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Understanding regional building energy patterns is the prerequisite to efficiently and effectively promote sustainable urban development. Previous studies have proposed various data-driven methods to investigate the relationship between building energy consumption and hundreds of potential influencing features. However, it is difficult to include all potential features in one single model since either some data could be unavailable or the model would be too complex. To identify the critical features, this study develops a data-driven random forest (RF) based framework with a dataset of Taipei City, consisting of 24,764 buildings in 881 city-blocks, to model the relationship between city-block-level building-oriented features and building energy consumption. The RF model is found to outperform other machine learning models including logistic regression, k-nearest neighborhood, support vector machine, and decision tree models in the predictive accuracy of the classification problem. Seven critical features related to the built year of buildings, building gross floor area, building density, and the ratio of commercial buildings in the block are identified from the 59 city-block-level building-oriented features. The developed framework could refine the features adopted in regional building energy models, and policymakers and city planners would get practical implications from the identified critical features.
Zhongnan Ye; Kuangly Cheng; Shu-Chien Hsu; Hsi-Hsien Wei; Clara Man Cheung. Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach. Applied Energy 2021, 301, 117453 .
AMA StyleZhongnan Ye, Kuangly Cheng, Shu-Chien Hsu, Hsi-Hsien Wei, Clara Man Cheung. Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach. Applied Energy. 2021; 301 ():117453.
Chicago/Turabian StyleZhongnan Ye; Kuangly Cheng; Shu-Chien Hsu; Hsi-Hsien Wei; Clara Man Cheung. 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach." Applied Energy 301, no. : 117453.
This paper is aimed at the usage of an augmented reality assisted system set up on the smart-glasses for training activities. Literature review leads us to a comparison among related technologies, yielding that Mask Regions with Convolutional Neural Network (R-CNN) oriented approach fits the study needs. The proposed method including (1) pointing gesture capture, (2) finger-pointing analysis, and (3) virtual tool positioning and rotation angle are developed. Results show that the recognition of object detection is 95.5%, the Kappa value of recognition of gesture detection is 0.93, and the average time for detecting pointing gesture is 0.26 seconds. Furthermore, even under different lighting, such as indoor and outdoor, the pointing analysis accuracy is up to 79%. The error between the analysis angle and the actual angle is only 1.32 degrees. The results proved that the system is well suited to present the effect of augmented reality, making it applicable for real world usage.
Mu-Chun Su; Jieh-Haur Chen; Vidya Trisandini Azzizi; Hsiang-Ling Chang; Hsi-Hsien Wei. Smart training: Mask R-CNN oriented approach. Expert Systems with Applications 2021, 185, 115595 .
AMA StyleMu-Chun Su, Jieh-Haur Chen, Vidya Trisandini Azzizi, Hsiang-Ling Chang, Hsi-Hsien Wei. Smart training: Mask R-CNN oriented approach. Expert Systems with Applications. 2021; 185 ():115595.
Chicago/Turabian StyleMu-Chun Su; Jieh-Haur Chen; Vidya Trisandini Azzizi; Hsiang-Ling Chang; Hsi-Hsien Wei. 2021. "Smart training: Mask R-CNN oriented approach." Expert Systems with Applications 185, no. : 115595.
The objective of this research was to explore the impact factors of sustainable corporate governance for top consulting engineering companies in Taiwan, to facilitate managers in meeting stakeholders’ needs and adapting to the challenges of the global markets. Nine hypotheses derived from a literature review were proposed and used to develop a survey. Based on the concept of structural equation modeling (SEM) and these hypotheses, a questionnaire containing six aspects and comprising 46 stems was developed using the Likert 5-scale format. The survey took around four months to administer with 324 effective returns, with only five hypotheses confirmed. This was followed by factor analysis to determine the weight sequence for the 28 impact factors and four aspects. The contributions of the findings are as follows: (1) the weighted factors provide practitioners with guidelines for the proper order for the implementation of measures to improve corporate governance, and (2) they answer questions about the degree of influence and the relationship among all aspects and factors for sustainable corporate governance.
Jieh-Haur Chen; Tien-Sheng Chou; Jui-Pin Wang; Hsi-Hsien Wei; Tzu-Han Yang. Sustainable Corporate Governance: The Impact Factors for Top Consulting Engineering Companies in Taiwan. Sustainability 2021, 13, 7604 .
AMA StyleJieh-Haur Chen, Tien-Sheng Chou, Jui-Pin Wang, Hsi-Hsien Wei, Tzu-Han Yang. Sustainable Corporate Governance: The Impact Factors for Top Consulting Engineering Companies in Taiwan. Sustainability. 2021; 13 (14):7604.
Chicago/Turabian StyleJieh-Haur Chen; Tien-Sheng Chou; Jui-Pin Wang; Hsi-Hsien Wei; Tzu-Han Yang. 2021. "Sustainable Corporate Governance: The Impact Factors for Top Consulting Engineering Companies in Taiwan." Sustainability 13, no. 14: 7604.
The outbreak of the COVID-19 pandemic poses great challenges to the current government subsidy models in the renewable energy sector for recovering in the post-pandemic economy. Although, many subsidy models have been applied to accelerate renewable energy investment decisions. However, it is important to develop a new model to ensure the sustainability of the renewable energy supply network under disruptions on both the supply and demand sides due to hazardous events. This study investigates different subsidy models (renewable credit, supplier subsidy, and retailer subsidy) to find a win-win subsidy model for sustainable energy supply under disruption risks. The objective is to determine the optimal capacity of renewable energy added to the grid, the optimal wholesale price of the power plant, and the optimal retail price of the aggregator under different subsidy models to maximize the economic, social, and environmental benefits of the whole network. A novel scenario-based robust fuzzy optimization approach is proposed to capture the uncertainties of business-as-usual operations (e.g., some relevant costs and demand) and hazardous events (e.g., COVID-19 pandemic). The proposed model is tested in a case study of the Vietnamese energy market. The results show that for a high negative impact level of hazardous events on the supply side, the renewable credit and supplier subsidy models should be considered to recovery the renewable energy market. Further, the proposed approach has a better performance in improving the power plant's robust profit for most of the hazard scenarios than the robust optimization model.
Yu-Chung Tsao; Vo-Van Thanh; Yi-Ying Chang; Hsi-Hsien Wei. COVID-19: Government subsidy models for sustainable energy supply with disruption risks. Renewable and Sustainable Energy Reviews 2021, 150, 111425 .
AMA StyleYu-Chung Tsao, Vo-Van Thanh, Yi-Ying Chang, Hsi-Hsien Wei. COVID-19: Government subsidy models for sustainable energy supply with disruption risks. Renewable and Sustainable Energy Reviews. 2021; 150 ():111425.
Chicago/Turabian StyleYu-Chung Tsao; Vo-Van Thanh; Yi-Ying Chang; Hsi-Hsien Wei. 2021. "COVID-19: Government subsidy models for sustainable energy supply with disruption risks." Renewable and Sustainable Energy Reviews 150, no. : 111425.
Several frameworks are introduced to address occupancy-based building performance. However, the performance predictions obtained using these frameworks deviate from real performance. So, this study’s aim is to represent a new framework for the automatic assessment of occupants’ comfort and building indoor performance using BIM and the SD-ABM platform. Initially, an office space in Hong Kong, consisting of 10 occupants, was considered for the BIM model construction. The occupancy, indoor data and required equations are defined using the SD-ABM model. Essential data from the BIM model can be transferred using the Dynamo-Excel platform. Furthermore, a validation study was conducted using a paper-based survey from the occupants and sensor data for environmental data monitoring while error metrics were also calculated. The framework actively predicts occupant presence, comfort level, temperatures, and CO2 concentration in the office space. However, a comprehensive usability and feasibility study is required to assess the efficiency of the framework.
M.N. Uddin; Q. Wang; Hsi Hsien Wei; Hung Lin Chi; Meng Ni. Building information modeling (BIM), System dynamics (SD), and Agent-based modeling (ABM): Towards an integrated approach. Ain Shams Engineering Journal 2021, 1 .
AMA StyleM.N. Uddin, Q. Wang, Hsi Hsien Wei, Hung Lin Chi, Meng Ni. Building information modeling (BIM), System dynamics (SD), and Agent-based modeling (ABM): Towards an integrated approach. Ain Shams Engineering Journal. 2021; ():1.
Chicago/Turabian StyleM.N. Uddin; Q. Wang; Hsi Hsien Wei; Hung Lin Chi; Meng Ni. 2021. "Building information modeling (BIM), System dynamics (SD), and Agent-based modeling (ABM): Towards an integrated approach." Ain Shams Engineering Journal , no. : 1.
The study objective is to establish the learning curve model for precast component productivity in construction, verified using cross-validation empirical data for over 90% of these facilities’ precast component production activities over the past 5 years, with a total of 373,077 datasets across 14 production activities, sorted among a total of 4352 workers. By applying the learning curve theory to the analysis, the results show that relative to the straight-line model, the learning curve was established using exponential models. The exponential model can effectively mitigate the unreasonable fluctuations present in the cubic model’s representations of learning curves during initial training periods. This study therefore suggests the adoption of the Exponential model to model the learning curves for production workers learning to make precast components. The model has a satisfactory degree of fit (R2 > 0.88), and the post-cross-validation results also show that the model has a highly accurate prediction capability (MAPE value < 10%). The finding can serve as an important reference for the creation of production personnel allocation plans, personnel reserve plans, and training plans at precast factories in the construction industry.
Hsing-Wei Tai; Jieh-Haur Chen; Jiun-Yao Cheng; Shu-Chien Hsu; Hsi-Hsien Wei. Learn Curve for Precast Component Productivity in Construction. International Journal of Civil Engineering 2021, 19, 1179 -1194.
AMA StyleHsing-Wei Tai, Jieh-Haur Chen, Jiun-Yao Cheng, Shu-Chien Hsu, Hsi-Hsien Wei. Learn Curve for Precast Component Productivity in Construction. International Journal of Civil Engineering. 2021; 19 (10):1179-1194.
Chicago/Turabian StyleHsing-Wei Tai; Jieh-Haur Chen; Jiun-Yao Cheng; Shu-Chien Hsu; Hsi-Hsien Wei. 2021. "Learn Curve for Precast Component Productivity in Construction." International Journal of Civil Engineering 19, no. 10: 1179-1194.
Risk and resilience assessments have been both widely, but separately, used as tools for guiding policymakers to formulate disaster-risk reduction policies. On the one hand, risk assessment is utilized to estimate the risk associated with disasters in terms of operational metrics such as monetary or casualties’ loss; on the other hand, most resilience analysis assesses and represent community resilience as an index, without a specific unit metric, to gauge levels of disparity in community’s post-disaster recovery capability among the areas of interest. Although disaster-risk reduction policies should be best informed by both risk and resilience assessments, an informative integrated assessment approach accounting for both seems to be lacked in current research, insofar as the difficulty in properly integrating their distinct measurement metrics. This paper commences with a literature review of risk assessment and community resilience. It then proposes an integrated framework that can comprehensively assess both seismic risk and resilience, by taking into account the casualties and economic losses associated with earthquakes resulted from a risk assessment, and the infrastructure-system resilience and community socioeconomic–demographic resilience resulted from a resilience assessment. More specifically, an integrated tool, risk-based resilience-concentration curve, is proposed for assessing the inequality of given types of risk in the community’s infrastructure-system resilience, and socioeconomic–demographic resilience, respectively. A case study is presented using the data from a city in Israel: the first phase of the case study focused on the concentration of casualties’ risk in community’s infrastructure-system resilience, and the second on the concentration of economic risk in community’s socioeconomic–demographic resilience. The results show that unevenly distributed risk and community resilience can cause inequality of risk in resilience capacity in certain administrative tracts of the city. Based on these findings, the paper recommends a range of risk-reduction strategies for different administrative tracts based on their risk-based resilience concentration curves.
Tingting Ji; Hsi-Hsien Wei; Igal M. Shohet; Feng Xiong. Risk-based resilience concentration assessment of community to seismic hazards. Natural Hazards 2021, 108, 1731 -1751.
AMA StyleTingting Ji, Hsi-Hsien Wei, Igal M. Shohet, Feng Xiong. Risk-based resilience concentration assessment of community to seismic hazards. Natural Hazards. 2021; 108 (2):1731-1751.
Chicago/Turabian StyleTingting Ji; Hsi-Hsien Wei; Igal M. Shohet; Feng Xiong. 2021. "Risk-based resilience concentration assessment of community to seismic hazards." Natural Hazards 108, no. 2: 1731-1751.
Data-driven housing-market segmentation has been given increasing prominence for its objectiveness in identifying submarkets based on the housing data’s underlying structures. However, when handling high-dimensionality housing dataset, traditional statistical-clustering methods have been found to tend to lose low-variance information of the dataset and be deficient in deriving the globally optimal number of submarkets. Accordingly, with the intention of achieving more rigorous high-dimensionality housing market segmentation, a swarm-inspired projection (SIP) algorithm is introduced by this study. Using a high-dimensionality Taipei city’s housing dataset in a case study, a comparison of the proposed SIP algorithm and a statistical-clustering method using the combination of principal component analysis (PCA) and K-means clustering is conducted in evaluating the predictive accuracy of hedonic price models of the housing submarkets. The results show that, as compared to the original single market, the segmented submarkets resulting from SIP algorithm are more homogenous and distinctive, where the resulted hedonic price models have high-level statistical explanation and disparate sets of hedonic prices for different submarkets. In addition, as compared to the use of a statistical-clustering method, SIP algorithm is found to obtain a more optimal number of submarkets, where the resulted hedonic price models are found to achieve greater improvement of statistical explanation and more stable reduction of prediction error. These findings highlight the advantages of our proposed SIP algorithm in high-dimensionality housing market segmentation, and thus it is hoped that the present research will serve as a practical tool to better inform further studies aimed at market-segmentation-related problems.
Jieh-Haur Chen; Tingting Ji; Mu-Chun Su; Hsi-Hsien Wei; Vidya Trisandini Azzizi; Shu-Chien Hsu. Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city. Journal of Housing and the Built Environment 2021, 1 -25.
AMA StyleJieh-Haur Chen, Tingting Ji, Mu-Chun Su, Hsi-Hsien Wei, Vidya Trisandini Azzizi, Shu-Chien Hsu. Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city. Journal of Housing and the Built Environment. 2021; ():1-25.
Chicago/Turabian StyleJieh-Haur Chen; Tingting Ji; Mu-Chun Su; Hsi-Hsien Wei; Vidya Trisandini Azzizi; Shu-Chien Hsu. 2021. "Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city." Journal of Housing and the Built Environment , no. : 1-25.
Postdisaster emergency restoration of damaged highway–bridge networks are crucial to those providing timely emergency assistance to disaster-damaged areas. Ideally, inspection routing and restoration scheduling should complement each other, such that multiple inspection and restoration crews can operate simultaneously and optimally in the immediate aftermath of disaster events. Therefore, it is necessary to understand the interaction between inspection and restoration in postdisaster emergency restoration processes of highway–bridge networks, as well as their impacts on the inspection routing, restoration scheduling, and overall process. This paper proposes an integer program for modeling such inspection-routing and restoration-scheduling problems, accounting for the inspection-restoration interactions, for determining the optimal inspection routes and restoration schedules for damaged highway–bridge networks, with the specific aim of maximizing the networks’ travel time as their resilience metric. A hybrid genetic algorithm coupled with an early termination test is also developed to improve the proposed integer program’s computational efficiency. The results of a case study using the proposed method and data from China’s 2008 Wenchuan earthquake show that, as compared to a traditional sequential inspection-restoration model, simultaneously and optimally performing inspection and restoration can significantly improve highway–bridge-network resilience.
Zhenyu Zhang; Hsi-Hsien Wei. Modeling Interaction of Emergency Inspection Routing and Restoration Scheduling for Postdisaster Resilience of Highway–Bridge Networks. Journal of Infrastructure Systems 2021, 27, 04020046 .
AMA StyleZhenyu Zhang, Hsi-Hsien Wei. Modeling Interaction of Emergency Inspection Routing and Restoration Scheduling for Postdisaster Resilience of Highway–Bridge Networks. Journal of Infrastructure Systems. 2021; 27 (1):04020046.
Chicago/Turabian StyleZhenyu Zhang; Hsi-Hsien Wei. 2021. "Modeling Interaction of Emergency Inspection Routing and Restoration Scheduling for Postdisaster Resilience of Highway–Bridge Networks." Journal of Infrastructure Systems 27, no. 1: 04020046.
Energy consumption in buildings depends on several physical factors, including its physical characteristics, various building services systems/appliances used, and the outdoor environment. However, the occupants’ behavior that determines and regulates the building energy conservation also plays a critical role in the buildings’ energy performance. Compared to physical factors, there are relatively fewer studies on occupants’ behavior. This paper reports a systematic review analysis on occupant behavior and different modeling approaches using the Scopus and Science Direct databases. The comprehensive review study focuses on the current understanding of occupant behavior, existing behavior modeling approaches and their limitations, and key influential parameters on building energy conservation. Finally, the study identifies six significant research gaps for future development: occupant-centered space layout deployment; occupant behavior must be understood in the context of developing or low-income economies; there are higher numbers of quantitative occupant behavior studies than qualitative; the extensive use of survey or secondary data and the lack of real data used in model validation; behavior studies are required for diverse categories building; building information modeling (BIM) integration with existing occupant behavior modeling/simulation. These checklists of the gaps are beneficial for researchers to accomplish the future research in the built environment.
Mohammad Nyme Uddin; Hsi-Hsien Wei; Hung Lin Chi; Meng Ni. Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach. Buildings 2021, 11, 41 .
AMA StyleMohammad Nyme Uddin, Hsi-Hsien Wei, Hung Lin Chi, Meng Ni. Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach. Buildings. 2021; 11 (2):41.
Chicago/Turabian StyleMohammad Nyme Uddin; Hsi-Hsien Wei; Hung Lin Chi; Meng Ni. 2021. "Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach." Buildings 11, no. 2: 41.
A disaster resilience index aggregates numerous observed individual indicators into a numeric value, for the purpose of gauging various communities’ disparate disaster resilience capacities as part of decision-making in resilience management. There have been abundant studies on the creation of such indices, but only a few have sought to empirically validate individual indicators’ practical efficacy in explaining disaster-related outcomes. Therefore, this study performs such disaggregated empirical validation of nine disaster-resilience indicators’ efficacy at explaining two outcome measures: the resistant capacity and recovery capacity of households in Hong Kong. It reveals that certain indicators including education, income, and place attachment can be empirically valid, but that their explanatory power varies substantially across the two outcome measures. For instance, place attachment has divergent relationships with households’ resistant and recovery capacities. The robustness of the indicators’ explanatory power is also unequal, due to the disparate effect sizes of the outcome measures and the indicators’ interdependence. Based on these findings, we provide recommendations on indicator selection and index creation that should be useful to those seeking to create parsimonious and robust sets of indicators that are explanatory of the actual resilience capacities of local communities.
Tingting Ji; Hsi-Hsien Wei; Timothy Sim; Liang Emlyn Yang; Jürgen Scheffran. Disaggregated validation of disaster-resilience indicators using household survey data: A case study of Hong Kong. Sustainable Cities and Society 2021, 67, 102726 .
AMA StyleTingting Ji, Hsi-Hsien Wei, Timothy Sim, Liang Emlyn Yang, Jürgen Scheffran. Disaggregated validation of disaster-resilience indicators using household survey data: A case study of Hong Kong. Sustainable Cities and Society. 2021; 67 ():102726.
Chicago/Turabian StyleTingting Ji; Hsi-Hsien Wei; Timothy Sim; Liang Emlyn Yang; Jürgen Scheffran. 2021. "Disaggregated validation of disaster-resilience indicators using household survey data: A case study of Hong Kong." Sustainable Cities and Society 67, no. : 102726.
City administrators hoping to achieve people-centric smart city (SC) development require a clear understanding of citizens’ preferences and perceptions about SC services. This study fills that need by presenting evidence-based research on such preferences and perceptions from the perspective of need theories, taking Taiwan as a case study. Specifically, we investigated Taiwanese citizens’ preferences for 35 SC services of seven dimensions classified in two domains, as well as their three perceptions of the usefulness of these SC services in the realization of human needs. The results show that most of our respondents clearly perceived SC services as both important and useful to their existence, relatedness, and growth needs, and that they expressed relatively higher preferences for such services to operate in the “hard” domain – e.g., smart energy, smart transport, or smart safety –than the “soft” one, e.g., smart living. Based on these findings, this study provides policy recommendations that, if implemented, could be expected to advance SC development by increasing citizens’ usage of SC services in both the hard and soft domains, and serve the wider aim of improving their well-being and quality of life.
Tingting Ji; Jieh-Haur Chen; Hsi-Hsien Wei; Yu-Ching Su. Towards people-centric smart city development: Investigating the citizens’ preferences and perceptions about smart-city services in Taiwan. Sustainable Cities and Society 2021, 67, 102691 .
AMA StyleTingting Ji, Jieh-Haur Chen, Hsi-Hsien Wei, Yu-Ching Su. Towards people-centric smart city development: Investigating the citizens’ preferences and perceptions about smart-city services in Taiwan. Sustainable Cities and Society. 2021; 67 ():102691.
Chicago/Turabian StyleTingting Ji; Jieh-Haur Chen; Hsi-Hsien Wei; Yu-Ching Su. 2021. "Towards people-centric smart city development: Investigating the citizens’ preferences and perceptions about smart-city services in Taiwan." Sustainable Cities and Society 67, no. : 102691.
Hand-gesture based control has enormous potential both theoretically and for practical applications due to its convenience and intuitiveness. This study presents a real-time interactive control system for household appliances. The interactive control system allowing wireless control of household appliances using a combination of 11 hand gestures and 2 waving motions is tested on hundreds of samples. It is implemented using a regular personal computer (PC) and existing digital single processing (DSP) platforms. The evaluation results show that the system performs efficiently reaching an accuracy recognition rate of 91% and spending around 30 seconds to complete the control operation for household appliances. The contributions of this work are both academic (1) successful demonstration of the integration of algorithms for solving image detection, processing, and pattern recognition, and practical (2) showing its feasibility and using commonly available hardware and software configurations for practical uses, and finally (3) establishing a mechanism for intuitively interactive control system that facilitates smart living.
Mu-Chun Su; Jieh-Haur Chen; Achmad Muhyidin Arifai; Sung-Yang Tsai; Hsi-Hsien Wei. Smart living: an interactive control system for household appliances. IEEE Access 2021, 9, 1 -1.
AMA StyleMu-Chun Su, Jieh-Haur Chen, Achmad Muhyidin Arifai, Sung-Yang Tsai, Hsi-Hsien Wei. Smart living: an interactive control system for household appliances. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleMu-Chun Su; Jieh-Haur Chen; Achmad Muhyidin Arifai; Sung-Yang Tsai; Hsi-Hsien Wei. 2021. "Smart living: an interactive control system for household appliances." IEEE Access 9, no. : 1-1.
Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.
Muchun Su; Diana Wahyu Hayati; Shaowu Tseng; Jiehhaur Chen; Hsihsien Wei. Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition. Applied Sciences 2020, 11, 10 .
AMA StyleMuchun Su, Diana Wahyu Hayati, Shaowu Tseng, Jiehhaur Chen, Hsihsien Wei. Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition. Applied Sciences. 2020; 11 (1):10.
Chicago/Turabian StyleMuchun Su; Diana Wahyu Hayati; Shaowu Tseng; Jiehhaur Chen; Hsihsien Wei. 2020. "Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition." Applied Sciences 11, no. 1: 10.
Windstorms have caused a range of damage on the built environment. Although several risk assessment models for estimating such damage have been widely developed, the results generated by these models often turn inaccurate due to the building information required for such models at a regional scale are usually incomplete, or of a poor quality. Alternatively, this study utilizes an insurance company’s loss data pertaining to the high winds of Typhoon Maemi in South Korea in 2003 for calculating building damage in terms of damage ratios. Next, these damage ratios and storm-wind speeds are utilized for constructing vulnerability curves that can be used to predict levels of damage to designated building types subject to given wind speeds. Lastly, geographical information systems spatial data is combined with those vulnerability curves to arrive at four distinct wind-damage levels. It is hoped that the present research will serve as a reference for further studies of developing building vulnerability curves for storm winds.
Sang-Guk Yum; Ji-Myong Kim; Hsi-Hsien Wei. Development of vulnerability curves of buildings to windstorms using insurance data: An empirical study in South Korea. Journal of Building Engineering 2020, 34, 101932 .
AMA StyleSang-Guk Yum, Ji-Myong Kim, Hsi-Hsien Wei. Development of vulnerability curves of buildings to windstorms using insurance data: An empirical study in South Korea. Journal of Building Engineering. 2020; 34 ():101932.
Chicago/Turabian StyleSang-Guk Yum; Ji-Myong Kim; Hsi-Hsien Wei. 2020. "Development of vulnerability curves of buildings to windstorms using insurance data: An empirical study in South Korea." Journal of Building Engineering 34, no. : 101932.
This paper presents a novel approach for estimating the vulnerability level of critical infrastructure confronting potential terrorist threats and assessing the usefulness of various protection strategies for critical infrastructure (CI). A methodology, utilizing a combination of topological network analysis and game theory, is presented to evaluate the effectiveness of protection strategies for certain components in the infrastructure under various attack scenarios. This paper focuses on protective strategies that are based on different attack scenarios as well as on the connectivity of the critical infrastructure components. The methodology proposed allows optimization of protection strategies in terms of investment in critical infrastructure protection in order to reduce expenditures on local infrastructure protection or on a single critical infrastructure for small projects. A case study of a power-supply substation is included to validate the analytical framework. The results indicate that the framework is highly applicable to other types of critical infrastructures facing similar threats. The results suggest that when only terrorist attacks are considered, improving the robustness of CI has a much higher effectiveness and efficiency than improving CI redundancy. The research methodology in this paper can be applied to a wide range of critical infrastructures and systems that may be at risk from manmade extreme events.
Xijun Yao; Hsi-Hsien Wei; Igal M. Shohet; Miroslaw J. Skibniewski. Assessment of Terrorism Risk to Critical Infrastructures: The Case of a Power-Supply Substation. Applied Sciences 2020, 10, 7162 .
AMA StyleXijun Yao, Hsi-Hsien Wei, Igal M. Shohet, Miroslaw J. Skibniewski. Assessment of Terrorism Risk to Critical Infrastructures: The Case of a Power-Supply Substation. Applied Sciences. 2020; 10 (20):7162.
Chicago/Turabian StyleXijun Yao; Hsi-Hsien Wei; Igal M. Shohet; Miroslaw J. Skibniewski. 2020. "Assessment of Terrorism Risk to Critical Infrastructures: The Case of a Power-Supply Substation." Applied Sciences 10, no. 20: 7162.
Behavior-driven energy conservation has been a promising strategy for reducing building energy consumption as well as carbon emissions. With the intention of revealing the impacts of an individual’s personality basis on energy conservation behavioral attitudes and intentions in households and offices, the present study proposes and conducts an experiment in Xi’an, China with two groups for the investigation of such attitudes towards household energy-saving behavior (HESB) and office energy-saving behavior (OESB), respectively. The research adopts structural equation modeling for experiment data analysis. The analysis results suggest that the two personality traits, Agreeableness and Neuroticism, are significantly related to both HESB and OESB attitudes. Especially, agreeable people tend to present stronger energy-saving attitudes, while individuals with higher Neuroticism are less likely to do so. The results indicate that the impacts of these two traits on energy-saving attitude are found to be less influenced by different environment settings. Further, the results find that Extraversion positively influences energy-saving attitude in the office environment, while Openness only significantly works in the household environment. It is hoped that the findings of the present study can provide informative references to energy-saving intervention design as well as further studies on the spillover of pro-environmental behaviors.
Qian-Cheng Wang; Yi-Xuan Wang; Izzy Yi Jian; Hsi-Hsien Wei; Xuan Liu; Yao-Tian Ma. Exploring the “Energy-Saving Personality Traits” in the Office and Household Situation: An Empirical Study. Energies 2020, 13, 3535 .
AMA StyleQian-Cheng Wang, Yi-Xuan Wang, Izzy Yi Jian, Hsi-Hsien Wei, Xuan Liu, Yao-Tian Ma. Exploring the “Energy-Saving Personality Traits” in the Office and Household Situation: An Empirical Study. Energies. 2020; 13 (14):3535.
Chicago/Turabian StyleQian-Cheng Wang; Yi-Xuan Wang; Izzy Yi Jian; Hsi-Hsien Wei; Xuan Liu; Yao-Tian Ma. 2020. "Exploring the “Energy-Saving Personality Traits” in the Office and Household Situation: An Empirical Study." Energies 13, no. 14: 3535.
Changing energy consumption behavior is a promising strategy to enhance household energy efficiency and to reduce carbon emission. Understanding the role of psychological and demographic factors in the context of energy-conservation behaviors is critical to promote energy-saving behaviors in buildings. This study first proposes a theoretical framework built on the Theory of Planned Behavior (TPB). Based on the collected survey data from 207 families (553 residents) in three communities in Xi’an, a typical city in northwest China, the research examines how three standard TPB predictors, namely attitude, subjective norm, and perceived behavioral control, as well as their interactive effects and three socio-demographic factors (i.e., house ownership, education and household income) influence building occupants’ energy-saving intention at home. Through structural equation modeling and keyword analysis, this study reveals that two interaction terms, namely attitude and subjective norms, as well as attitude and perceived behavior control, significantly influence building occupants’ energy-saving intention. Furthermore, this study implies that household income may positively associate with occupants’ energy-saving intention. The model in this study would be conducive to architects and property managers to mitigate severe building energy overuse problem in design and operation stages. Based on a qualitative analysis, the study then discusses the limitations of the study and further research direction. The results of this study would be conducive to building designers and operators to develop customized architectural or informatic interventions and to mitigate the severe energy overuse problem in the residential sector in northwest China.
Xuan Liu; Qiancheng Wang; Hsi-Hsien Wei; Hung-Lin Chi; Yaotian Ma; Izzy Yi Jian. Psychological and Demographic Factors Affecting Household Energy-Saving Intentions: A TPB-Based Study in Northwest China. Sustainability 2020, 12, 836 .
AMA StyleXuan Liu, Qiancheng Wang, Hsi-Hsien Wei, Hung-Lin Chi, Yaotian Ma, Izzy Yi Jian. Psychological and Demographic Factors Affecting Household Energy-Saving Intentions: A TPB-Based Study in Northwest China. Sustainability. 2020; 12 (3):836.
Chicago/Turabian StyleXuan Liu; Qiancheng Wang; Hsi-Hsien Wei; Hung-Lin Chi; Yaotian Ma; Izzy Yi Jian. 2020. "Psychological and Demographic Factors Affecting Household Energy-Saving Intentions: A TPB-Based Study in Northwest China." Sustainability 12, no. 3: 836.
The objective of the research is aimed for a solution that is to establish the dynamic impact function of surrounding multi-attribute for house pricing. It is also able to measure the ripple effect and allows the hedonic parameter estimates to vary from point-to-point. A comprehensive literature review is carried out to obtain an adequate theoretical basis for the corresponding hypothesis and concepts. The proposed dynamic impact function for multi- attributes is then constructed based on the characteristics of surrounding facilities. Adopting the convenience sampling criteria of 95% confidence level on the data sampling and 10% limit of error in a 5−95% proportion, we collect the empirical data of 39 yearly house sales in the investigated urban areas of Taipei city focusing on housing prices and then utilize them for evaluating and adjusting the function. The actual house price and that of proposed function affected by Mass Rapid Transit (MRT) stations are analysed, resulting in the correlation coefficient at 0.946 (single attribute) and 0.944 (multi-attribute), respectively. The findings support that proposed function can highly represent the house pricing pattern and be an accurate tool for appraisers.
Jieh-Haur Chen; Li-Ren Yang; Vidya Trisandin Azzizi; Eric Chu; Hsi-Hsien Wei. ESTABLISHING DYNAMIC IMPACT FUNCTION FOR HOUSE PRICING BASED ON SURRENDING MULTI-ATTRIBUTES: EVIDENCE FROM TAIPEI CITY, TAIWAN. International Journal of Strategic Property Management 2020, 24, 119 -129.
AMA StyleJieh-Haur Chen, Li-Ren Yang, Vidya Trisandin Azzizi, Eric Chu, Hsi-Hsien Wei. ESTABLISHING DYNAMIC IMPACT FUNCTION FOR HOUSE PRICING BASED ON SURRENDING MULTI-ATTRIBUTES: EVIDENCE FROM TAIPEI CITY, TAIWAN. International Journal of Strategic Property Management. 2020; 24 (2):119-129.
Chicago/Turabian StyleJieh-Haur Chen; Li-Ren Yang; Vidya Trisandin Azzizi; Eric Chu; Hsi-Hsien Wei. 2020. "ESTABLISHING DYNAMIC IMPACT FUNCTION FOR HOUSE PRICING BASED ON SURRENDING MULTI-ATTRIBUTES: EVIDENCE FROM TAIPEI CITY, TAIWAN." International Journal of Strategic Property Management 24, no. 2: 119-129.
A given region's volume of air passengers and cargo is frequently taken to represent its economic development. This research proposes a practical methodology for investigating the inherent patterns of the relationships between air-traffic volume and macroeconomic development, utilizing data-mining techniques, including K-means clustering and Decision Tree C5.0 classification. Using the case of Taiwan from 2001 to 2014, 32 potential macroeconomic factors ascertained from a literature review were combined with air-traffic volume data to establish a 168-month dataset. After this dataset was grouped into five clusters, decision trees were implemented to determine its critical macroeconomic characteristics. The resulting four critical factors and their thresholds were the Information and Electronics Industrial Production Index (IE Index), at 83.22; National Income Per Capita, at US$3,222; Employed Population, at 10.134 million; and the Japanese Nikkei 225 Stock Average, at 10564.44. Among these, the IE Index was found to be the first critical factor relating to air-traffic volume as well as the only characteristic to distinguish Cluster V – 58 consecutive months from March 2010 to December 2014 inclusive – among others, and the reasonableness of this finding was confirmed via examination of detailed air-traffic statistics. Besides, the effectiveness of the four identified critical factors as predictive variables were validated by comparing forecasted results with actual air traffic volume from 2015 to 2016. Understanding these four critical factors and their relative importance is of great value to policymakers seeking to allocate limited resources optimally and objectively. Therefore, as an effective and efficient means of capturing significant and explainable macroeconomic factors influencing air-traffic volume, the proposed methodology can be applied to strategy formulation, operations management, and investment planning by governments, airports, airlines, and related entities.
Jieh-Haur Chen; Hsi-Hsien Wei; Chih-Lin Chen; Hsin-Yi Wei; Yi-Ping Chen; Zhongnan Ye. A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification. Journal of Air Transport Management 2019, 82, 101743 .
AMA StyleJieh-Haur Chen, Hsi-Hsien Wei, Chih-Lin Chen, Hsin-Yi Wei, Yi-Ping Chen, Zhongnan Ye. A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification. Journal of Air Transport Management. 2019; 82 ():101743.
Chicago/Turabian StyleJieh-Haur Chen; Hsi-Hsien Wei; Chih-Lin Chen; Hsin-Yi Wei; Yi-Ping Chen; Zhongnan Ye. 2019. "A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification." Journal of Air Transport Management 82, no. : 101743.