• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.213-216
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• 2018

본 논문에서는 사람의 인지 시각 특성 중 하나인 JND(Just Noticeable Difference)를 이용한 인지 비디오 부호화 기법을 제안한다. JND 기반 인지 부호화 방법은 사람의 인지 시각 특성을 이용해 시각적으로 인지가 잘 되지 않는 인지 신호를 제거함으로 부호화 효율을 높이는 방법이다. 제안된 방법은 기존 수학적 모델 기반의 JND 기법이 아닌 최근 각광 받고 있는 데이터 중심(data-driven) 모델링 방법인 심층 신경망 기반 JND 모델 생성 기법을 제안한다. 제안된 심층 신경망 기반 JND 모델은 비디오 부호화 과정에서 입력 영상에 대한 전처리를 통해 입력 영상의 인지 중복(perceptual redundancy)를 제거하는 역할을 수행한다. 부호화 실험에서 제안된 방법은 동일하거나 유사한 인지화질을 유지한 상태에서 평균 16.86 %의 부호화 비트를 감소 시켰다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9A
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• pp.898-904
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• 2007

I must receive node discernment address for communication between node that participate to network in MANETs(Mobile Ad-hoc Networks). Address is created by node confidence or different node. I achieve address redundancy check (Duplicate Address Detection) to examine whether this address is available unique address. However, this method happens problem that MANETs' extensity drops. This paper can manage by group unit binding transfer nodes to group in MANETs. I suggest method that apply special quality of cluster that exchange subordinate decrease and mobility government official of control message are easy in address assignment protocol minimize time required in redundancy check and solves extensity problem. Method that propose in this paper shows excellent performance according to node number increase than wave and MANETConf [2] through simulation.

• The KIPS Transactions:PartC
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• v.10C no.2
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• pp.199-208
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• 2003

LDAP (Lightweight Directory Access Protocol) Directory Service provides information for locating resources like files and devices over the network such as Internet or Intranet. Since LDAP is widely accepted as one of the standard directory service structure for the Internet, it is desirable that a group of LDAP servers works transparently and continuously even if the related network partitions temporally, through maintaining replicated directory information among those LDAP servers. In this paper, we describe the design and implementation of a robust LDAP sewer, which runs as a process group in JACE group communication system, and the associated LDAP service provider which enables Java applications to use the developed LDAP directory service.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.95-99
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• 2007

오버레이 멀티캐스트는 응용 계층을 기반으로 멀티캐스트 트리를 구성함으로써 서비스 보급을 위한 비용 소모를 최소화하고 확장성 있는 일대다 통신 구조를 지원한다. 그러나 이를 이동망에 적용하기 위해서는 이동성 지원 메커니즘과 패킷 손실 및 패킷 중복 수신 방지 기능을 함께 지원하는 오버레이 멀티캐스트 기법이 요구된다. 본 논문에서는 이를 해결하기 위하여 오버레이 멀티캐스트 기법인 mRMCP를 제안한다. 이 기법은 응용 계층 기반의 Advertisement 기능을 이용하여 하위계층의 지원 없이 단말의 이동성을 지원한다. 또한 중계기와 단말 간 데이터 전송 시 독립적인 멀티캐스트 채널을 활용함으로써 패킷 중복 수신을 최소화한다. 이와 같은 기능을 통해 mRMCP는 이동 환경에서 확장성 있는 멀티캐스트 서비스를 제공한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.773-775
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• 2003

이동성을 갖는 장치들의 위치 정보를 관리하기 위하여 이동체 데이터베이스에 관한 연구가 필요하게 되었다. 이동체 색인의 검색에서 영역 질의와 궤적 질의는 공간 근접성과 궤적 연결성과 같이 상반된 특징으로 인하여 함께 고려되지 않았다. 이동체 색인에서 영역 질의의 성능개선을 위해서는 노드간의 심한 중복과 사장 공간(Dead space)을 줄여야 하고, 궤적 질의의 성능 개선을 위해서는 이동체의 궤적 보전이 이루어져야 한다. 이와 같은 요구 조건을 만족하기 위해, 이 논문에서는 R-tree를 기반의 색인 구조에서 새로운 분할 정책을 제안한다. 제시하는 색인 구조에서 단말 노드의 엔트리는 궤적이며, 비단말 노드의 엔트리는 자식 노드이다. 단말 노드 분할 정책에서 동일 궤적을 그룹화해서 분할 하는 공간 축 분할 정책과 공간 활용도를 높이는 시간 축 분할 정책을 제안한다. 시간 축 분할 후 사장영역이 클 경우에는 다중 분할을 수행하여 사장 공간을 줄이고 노드간의 중복을 최소화한다. 비 단말 노드 분할 정책에서는 같은 궤적을 저장하는 노드들을 연결 노드(Connected Node)라고 정의하고, 엔트리의 궤적 연결성을 기준으로 분할한다.

• Journal of Fisheries and Marine Sciences Education
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• v.9 no.2
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• pp.222-235
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• 1997

On the basis of the seven species of fish caught by fishing boats with one crewmen belonging to the Iwawada Fisheries Cooperative of Chiba Prefecture, the fishing boats were classified by species with high market values, and the results obtained were reclassified by discriminant function. As a result, the fishing boats were classified into six groups. These six groups are : G1 featuring the main catches of yellowtails ; G2 flounders ; G3 skipjack tunas, G4 squids ; G5 demersal fish, and G6 other miscellaneous species. Furthermore, principal component analysis were carried out on fish catches of the seven species in terms of the value obtained from a catch from the scores of the first, second, third and fourth principal components. The results of analysis show that fishing boats with one crewman can be broadly classified into three groups ; i.e., Groups G1/G2, Groups G3/G4/G5 and Group G6.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.40-49
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• 2020

This study analyzed the factors affecting the ruling of litigation on the housing reconstruction and housing redevelopment project based on the cross analysis and logistic regression analysis. According to cross analysis result, the defensive process prerequisite group was showed significant in many variables which were past legal relation, no ownership or association member status, double lawsuit, abuse of lawsuit right·litigation trust, existence of claim-preclusion, no standing to sue·no standing to be sued, lapse of litigation period, no legal interest, no own defect of approval. On the other hand, the offensive cause of action group was found to have no significant variable. According to logistic regression result, the defensive process prerequisite group was showed significant in many variables which were past legal relation, no standing to sue·no standing to be sued, no ownership or association member status, double lawsuit, no own defect of approval, abuse of lawsuit right·litigation trust, existence of claim-preclusion. Meanwhile, the offensive cause of action group was showed significant in only one variable that was defect in relation with articles of association. Overall, it is noteworthy that the offensive cause group showed very low significant results compared with the defensive process prerequisite group.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.153-169
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• 2011

Today using Internet environment is considered absolutely essential for establishing corporate marketing strategy. Companies have promoted their products and services through various ways of on-line marketing activities such as providing gifts and points to customers in exchange for participating in events, which is based on customers' membership data. Since companies can use these membership data to enhance their marketing efforts through various data analysis, appropriate website membership management may play an important role in increasing the effectiveness of on-line marketing campaign. Despite the growing interests in proper membership management, however, there have been difficulties in identifying inappropriate members who can weaken on-line marketing effectiveness. In on-line environment, customers tend to not reveal themselves clearly compared to off-line market. Customers who have malicious intent are able to create duplicate IDs by using others' names illegally or faking login information during joining membership. Since the duplicate members are likely to intercept gifts and points that should be sent to appropriate customers who deserve them, this can result in ineffective marketing efforts. Considering that the number of website members and its related marketing costs are significantly increasing, it is necessary for companies to find efficient ways to screen and exclude unfavorable troublemakers who are duplicate members. With this motivation, this study proposes an approach for managing duplicate membership based on the social network analysis and verifies its effectiveness using membership data gathered from real websites. A social network is a social structure made up of actors called nodes, which are tied by one or more specific types of interdependency. Social networks represent the relationship between the nodes and show the direction and strength of the relationship. Various analytical techniques have been proposed based on the social relationships, such as centrality analysis, structural holes analysis, structural equivalents analysis, and so on. Component analysis, one of the social network analysis techniques, deals with the sub-networks that form meaningful information in the group connection. We propose a method for managing duplicate memberships using component analysis. The procedure is as follows. First step is to identify membership attributes that will be used for analyzing relationship patterns among memberships. Membership attributes include ID, telephone number, address, posting time, IP address, and so on. Second step is to compose social matrices based on the identified membership attributes and aggregate the values of each social matrix into a combined social matrix. The combined social matrix represents how strong pairs of nodes are connected together. When a pair of nodes is strongly connected, we expect that those nodes are likely to be duplicate memberships. The combined social matrix is transformed into a binary matrix with '0' or '1' of cell values using a relationship criterion that determines whether the membership is duplicate or not. Third step is to conduct a component analysis for the combined social matrix in order to identify component nodes and isolated nodes. Fourth, identify the number of real memberships and calculate the reliability of website membership based on the component analysis results. The proposed procedure was applied to three real websites operated by a pharmaceutical company. The empirical results showed that the proposed method was superior to the traditional database approach using simple address comparison. In conclusion, this study is expected to shed some light on how social network analysis can enhance a reliable on-line marketing performance by efficiently and effectively identifying duplicate memberships of websites.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06d
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• pp.28-30
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• 2006

스트리밍 서비스는 인터넷 트래픽의 많은 부분을 차지할 정도로 인기 있는 서비스가 되었고, 확장성을 위해 P2P기반의 스트리밍 서비스가 제안되었다. P2P기반 스트리밍 환경은 빈번한 피어들의 떠남과 합류가 일어난다. 이러한 멀티캐스트 그룹의 변화에 대처하기 위해서 다중 멀티캐스트 트리가 제안되었다. 이는 중복성을 통해 멀티캐스트 그룹의 변화에 따른 영향을 줄였다. 하지만 노드의 능력 차이를 고려하지 않았기 때문에 트리가 길어지고, 불안정해질 수 있다. 이를 위해 본 논문은 노드의 능력을 고려한 내구적 멀티캐스트 트리 생성 기법(R-CAT)을 제시하여 우수 노드를 트리의 상층부에 위치시킴으로써 트리의 길이를 줄이고 트리 상층부의 안정화 문제를 해결할 수 있다. 또한 제시한 기법의 유효성을 증명하기 위해 기존의 SplitStream을 확장해서 R-CAT을 구현, 비교 검증한다.