• The Transactions of the Korea Information Processing Society
• /
• v.3 no.6
• /
• pp.1468-1472
• /
• 1996

In this study, the performance of the CART(Classification and Regression Tree) is compared with that of discriminant analysis method. In most experiments using bank data, discriminant analysis shows better performance in terms of the total cost. In contrast, most experiments using insurance data show that the CART is better than discriminant analysis in terms of the total cost. The contradictory result are analysed by using the characteristics of the data sets. The performances of both the Classification and Regression Tree and discriminant analysis depend on the parameters:failure prior probability, data used, type I error, type II error cost, and validation method.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.35
• /
• pp.17-24
• /
• 1995

The classification of quality costs by item is essencial to sum up the entire quality cost but the reality of classifying the quality cost by the firm is facing with difficulty in terms of grouping the concerned items. Meanwhile, the classification of items and calculating method of quality costs should be prepared in adcance with a certain standards and or regulations to figure out the accurate quality costs successfuly. This case study provides the contents of quality costs calulated by item and the method of calculation in detail which is applicable to automobile component industry md, also introduce how to set up the computing system of quality costs.

• Geotechnical Engineering
• /
• v.14 no.1
• /
• pp.29-36
• /
• 1998

The application of a multiple rock classification method, which is a generalization of a binary rock classification, is studied in this paper. In particular, this paper shows how to incorporate qualitative data through a case study. The method suggested in this paper can be effectively used for a systematic multiple rock classification such as RMR system developed by Bieniawski. It will be very useful for rock classifications. In addition, it is known that the expected cost of errors can be atopted to indicate how well a investigation plan is made.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.176-181
• /
• 2011

A role of cost modeler is that of facilitating design process by the systematic application of cost factors so as to maintain sensible and economic relationships between cost, quantity, utility and appearance. These relationships help to achieve the client's requirements within an agreed budget. The purpose of this study is to develop a parametric cost estimating model for the early design stage by using the multi-dimensional system of OLAP (On-line Analytical Processing) based on the case of quantity data related to architectural design features. The parametric cost estimating models have been adopted to support decision making in the early design stage. These models typically use a similar instance or a pattern of historical case. In order to effectively use this type of data model, it is required to set data classification and prediction methods. One of the methods is to find the similar class in line with attribute selection measure in the multi-dimensional data model. Therefore, this research is to analyze the relevance attribute influenced by architectural design features with the subject of case-based quantity data used for the parametric cost estimating model. The relevance attributes can be analyzed by Analytical Characterization. It helps determine what attributes to be included in the OLAP multi-dimension.

• Journal of Advanced Research in Ocean Engineering
• /
• v.2 no.4
• /
• pp.202-208
• /
• 2016

In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

• Journal of the Korea Safety Management & Science
• /
• v.4 no.4
• /
• pp.15-26
• /
• 2002

The quantitative evaluation method of the safety management cost was suggested to prevent a gas accident as a major industrial accident. In a gas governor station, process risk assessments such as the fault tree analysis(FTA) and the consequence analysis were performed. Based on process risk assessments, potential accident costs were estimated and the cost-benefit analysis(CBA) was performed. From the cost-benefit analysis for five classification items of safety management cost, the order of the cost/benefit ratio was estimated.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.3
• /
• pp.281-292
• /
• 2021

With the development of deep learning technology, researchers and technicians keep attempting to apply deep learning in various industrial and academic fields, including the defense. Most of these attempts assume that the data are balanced. In reality, since lots of the data are imbalanced, the classifier is not properly built and the model's performance can be low. Therefore, this study proposes cost-sensitive learning as a solution to the imbalance data problem of image classification in the defense field. In the proposed model, cost-sensitive learning is a method of giving a high weight on the cost function of a minority class. The results of cost-sensitive based model shows the test F1-score is higher when cost-sensitive learning is applied than general learning's through 160 experiments using submarine/non-submarine dataset and warship/non-warship dataset. Furthermore, statistical tests are conducted and the results are shown significantly.

• Korean Institute of Interior Design Journal
• /
• no.23
• /
• pp.68-73
• /
• 2000

The purpose of this research is to develop interior construction cost-estimating system at the early stage of the project. Though general construction estimates are typically in quantitative, interior construction should be in rather qualitative. Therefore, design-concerned cost-estimating methods should be developed to manage interior projects from the early statge. 30 estimates of VIP-Zone interior projects, were examined to develop the general type of composition and material classification. The cost has been classified by construction parts such as wall, ceiling, floor, and doors & windows and their treatments. The composition and material related estimating system (CMRES) was consisted of the unit average costs of classification and the variation coefficients. The CMRESS was verified by the case study, and the results sowed that the difference between the actual estimate and the CMRES was competitively confident.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.13 no.4
• /
• pp.91-98
• /
• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• Geotechnical Engineering
• /
• v.11 no.2
• /
• pp.71-78
• /
• 1995

In this paper, a study was performed on classifying a rock mass into multiple classes as in rock mass classification systems, such as RMR system and Q system etc. In a situation with only limited quantitative data available, it was sought to employ a way of incorporating qualitative data in a systematical and reasonable manner. It is based on the realm of Geostatistics. In particular, indicator kriging technique, which is one of non-parametric approaches, was used. As a selection criterion for an optimal classification, the cost of errors was adopted. As a result, the binary rock classification method developed before was extended and generalized for multiple rock classification with its total number of classes unlimited.