• Korean Management Science Review
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• v.18 no.2
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• pp.125-134
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• 2001

This paper is concerned with the development of a GIS-based distribution system with service consideration. The proposed model could be used for a wide range of logistics applications in planning, engineering and operational purpose for logistics system. This research addresses the formulation of those complex prob1ems of two-echelon logistics system to plan the incorporating supply center locations and distribution problems based on GIS. We propose an integrated logistics model for determining the optimal patterns of supply centers and inventory allocations (customers) with a three-step sequential approach. 1) First step, Developing GIS-distance model and stochastic set-covering program to determine Optimel pattern of supply center location. 2) Second step, Optimal sector-clustering to support customers. 3) Third step, Optimal vehicle rouse scheduling based on GIS, GIS-VRP In this research we developed GUI-tree program, the GIS-VRP provide the vehicle to users and freight information in real time. We applied a set of sample examples to this model and demonstrated samp1e results. It has been found that the proposed model is potentially efficient and useful in solving multi-depot problem through examples. However the proposed model can provide logistics decision makers to get the best supply schedule.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3203-3215
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• 2015

Due to the high growth of SNS population, service scalability is one of the critical issues to be addressed. The cloud environment provides the flexible computing and storage resources for services deployment, which fits the characteristics of scalable SNS deployment. However, if the SNS related information is not properly placed, it will cause unbalance load and heavy transmission cost on the storage virtual machine (VM) and cloud data center (CDC) network. In this paper, we characterize the SNS into a graph model based on the users' associations and interest correlations. The node weight represents the degree of associations, which can be indexed by the number of friends or data sources, and the link weight denotes the correlation between users/data sources. Then, based on the SNS graph, the two-step algorithm is proposed in this paper to determine the placement of SNS related data among VMs. Two k-means based clustering schemes are proposed to allocate social data in proper VM and physical servers for pre-configured VM and dynamic VM environment, respectively. The experimental example was conducted and to illustrate and compare the performance of the proposed schemes.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.

• Journal of Multimedia Information System
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• v.7 no.3
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• pp.197-204
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• 2020

Superpixel methods are widely used in the preprocessing stage as a method to reduce computational complexity by simplifying images while maintaining the characteristics of the images in the computer vision applications. It is common to generate superpixels of similar size and shape based on the pixel values rather than considering the characteristics of the image. In this paper, we propose a method to control the sizes and shapes of generated superpixels, considering the contents of an image. The proposed method consists of two steps. The first step is to over-segment an image so that the boundary information of the image is well preserved. In the second step, generated superpixels are merged based on similarity to produce the target number of superpixels, where the shapes of superpixels are controlled by limiting the maximum size and the proposed roundness metric. Experimental results show that the proposed method preserves the boundaries of the objects in an image more accurately than the existing method.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.803-805
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• 2000

In this paper, we propose fuzzy-neural Networks(FNN) which is useful for identification algorithms. The proposed FNN model consists of two steps: the first step, which determines premise and consequent parameters approximately using FCM_RI method, the second step, which adjusts the premise and consequent parameters more precisely by gradient descent algorithm. The FCM_RI algorithm consists FCM clustering algorithm and Recursive least squared(RLS) method, this divides the input space more efficiently than convention methods by taking into consideration correlations between components of sample data. To evaluate the performance of the proposed FNN model, we use the time series data for gas furnace.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.43-46
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• 2008

Centrality has been actively studied in network analysis field. In this paper we show a calculation method of closeness centrality for WSN. Since nodes in a sensor network are very scarce in energy and computation capability the calculation of the closeness is done in two tiers by dividing network into clusters. In first step closeness centrality for cluster heads is calculated. In the second step closeness of member nodes of the chosen cluster is computed in respect to that cluster itself.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.7
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• pp.340-353
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• 2008

A computational color palette extraction model is introduced to describe paint brush objectively and efficiently. In this model, a color palette is defined as a minimum set of colors in which a painting can be displayed within error allowance and extracted by the two step processing of color grouping and major color extraction. The color grouping controls the resolution of colors adaptively and produces a basic color set of given painting images. The final palette is obtained from the basic color set by applying weighted k-means clustering algorithm. The extracted palettes from several famous painters are displayed in a 3-D color space to show the distinctive palette styles using RGB and CIE LAB color models individually. And the two experiments of painter classification and color transform of photographic image has been done to check the performance of the proposed method. The results shows the possibility that the proposed palette model can be a computational color analysis metric to describe the paint brush, and can be a color transform tool for computer graphics.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.703-710
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• 2003

Because of their human-like synthesized speech quality, recently Corpus-Based Text-To-Speech(CB-TTS) have been actively studied worldwide. However, due to their large size speech database (DB), their application is very restricted. In this paper we propose and evaluate three DB reduction algorithms to which are designed to solve the above drawback. The first method is based on a K-means clustering approach, which selects k-representatives among multiple instances. The second method is keeping only those unit instances that are selected during synthesis, using a domain-restricted text as input to the synthesizer. The third method is a kind of hybrid approach of the above two methods and is using a large text as input in the system. After synthesizing the given sentences, the used unit instances and their occurrence information is extracted. As next step a modified K-means clustering is applied, which takes into account also the occurrence information of the selected unit instances, Finally we compare three pruning methods by evaluating the synthesized speech quality for the similar DB reduction rate, Based on perceptual listening tests, we concluded that the last method shows the best performance among three algorithms. More than this, the results show that the last method is able to reduce DB size without speech quality looses.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.664-677
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• 2011

In order to control interference and improve spectrum efficiency in the femtocell and macrocell overlaid system (FMOS), we propose a joint frequency bandwidth dynamic division, clustering and power control algorithm (JFCPA) for orthogonal-frequency-division-multiple access-based downlink FMOS. The overall system bandwidth is divided into three bands, and the macro-cellular coverage is divided into two areas according to the intensity of the interference from the macro base station to the femtocells, which are dynamically determined by using the JFCPA. A cluster is taken as the unit for frequency reuse among femtocells. We map the problem of clustering to the MAX k-CUT problem with the aim of eliminating the inter-femtocell collision interference, which is solved by a graph-based heuristic algorithm. Frequency bandwidth sharing or splitting between the femtocell tier and the macrocell tier is determined by a step-migration-algorithm-based power control. Simulations conducted to demonstrate the effectiveness of our proposed algorithm showed the frequency-reuse probability of the FMOS reuse band above 97.6% and at least 70% of the frequency bandwidth available for the macrocell tier, which means that the co-tier and the cross-tier interference were effectively controlled. Thus, high spectrum efficiency was achieved. The simulation results also clarified that the planning of frequency resource allocation in FMOS should take into account both the spatial density of femtocells and the interference suffered by them. Statistical results from our simulations also provide guidelines for actual FMOS planning.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.179-187
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• 2009

Most real world systems contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of them. A two-step methodology comprised of clustering and then model creation is proposed for the analysis on time series data. An interface is designed for CRM(Customer Relationship Management) that provides user with 1:1 customized information using system modeling. It was confirmed from experiments that better clustering would be derived from model based approach than similarity based one. Clustering is followed by model creation over the clustered groups, by which future direction of time series data movement could be predicted. The effectiveness of the method was validated by checking how similarly predicted values from the models move together with real data such as stock prices.