• Proceedings of the Korea Society for Industrial Systems Conference
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• 1997.03a
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• pp.129-139
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• 1997

다양한 실세계의 표현은 주관적인 의미가 내포되어 있어 데이터의 모델링 과정이 보다 중요하며 이 과정에서 데이터 손실을 최소화시켜야 한다. 이러한 성향의 모든 데이터를 수용하기위한 퍼지데이터베이스 시스템 구축시에는 데이터가 퍼지 집합으로 표현되어야 하고 불확실하고 다양한 형태의 질의가 가능하며 신뢰성 있는 응답 제시되어야 한다. 본 논문에서는 불활실함의 의미를 최대한 반영하여 표현을 다양화 시키고 사용자의 주관적인 인식수용을 위한 데이터의 개별화와 레벨의 다양화를 위한 클러스터링(clustering)기법을 보인다. 이를 통해 영역구조를 병합 분리시켜 데이터베이스의 릴레이션에서의 도메인요소의 첨가와 삭제를 통하여 자유로운 질의에 대한 불확실성이 감소된 응답과 융통성이 부여된 퍼지질의 처리를 보여준다.

• Annual Conference of KIPS
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• 2015.04a
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• pp.706-709
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• 2015

영향력 전파 문제는 주어진 네트워크 환경에서 영향력을 최대화 할 수 있는 top-k 노드를 찾는 문제로 데이터 마이닝 분야에서 활발히 연구되어왔다. 본 논문에서는 그래프 클러스터링 기법을 사용하여 영향력을 전파하는 방법을 제안하고자 한다. 이러한 방법에는 두 가지 이점이 있는데 먼저 서로 다른 시드 사이에 영향력이 중복되는 블로킹 현상을 제거하여 수행시간을 단축시킬 수 있다. 다음으로는 유 방향 그래프인 경우 기존의 탐욕 알고리즘보다 더 많은 노드에 전파를 가능하게 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.630-637
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• 2018

Harmony search (HS) is a recently developed metaheuristic optimization algorithm. HS is inspired by the process of musical improvisation and repeatedly searches for the optimal solution using three operations: random selection, memory recall (or harmony memory consideration), and pitch adjustment. HS has been applied by many researchers in various fields. The increasing complexity of real-world optimization problems has created enormous challenges for the current technique, and improved techniques of optimization algorithms and HS are required. We propose an improved clustering harmony search (ICHS) that uses a clustering technique to group solutions in harmony memory based on their objective function values. The proposed ICHS performs modified harmony memory consideration in which decision variables of solutions in a high-ranked cluster have higher probability of being selected than those in a low-ranked cluster. The ICHS is demonstrated in various optimization problems, including mathematical benchmark functions and water distribution system pipe design problems. The results show that the proposed ICHS outperforms other improved versions of HS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.9-16
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• 2016

Wireless sensor networks (WSN) are composed of sensor nodes and a base station. The sensor nodes deploy a non-accessible area, receive critical information, and transmit it to the base station. The information received is applied to real-time monitoring, distribution, medical service, etc.. Recently, the WSN was extended to mobile wireless sensor networks (MWSN). The MWSN has been applied to wild animal tracking, marine ecology, etc.. The important issues are mobility and energy consumption in MWSN. Because of the limited energy of the sensor nodes, the energy consumption for data transmission affects the lifetime of the network. Therefore, efficient data transmission from the sensor nodes to the base station is necessary for sensing data. This paper, proposes an energy consumption model using two-tier clustering in mobile sensor networks (TTCM). This method divides the entire network into two layers. The mobility problem was considered, whole energy consumption was decreased and clustering methods of recent researches were analyzed for the proposed energy consumption model. Through analysis and simulation, the proposed TTCM was found to be better than the previous clustering method in mobile sensor networks at point of the network energy efficiency.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.90-99
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• 2016

Forecasting of box office performance after a film release is very important, from the viewpoint of increase profitability by reducing the production cost and the marketing cost. Analysis of psychological factors such as word-of-mouth and expert assessment is essential, but hard to perform due to the difficulties of data collection. Information technology such as web crawling and text mining can help to overcome this situation. For effective text mining, categorization of objects is required. In this perspective, the objective of this study is to provide a framework for classifying films according to their characteristics. Data including psychological factors are collected from Web sites using the web crawling. A clustering analysis is conducted to classify films and a series of one-way ANOVA analysis are conducted to statistically verify the differences of characteristics among groups. The result of the cluster analysis based on the review and revenues shows that the films can be categorized into four distinct groups and the differences of characteristics are statistically significant. The first group is high sales of the box office and the number of clicks on reviews is higher than other groups. The characteristic of the second group is similar with the 1st group, while the length of review is longer and the box office sales are not good. The third group's audiences prefer to documentaries and animations and the number of comments and interests are significantly lower than other groups. The last group prefer to criminal, thriller and suspense genre. Correspondence analysis is also conducted to match the groups and intrinsic characteristics of films such as genre, movie rating and nation.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.4
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• pp.41-50
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• 2012

Network life time is very important issue for wireless sensor network(WSN). It is very important to design sensor networks for sensors to utilize their energies in effective ways. A-PEGASIS that basically bases on PEGASIS and enhances in two aspects-an elegant chain generation algorithm and periodical update of chains. However, it has problems in the chain generation mechanism and some possibility of network partitioning or sensing hole problem in the network, in LEACH related protocols. This dissertation proposes a new clustering protocol to solve the co-shared problems in the previous protocols. The basic idea of our scheme is using the table for node connectivity. The results show that the network life time would be extended in about 1.8 times longer than LEACH and 1.5 times longer than PEGASIS-A.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.181-193
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• 2009

This paper presents a framework for controlling mobile multiple robots connected by communication networks. This framework provides novel methods to control coordinated systems using mobile agents. The combination of the mobile agent and mobile multiple robots opens a new horizon of efficient use of mobile robot resources. Instead of physical movement of multiple robots, mobile software agents can migrate from one robot to another so that they can minimize energy consumption in aggregation. The imaginary application is making "carts," such as found in large airports, intelligent. Travelers pick up carts at designated points but leave them arbitrary places. It is a considerable task to re-collect them. It is, therefore, desirable that intelligent carts (intelligent robots) draw themselves together automatically. Simple implementation may be making each cart has a designated assembly point, and when they are free, automatically return to those points. It is easy to implement, but some carts have to travel very long way back to their own assembly point, even though it is located close to some other assembly points. It consumes too much unnecessary energy so that the carts have to have expensive batteries. In order to ameliorate the situation, we employ mobile software agents to locate robots scattered in a field, e.g. an airport, and make them autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO). ACO is the swarm intelligence-based methods, and a multi-agent system that exploit artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. In this paper, we focus on the implementation of the controlling mechanism of the multi-robots using the mobile agents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.263-272
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• 2016

Modular product design has contributed to flexible product modification and development, production lead time reduction, and increasing product diversity. Modular product design aims to develop a product architecture that is composed of detachable modules. These modules are constructed by maximizing the similarity of components based on physical and functional interaction analysis among components. Accordingly, a systematic procedure for clustering the components, which is a main activity in modular product design, is proposed in this paper. The first phase in this procedure is to build a component-to-component correlation matrix by analyzing physical and functional interaction relations among the components. In the second phase, network flow modeling is applied to find clusters of components, maximizing their correlations. In the last phase, a network flow model formulated with linear programming is solved to find the clusters and to make them modular. Finally, the proposed procedure in this research and its application are illustrated with an example of modularization for a vacuum cleaner.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.453-462
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• 2016

Since the Internet provides a way of expressing and sharing Internet users' mindsets, corporate marketers want to acquire measurable and actionable insights from web data. In the past, companies used to analyze the attitude, satisfaction, and loyalty of consumers toward their brands using survey data, whereas nowadays this is done using the big data extracted from Social Network Services. In this study, we propose a framework for clustering brand names using the social metrics gathered on social media. We also conduct a case study of the automobile industry to verify the feasibility of the proposed framework. We calculate the brand name distance for each pair of brand names based on the total number of times that they are mentioned together. These distances are used to project the brand name onto a 3-dimensional space using multidimensional scaling. After the projection, we found the clusters of brand names and identified the characteristics of each cluster. Furthermore, we concluded this paper with a discussion of the limitations and future directions of this research.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.119-127
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• 2014

This study tried to develop a basis for quantitative index of working postures associated with WMSDs (Work-related Musculoskeletal Disorders) that could overcome realistic restriction during application of typical checklists for WMSDs evaluation. The baseline data(for a total of 603 jbs) for this study was obtained from automobile manufacturing company. Specifically, data for back posture was analyzed in this study to have a better and more objective method in terms of job relevance than typical methods such as OWAS, RULA, and REBA. Major statistical tools were clustering, logistic regression and so on. The main results in this study could be summarized as follows; 1) The relationship between working posture and WMSDs symptom at back was statistically significant based on the results from logistic regression, 2) Based on clustering analysis, three levels for WMSDs risk at back were produced for flexion as follows: low risk(< $18.5^{\circ}$), medium risk($18.5^{\circ}{\sim}36.0^{\circ}$), high risk(> $36.0^{\circ}$), 3) The sensitivities on risk levels of back flexion was 93.8% while the specificities on risk levels of back flexion was 99.1%. The results showed that the data associated with back postures in this study could provide a good basis for job evaluation of WMSDs at back. Specifically, this evaluation methodology was different from the methods usually used at WMSDs study since it tried to be based on direct job relevance from real working situation. Further evaluation for other body parts as well as back would provide more stability and reliability in WMSDs evaluation study.