Browsing by Author "Wu, Che-I"

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An intelligent slope disaster prediction and monitoring system based on WSN and ANP
(2014-08-01)
Wu, Che-I
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Kung, Hsu-Yang
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Chen, Chi-Hua
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Kuo, Li-Chia
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資訊管理與財務金融系註：原資管所+財金所
Taiwan generally has large-scale landslides and torrential rainfall during the typhoon season. As Wireless Sensor Networks (WSN) and mobile communication technologies advance rapidly, state-of-the-art technologies are adopted to build a model to reliably predict and monitor disasters, as well as accumulate environmental variation-related information. By integrating WSN and Analytic Network Process (ANP), this study evaluates the weight of disaster factors that adopt the consistency index of pair comparisons on hillslopes. The weight estimation and classification of disaster factors are based on the K-means model to build the hillslope prediction model. The Portrait-based Disaster Alerting System (PDAS) is designed and implemented using the proposed disaster prediction model. The PDAS adopts Web-GIS to visualize the environmental information. Evaluation results of the system indicate that the proposed prediction model achieves more accurate disaster determination than the conventional method. (C) 2014 Elsevier Ltd. All rights reserved.
Mobile merchandise evaluation service using novel information retrieval and image recognition technology
(2011-02-15)
Lo, Chi-Chun
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Kuo, Ting-Huan
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Kung, Hsu-Yang
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Kao, Hsiang-Ting
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Chen, Chi-Hua
Consumers' purchasing behavior has obviously changed in recent years with developments in social economics. This change has been evident in the decreased ratio of planned purchases but not in the increase of planned (or spontaneous) purchases. This act of spontaneous or otherwise unplanned purchasing is called "impulse buying". However, buying under these conditions costs more money always comes with negative responses, such as complaints and regret. Therefore, we propose and have designed a new merchandise recommendation system, the Mobile Merchandise Evaluation Service Platform (MMESP). This is a three-tier system composed of Real-time Merchandise Identifying System (RMIS), Real-time Merchandise Evaluation System (RMES), and Real-time Merchandise Recommendation System (RMRS). With this system, Mobile Users (MUs) take pictures of merchandise and send them to MMESP. RMIS integrates Region Adjacency Graph (RAG) and Self-Organizing Maps (SOM) to gather information on the merchandise through those photographs, and. RMES and RMRS provide Intelligence Agents (IAs) and Multiple Document Summarization (MDS) to summarize recommendations on merchandise for MUs, all in real time. (C) 2010 Elsevier B.V. All rights reserved.
Traffic Information Estimation Methods From Handover Events
(2016-01)
Wu, Che-I
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Chen, Chi-Hua
;
Lin, Bon-Yeh
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Lo, Chi-Chun
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資訊管理與財務金融系註：原資管所+財金所
Fast growth of the economy and technology upgrades have led to improvements in the quality of traditional transport systems. As such, the use of intelligent transportation systems (ITS) has become more and more popular. The implementation and improvement of real-time traffic information systems are an important parts of ITS. Compared with other traditional methods, traffic information estimations from cellular network data are now readily available, more cost-effective, and easier to deploy and maintain. This study assumed that nonvehicle calls could be filtered out and vehicles could be tracked on road segments. A novel ITS model was proposed to indicate the relationship between call arrival rate and traffic density. Moreover, the vehicle speed and traffic flow were estimated by using cellular floating vehicle data (CFVD) and the proposed novel ITS model. In experiments, this study used a VISSIM traffic simulator and adopted the average call inter-arrival time and call holding time to simulate communication behavior on road segments. The estimated traffic information was compared with the simulated traffic information from stationary vehicle detectors (VD). The results indicated that the average accuracies for vehicle speed estimation, traffic flow estimation, and traffic density estimation in the congested flow case were 97.63, 89.72, and 90.45 %, respectively. Therefore, this approach was feasible to estimate traffic information for ITS improvement.