• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.435-440
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• 2004

연안침수지도는 태풍이나 해일에 의한 연안침수 발생 시 방조제의 월류 및 붕괴로 인한 예상 침수구역을 강우빈도(200년)별로 나타내고, 침수면적과 깊이를 표현한 지도로서 국외 선진국에서 방재형 국토관리 정책결정과 침수피해에 대한 대민 홍보의 수단으로 활용되고 있다. 국내에서도 연안침수에 의한 피해가 나날이 커지고 국토의 개발에 따른 자연 재해가 많아짐에 따라 보다 방재형 국토개발로 국민의 안전과 복리를 증진하고, 연안침수에 의한 피해를 최소화하기 위한 노력의 일환으로 연안침수지도의 제작 필요성이 증대되고 있다. 본 연구의 시범지역인 대부도 남부지역의 GIS 데이터베이스 구축은 문헌조사 및 기구축 데이터 확보, 지역의 특성에 맞는 신규 GIS 데이터베이스 구축 방법을 제시하였다. 침수구역을 예측하기 위한 모델링을 지원하기 위한 정확도와 경제성, 객관성, 현실성이 높은 지형공간자료의 효율적인 구축이 강조되었다. 구축한 데이터베이스의 세부 유형은 침수위험구역, 침수경계구역, 침수량 화살표이며, 기존에 구축된 데이터를 활용한 레이어는 해안선, 도로, 건물, 등고선, 등심선, 연안 표고점 측량데이터이다. 연안침수지도는 효율적인 재난관리를 위하여 다양하게 활용될 수 있을 것으로 사료되며 나아가 주민의 침수에 대한 경각심 고취와 사전 교육, 지방자치단체의 침수위험 관리업무의 활용이 가능할 것으로 사료된다. 본 연구에서 제작된 연안침수지도는 지자체 주민들을 위해 보기 쉽고 다양한 정보를 얻을 수 있도록 제작되었으며, 이를 위해 사진과 삽화를 추가하여 주민들에게 침수피해에 대한 경각심과 이해를 높이는데 중점을 두었다. 유지관리 후 활용체계 정비의 중요성’을 점검하고, ‘변동자료 발생원인의 분석’, ‘명확한 변동자료 입력 근거의 확보’, ‘갱신주체별 역할의 정의 및 유지관리 기준의 설정’, ‘분야별업무 특성을 고려한 관련 기준의 마련 및 타 시스템과 연계되는 항목을 고려한 절차 정의’ 등에 대한 다양한 접근을 시도하였다. 본 연구에서 제시하는 유지관리 모델을 기반으로 각 지자체별로 적절한 컨설팅이 진행되고 이에 따라 담당자의 실천이 이루어진다면 지자체 GIS의 투자대비 효과에 대한 기대는 이상이 아닌 현실로 다가오게 될 것이다.가오게 될 것이다. 동일하게 25%의 소유권을 가지고 있다. ？스굴 시추사업은 2008년까지 수행될 계획이며, 시추작업은 2005년까지 완료될 계획이다. 연구 진행과 관련하여, 공동연구의 명분을 높이고 분석의 효율성을 높이기 위해서 시료채취 및 기초자료 획득은 4개국의 연구원이 모여 공동으로 수행한 후의 결과물을 서로 공유하고, 자세한 전문분야 연구는 각 국의 대표기관이 독립적으로 수행하는 방식을 택하였다 ？스굴에 대한 제1차 시추작업은 2004년 3월 말에 실시하였다. 시추작업 결과, 약 80m의 시추 코아가 성공적으로 회수되어 현재 러시아 이르쿠츠크 지구화학연구소에 보관중이다. 이 시추코아는 2004년 8월 중순경에 4개국 연구팀원들에 의해 공동으로 기재된 후에 분할될 계획이다. 분할된 시료는 국내로 운반되어 다양한 전문분야별 연구에 이용될 것이다. 한편, 제2차 시추작업은 2004년 12월에서 2005년 2월 사이에 실시될 계획이다. 수백만년에 이르는 장기간에 걸쳐 지구환경변화 기록이 보존되어 있는

• Spatial Information Research
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• v.22 no.5
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• pp.9-18
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• 2014

According to the recent development of the ubiquitous computing environment, the use of spatio-temporal data from sensors with GPS is increasing, and studies on the Semantic Sensor Web using spatio-temporal data for providing different kinds of services are being actively conducted. Especially, the W3C developed the SSNO(Semantic Sensor Network Ontology) which uses sensor-related standards such as the SWE(Sensor Web Enablement) of OGC and defines classes and properties for expressing sensor data. Since these studies are available for the query processing about non-spatio-temporal sensor data, it is hard to apply them to spatio-temporal sensor data processing which uses spatio-temporal data types and operators. Therefore, in this paper, we developed the SWE based on SSNO which supports the spatio-temporal sensor data types and operators expanding spatial data types and operators in "OpenGIS Simple Feature Specification for SQL" by OGC. The system receives SensorML(Sensor Model Language) and O&M (Observations and Measurements) Schema and converts the data into SSNO. It also performs the efficient query processing which supports spatio-temporal operators and reasoning rules. In addition, we have proved that this system can be utilized for the web service by applying it to a virtual scenario.

• Korean journal of communication and information
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• v.47
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• pp.23-49
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• 2009

The purpose of the study is to suggest contents, and a range of a Nonnative Theory and a practice for overcoming a crisis of the Mass media. In order to achieve the purpose of the study, a limitation and implication of Social Responsibility Theory was looked into by investigating a Hutchins Report. Hutchins Report suggested the related points at moral duty and legal obligation in implementing mass media's Social Responsibility and Accountability, with relations in publics and community. However, within a view point of a lack of Accountability, Social Responsibility Theory limited responsibility to the realm of 'function', 'self-regulation' and 'self-ethics', In order to promote mass media's quality and serve to rehabilitate its reliability under the pluralistic media system, the contents of Social Responsibility and its scope should be suggested. Media Accountability is a starting point to draw out the contents and scope of Social Responsibility and also a valid democratic plan for mass media to 'social self-regulation' through the mutual communication with civil society. For future Social Responsibility and Media Accountability that is suitable for various mass media, and contents and a range should be defined.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.67-81
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• 2006

While the existing Web focuses on the interface with human users based on HTML, the next generation Web will focus on the interaction among software agents by using XML and XML-based standards and technologies. The inference engine, which will serve as brains of software agents in the next generation Web, should thoroughly understand the Semantic Web, the standard language of the next generation Web. As abasis for the service, the W3C (World Wide Web Consortium) has recommended SWRL (Semantic Web Rule Language) which had been made by compounding OWL (Web Ontology Language) and RuleML (Rule Markup Language). In this research, we develop a backward chaining inference engine SMART-B (SeMantic web Agent Reasoning Tools -Backward chaining inference engine), which uses SWRL and OWL to represent rules and facts respectively. We analyze the requirements for the SWRL-based backward chaining inference and design analgorithm for the backward chaining inference which reflects the traditional backward chaining inference algorithm and the requirements of the next generation Semantic Web. We also implement the backward chaining inference engine and the administrative tools for fact and rule bases into Java components to insure the independence and portability among different platforms under the environment of Ubiquitous Computing.

• The KIPS Transactions:PartA
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• v.17A no.5
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• pp.229-236
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• 2010

With an increasing number of documents in the Internet or enterprises, it becomes crucial to efficiently support users' queries on those documents. In that situation, the full-text search technique is accepted in general, because it can answer uncontrolled ad-hoc queries by automatically indexing all the keywords found in the documents. The size of index files made for full-text searches grows with the increasing number of indexed documents, and thus the disk cost may be too large to process multi-keyword queries against those enlarged index files. To solve the problem, we propose both of the index file structure and its management scheme suitable to the processing of multi-keyword queries against a large volume of index files. For this, we adopt the structure of inverted-files, which are widely used in the multi-keyword searches, as a basic index structure and modify it to a hierarchical structure for join operations and ranking operations performed during the query processing. In order to save disk costs based on that index structure, we dynamically store in the main memory the results of join operations between two keywords, if they are highly expected to be entered in users' queries. We also do performance comparisons using a cost model of the disk to show the performance advantage of the proposed scheme.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.543-548
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• 2015

Recently top-k query processing has been extremely important along with the explosion of data produced by a variety of applications. Top-k queries return the best k results ordered by a user-provided monotone scoring function. As cloud computing service has been getting more popular than ever, a hot attention has been paid to cloud-based data outsourcing in which clients' data are stored and managed by the cloud. The cloud-based data outsourcing, though, exposes a critical secuity concern of sensitive data, resulting in the misuse of unauthorized users. Hence it is essential to encrypt sensitive data before outsourcing the data to the cloud. However, there has been little attention to efficient top-k processing on the encrypted cloud data. In this paper we propose a novel top-k processing algorithm that can efficiently process a large amount of encrypted data in the cloud. The main idea of the algorithm is to prune unpromising intermediate results at the early phase without decrypting the encrypted data by leveraging an order-preserving encrypted technique. Experiment results show that the proposed top-k processing algorithm significantly reduces the overhead of client systems from 10X to 10000X.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.9
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• pp.279-292
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• 2014

Energy aware server cluster aims to reduce power consumption at maximum while keeping QoS(quality of service) as much as energy non-aware server cluster. In the existing methods of energy aware server cluster, they calculate the minimum number of active servers needed to handle current user requests and control server power mode in a fixed time interval to make only the needed servers ON. When loads change rapidly, QoS of the existing methods become degraded because they cannot increase the number of active servers so quickly. To solve this QoS problem, we classify load change situations into five types of rapid growth, growth, normal, decline, and rapid decline, and apply five different thresholds respectively in calculating the number of active servers. Also, we use a flexible scheme to adjust the above classification criterion for multi threshold, considering not only load change but also the remaining capacity of servers to handle user requests. We performed experiments with a cluster of 15 servers. A special benchmarking tool called SPECweb was used to generate load patterns with rapid change. Experimental results showed that QoS of the proposed method is improved up to the level of energy non-aware server cluster and power consumption is reduced up to about 50 percent, depending on the load pattern.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.35-44
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• 2011

Advances in science and technology have made a lot of changes in our life. Especially, sensors have used in various ways to monitor in real time and analyze the world effectively. Traditional sensor networks, however, have used their own protocols and architecture so it had to be paid a lot of additional cost. In the past 8 years, OGC and ISO have been formulating standards and protocols for the geospatial Sensor Web. Although the OGC SWE initiatives have deployed some components, attempts have been made to access sensor data. All spatial operations had to calculate on the client side because traditional SOS architecture did not consider spatial operation for GeoSensor. As a result, clients have to implement and run spatial operations, and it caused a lot of overload on them and decreased approachableness. In this paper we propose S-SOS for in-situ and moving GeoSensor that extends 52 North SOS and provides spatialFilter and spatialFinder operations. The proposed S-SOS provides an architecture that does not need to edit already deployed SOSs and can add spatial operations as occasion. Additionally we explain how to express the spatial queries and to be used effectively for various location based services.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.91-98
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• 2010

Large corpus-based concatenating Text-to-Speech (TTS) systems can generate natural synthetic speech without additional signal processing. Because the improvements in the natualness, personality, speaking style, emotions of synthetic speech need the increase of the size of speech DB, it is necessary to prune the redundant speech segments in a large speech segment DB. In this paper, we propose a new method to construct a segmental speech DB for the Korean TTS system based on a clustering algorithm to downsize the segmental speech DB. For the performance test, the synthetic speech was generated using the Korean TTS system which consists of the language processing module, prosody processing module, segment selection module, speech concatenation module, and segmental speech DB. And MOS test was executed with the a set of synthetic speech generated with 4 different segmental speech DBs. We constructed 4 different segmental speech DB by combining CM1(or CM2) tree clustering method and full DB (or reduced DB). Experimental results show that the proposed method can reduce the size of speech DB by 23% and get high MOS in the perception test. Therefore the proposed method can be applied to make a small sized TTS.