• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1239-1246
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• 2003

Registration is the process aligning the range data sets from different views in a common coordinate system. In order to achieve a complete 3D model, we need to refine the data sets after coarse registration. One of the most popular refinery techniques is the iterative closest point (ICP) algorithm, which starts with pre-estimated overlapping regions. This paper presents an improved ICP algorithm that can automatically register multiple 3D data sets from unknown viewpoints. The sensor projection that represents the mapping of the 3D data into its associated range image is used to determine the overlapping region of two range data sets. By combining ICP algorithm with the sensor projection constraint, we can make an automatic registration of multiple 3D sets without pre-procedures that are prone to errors and any mechanical positioning device or manual assistance. The experimental results showed better performance of the proposed method on a couple of 3D data sets than previous methods.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.319-332
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• 2008

Data cube, which is multi-dimensional data model, have been successfully applied in many cases of multi-dimensional data analysis, and is still being researched to be applied in data stream analysis. Data stream is being generated in real-time, incessant, immense, and volatile manner. The distribution characteristics of data arc changing rapidly due to those characteristics, so the primary rule of handling data stream is to check once and dispose it. For those characteristics, users are more interested in high support attribute values observed rather than the entire attribute values over data streams. This paper propose dynamic data cube for applying data cube to data stream environment. Dynamic data cube specify user's interested area by the support ratio of attribute value, and dynamically manage the attribute values by grouping each other. By doing this it reduce the memory usage and process time. And it can efficiently shows or emphasize user's interested area by increasing the granularity for attributes that have higher support. We perform experiments to verify how efficiently dynamic data cube works in limited memory usage.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.22-24
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• 2003

최근 웹 확산에 따른 일반인들의 인터넷 이용이 급증하였고 이에 따른 정보 기술도 빠르게 변화하고 있다. GIS 분야에서도 시대의 흐름에 맞춰 공간 데이타를 웹상에서 제공하는 웹 GIS 분야가 등장하게 되었다. 그러나, 상호이질적인 공간 데이타를 웹 기반 분산 환경에서 서비스하기 위해 표준 인터페이스와 각각의 웹 서비스 모델을 정의한 통합 시스템이 요구되고 있다. OGC에서는 기존 OPenGIS의 상호운용성을 웹 환경에서 지원하고, 또한 피쳐 인스턴스 단위의 지리정보를 접근하기 위해 새로운 웹 피쳐 서비스 구현 명세를 제안하였다. 웹 피쳐 서비스 구현 명세에서는 클라이언트로부터 HTTP를 통하여 요청된 공간.비공간 질의를 처리하기 위한 URL 컴포넌트를 정의하고 있다. 이 컴포넌트는 클라이언트 질의를 수행하여 피쳐 인스턴스 단위로 데이타를 검색 및 조작하고, 검색된 데이타는 GML로 정의하여 클라이언트로 제공한다. 본 논문에서는 OGC가 제안한 XML 기반의 표준 공간 데이타 포맷인 GML과 웹 피쳐 서비스 인터페이스를 이용해 분산된 지리정보 데이타를 웹 브라우저를 통하여 서비스할 수 있는 웹 피쳐 서비스 시스템을 설계 및 구현하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.57-66
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• 1996

For the effective use of spatial data, it is necessary to translate the diverse digital map data (here, digital map for car navigation system) into common GIS data structure(here, ARC/INFO). For this purpose, analysis on the structure of the established digital map was fulfilled, the data model for effective translation tool was designed. As a result, the possibility to translate diverse digital map structures (database) into GIS tools and to share them, with the users' point of view, was successfully verified.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.764-766
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• 2003

시공간 데이타베이스에서 저장대상이 되는 이동객체는 실시간으로 위치가 변화하는 동적인 데이타이다. 동일한 객체의 위치정보가 여러 데이타베이스 서버에 분산 저장 될 수 있으며, 질의 역시 여러 서버에 걸쳐서 분산질의 형태로 수행될 필요성이 있다. 이를 위해서 본 논문에서는 실시간 객체 모델인 TMO(Time-triggered Message-triggered Object)를 이용하여 이동 객체를 발생시키고 객체의 위치를 저장, 질의하는 시스템을 설계 및 구현하였다. TMO를 이용한 분산 저장을 통해 입력 시 발생할 수 있는 오버헤드를 줄이고. 데이타베이스간에 완전 연결 네트워크가 형성되어 각 데이타베이스 서버간의 상호 작용을 최소화 할 수 있게 된다. 이 시스템은 실시간으로 분산 위치정보를 관리해야 하는 여러 응용 분야에서 효과적으로 활용될 수 있다.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1259-1265
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• 2004

In many applications, producing labeled data is costly and time consuming while an enormous amount of unlabeled data is available with little cost. Therefore, it is natural to ask whether we can take advantage of these unlabeled data in classification teaming. In machine learning literature, the co-training method has been widely used for this purpose. However, the current co-training method requires the entire features to be split into two independent sets. Therefore, in this paper, we improved the current co-training method in a number of ways, and proposed a new co-training method which do not need the feature split. Experimental results show that our proposed method can significantly improve the performance of the current co-training algorithm.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.243-251
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• 2004

Most programs of bioinformatics provide biochemists and biologists retrieve and analysis services of gene and protein database. As these services retrieve database for each arrival of user's request, it takes a long time and increases server's load and response time. In this paper. by utilizing database retrieval patterns of sequence alignment programs in bioinformatics, grouping method is proposed to share database retrieval between many requests. Carpool method is also proposed to reduce response time as well as to increase system expandability by combining new arriving requests with the previous on going requests. The performance of our two proposed schemes is verified by mathematic analysis and simulation.

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.422-431
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• 2004

In recent years, different models in data delivery have been explored in mobile computing systems. Particularly, there were a lot of research efforts in the periodic push model where the server repetitively disseminates information without explicit request. However, average waiting time per data operation highly depends on the length of a broadcast cycle and different access pattern among clients may deteriorate the response time considerably. In this case, clients are preferably willing to send a data request to the server explicitly through backchannel in order to obtain optimal response time. We call the broadcast model supporting backchannel as hybrid broadcast. In this paper, we devise a new transaction processing algorithm(O-PreH) in hybrid broadcast environments. The data objects which the server maintains are divided into Push_Data for periodic broadcasting and Pull_Data for on-demand processing. Clients tune in broadcast channel or demand the data of interests according to the data type. Periodic invalidation reports from the server support maintaining transactional consistency. If one or more conflicts are found, conflict orders are determined not to violate the consistency(pre-reordering) and then the remaining operations have to be executed pessimistically. Through extensive simulations, we demonstrate the improved throughput of the proposed algorithm.

• Annual Conference on Human and Language Technology
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• 1991.10a
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• pp.307-320
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• 1991

본 연구는 한국어 음성합성 시스팀에서 한글 텍스트를 음소로 변환 시키는 규칙기반과 신경망을 결합한 한글-음소 변환 시스팀을 제안하고 이를 위해 시스팀 모델을 설계하고 시스팀의 각 구성요소들을 설명하며 한국어 음운 변동 규칙중 설측음화 데이타와 설측음화에 상충되는 데이타를 사용하여 시스팀을 실험하고 제안된 모형의 타당성을 분석한다.

• Journal of KIISE:Software and Applications
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• v.27 no.8
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• pp.795-803
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• 2000

This paper presents a shared data decomposition model for improving concurrency in multi-user, distributed software developments. In our model, the target software system is decomposed into the independent components based on project roles to be distributed over clients. The distributed components are decomposed into view objects and core objects to replicate only view objects in a distributed collaboration session. The core objects are kept in only one client and the locking is used to prevent inconsistencies. The grain size of a lock is a role instead of a class which is commonly used as the locking granularity in the existing systems. The experimental result shows that our model reduces response time by 12${\sim}$18% and gives good scalability.