• Fashion & Textile Research Journal
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• v.1 no.3
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• pp.231-238
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• 1999

The purpose of this study was to identify characteristics and consumer attitude on purchasing apparel of Cable TV home-shopping viewer: A questionnaire was developed to measure watching attributes, consumers' attitude and actual purchasing condition of Cable TV home shopping, and demographic variables. The questionnaire was administered to 277 adult, and the data were analyzed by using frequency; crosstab, t-test, ANOVA. The results of this study were as follows: 1. main view time were 3~5 p.m. 11 p.m, 10~12 a.m. home shopping through Cable Tv. Women's main terms were afternoon, whereas men's main terms were night. Chiefly view program was about apparel and fashion items. The reason why they watched the home-shopping channel was to purchase more cheaper items. 2. Favor about Cable TV home-shopping was relatively affirmative, but purchasing intention through home-shopping was still negative. But affirmative response was gradually increasing a few years ago. 3. Purchasing experience through Cable TV home shopping was 61.0% and clothing purchaser within recently 6 months was 28.5% of total sample. Withspreading Cable TV widely; Cable TV home shopper was gradually increasing. Heavy purchasing items through Cable TV home-shopping were under-wear; muffler, shawl, because those items are standardized in size and style. When home shopping user made purchasing decision, the most significant factors were color; and size (fit), price in order of importance.

• ETRI Journal
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• v.31 no.2
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• pp.173-181
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• 2009

The growth of the World Wide Web and web-based applications is creating demand for high performance web servers to offer better throughput and shorter user-perceived latency. This demand leads to widely used cluster-based web servers in the Internet infrastructure. Load balancing algorithms play an important role in boosting the performance of cluster web servers. Previous load balancing algorithms suffer a significant performance drop under dynamic and database-driven workloads. We propose an estimation-based load balancing algorithm with admission control for cluster-based web servers. Because it is difficult to accurately determine the load of web servers, we propose an approximate policy. The algorithm classifies requests based on their service times and tracks the number of outstanding requests from each class in each web server node to dynamically estimate each web server load state. The available capacity of each web server is then computed and used for the load balancing and admission control decisions. The implementation results confirm that the proposed scheme improves both the mean response time and the throughput of clusters compared to rival load balancing algorithms and prevents clusters being overloaded even when request rates are beyond the cluster capacity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.123-133
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• 2011

The advanced planning and scheduling(APS) is an well known enterprise information system that provides optimal production schedules and supports to complete production on time by solving the complex scheduling problems including capacity and due dates. In this paper, we focused on the improvement of the APS that is already established on a real company. The existing APS had several drawbacks, thus utilization and satisfaction were very low. We performed the focused group interviews and the process analysis and could find that the end users and developers have various objectives and the frequently used functions are different. We applied the analytical hierarchy process(AHP) to converge opinions of them on quantitative data. The results show that it is necessary to enhance visibility, to improve user interfaces and response speed, and to reconcile the real business process and the APS's process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2B
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• pp.129-136
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• 2006

We propose a new data carousel scheduling algorithm for on-demand data broadcasting in DMB. The server divides data items into two sets named hot and cold, according to request statistics from clients. When constructing a data carousel, hot items are placed periodically with their upper limit of broadcasting frequency. If there are empty slots after placing hot items, cold items with high request ratio are placed until the carousel is full. A cold item is broadcast only once in the caroulsel. For the response on clients' requests, our proposed scheme is shown to have high success ratio with short waiting time.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.411-425
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• 2000

Information retrieval systems for structured documents which are written in SGML or XML support partial retrieval of document. In order to efficiently process queries based on document structures, low memory overhead for indexing, quick response time for queries, supports to powerful types of user queries, and minimal updates of index structure for document updates are required. This paper suggests the Global Document Instance Tree(GDIT) and proposes an effective indexing scheme and query processing algorithms based on the GDIT. The indexing scheme keeps up indexing and retrieval effciency and also guarantees minimal updates of the index structure when document structures are updated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.985-987
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• 2010

In this paper, we propose a maximum entropy-based emotion recognition model using individual average difference. In order to accurately recognize an user' s emotion, the proposed model utilizes the difference between the average of the given input physiological signals and the average of each emotion state' signals rather than only the input signal. For the purpose of alleviating data sparse -ness, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of physiological signals based on a second rather than the longer total emotion response time. With the aim of easily constructing the model, it utilizes a simple average difference calculation technique and a maximum entropy model, one of well-known machine learning techniques.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.148-157
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• 2002

Caching in meta-search engines can improve the response time of users' request. We describe the cache scheme in our meta-search engine in terms of its architecture and operational flow. In particular, we propose a popularity-driven cache algorithm that utilizes popularities of queries to determine cached data to be purged. The popularity is a value that represents the normalized occurrence frequency of user queries. This paper presents how to collect popular queries and how to calculate query popularities. An empirical performance evaluation of the popularity-driven caching with the traditional schemes (i.e., least recently used (LRU) and least frequently used (LFU)) has been carried out on a collection of real data. In almost all cases, the proposed replacement policy outperforms LRU and LFU.

• Journal of KIISE:Software and Applications
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• v.27 no.8
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• pp.795-803
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• 2000

This paper presents a shared data decomposition model for improving concurrency in multi-user, distributed software developments. In our model, the target software system is decomposed into the independent components based on project roles to be distributed over clients. The distributed components are decomposed into view objects and core objects to replicate only view objects in a distributed collaboration session. The core objects are kept in only one client and the locking is used to prevent inconsistencies. The grain size of a lock is a role instead of a class which is commonly used as the locking granularity in the existing systems. The experimental result shows that our model reduces response time by 12${\sim}$18% and gives good scalability.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1346-1357
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• 2021

The drone technology, which is receiving a lot of attention due to the 4th industrial revolution, requires an Unmanned Aerial Vehicles'(UAVs) flight path search algorithm for automatic operation and driver assistance. Various studies related to flight path prediction and recommendation algorithms are being actively conducted, and many studies using the A-Star algorithm are typically performed. In this paper, we propose an Optimal 3D Flight Path Recommendation System for unmanned aerial vehicles. The proposed system was implemented and simulated in Unity 3D, and by indicating the meaning of the route using three different colors, such as planned route, the recommended route, and the current route were compared each other. And obstacle response experiments were conducted to cope with bad weather. It is expected that the proposed system will provide an improved user experience compared to the existing system through accurate and real-time adaptive path prediction in a 3D mixed reality environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5269-5286
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• 2018

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.