• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.150-152
• /
• 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.

• Journal of the Korea Society for Simulation
• /
• v.15 no.3
• /
• pp.115-124
• /
• 2006

XML is rapidly becoming technologies for information exchange and representation. It causes many research issues such as semantic modeling methods, security, conversion far interoperability with other models, and so on. Especially, the most important issue for its practical application is how to achieve the interoperability between XML model and relational model. Until now, many suggestions have been proposed to achieve it. However several problems still remain. Most of all, the exiting methods do not consider implicit referential integrity relations, and it causes incorrect data delivery. One method to do this has been proposed with the restriction where one semantic is defined as only one same name in a given database. In real database world, this restriction cannot provide the application and extensibility. This paper proposes a noble conversion (RDB-to-XML) algorithm based on the similarity checking technique. The key point of our method is how to find implicit referential integrity relations between different field names presenting one same semantic. To resolve it, we define an enhanced implicity referentiai integrity relations extraction algorithm based on a widely used ontology, WordNet. The proposed conversion algorithm is more practical than the previous-similar approach.

• Journal of KIISE:Databases
• /
• v.33 no.5
• /
• pp.526-537
• /
• 2006

The most representative approach for efficient storing of XML data is to store XML data in relational databases. The merit of this approach is that it can easily accept the realistic status that most data are still stored in relational databases. This approach needs to convert XML data into relational data or relational data into XML data. The most important issue in the translation is to reflect structural and semantic relations of RDB to XML schema model exactly. Many studies have been done to resolve the issue, but those methods have several problems: Not cover structural semantics or just support explicit referential integrity relations. In this paper, we propose an algorithm for extracting implicit referential integrities automatically. We also design and implement the suggested algorithm, and execute comparative evaluations using translated XML documents. The proposed algorithm provides several good points such as improving semantic information extraction and conversion, securing sufficient referential integrity of the target databases, and so on. By using the suggested algorithm, we can guarantee not only explicit referential integrities but also implicit referential integrities of the initial relational schema model completely. That is, we can create more exact XML schema model through the suggested algorithm.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.36 no.2
• /
• pp.73-78
• /
• 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.

• Journal of Internet Computing and Services
• /
• v.2 no.4
• /
• pp.91-99
• /
• 2001

Since roost of the program execution time is spent in the loop structure, the problem of extracting parallelism from sequential loop has been one of the most important research issues. However. roost programs have Implicit interprocedure parallelism. This paper presents a generalized method extracting parallelism in loops having the procedure calls. Most parallelization of loops having procedure calls focus on the uniform code where data dependency distance is constant. We present algorithms which can be applied to uniform code, nonuniform code, and complex code. The performance of the proposed algorithm, loop extraction, loop embedding and procedure cloning transformation methods have been evaluated using CRAY-T3E. The result shows the effective of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.3
• /
• pp.366-370
• /
• 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.

• The Journal of Information Technology and Database
• /
• v.6 no.1
• /
• pp.89-101
• /
• 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.

• Journal of computational fluids engineering
• /
• v.7 no.4
• /
• pp.1-7
• /
• 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
• /
• v.6 no.1
• /
• pp.81-87
• /
• 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.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.45
• /
• pp.101-112
• /
• 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.