• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• 한국시뮬레이션학회논문지
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• 제15권3호
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• pp.115-124
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• 2006

XML은 정보 교환과 표현을 위해 빠르게 발전해 오고 있는 기술이다. XML을 통한 시멘틱 모델링 방법론, 보안, 다른 모델들과의 상호 운용성을 위한 변환과 같은 많은 연구들이 이슈화 되었다. 특히, 실질적인 응용분야의 가장 중요한 이슈는 XML 모델과 관계형 모델들과의 상호 운용성을 위해 많은 방법들에 제기되어 왔다. 하지만, 여전히 몇 가지 문제점이 있다. 대부분의 기존의 방법들은 묵시적인 참조 무결성 관계를 고려하지 않기 때문에, 부정확한 데이터 전달이 야기된다. 데이터베이스에서 하나의 의미가 정의 될 때 오직 하나의 이름만 가진다는 제약조건하에서 위의 문제를 해결하기 위한 한 가지 방법이 제안되었다. 하지만, 실제 데이터베이스에서 응용과 확장을 위해서 이 제약사항을 적용할 수는 없다. 그래서 이 논문에서는 유사성 검사 기법을 기반하는 한 RDB-to-XML 변환 알고리즘을 제안한다. 이 방법의 핵심은 하나의 같은 의미에 대해 다른 이름으로 표현되는 속성들 간의 묵시적인 참조 무결성 관계를 추출하는 알고리즘을 정의하였다. 제안된 변환 알고리즘은 이전의 유사한 접근 방법 보다 더욱 실질적이다.

• 한국정보과학회논문지:데이타베이스
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• 제33권5호
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• pp.526-537
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• 2006

XML 데이타의 효율적인 저장을 위한 가장 대표적인 접근방법은 XML 데이타를 관계형 데이타베이스에 저장하는 것으로 대부분의 데이타가 여전히 관계형 데이타베이스에 저장되어 있다는 현실적 상황을 쉽게 수용할 수 있다는 장점을 지닌다. 이러한 접근 방법은 XML 데이타를 관계형 데이타로 혹은 관계형 데이타를 XML 데이타로 변환 과정이 필수적으로 요구하며, 변환 과정에서 가장 중요한 점은 관계형 스키마 모델의 구조적, 의미적 관계 정보를 XML 스키마 모델에 정확히 반영하는 것이다 지금까지 많은 변환 방법들이 제안되었으나 구조적 의미를 반영하지 못하거나 단순히 명시적으로 정의된 참조 무결성 관계(Referential Integrity Relations)만을 지원하는 문제점을 지닌다. 이 논문에서는 관계형 스키마 모델의 XML 스키마 모델로의 변환 시 초기 관계형 데이타베이스의 묵시적 참조 무결성 관계를 자동적으로 추출하여 이를 변환에 반영할 수 있는 알고리즘을 제안한다. 제안된 알고리즘은 초기 관계형 데이타베이스에 명시적으로 정의되어 있는 참조 무결성 관계는 물론 묵시적인 참조 무결성 관계까지 변환 과정에 반영함으로써 보다 정확한 XML 데이타 모델 생성을 가능하게 한다.

• 동의생리병리학회지
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• 제36권2호
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• pp.73-78
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• 2022

Since Traditional Korean medicine (TKM) doctors use various knowledge systems during treatment, diagnosis results may differ for each TKM doctor. However, it is difficult to explain all the reasons for the diagnosis because TKM doctors use both explicit and implicit knowledge. In this study, an upgraded random forest (RF)-based evaluation tool was proposed to extract clinical knowledge of TKM doctors. Also, it was confirmed to what extent the professor's clinical knowledge was delivered to the trainees by using the evaluation tool. The data used to construct the evaluation tool were targeted at 106 people who visited the Sasang Constitutional Department at Kyung Hee University Korean Medicine Hospital at Gangdong. For explicit knowledge extraction, four TKM doctors were asked to express the importance of symptoms as scores. In addition, for implicit knowledge extraction, importance score was confirmed in the RF model that learned the patient's symptoms and the TKM doctor's constitutional determination results. In order to confirm the delivery of clinical knowledge, the similarity of symptoms that professors and trainees consider important when discriminating constitution was calculated using the Jaccard coefficient. As a result of the study, our proposed tool was able to successfully evaluate the clinical knowledge of TKM doctors. Also, it was confirmed that the professor's clinical knowledge was delivered to the trainee. Our tool can be used in various fields such as providing feedback on treatment, education of training TKM doctors, and development of AI in TKM.

• 인터넷정보학회논문지
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• 제2권4호
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• pp.91-99
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• 2001

프로그램 수행시간의 대부분이 루프 구조에서 소비되고 있기 때문에 루프 구조를 가진 순차 프로그램에서 병렬성을 추출하는 연구들이 많이 행해지고 있고 그 연구들은 하나의 프로시저 내 루프 구조의 변환에 치중되고 있다. 그러나 대부분의 프로그램들은 프로시저 간 잠재된 병렬성을 가지고 있다. 본 논문에서는 프로시저 호출을 가진 루프에서 병렬성 추출 방식을 제안한다. 프로시저 호출을 포함하는 루프의 병렬화는 대부분 자료종속거리가 uniform 형태의 코드에서만 집중되었다. 본 논문에서는 자료종속거리가 uniform 코드, nonuniform 코드 그리고 복합된(complex) 코드를 가진 프로그램에서 적용 가능한 알고리즘을 제시하였으며, 제안된 알고리즘과 loop extraction, loop embedding 그리고 procedure cloning변환 방법을 CRAY-T3E로 성능 평가하였다. 성능평가 결과는 제안된 알고리즘이 효율적이라는 것을 보여준다.

• 한국지능시스템학회논문지
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• 제13권3호
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• 정보기술과데이타베이스저널
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• 제6권1호
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• pp.89-101
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• 1999

Increasing number of companies recognize that the understanding of customers and their markets is indispensable for their survival and business success. The companies are rapidly increasing the amount of investments to develop customer databases which is the basis for the database marketing activities. Database marketing is closely related to data mining. Data mining is the non-trivial extraction of implicit, previously unknown and potentially useful knowledge or patterns from large data. Data mining applied to database marketing can make a great contribution to reinforce the company's competitiveness and sustainable competitive advantages. This paper develops the classification model to select the most responsible customers from the customer databases for telemarketing system and evaluates the performance of the developed model using LIFT measure. The model employs the decision tree algorithm, i.e., CHAID which is one of the well-known data mining techniques. This paper also represents the effective database marketing strategy by applying the data mining technique to a credit card company's telemarketing system.

• 한국전산유체공학회지
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• 제7권4호
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• pp.1-7
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• 2002

The phase-change transformation processes are relevant in many engineering applications. In particular, this phenomenon plays an important role in the extraction and fabrication operations in the metallurgical industry. The control of the heat transfer and fluid flow patterns is important to achieve casting quality and competitive production times. In the present study, a simple finite-element algorithm is developed for solid-liquid phase change problems. Natural convection in the liquid phase due to the temperature dependency of water density is considered by a numerical model. The predictions are compared with measurements by the particle image velocimetry(PIV). to show that the calculation results are in good agreement with the experiment results.

• International Journal of CAD/CAM
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• 제6권1호
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• pp.81-87
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• 2006

This paper describes a method for extracting surfaces from multi-material CT (Computed Tomography) data. Most contouring methods such as Marching Cubes algorithm assume that CT data are composed of only two materials. Some extended methods such as [3, 6] can extract surfaces from the multi-material (non-manifold) implicit representation. However, these methods are not directly applicable to CT data that are composed of three or more materials. There are two major problems that arise from fundamentals of CT. The first problem is that we have to use n(n-1)/2 threshold values for CT data contains n materials and select appropriately one threshold value for each boundary area. The second is that we cannot reconstruct only from CT data in which area three or more materials are adjacent each other. In this paper, we propose a method to solve the problems by using image analysis and demonstrate the effectiveness of the method with application examples construct polygon models from CT data of machine parts.

• 산업경영시스템학회지
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• 제21권45호
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• pp.101-112
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• 1998

Data mining, which is also referred to as knowledge discovery in database, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases. The discovered knowledge can be applied to information management, decision making, and many other applications. In this paper, a new data mining problem, discovering sequential patterns, is proposed which is to find all sequential patterns using sampling method. Recognizing that the quantity of database is growing exponentially and transaction database is frequently updated, sampling method is a fast algorithm reducing time and cost while extracting the trend of customer behavior. This method analyzes the fraction of database but can in general lead to results of a very high degree of accuracy. The relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time. The superiority of the proposed algorithm will be shown through analyzing accuracy and efficiency by comparing with Apriori All algorithm.