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 27

Using cloud computing technology to design and implementation of smart shop floor control system
(1970-01-01)
Li, Jianxin
;
Chen, Dongsheng
;
Peng, Yo-So
;
Zhang, Zhiyu
;
Tian, Yuan
Smart manufacturing has evolved and become more automated, computerized and cloud computing. The current shop floor control system (SFCS) has been implemented for many years, and has gradually encountered many problems. It moves towards the flexible smart manufacturing owing to the multiplex customer demand. Therefore, the current SFCS has been customized in many versions, which makes the low maintenance efficiency and the version control difficult. This paper is using cloud computing as a platform of developing and integrating multiplex customer demand to establish the integrity smart system function, and using Team Server to centralize the program versions to solve version management. Besides, the maintenance efficiency is raised by using production parameter configuration of the service-oriented design. The cloud based method enables deploying to be performed in any place other than the production line, which also facilitates the promotion of maintenance efficiency. The subscription function offered by the reporting service provides reports at specific time. The result of this research is to develop the cloud-based smart SFCS, which can achieve the goal of integrating production process and the promotion of production efficiency. With the system report, cloud-based smart SFCS can support managers to make right decision to promote the product quality.
Workload and Capacity Optimization for Cloud-Edge Computing Systems with Vertical and Horizontal Offloading
(2020-03-01)
Thai, Minh-Tuan
;
Lin, Ying-Dar
;
Lai, Yuan-Cheng
;
Chien, Hsu-Tung
;
交大名義發表
A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.
Using Cache Optimization Method to Reduce Network Traffic in Communication Systems Based on Cloud Computing
(2019-01-01)
Kang, Lanlan
;
Chen, Ruey-Shun
;
Chen, Yeh-Cheng
;
Wang, Chung-Chei
;
Li, Xingguan
The overwhelming increase of population will lead to an increase network traffic usage. In most products, local cache mechanism is implemented for the purpose of reducing the network traffics. However, Due to storage space limitations, lot of times, cache will get purged when there are lots of queries sent and replaced by other newest queries. It is not an efficient by using traditional methodology to build or forming cache. In order to reduce the network traffic usage, the paper propose a solution, which utilizes data mining technique with clustering concept, by gathering the current feedback data that have from our SPN (Smart Protection Network). we are able to form these data in groups with similarity, and by deploying these data to client side, to achieve the reduction of traffic usage. In prototype, this design can really reduce network traffic more than 8%.
A Cross Language Plagiarism Detection Based on Cloud Computing
(2015-01-01)
Fan, Chih-Tien
;
Nguyen Dang Minh
;
Muhammad, Husaini
;
交大名義發表
;
National Chiao Tung University
Nowadays, language barrier is getting lower, especially for the students. Most of them know at least one foreign language. Even that, translation through the cloud is getting popular. There are many machine translation services can be access easily via Internet. These services work accurately and support many languages. They are even embedded by default in web browser of users and in mobile devices. This is easiest way for a student to translate a sentence from any language, and of course easy for cross-language plagiarism too. It makes plagiarism is getting difficult to detect. In this paper, we exploited cloud services and cloud computing model for detecting cross-language plagiarism. We downloaded a translation of the article from a website of Vietnam and Vietnamese local news provider then used as a test dataset. These articles were first translated into the English using translation services on the Web. Again, each translated sentences were sent to the web search engine to find related articles. Then, we compared this sentence with each sentence in the related articles to find out its score of plagiarism. Finally, we computed the score of plagiarism of this article. Experiment results show that the accuracy of detection is slightly higher than 0.5.
Dynamic Resource Provisioning with Hotspot Anticipation for MMOG Clouds
(2015-01-01)
Chang, Yenchih
;
Wang, Kuochen
;
Hsu, Yi-Huai
;
資訊工程學系
;
Department of Computer Science
We propose a dynamic resource provisioning with hotspot anticipation scheme, called NN-Player+DRP-HA that employs a finite state machine model to monitor the movement of avatars in a virtual world. Furthermore, we use a finite state machine to represent possible avatar states and state transitions. By combining the state of each avatar in a game zone with a neural network (NN) predictor, we may figure out potential workload produced by hotspots, and then allocate appropriate computing resources to support the game zone. Experimental results support that the proposed NN-Player+DRP-HA scheme can avoid most of under-allocation events with an acceptable over-allocation rate. Compared with a representative dynamic resource provisioning method, called NN-Player+DRP, the proposed NN-Player+DRP-HA reduces the probability of under-allocation events from 2.16% to 0.42% (80% improvement) in terms of CPU capacity of a VM, under the premise of controlling the CPU over-allocation rate within the CPU capacity of one VM.
An Efficient Geometry Data Allocation Algorithm in Cloud Computing Environments
(2012-01-01)
Wang, Kun-Wei
;
Huang, Bo-Wei
;
Peng, Wen-Chih
;
資訊工程學系
;
Department of Computer Science
The number of location-based services is growing and developing. Usually, these services put a huge amount of effort into geometry data computation. Thus, their workload is generally high. By exploring cloud computing techniques, one could utilize a number of computing nodes to distribute the workload of the systems. However, the workload is usually not equally balanced across computing nodes, if data is not well-distributed. To make the best use of computing nodes, we propose a sophisticated data distribution technology for geometry computation processing. Intuitively, one can simply divide geometry data into tiles so that the geometry data in each tile can be stored on one computing node. Unfortunately, since data in a tile shares spatial-proximity, processing a geometry computation on spatial-proximity data still incurs a huge workload. To address this issue, we propose a new data distribution approach, Reversed K-means, to distribute geometry data that shares spatial-proximity across different computing nodes. In this way, we can use more computing nodes to process geometry computation and get better performance. To evaluate the performance of our proposed algorithm, we evaluate the utility of computing nodes and the response time when performing geometry computations. The experimental results show that the utility of the computing nodes is higher than existing methods, and the response time is the fastest of all methods.
Distributed and scalable sequential pattern mining through stream processing
(2017-11-01)
Chen, Chun-Chieh
;
Shuai, Hong-Han
;
Chen, Ming-Syan
;
電機工程學系
;
Department of Electrical and Computer Engineering
Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data. Previous work, namely sequential pattern mining on the cloud (SPAMC), has already addressed the scalability problem. It supports the MapReduce cloud computing architecture for mining frequent sequential patterns on large datasets. However, this existing algorithm does not address the iterative mining problem, which is the problem that reloading data incur additional costs. Furthermore, it did not study the load balancing problem. To remedy these problems, we devised a powerful sequential pattern mining algorithm, the sequential pattern mining in the cloud-uniform distributed lexical sequence tree algorithm (SPAMC-UDLT), exploiting MapReduce and streaming processes. SPAMC-UDLT dramatically improves overall performance without launching multiple MapReduce rounds and provides perfect load balancing across machines in the cloud. The results show that SPAMC-UDLT can significantly reduce execution time, achieves extremely high scalability, and provides much better load balancing than existing algorithms in the cloud.
An Integrated Home Financial Investment Learning Environment Applying Cloud Computing in Social Network Analysis
(2011)
Wen, Mao-Ping
;
Lin, Hsio-Yi
;
Chen, An-Pin
;
Yang, Chyan
;
資訊管理與財務金融系註：原資管所+財金所
This paper tried to apply cloud computing technology in social network analysis for a comprehensive home financial learning environment that individual investors may use as a reference in establishing web-based learning and investment platforms. The major contributions were described in three parts. First, this paper advanced the social network analysis technology to be able to handle millions of nodes and links. Second, we demonstrate how cloud computing can be applied to advanced computing in social network. Third, we performed several intelligent analyses on a very popular social network, IHFILE, to identify some interesting and important features of it. In addition to analyzing a homogeneous social network such as IHFILE, we also propose direction of how cloud computing can be performed on a social network analysis as our future work.
Evaluating Cloud Computing Based Telecommunications Service Quality Enhancement by Using a New Hybrid MCDM Model
(2012)
Huang, Chi-Yo
;
Hsu, Pei-Chu
;
Tzeng, Gwo-Hshiung
;
科技管理研究所
;
Institute of Management of Technology
The fast emergence of the cloud based computation fulfills modern telecommunication serve firms\' requirements for massive computation and storage space with very low costs. However, how the cloud-based service quality can be enhanced, how customers\' needs can be fulfilled, and how firms\' profits can be maximized were seldom discussed. Thus, this research aims to provide a new hybrid MCDM model combining DEMATEL and ANP based framework and Grey Relational Analysis (GRA) for improving to close the service quality gap, fulfill customers\' satisfactory and maximize profits in interdependence and feedback problems among dimensions/criteria. An empirical case study bases on enhancing the service quality of a Taiwanese mobile telecommunication firm\'s application of cloud computation as the billing system. In results of this case study, the expertise in dynamic capacity management and new education and training should be the most important strategies for service quality enhancement to be introduced.
Design and Development of a Cloud-based Trip-Tracking System
(2015)
Thanh An Nguyen Thi
;
Chen, Yaw-Chung
;
資訊工程學系
;
Department of Computer Science
A trip-tracking system records people traveled route which could be retrieved immediately or latter for certain purposes. The recent tracking application does not integrate both passive and real-time tracking function into one system. The aim of this work is to build a tracking system that integrates both passive and real-time tracking functions. A new system architecture consisting the client side which is a map tracking application on smart devices, and the server side which includes a server and a database in the cloud, was developed. Because of the ability to store and process database on the cloud, the system would be more secure, available and durable. More importantly, it can be tracked at any time and from anywhere. The results demonstrated a complete system with three real-world experiments on Android application and web interface.

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