Browsing by Author "Kung, Hsu-Yang"

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A Field Wireless Sensor System for Mangrove Ecology Environment in Taiwan: Design and Implementation
(2012)
Lin, Mei-Hsien
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Kung, Hsu-Yang
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Li, Chia-Ling
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Chen, Chi-Hua
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Lai, Wei Kuang
This study is the first investigation of using wireless sensor networks (WSNs) for Mangroves protection and observation in Taiwan. In this paper, this study proposes a natural ecology sensing system (NESS), which consists of ecological environment sensor (EES), nature resource integrated computing server (NRICS), mobile user (MU), and ecological information service system (EISS). NESS can improve the traditional long-term observation method by providing a real-time and networked surveillance system. This paper considers the practical issues of deploying ecological environment sensors in the Mangroves protection area. In additions, feasible solutions are proposed to achieve sustainable energy supply and reliable data delivery with high accuracy. We also evaluate system performance in terms of energy consumption and packet loss rate at different duty cycles, and compare lifetime and data accuracy with different energy supply sources. According to implementation results, the proposed system is efficient and feasible for Mangroves observation, protection, and surveillance. [Mei-Hsien Lin, Hsu-Yang Kung, Chia-Ling Li, Chi-Hua Chen, Wei Kuang Lai. A Field Wireless Sensor System for Mangrove Ecology Environment in Taiwan: Design and Implementation. Life Sci J 2012;9(4):5759-5767] (ISSN:1097-8135). http://www.lifesciencesite.com. 857
Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method
(2016-08-01)
Kung, Hsu-Yang
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Kuo, Ting-Huan
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Chen, Chi-Hua
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Tsai, Pei-Yu
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資訊管理與財務金融系註：原資管所+財金所
With the rise and development of information technology (IT) services, the amount of data generated is rapidly increasing. Data from many different places are inconsistent. Data capture, storage and analysis have major challenges. Most data analysis methods are unable to handle such large amounts of data. Many studies employ neural networks, mostly specifying the number of hidden layers and neurons according to experience or formula. Different sets of network topologies have different results, and the best network model is selected. This investigation proposes a system based on the ensemble neural network (ENN). It creates multiple network models, each with different numbers of hidden layers and neurons. A model that does not achieve the accuracy rate is discarded. The proposed system derives the weighted average of all remaining network models to improve the accuracy of the prediction. This study applies the proposed method to generate agricultural yield predictions. The agricultural production process in Taiwan is more complex than those of manufacturing or other industries. The Council of Agriculture provides agricultural forecasting primarily based on the planted area and experience to predict the yield, but without consideration of the overall planting environment. This work applies the proposed data analysis method to agriculture. The method based on ENN has a much lower error rate than traditional back-propagation neural networks, while multiple regression analysis has an error rate of 12.4%. Experimental results reveal that the ENN method is better than traditional back-propagation neural networks and multiple regression analysis.
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.
Context-Aware Services for Mobile Multimedia Application
(2015-07-01)
Kung, Hsu-Yang
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Chen, Chi-Hua
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Lin, Ching-Yu
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Advancements are rapidly being made in mobile computing environments and context aware multimedia streaming services that are based on information about a user\'s context. This work proposes a Context-Aware Embedded Multimedia Presentation System (CEMP) that is based on the context vocabulary ontology to provide a formal explicit description of the multimedia information domain and the formal context process model. The designed context recognition and context representation reasoning mechanism can perform context classification to deliver multimedia services of the required quality. Additionally, the CEMP system includes an adaptation reasoning mechanism that identifies the best adaptation control when the context changes. However, context modification may cause a context conflict problem among multiple adaptation requirements and resource limitations. The CEMP system provides a conflict resolution mechanism that calculates the context\'s priority and the context weight to improve context conflict resolution results.
Designing intelligent disaster prediction models and systems for debris-flow disasters in Taiwan
(2012-04-01)
Kung, Hsu-Yang
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Chen, Chi-Hua
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Ku, Hao-Hsiang
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Effective disaster prediction relies on using correct disaster decision model to predict the disaster occurrence accurately. This study proposes three effective debris-flow prediction models and an inference engine to predict and decide the debris-flow occurrence in Taiwan. The proposed prediction models are based on linear regression, multivariate analysis, and back-propagation networks. To create a practical simulation environment, the decision database is the pre-analyzed 181 potential debris-flows in Taiwan. According to the simulation results, the prediction model based on back-propagation networks predicted the debris flow most accurately. Moreover, a Real-time Mobile Debris Flow Disaster Forecast System (RM(DF)(2)) was implemented as a three-tier architecture consisting of mobile appliances, intelligent situation-aware agents and decision support servers based on the wireless/mobile Internet communications. The RM(DF)(2) system provides real-time communication between the disaster area and the rescue-control center, and effectively prevents and manages debris-flow disasters. (C) 2011 Elsevier Ltd. All rights reserved.
Information Management and Applications of Intelligent Transportation System
(2015-01-01)
Lo, Chi-Chun
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Chao, Kuo-Ming
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Kung, Hsu-Yang
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Chen, Chi-Hua
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Chang, Maiga
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.
Ubiquitous Healthcare Service System with Context-awareness Capability: Design and Implementation
(2011-04-01)
Lo, Chi-Chun
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Chen, Chi-Hua
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Cheng, Ding-Yuan
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Kung, Hsu-Yang
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資訊管理與財務金融系註：原資管所+財金所
The rises of the life index quality together with the medical technology improvement lead to a longer life expectancy. Thus a better health care program, especially for elderly, is needed. The common health problems facing those senior citizens are changed from acute diseases to chronic diseases, such as diabetes, hypertension, etc. Along with these changes, medical tourism is becoming the trend of the future. In this paper, we propose a decision support systems, the Ubiquitous Context-aware Healthcare Service System (UCHS), which uses micro sensors integrate RFID to sense user's life vital signal, such as electrocardiogram (ECG/EKG), heart rate (HR), respiratory rate (RR), blood pressure (BP), blood sugar (BS), and temperature and light. The UCHS is composted of Situation-Aware Medical Tourism Service Search Subsystem (SAMTS(3)), Healthy-life Map Guiding Subsystem (HMGS), Intelligent Curative Food Decision Support Subsystem (ICFDSS), and 4D Emergency Indication and Ambulance Dispatch Subsystem (4DEIADS) to provide relevant nature medicine recommendations to its user. The UCHS built upon an integrated service platform in which medical experts' knowledge and all position and negative influence of the proposed therapy are inferred by using semantic network. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
Using RFID Technology and SOA with 4D Escape Route
(2008)
Kung, Hsu-Yang
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Wu, Che-, I
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Chen, Chi-Hua
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Lan, Yuan-Hung
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資訊管理與財務金融系註：原資管所+財金所
When the heavy rainfalls or typhoons occurred, many counties were unavoidable to suffer the debris-flow and flood disasters in Taiwan. Therefore, it is urgently required to obtain and inform the real-time disaster information to display the situation, and it is important for people to design an effective disaster information system to assist the disaster protection and alerting works. To plan and design a Multimedia Escape and Survival System (MESS), which is a three-tier system composed of the mobile users, multimedia server, and disaster decision server, and the system combines mobile communication technology. MESS combines RFID technology, CPS, GIS and GPRS/3G to find out the 4D safety paths and roads in the disaster areas, and the disaster decision is packed as a reusable web services which can be used in MESS or other systems for reducing the cost and speeding up the efficiency of system development in the future. Mobile users use mobile devices with GPS to locate the longitude and latitude, and transmitting these coordinates to the CIS server. According to the longitude and latitude, the CIS will draw VR map of disaster area using CIS relevant database and show the simulated safety way to users. The MESS can draw the points on the VR map that includes all users\' position and announces the best refuge and escape path. The reasoning engine of MESS is used Knapsack Problem (KP) and the Adaptive Path Algorithm (APA) which consider distance, security, traffic volume, cost, and refuges where RFID readers were installed to infer reason the 4D safety paths and escape routes.