Browsing by Author "Kuo, Ting-Huan"

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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.
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.
Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
(2016-02-01)
Lai, Wei-Kuang
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Kuo, Ting-Huan
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Chen, Chi-Hua
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傳播與科技學系
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電機工程學系
Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.