, Proceedings, 2010
... made with different SAMs as: 'device a' for Si-MU SAM, 'device b&a... more ... made with different SAMs as: 'device a' for Si-MU SAM, 'device b' for Si-MP SAM, 'device c' for Si-PP SAM and Ag-NPs ... mA cm-2). In summary, we have presented a systematic study on the effect of monolayer properties and localized surface plasmon of silver nanoparticles on ...
IEEE International Conference on Industrial Engineering and Engineering Management, 2011
Bankruptcy prediction has been approached by data mining techniques. However, since data pre-proc... more Bankruptcy prediction has been approached by data mining techniques. However, since data pre-processing including feature selection or dimensionality reduction and data reduction is a very important stage for successful data mining, very few consider performing both tasks to examine the impact of data pre-processing on prediction performance. This paper applies genetic algorithms, which have been widely used for the data
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Papers by Jui-Sheng Chou