• Journal of the Korean Institute of Oriental Medical Informatics
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• v.15 no.1
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• pp.1-10
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• 2009

In these days everyone search the information easily with the Internet as the rapid distribution and active usage of the Internet. The search engines were developed specially to accuracy of information retrieval. User search the information more quickly and variously with them. The search portal system will be embossed with representation and basic services. The Internet user needs the result of text, image and video, knowledge search. The keyword based search is used generally for getting result of the information retrieval and another method is category based search. This paper investigates the classification of knowledge search structure for oriental medicine in market leader of search portal system by ranking web site. As a result, each classification system is unified and there is a possibility of getting up a many confusion to the user who approaches with classification systematic search method. This treatise proposed the improved oriental medicine classification system of internet information retrieval in knowledge search area. if the service provider amends about the classification system, there will be able to guarantee the compatibility of data. Also the proper access path of the knowledge which seeks is secured to user.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.288-295
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• 1998

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity theshold value is very important for the accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that (1) some tolerant objects are required to be included in the same class as many as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grounded into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method that all data are classified by using the lower approxi ation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification. problem and compare its classification performance and learning time with those of the feed forward neural network's back propagation algorithm.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.367-373
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• 2000

Natural and artificial land features are very dynamic, changing somewhat repidly in our lifetime. It is important that such changes are inventoried accurately so that the physical and human processes at work can be more fully understood. Change detection is a technique used to determine the change between two or more time periods of a particular object of study. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution in the population of interest. The purpose of this research is to detect environmental changes surrounding an area of Mountain Moscow, Idaho using Landsat Thematic Maper (TM) images of (July 8, 1990 and July 20, 1991). For accurate classification, the Image enhancement process was performed for improving the image quality of each image. A SPOT image (Aug. 14, 1992) was used for image merging in this research. Supervised classification was performed using the maximum likelihood method. Accuracy assessments were done for each classification. Two images were compared on a pixel-by-pixel basis using the post-classification comparison method that is used for detecting the changes of the study area in this research. The 'from-to' change class information can be detected by post classification comparison using this method and we could find which class change to another.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.119-131
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• 2019

Among the themes for CSR(Corporate Social Responsibility), consumer issues include protecting the health and safety of consumers who purchase and use the products. In particular, ensuring product safety is a major theme of consumer issues for corporate social responsibility. Currently, the government implements the Electrical Appliances and Consumer Products Safety Control Act for product safety management and selects products that may harmful to consumers as safety control items, and manages the products by designating them as 4 types of safety certification, safety confirmation, supplier conformity verification, and safety standard compliance. In this paper, we propose management plans for the establishment of a more reasonable classification type of safety management target for 48 items of consumer products to be controlled by the act, and confirm the validity of the plan. First, we perform cluster analysis using data for CISS (Consumer Injury Surveillance System) to derive a new classification type of the safety management target. Next, we compare the results of the cluster analysis with the classification type of the act and the existing scenario classification method RAS (Risk Assessment by Scenario) and the causal network method RAMP (Risk Assessment Method based on Probability). Based on these results, we propose two new plans of safety management target classification and verify its validity.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1336-1342
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• 2022

Risk-informed safety classification of structures, systems and components (SSCs) is very important for ensuring the safety and economic efficiency of nuclear power plants (NPPs). However, previous methods for safety classification of SSCs do not take the plant operating modes or the operational process of SSCs into consideration, thus cannot concentrate on the safety and economic efficiency accurately. In this contribution, a new method for safety classification of SSCs based on the categorization of plant operating modes is proposed, which considers the NPPs operating history to improve the economic efficiencies while maintaining the safety. According to the time duration of plant configurations in plant operating modes, average importances of SSCs are accessed for an NPP considering the operational process, and then safety classification of SSCs is performed for plant operating modes. The correctness and effectiveness of the proposed method is demonstrated by application in an NPP's safety classification of SSCs.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.768-771
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• 2004

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital signal processing approach. From the 20 second query audio file, 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS (Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we verify the superior performance of the SFS method that provides near $90{\%}$ success rate for the genre classification which means $10{\%}$-$20{\%}$ improvements over the previous methods

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.3
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• pp.565-572
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• 1994

A method for the classification of kinematic chains according to the similarity in their structures is proposed. Classifcation code is defined from the contracted graph of the kinematic chain. This method of classifying kinematic chains can be effectively used for the systematic enumeration of structurally distinct kinematic chains given the number of links and degrees of freedom of the kinematic chains. Two separate steps for the enumeration are developed in the study. The first step is to generated all the possible classification codes and the next step is to generate individual kinematic chains belonging to each classification code generated. Using this two step procedure, kinematic chains up to 12 links are successfully enumerated in the present study. It is concluded that the two step method can be efficiently used for the type synthesis of mechanisms.

• Journal of Environmental Science International
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• v.12 no.12
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• pp.1215-1225
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• 2003

The purpose of this research was to investigate the characteristics of urban ecotopes and to classify ecotopes systematically from them. Total of 15 characteristics for classification of ecotopes were selected, and there were categorized 3 factors, that is abiotic, biotic and anthropological factors. The ecotope types in the study area were classified into 67. The classification of ecotope was made with SPSS for Windows Version 10.0 on the basis of the 15 characteristics. As the results of cluster analysis using the average linkage method between groups, groups of ecotope type were divided into 15 clusters. It was known that there was not a great difference in an affinity as the result of overlapping the maps of ecotope type and land use type. This research suggested characteristics for classification of ecotopes, but there was a limit to Set the objective method for grade classification because of lacking in the basic data, the research of characteristics will be accomplished continuously.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.429-432
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• 1999

Vegetation cover classification is examined based on a time series NOAA/AVHRR data. Time series data analysis methods including Fourier transform, Auto-Regressive (AR) model and temporal signature similarity matching are developed to extract phenological features of vegetation from a time series NDVI data from NOAA/AVHRR and to classify vegetation types. In the Fourier transform method, typical three spectral components expressing the phenological features of vegetation are selected for classification, and also in the AR model method AR coefficients are selected. In the temporal signature similarity matching method a new index evaluating the similarity of temporal pattern of the NDVI is introduced for classification.

• MALSORI
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• no.66
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• pp.61-72
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• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.