• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• Journal of Advanced Research in Ocean Engineering
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• 제2권4호
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• pp.202-208
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• 2016

In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

• The Journal of Asian Finance, Economics and Business
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• 제8권10호
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• pp.147-158
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• 2021

Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.

• Journal of Contemporary Eastern Asia
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• 제18권1호
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• pp.7-29
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• 2019

This study describes an approach for comparative bibliometric analysis of scientific publications related to (i) individual or several departments comprising a university, and (ii) broader integrated subject areas using multiple disciplinary schemes. It uses a custom dataset of scientific publications (ca. 15,000 articles and reviews, published during 2009-2013, and recorded in the Web of Science Core Collections) with author affiliations to the research departments, dedicated to science, technology, engineering, mathematics, and medicine (STEMM), of a comprehensive university. The dataset was subjected, at first, to the department level and discipline level analyses using the newly available KAKEN-L3 classification (based on MEXT/JSPS Grants-in-Aid system), hierarchical clustering, correspondence analysis to decipher the major departmental and disciplinary clusters, and visualization of the department-discipline relationships using two-dimensional stacked bar diagrams. The next step involved the creation of subsets covering integrated subject areas and a comparative analysis of departmental contributions to a specific area (medical, health and life science) using several disciplinary schemes: Essential Science Indicators (ESI) 22 research fields, SCOPUS 27 subject areas, OECD Frascati 38 subordinate research fields, and KAKEN-L3 66 subject categories. To illustrate the effective use of the science mapping techniques, the same subset for medical, health and life science area was subjected to network analyses for co-occurrences of keywords, bibliographic coupling of the publication sources, and co-citation of sources in the reference lists. The science mapping approach demonstrates the ways to extract information on the prolific research themes, the most frequently used journals for publishing research findings, and the knowledge base underlying the research activities covered by the publications concerned.

• 대한산업공학회지
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• 제39권6호
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• pp.577-586
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• 2013

In a shipyard, it is hard to predict block movement due to the uncertainty caused during the long period of shipbuilding operations. For this reason, block movement is rarely scheduled, while main operations such as assembly, outfitting and painting are scheduled properly. Nonetheless, the high operating costs of block movement compel task managers to attempt its management. To resolve this dilemma, this paper proposes a new block movement analysis framework consisting of the following operations: understanding the entire process, log clustering to obtain manageable processes, discovering the process model and detecting exceptional processes. The proposed framework applies fuzzy mining and trace clustering among the process mining technologies to find main process and define process models easily. We also propose additional methodologies including adjustment of the semantic expression level for process instances to obtain an interpretable process model, definition of each cluster's process model, detection of exceptional processes, and others. The effectiveness of the proposed framework was verified in a case study using real-world event logs generated from the Block Process Monitoring System (BPMS).

• 정보관리연구
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• 제42권1호
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• pp.47-62
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• 2011

이상적 저자-망은 그 노드가 저자를 표현하도록 정의된다. 그러나 실제 자동 생성되는 대부분 저자망의 노드는 저자명을 저자 식별자로 사상시키는 어려움으로 인해 단순히 저자명으로 표현된다. 실 세계 저자를 표현하기 위해 이처럼 저자명을 사용하여 저자망을 구성하는 것은 서로 다른 동명 저자들이 하나의 저자명 노드로 병합됨으로 인해 저자망의 특성을 왜곡하는 문제가 발생한다. 이 연구는 공저 관계에 의존하여 저자명이 갖는 중의성을 해소하고 저자 노드로 구성된 저자망을 자동 생성하는 알고리즘을 제시한다. 공저자 자질의 특성상 이 알고리즘은 과소군집오류를 희생하면서 과다군집오류를 최소화하는 군집 결과를 만든다. 실험에서는 한글 동명 저자명이 출현한 실제 서지레코드 집합을 대상으로 알고리즘의 적용 결과를 제시한다.

• 정보처리학회논문지B
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• 제13B권2호
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• pp.149-154
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• 2006

문서요약은 여러 개의 하위 주제로 구성되어 있는 문서에 대해 문서의 복잡도를 줄이면서 하위 주제를 모두 포함하는 요약문을 생성하는 것이 목적이다. 본 논문은 그래프 분할을 이용하여 하위 주제별로 중요 문장을 추출하는 요약시스템을 제안한다. 문장별 공기정보에 의한 단어의 연관성 분석을 통해 선정된 대표어를 이용하여 문서를 그래프로 표현한다. 그래프는 연결정보에 의해 하위 주제를 의미하는 부분 그래프로 분할되며 부분 그래프는 긴밀한 관계를 갖는 문장들이 클러스터링된 형태이다. 부분 그래프별로 중요 문장을 추출하면 하위 주제별 핵심 내용들로만 요약문을 구성하게 되어 요약 성능이 향상된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.341-360
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• 2012

The network topology for a wide area sensor network has to support connectivity and a prolonged lifetime for the many applications used within it. The concepts of structure and group in sociology are similar to the concept of cluster in wireless sensor networks. The clustering method is one of the preferred ways to produce a topology for reduced electrical energy consumption. We herein propose a cluster topology method based on sociological structures and concepts. The proposed sociological clustering topology (SOCT) is a method that forms a network in two phases. The first phase, which from a sociological perspective is similar to forming a state within a nation, involves using nodes with large transmission capacity to set up the global area for the cluster. The second phase, which is similar to forming a city inside the state, involves using nodes with small transmission capacity to create regional clusters inside the global cluster to provide connectivity within the network. The experimental results show that the proposed method outperforms other methods in terms of energy efficiency and network lifetime.

• 식물분류학회지
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• 제49권1호
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• pp.13-27
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• 2019

Numerical taxonomy is employed to determine the phenetic proximity of the Egyptian taxa belonging to the genus Ononis L. A classical clustering analysis and a principal component analysis (PCA) were used to separate 57 macro- and micromorphological characters in order to circumscribe 11 taxa of Ononis. A clustering analysis using the unweighted pair-group method with the arithmetic means (UPGMA) method gives the highest co-phenetic correlation. Results from clustering and PCA revealed the segregation of five groups. Our results are in line, to some certain degree, with the traditional sub-sectional concept, as can be seen in the grouping of the representative members of the subsections Diffusae and Mittisimae together and the representative members of the subsections Viscosae and Natrix. The phenetic uniqueness of Ononis variegata and O. reclinata subsp. mollis was formally established. However, our findings contradict the classic sectional concept; this opinion was suggested earlier in previous phylogenetic circumscriptions of the genus. The most useful characters that provide taxonomic clarity were discussed.

• 한국문헌정보학회지
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• 제49권1호
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• pp.129-148
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• 2015

This study grasped the true nature of the inquiry domain by analysing the requests for collaborative reference service in the social science field using the co-word technique, and schematized the intellectual structure. First, this study extracted 748 uncontrolled keywords from inquiries for reference in the field of social science. Second, calculated similarity indices between the words on the basis of co-occurrence frequency, and performed not only clustering but also MDS mapping. Third, to grasp the difference in inquiries for reference by period, dividing the period into two parts, and performed comparative analysis. As a result, there formed 5 clusters and "Korea Education" showed an overwhelming size with 40.3% among those clusters. The result of the analysis through the period division showed there were many questions about "Education" during the first half, while a lot of inquiries with focus on "welfare and business information" during the second half.