學術出版;;Publications

Permanent URI for this communityhdl:11536/1

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.

Browse

🏛️國立陽明交通大學機構典藏

Search Results

Now showing 1 - 10 of 28

An Augmented Reality Question Answering System Based on Ensemble Neural Networks
(2017-01-01)
Chen, Chi-Hua
;
Wu, Chen-Ling
;
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.
大型資料庫中循序樣式之勘測與維護
(2000)
李素瑛
;
LEE SUH-YIN
;
交通大學資訊工程系
互動式勘測大型資料庫中循序樣式之研究
(2001)
李素瑛
;
LEE SUH-YIN
;
國立交通大學資訊工程學系
An integration method combining Rough Set Theory with formal concept analysis for personal investment portfolios
(2010-08-01)
Shyng, Jhieh-Yu
;
Shieh, How-Ming
;
Tzeng, Gwo-Hshiung
;
科技管理研究所
;
Institute of Management of Technology
The classical Rough Set Theory (RST) always generates too many rules, making it difficult for decision makers to choose a suitable rule. In this study, we use two processes (pre process and post process) to select suitable rules and to explore the relationship among attributes In pre process, we propose a pruning process to select suitable rules by setting up a threshold on the support object of decision rules, to thereby solve the problem of too many rules The post process used the formal concept analysis from these suitable rules to explore the attribute relationship and the most important factors affecting decision making for choosing behaviours of personal investment portfolios In this study, we explored the main concepts (characteristics) for the conservative portfolio the stable job, less than 4 working years, and the gender is male, the moderate portfolio. high school education, the monthly salary between NT$30,001 (US$1000) and NT$80,000 (US$2667), the gender is male, and the aggressive portfolio. the monthly salary between NT$30,001 (US$1000) and NT$80,000 (US$2667), less than 4 working years, and a stable job The study result successfully explored the most important factors affecting the personal investment portfolios and the suitable rules that can help decision makers. (C) 2010 Elsevier B.V All rights reserved.
Using decision rules to achieve mass customization of airline services
(2010-09-16)
Liou, James J. H.
;
Yen, Leon
;
Tzeng, Gwo-Hshiung
;
科技管理研究所
;
Institute of Management of Technology
This paper uses the Dominance-based Rough Set Approach (DRSA) to formulate airline service strategies by generating decision rules that model passenger preference for airline service quality. DRSA could help airlines eliminate some services associated with dispensable attributes without affecting passenger perception of service quality. DRSA could also help airlines achieve mass customization of airline services and generate additional revenues by active or passive targeting of quality services to passengers. (C) 2009 Published by Elsevier B.V.
Using SVM based method for equipment fault detection in a thermal power plant
(2011-01-01)
Chen, Kai-Ying
;
Chen, Long-Sheng
;
Chen, Mu-Chen
;
Lee, Chia-Lung
;
運輸與物流管理系註：原交通所+運管所
Due to the growing demand on electricity, how to improve the efficiency of equipment in a thermal power plant has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependant upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Data mining techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work proposes a support vector machines (SVM) based model which integrates a dimension reduction scheme to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness of the proposed SVM based model. Experimental results show that SVM outperforms linear discriminant analysis (LDA) and back-propagation neural networks (BPN) in classification performance. (C) 2010 Elsevier B.V. All rights reserved.
Mining group-based knowledge flows for sharing task knowledge
(2011-01-01)
Liu, Duen-Ren
;
Lai, Chin-Hui
;
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 an organization, knowledge is the most important resource in the creation of core competitive advantages. It is circulated and accumulated by knowledge flows (KFs) in the organization to support workers' task needs. Because workers accumulate knowledge of different domains, they may cooperate and participate in several task-based groups to satisfy their needs. In this paper, we propose algorithms that integrate information retrieval and data mining techniques to mine and construct group-based KFs (GKFs) for task-based groups. A GKF is expressed as a directed knowledge graph which represents the knowledge referencing behavior, or knowledge flow, of a group of workers with similar task needs. Task-related knowledge topics and their relationships (flows) can be identified from the knowledge graph so as to fulfill workers' task needs and promote knowledge sharing for collaboration of group members. Moreover, the frequent knowledge referencing path can be identified from the knowledge graph to indicate the frequent knowledge flow of the workers. To demonstrate the efficacy of the proposed methods, we implement a prototype of the GKF mining system. Our GKF mining methods can enhance organizational learning and facilitate knowledge management, sharing, and reuse in an environment where collaboration and teamwork are essential. (C) 2010 Elsevier B.V. All rights reserved.
A regression-based approach for mining user movement patterns from random sample data
(2011-01-01)
Hung, Chih-Chieh
;
Peng, Wen-Chih
;
資訊工程學系
;
Department of Computer Science
Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users. (C) 2010 Elsevier B.V. All rights reserved.
An incremental algorithm for clustering spatial data streams: exploring temporal locality
(2013-11-01)
Wei, Ling-Yin
;
Peng, Wen-Chih
;
資訊工程學系
;
Department of Computer Science
Clustering sensor data discovers useful information hidden in sensor networks. In sensor networks, a sensor has two types of attributes: a geographic attribute (i.e, its spatial location) and non-geographic attributes (e.g., sensed readings). Sensor data are periodically collected and viewed as spatial data streams, where a spatial data stream consists of a sequence of data points exhibiting attributes in both the geographic and non-geographic domains. Previous studies have developed a dual clustering problem for spatial data by considering similarity-connected relationships in both geographic and non-geographic domains. However, the clustering processes in stream environments are time-sensitive because of frequently updated sensor data. For sensor data, the readings from one sensor are similar for a period, and the readings refer to temporal locality features. Using the temporal locality features of the sensor data, this study proposes an incremental clustering (IC) algorithm to discover clusters efficiently. The IC algorithm comprises two phases: cluster prediction and cluster refinement. The first phase estimates the probability of two sensors belonging to a cluster from the previous clustering results. According to the estimation, a coarse clustering result is derived. The cluster refinement phase then refines the coarse result. This study evaluates the performance of the IC algorithm using synthetic and real datasets. Experimental results show that the IC algorithm outperforms exiting approaches confirming the scalability of the IC algorithm. In addition, the effect of temporal locality features on the IC algorithm is analyzed and thoroughly examined in the experiments.
Fast and memory efficient mining of high-utility itemsets from data streams: with and without negative item profits
(2011-09-01)
Li, Hua-Fu
;
Huang, Hsin-Yun
;
Lee, Suh-Yin
;
資訊工程學系
;
Department of Computer Science
Mining utility itemsets from data steams is one of the most interesting research issues in data mining and knowledge discovery. In this paper, two efficient sliding window-based algorithms, MHUI-BIT (Mining High-Utility Itemsets based on BITvector) and MHUI-TID (Mining High-Utility Itemsets based on TIDlist), are proposed for mining high-utility itemsets from data streams. Based on the sliding window-based framework of the proposed approaches, two effective representations of item information, Bitvector and TIDlist, and a lexicographical tree-based summary data structure, LexTree-2HTU, are developed to improve the efficiency of discovering high-utility itemsets with positive profits from data streams. Experimental results show that the proposed algorithms outperform than the existing approaches for discovering high-utility itemsets from data streams over sliding windows. Beside, we also propose the adapted approaches of algorithms MHUI-BIT and MHUI-TID in order to handle the case when we are interested in mining utility itemsets with negative item profits. Experiments show that the variants of algorithms MHUI-BIT and MHUI-TID are efficient approaches for mining high-utility itemsets with negative item profits over stream transaction-sensitive sliding windows.

學術出版;;Publications

Browse

Filters

Settings

Sort By

Results per page

🏛️國立陽明交通大學機構典藏

Search Results