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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Now showing 1 - 10 of 44

A Fuel-Efficient Route Plan Method Based on Game Theory
(2019-01-01)
Lo, Chi-Lun
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Chen, Chi-Hua
;
Hu, Jin-Li
;
Lo, Kuen-Rong
;
Cho, Hsun-Jung
This study adopts a fuel consumption estimation method to measure the consumed fuel quantity of each vehicle speed interval (i.e., a cost function) in accordance with individual behaviors. Furthermore, a mobile app is designed to consider the best responses of other route plan apps (e.g., the shortest route plan app and the fast route plan app) and plan the most fuel-efficient route according to the consumed fuel quantity. The numerical analysis results show that the proposed fuel-efficient route plan app can effectively support fuel-saving for logistics industries.
Combining the Technology Acceptance Model and Uses and Gratifications Theory to examine the usage behavior of an Augmented Reality Tour-sharing Application
(2017-07-01)
Lin, Hui-Fei
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Chen, Chi-Hua
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電機工程學系
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Department of Electrical and Computer Engineering
An intelligent tour service system including an augmented reality (AR) tour-sharing Application (APP) and a query-answering server was developed in this study to promote tourist attractions involving local Hakka culture in Thailand. Subsequently, use of this APP to navigate Hakka culture tourist attractions in Thailand was observed. The novel random neural networks (RNNs) were proposed to obtain query-answering services, and the practical experimental results showed that the accuracy of RNNs was 99.51%. This study also integrated the Technology Acceptance Model with Uses and Gratifications Theory to predict the gratification, usage intention, and user attitudes toward marketed attractions of the AR tour-sharing APP. A questionnaire survey was conducted, and 446 valid questionnaires were returned. The following results were obtained: (a) self-presentation and perceived usefulness (PU) directly influenced gratification; (b) perceived entertainment indirectly influenced gratification through perceived ease of use and PU, and information sharing indirectly influenced gratification through PU; and (c) gratification was significantly and positively related to usage intention and attitude toward attractions. Based on these results, suggestions that new technology marketing can be used to promote causes other than Hakka tourist attractions established in Thailand can be contrived. For example, the tour-sharing APP developed in this study could be applied to emphasize the characteristics of Thai Hakka culture; users' fondness for self-presentation and information sharing can be used for word-of-mouth marketing to attract additional visitors. In addition, this research provides a reference for enterprises and marketers regarding the use of AR tour-sharing APPs to market tourist attractions, and also for future related studies.
Fuel Consumption Estimation System and Method with Lower Cost
(2017-07-01)
Lo, Chi-Lun
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Chen, Chi-Hua
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Kuan, Ta-Sheng
;
Lo, Kuen-Rong
;
Cho, Hsun-Jung
This study proposes a fuel consumption estimation system and method with lower cost. On-board units can report vehicle speed, and user devices can send fuel information to a data analysis server. Then the data analysis server can use the proposed fuel consumption estimation method to estimate the fuel consumption based on driver behaviours without fuel sensors for cost savings. The proposed fuel consumption estimation method is designed based on a genetic algorithm which can generate gene sequences and use crossover and mutation for retrieving an adaptable gene sequence. The adaptable gene sequence can be applied as the set of fuel consumption in accordance with the pattern of driver behaviour. The practical experimental results indicated that the accuracy of the proposed fuel consumption estimation method was about 95.87%.
An Augmented Reality Question Answering System Based on Ensemble Neural Networks
(2017-01-01)
Chen, Chi-Hua
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Wu, Chen-Ling
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Lo, Chi-Chun
;
Hwang, Feng-Jang
;
資訊管理與財務金融系註：原資管所+財金所
This paper proposes a classification algorithm based on ensemble neural networks. In the training phase, the proposed algorithm uses a random number of training data to develop multiple random artificial neural network (ANN) models until those ANN models converge. Those models with lower accuracy than the threshold are filtered out. The remaining highly accurate models will be used to predict the output in the testing phase. Meanwhile, the accuracy of ANN models is presented as a weighting value in the testing phase. In the testing phase, the testing data are loaded into the selected ANN models to predict the output class. The output values are multiplied by the corresponding weighting values of ANN models. Then the weighted average of the outputs can be obtained. Finally, the predicted output is converted into the predicted class. We design an augmented reality question answering system (AR-QAS) applying and implementing the proposed algorithm on mobile devices. AR-QAS offers an interactive user interface and automatically replies according to user's queries. By comparing with the logistic regression method and the ANN method, the experiment results demonstrate that the proposed algorithm offers the highest accuracy.
Vehicle Localization and Velocity Estimation Based on Mobile Phone Sensing
(2016-01-01)
Chen, Chi-Hua
;
Lee, Chi-Ao
;
Lo, Chi-Chun
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In recent years, smart devices have become ubiquitous. Many of these devices are equipped with global positioning system (GPS), Wi-Fi, and other sensors. With their high mobility, the idea of mobile devices as probes has been attracting more and more attention. Mobility and flexibility offered by smart mobile devices are what traditional fixed sensors lack. However, mobile devices' power supplies are quite limited. Although GPS is accurate, its high power consumption somewhat limits its accessibility and sustainability. In contrast, Wi-Fi is less power hungry, but at the same time, less accurate. For the sake of sustainability, by adopting Wi-Fi as an alternative to GPS, longer operation is attainable at the cost of losing some degree of accuracy. In this paper, a Wi-Fi-based algorithm based on log-normal probability distribution of distances with respect to received signal strength is proposed. It is suitable for an outdoor environment, where Wi-Fi access points (APs) are abundant. Simulations are conducted over known AP locations, and results show that the proposed algorithm can save, on average, as much as 35% more battery power than GPS does. The average localization error is similar to 18 m, and the average velocity estimation error is similar to 25%.
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
;
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.
The frequency of CFVD speed report for highway traffic
(2015-04-10)
Chang, Ming-Feng
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Chen, Chi-Hua
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Lin, Yi-Bing
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Chia, Chung-Yung
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資訊工程學系
The control signals of cellular networks have been used to infer the traffic conditions of the road network. In particular, consecutive handover events are being used to estimate the traffic speed. During traffic congestion, consecutive handover events may be rare because vehicles move slowly, and thus very few or no speed reports would be generated from the congested area.However, the traffic speed report rate during traffic congestion has not been investigated in the literature. In this paper, we present an analytic model to estimate the speed report rate from cellular network signaling in steady traffic conditions, that is, the traffic speed and flow are assumed constant. Real field trial data were used to validate our analytic model. In addition, computer simulations were conducted to study how speed reports are generated in dynamic traffic conditions when traffic speed and flow change rapidly. Our study indicates that in a typical cell of length 1.5km with a typical expected call holding time of 1min, no speed report was generated from a congested three-lane highway. Our study demonstrates that the lack of speed reports from consecutive handover events during rush hours indicates severe traffic congestion, and new methods that can estimate traffic speed from cellular network data during severe traffic congestion need to be developed. Copyright (c) 2013 John Wiley & Sons, Ltd.
A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
(2016-06-01)
Chen, Chi-Hua
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Lin, Jia-Hong
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Kuan, Ta-Sheng
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Lo, Kuen-Rong
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資訊管理與財務金融系註：原資管所+財金所
Commercial vehicle operation (CVO) has been a popular application of intelligent transportation systems. Location determination and route tracing of an on-board unit (OBU) in a vehicle is an important capability for CVO. However, large location errors from global positioning system (GPS) receivers may occur in cities that shield GPS signals. Therefore, a highly efficient mobile positioning method is proposed based on the collection and analysis of the cellular network signals of CVO data. Parallel- and cloud-computing techniques are designed into the proposed method to quickly determine the location of an OBU for CVO. Furthermore, this study proposes analytical models to analyze the availability of the proposed mobile positioning method with various outlier filtering criteria. Experimentally, a CVO system was designed and implemented to collect CVO data from Chunghwa Telecom vehicles and to analyze the cellular network signals of CVO data for location determination. A case study found that the average errors of location determination using the proposed method vs. using the traditional cell-ID-based location method were 163.7 m and 521.2 m, respectively. Furthermore, the practical results show that the average location error and availability of using the proposed method are better than using GPS or the cell-ID-based location method for each road type, particularly urban roads. Therefore, this approach is feasible to determine OBU locations for improving CVO.
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.
A Novel Real-Time Speech Summarizer System for the Learning of Sustainability
(2015-04-01)
Wang, Hsiu-Wen
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Cheng, Ding-Yuan
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Chen, Chi-Hua
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Wu, Yu-Rou
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Lo, Chi-Chun
As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures). Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS) was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words) and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability.

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