• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.27-44
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• 1999

Digital maps produced in Korea are various in scale and include a lot of geographic and attribute data. In this study, it is argued that, to reduce the production cost and the difficulties for renewal, it is necessary to establish the already nationally drawn 1:5,000 scale digital maps as the base maps and simplify them as much as the TIGER files in the U.S. The comprehensive data included in the digital maps in Korea are mostly land use information, which are supposed to be established separately from the digital maps. The land use information system could be maintained and updated cheaply and frequently at the local government level. In response to common needs, the land use information could be imported to GIS and used for analyses. As technologies and societies changes, the Census questions and methodologies should be changed for better uses. Along with GIS, the Census would be developed and processed more reliably and efficiently. Also, it is recommended for Korean government to develop the Census Tract and Block Group system. Current Eup, Myon, Dong as basic units for Census information may not be useful or effective for micro level urban analyses and public service planning activities because of their large population and land areas. It is recommended that optimum population of a Census Tract be 5,000 and a Block Groups 1,500, and one Census Tract includes 1~9 Block Groups. It is recommend that Census Tract and Block Group boundary lines be decided flexibly in light of population, physical features, socio-economic attributes, and tradition. For urban analyses using GIS, socio-economic census data, city government's information such as parcel data and building permit data, survey data, and satellite image data could also be used. The existence of Census Tracts and Block Groups as well as GIS could help for the data and methods to be useful for urban analyses and public service provisions.

• Geotechnical Engineering
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• v.14 no.3
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• pp.25-46
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• 1998

This paper shows that there are the results of a series of model tests on the behavior of single pipe pile which is subjected to lateral load in, Nak-dong River sand. The purpose of the present paper is to estimate the effect of Non-homogeneity. constraint condition of pile head, lateral load velocity, relative density, and embedded length of pile on the behavior of single pile. These effects can be quantified only by the results of model tests. Also, these are compared with the results of the numerical methods (p-y method, modified Vlasov method; new ${\gamma}$ parameter, Characteristic Load Method'CLM). In this study, a new ${\gamma}$ parameter equation based on the Vlasov method was developed to calculate the modulus of subgrade reaction (E. : nhz.) proportional to the depth. The p-y method of analysis is characterized by nonlinear behavior. and is an effective method of designing deep foundations subjected to lateral loads. The new method, which is called the characteristic load method (CLM). is simpler than p-y analysis. but its results closely approximates p-y analysis results. The method uses dimensional analysis to characterize the nonlinear behavior of laterally loaded piles with respect to be relationships among dimensionless variables. The modulus of subgrade reaction used in p-y analysis and modified Vlasov method obtained from back analysis using direct shear test (DST) results. The coefficients obtained from DST and the modified ones used for the prediction of lateral behavior of ultimate soil reaction range from 0.014 to 0.05. and from 0.2 to 0.4 respectively. It is shown that the predicted numerical results by the new method (CLM), p-y analysis, and modified Vlasov method (new parameter) agree well with measured results as the relative density increases. Also, the characteristic load method established applicability on the Q-Mnu. relationship below y/D=0.2.

• Progress in Medical Physics
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• v.18 no.3
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• pp.134-138
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• 2007

The current PET/CT system with high quality CT images not only increases diagnostic value by providing anatomic localization, but also shortens the acquisition time for attenuation correction than primary PET system. All commercially available PET/CT system uses the CT scan for attenuation correction instead of the transmission scan using radioactive source such as $^{137}Cs,\;^{68}Ge$. However the CT scan may substantially increase the patient dose. The purpose of this study was to evaluate quality of PET images reconstructed by CT attenuation map using various tube currents. in this study, images were acquired for 3D Hoffman brain phantom and cylindrical phantom using GE DSTe PET/CT system. The emission data were acquired for 10 min using phantoms after injecting 44.03 MBq of $^{18}F-FDG$. The CT images for attenuation map were acquired by changing tube current from 10 mA to 95 mA with fixed exposure time of 8 sec and fixed tube voltage of 140 kVp. The PET images were reconstructed using these CT attenuation maps. Image quality of CT images was evaluated by measuring SD (standard deviation) of cylindrical phantom which was filled with water and $^{18}F-FDG$ solution. The PET images were evaluated by measuring the activity ratio between gray matter and white matter in Hoffman phantom images. SDs of CT images decrease by increasing tube current. When PET images were reconstructed using CT attenuation maps with various tube currents, the activity ratios between gray matter and white matter of PET images were almost same. These results indicated that the quality of the PET images using low dose CT data were comparable to the PET images using general dose CT data. Therefore, the use of low dose CT is recommended than the use of general dose CT, when the diagnostic high quality CT is not required. Further studies may need to be performed for other system, since this study is limited to the GE DSTe system used in this study.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1141-1153
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• 2010

This paper introduces ONLIS(Overlay Multicast Network for Live IPTV Service), a novel overlay multicast network optimized to deliver live broadcast IPTV stream. We analyzed IPTV reference model of ITU-T IPTV standardization group in terms of network and stream delivery from the source networks to the customer networks. Based on the analysis, we divide IPTV reference model into 3 networks; source network, core network and access network, ION(Infrastructure-based Overlay Multicast Network) is employed for the source and core networks and PON(P2P-based Overlay Multicast Network) is applied to the access networks. ION provides an efficient, reliable and stable stream distribution with very negligible delay while PON provides bandwidth efficient and cost effective streaming with a little tolerable delay. The most important challenge in live P2P streaming is to reduce end-to-end delay without sacrificing stream quality. Actually, there is always a trade-off between delay & stream quality in conventional live P2P streaming system. To solve this problem, we propose two approaches. Firstly, we propose DSPT(Distributed Streaming P2P Tree) which takes advantage of combinational overlay multicasting. In DSPT, a peer doesn't fully rely on SP(Supplying Peer) to get the live stream, but it cooperates with its local ANR(Access Network Relay) to reduce delay and improve stream quality. When RP detects bandwidth drop in SP, it immediately switches the connection from SP to ANR and continues to receive stream without any packet loss. DSPT uses distributed P2P streaming technique to let the peer share the stream to the extent of its available bandwidth. This means, if RP can't receive the whole stream from SP due to lack of SP's uploading bandwidth, then it receives only partial stream from SP and the rest from the ANR. The proposed distributed P2P streaming improves P2P networking efficiency.

• 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.

• Korean Security Journal
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• no.13
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• pp.351-382
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• 2007

This study seeks to analyze various aspects of women's fear of sexual crime committed against them within the Seoul metropolitan subway system, which takes center stage in public transportation today in Korea; that is, among different kinds of fear of crime, women's fear of sexual crime is empirically examined, and it is expected that the results of this study shall serve as an important basis for future policy-making, so that the fear of sexual crime against women in the subway system could be reduced. To the end, this study aims first, to investigate the real picture of women's using the subway and their attire, second, to look into the amount of information on such crime obtained and the level of its awareness, third, to analyze the characteristics of the fear of sexual crime in the subway system, and lastly, to empirically examine the relationship between women's regular women's regular attire/their level of information on such crime obtained and their fear of sexual crime. As a quantitative research method to discover facts, this study utilizes reality-analyztical and technical research methods, and for its final statistical analysis, uses questionnaire answered and returned by 509 women, out of a total of 520 female commuters on the Seoul metropolitan subway system who had originally been requested to participate in the survey. The result of this study demonstrates that the level of women's fear of sexual crime on the subway is relatively high. In detail, the higher their monthly income is, the more fearful women feel on the subway; it has also been found that women living in housing they own or in leased housing on deposits (Jeonse) fear sexual crime on the subway more than those living in the other forms of housing. However, the level of fear has been found to be low for those types of sexual crime judged to be relatively unlikely to be committed. Lastly the result of the relationship between women's regular attire/their level of information on such crime obtained and their fear of sexual crime is relatively high and very effective.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.359-365
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• 2013

Estimate breeding value can be used as single trait animal model was developed directly using the Fortran language program. The program is based on data computed by using the indirect method repeatedly. The program develops a common algorithm and imprves efficiency. Algorithm efficiency was compared between the two programs. Estimated using the solution is easy to farm and brand the service, pedigree data base was associated with the development of an improved system. The existing program that uses the single trait animal model and the comparative analysis of efficiency is weak because the estimation of the solution and the conventional algorithm programmed through regular formulation involve many repetition; therefore, the newly developed algorithm was conducted to improve speed by reducing the repetition. Single trait animal model was used to analyze Gauss-Seidel iteration method, and the aforesaid two algorithms were compared thorough the mixed model equation which is used the most commonly in estimating the current breeding value by applying the procedures such as the preparation of information necessary for modelling, removal of duplicative data, verifying the parent information of based population in the pedigree data, and assigning sequential numbers, etc. The existing conventional algorithm is the method for reading and recording the data by utilizing the successive repetitive sentences, while new algorithm is the method for directly generating the left hand side for estimation based on effect. Two programs were developed to ensure the accurate evaluation. BLUPF90 and MTDFREML were compared using the estimated solution. In relation to the pearson and spearman correlation, the estimated breeding value correlation coefficients were highest among all traits over 99.5%. Depending on the breeding value of the high correlation in Model I and Model II, accurate evaluation can be found. The number of iteration to convergence was 2,568 in Model I and 1,038 in Model II. The speed of solving was 256.008 seconds in Model I and 235.729 seconds in Model II. Model II had a speed of approximately 10% more than Model I. Therefore, it is considered to be much more effective to analyze large data through the improved algorithm than the existing method. If the corresponding program is systemized and utilized for the consulting of farm and industrial services, it would make contribution to the early selection of individual, shorten the generation, and cultivation of superior groups, and help develop the Hanwoo industry further through the improvement of breeding value based enhancement, ultimately paving the way for the country to evolve into an advanced livestock country.

• Journal of Distribution Science
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• v.10 no.1
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• pp.43-52
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• 2012

With the slowdown in environmental issues and the change of environmental consciousness, ecotourism is being discussed in various social fields. Ecotourism is being popularized for environmental protection, and now it is becoming a mainstream product from one of mass tourism. Ecotourism's emphasis on sustainable development in the tourism destination's society, economy, and environment, through ecotourism study and education, enable people to understand the core value of the ecological environment. 2011 was nominated as "the Year of World Forest" by the UN. In the recent years, forests are becoming increasingly important with their own values and functions in environment, economy, society, and culture. In particular, the global environmental issues caused by climate change are becoming an international agenda. Forests are the only effective solution for the carbon dioxide that causes global warming. Moreover, forests constitute a major part of ecotourism, and are now most used by ecotourists. For example, Korea, wherein 60% of the land is forest, attracts ecotourists. With the increasing interests in environment, the number of tourists visiting the ecosystem forest, which is highly valued for its conservation, is increasing significantly every year and is receiving considerable attention from the government. However, poor facilities in the forest ecotourism sites and improper market strategies are the reasons for the poor running of these sites. Furthermore, tourists' environmental awareness affects ecology environmental pollution or the optimization of forest ecotourism. In order to verify the relationships among tourist attractiveness, environmental consciousness, charm degrees of the attractions, and attitudes after tours, we established some scales based on existing research achievement. Then, using these scales, the researcher completed the questionnaire survey. From December 20, 2010 to February 20, 2011, after conducting surveys for 12 weeks, we finally obtained 582 valid questionnaires, from a total of 700 questionnaires, that could be used in statistical analysis. First, for the method of research and analysis, the researcher initially applied the Cronbach's (Alpha) for verifying the reliability, and subsequently applied the Exploratory factor analysis for verifying the validity. Second, in order to analyze the demographics, the researcher makes use of the Frequency analysis for the AMOS, measurement model, structural equation model computing, and also utilizes construct validity, convergent validity, discriminant validity, and nomological validity. Third, for the analysis of the ecotourists' environmental consciousness, impacts on tourist attractiveness, charm degrees of the attractions, and attitudes after the tour, the researcher uses AMOS 19, with the path analysis and equation of structure. After the research, researchers found that high awareness of natural protection lead to high tourist motivation and satisfaction and more positive attitude after the tour. Moreover, this research shows the psychological and behavioral reactions of the ecotourists to the ecotourist development. Accordingly, environmental consciousness does not affect the tourist attractiveness that has been interpreted as significant. Furthermore, people should focus on the change of natural protection consciousness and psychological reaction of ecotourists while ensuring the sustainable development of ecotourists and developing some ecotourist programs.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.