• Journal of the Economic Geographical Society of Korea
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• v.11 no.3
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• pp.428-441
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• 2008

This article verifies the actual conditions of the Wonju area medical devices cluster for presentation of method for increasing promotion. It examines the general status of companies, reason for location, competitive power and supporting system with a questionnaire survey and in-depth research. The Wonju area has 79 medical device companies. It comprised 9% of total sales and 11% of export sales of korea medical devices in 2007. For enhancement of the Wonju area medical devices ability to accumulate and attract of medical device companies and front-line and back-line industries, the followings is needed, a supply of highly qualified man power, a support base for developing modem technology and information marketing, adequate infrastructure for housing and education system, methodologies for sustaining new business and innovation fund-raising programs and marketing, and provide the highest degree of education for CEO.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.2
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• pp.184-191
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• 2017

This paper presents cluster based hybrid spectrum sensing system for M2M services. For each cluster, secondary nodes within the transmission radius of the primary node use hard decision method through local spectrum sensing to determine whether the primary node exists. And the other secondary nodes and the secondary nodes having poor radio channel conditions judge the existence of the primary node through the soft decision method of the values obtained by performing the cooperative spectrum sensing. In the proposed hybrid spectrum sensing system, the performance according to the number of secondary nodes is analyzed with the conventional system over Rayleigh fading channel. As the number of cooperative sensing users increased to 2, 3 and 4, the cluster error probability decreased to 0.5608, 0.5252 and 0.4001 at SNR of -10[dB] respectively. Since the proposed system uses less overhead traffic, it is found that it is more effective in terms of frequency usage than the conventional system using soft decision-soft decision and soft decision-hard decision methods.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.1-13
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• 2013

Recent advance and popularization of smart devices and web application services based on cloud computing have made end-users to directly produce and, at the same time, consume the image contents. This leads to demands of unified contents management services. Thus, this paper proposestag clustering method based on semantic similarity for effective image categorization. We calculate the cost of semantic similarity between tags and cluster tags that are closely related. If tags are in a cluster, we suppose that images with them are also in a same cluster. Furthermore, we could recommend tags for new images on the basis of initial clusters.

• Journal of the Korea Computer Industry Society
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• v.3 no.1
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• pp.77-84
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• 2002

An cluster system used consist of large number of running servers, one has the problem that does the low availability occured by the high chance of the server failures and it is difficult to provide occuring software aging. In this paper, running cluster web servers consists of n primary servers and k backup servers, based on the operational parameters such as number of running primary servers, number of backup severs, rejuvenation period, rejuvenation time, failure rate of sewers, repair rate of servers, unstable rate of servers. We calculate to evaluate the rejuvenation policy such steady-state probabilities, downtime, availability, and downtime cost. We validate the solutions of mathematical model by experiments based on various operation parameters and find that the software rejuvenation method can be adopted as prventive fault tolerant technique for stability of system. The failure rate and unstable rate of the servers are essential factors for decision making of the rejuvenation policies.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.451-462
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• 2019

Document-term frequency matrix is a type of data used in text mining. This matrix is often based on various documents provided by the objects to be analyzed. When analyzing objects using this matrix, researchers generally select only terms that are common in documents belonging to one object as keywords. Keywords are used to analyze the object. However, this method misses the unique information of the individual document as well as causes a problem of removing potential keywords that occur frequently in a specific document. In this study, we define data that can overcome this problem as proximity data. We introduce twelve methods that generate proximity data and cluster the objects through two clustering methods of multidimensional scaling and k-means cluster analysis. Finally, we choose the best method to be optimized for clustering the object.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.184-194
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• 2023

Purpose: The purpose of this study is to identify the characteristics of C02 emissions by road before establishing a policy to reduce greenhouse gas emissions. Method: As for the analysis method, the traffic volume and speed of the road were estimated using the traffic Assignment model targeting 27 arterial road axes in Incheon Metropolitan City. And, after estimating CO2 emissions by road axis by applying this, the characteristics of each group were analyzed through cluster analysis. Result: As a result of cluster analysis using total CO2 emissions, CO2 emissions by truck vehicles, and the ratio of truck vehicle emissions to total carbon dioxide emissions, four clusters were classified. When examining the characteristics of each road included in each group, it was analyzed that the characteristics of each group appeared according to the level of impact by CO2 emissions and truck vehicles. Conclusion: It is judged that it is necessary to establish a plan in consideration of CO2 emission characteristics for road CO2 management for greenhouse gas reduction.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.161-171
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• 2010

Sensor Networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects forged reports into the network through compromised nodes. Such attacks by compromised sensors can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. Ye et al. proposed the Statistical En-route Filtering scheme to overcome this threat. In statistical en-route filtering scheme, all the intermediate nodes perform verification as event reports created by center of stimulus node are forwarded to the base station. This paper applies a probabilistic verification method to the Static Statistical En-route Filtering for energy efficiency. It is expected that the farther from the base station an event source is, the higher energy efficiency is achieved.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.37-47
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• 2024

In the summer of 2018, the Korea-Japan (KJ) region experienced an extremely severe and prolonged heatwave. This study examines the GloSea6 model's prediction performance for the 2018 KJ heatwave event and investigates how its prediction skill is related to large-scale circulation patterns identified by the k-means clustering method. Cluster 1 pattern is characterized by a KJ high-pressure anomaly, Cluster 2 pattern is distinguished by an Eastern European high-pressure anomaly, and Cluster 3 pattern is associated with a Pacific-Japan pattern-like anomaly. By analyzing the spatial correlation coefficients between these three identified circulation patterns and GloSea6 predictions, we assessed the contribution of each circulation pattern to the heatwave lifecycle. Our results show that the Eastern European high-pressure pattern, in particular, plays a significant role in predicting the evolution of the development and peak phases of the 2018 KJ heatwave approximately two weeks in advance. Furthermore, this study suggests that an accurate representation of large-scale atmospheric circulations in upstream regions is a key factor in seasonal forecast models for improving the predictability of extreme weather events, such as the 2018 KJ heatwave.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.201-208
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• 2005

Recently, the container terminal requires the study of method to increase efficiency through change of its operation method. Loading plan is a very important part to increase the efficiency of container terminal. Loading Plan is largely divided into two cases, deciding loading location and loading scheduling and this Paper proposes a more efficient method of container loading scheduling. Container loading scheduling is a problem of combination optimization to consider several items of loading location and operation equipments. etc. An existing method of cluster composition that decides the order of container loading scheduling has a restriction to increase the efficiency of work owing to rehandling problem. Therefore, we Propose a more efficient method of container loading scheduling which composes containers with identical attribution, based on ship loading list and yard map, into stack units of cluster, applying to hierarchical clustering method, and defines the restriction of working order. In this process, we can see a possible working path among clusters by defining the restriction of working order and search efficiency will be increased because of restricted search for working path.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.1-8
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• 2018

K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, this method has the limitation to be used with fixed number of clusters because of only considering the intra-cluster distance to evaluate the data clustering solutions. Silhouette is useful and stable valid index to decide the data clustering solution with number of clusters to consider the intra and inter cluster distance for unsupervised data. However, this valid index has high computational burden because of considering quality measure for each data object. The objective of this paper is to propose the fast and simple speed-up method to overcome this limitation to use silhouette for the effective large-scale data clustering. In the first step, the proposed method calculates and saves the distance for each data once. In the second step, this distance matrix is used to calculate the relative distance rate ($V_j$) of each data j and this rate is used to choose the suitable number of clusters without much computation time. In the third step, the proposed efficient heuristic algorithm (Group search optimization, GSO, in this paper) can search the global optimum with saving computational capacity with good initial solutions using $V_j$ probabilistically for the data clustering. The performance of our proposed method is validated to save significantly computation time against the original silhouette only using Ruspini, Iris, Wine and Breast cancer in UCI machine learning repository datasets by experiment and analysis. Especially, the performance of our proposed method is much better than previous method for the larger size of data.