• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.509-515
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• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.197-203
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• 2020

The purpose of study is to identify the perception of image-making regulations change of airline cabin crew and analyze the relationship among the perception of regulations change, occupational choice motives and career decision of female university students majoring in airline service in Kwang-ju and Jeonnam area. The survey was in 203 for a month from November 18 to December 13, 2019. The collected data were analyzed using 'SPSS statistics 21.0.' Analytical methods such as frequency analysis, factor analysis, reliability analysis and multiple regression analysis were used. The findings of this study are presented as follows: Students majoring in airline service are positively aware of image-making regulations changes of airline cabin crew, positive perception of regulation changes has a statistically significant impact on occupational choice motives and career decision, and occupational choice motives have an effects on career decision. In conclusion, the study has implications for providing information to airlines to understand the applicants and to students preparing for employment. However, there is a limitation in that the sample of this study is limited to only female university students majoring in airline services in a specific area, and the size of the sample is not large.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.141-148
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• 1994

Both the neural network classifier utilizing multi-layer perceptron and the linear tree classifier composed of hierarchically structured linear discriminating functions can form arbitrarily complex decision boundaries in the feature space and have very similar decision making processes. In this paper, a new method for automatically choosing the number of neurons in the hidden layers and for initalzing the connection weights between the layres and its supporting theory are presented by mapping the sequential structure of the linear tree classifier to the parallel structure of the neural networks having one or two hidden layers. Experimental results on the real data obtained from the military ship images show that this method is effective, and that three exists no siginificant difference in the classification acuracy of both classifiers.

• Speech Sciences
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• v.7 no.4
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• pp.49-57
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• 2000

This paper proposes an effective decision method focused on evaluating the 'stress position'. Conventional methods usually extract the acoustic parameters and compare them to references in absolute scale, adversely producing unstable results as testing conditions change. To cope with environmental dependency, the proposed method is designed to be model-based and determines the stressed interval by making relative comparison over candidates. The stressed/unstressed models are then induced from normal phone models by adaptive training. The experimental results indicate that the proposed method is promising, and that it is useful for automatic detection of stress positions. The results also show that generating the stressed/unstressed model by adaptive training is effective.

• Smart Media Journal
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• v.3 no.3
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• pp.15-19
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• 2014

Fingerprint Recognition System is made up of Off-line treatment and On-line treatment; the one is registering all the information of there trier features which are retrieved in the digitalized fingerprint getting out of the analog fingerprint through the fingerprint acquisition device and the other is the treatment making the decision whether the users are approved to be accessed to the system or not with matching them with the fingerprint features which are retrieved and database from the input fingerprint when the users are approaching the system to use. In this paper, Among various biometrics recognition systems, statistical fingerprint recognition matching methods are considered using minutiae on fingerprints. We define similarity distance measures based on the coordinate and angle of the minutiae, and suggest a fingerprint recognition model following statistical distributions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.773-779
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• 1996

Dried oak mushroom have complex and various visual features. Grading and sorting of dried oak mushrooms has been done by the human expert. Though actions involved in human grading looked simple, a decision making underneath the simple action comes from the result of the complex neural processing of the visual image. Through processing details involved in human visual recognition has not been fully investigated yet, it might say human can recognize objects via one of three ways such as extracting specific features or just image itself without extracting those features or in a combined manner. In most cases, extracting some special quantitative features from the camera image requires complex algorithms and processing of the gray level image requires the heavy computing load. This fact can be worse especially in dealing with nonuniform, irregular and fuzzy shaped agricultural products, resulting in poor performance because of the sensitiveness to the crisp criteria or specific ules set up by algorithms. Also restriction of the real time processing often forces to use binary segmentation but in that case some important information of the object can be lost. In this paper, the neuro net based real time recognition algorithm was proposed without extracting any visual feature but using only the directly captured raw gray images. Specially formated adaptable size of grids was proposed for the network input. The compensation of illumination was also done to accomodate the variable lighting environment. The proposed grading scheme showed very successful results.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• IE interfaces
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• v.17 no.4
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• pp.414-425
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• 2004

This study presents the decision of priority for optimal design guide to improve disassembly process. Disassembly process is divided into recognition, transfer and disassembly of assembly point and recognition, transfer and remove of grasp point. Significant influential factors are derived from analyzing the above process. And those factors are used for making the check list to evaluate the properties of parts in each process. Furthermore, the weight with considering disassembly process is also used to determine weight of each process. On the base of the above sequence, qualitative score of disassemblability of each process that is enabled to compare different disassembly processes can be acquired. Ultimately the score helps to decide the priority of design guide for disassembly process.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.15 no.1
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• pp.54-60
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• 2007

According to U.S. NTSB, from 1989 to 1992, Situation Awareness (SA) was a major factor causing 80% of all aircraft accidents in scheduled airlines. Therefore, the prevention of accidents through effective training in SA became a pivot in aviation safety. Furthermore, during the past 10 years, since all helicopter accidents in Korea were caused by the factors related to SA, an appropriate countermeasure has been required. This study, which uses survey data, examines various factors related to SA that could affect helicopter pilots. Recognition of and countermeasures for the factors in emergency situations were analyzed. The results show that, while the factors associated with SA and vigilance have lower correlations with each other, the factors associated with recognition, diagnosis, and generation and implementation of solutions have higher correlations with each other. Thus, the results demonstrate the need for better SA through educational training.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.147-159
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• 2002

This paper proposes a method to recognize the noisiness of road images connected with the extraction of lane-related information in order to prevent the usage of erroneous information. The proposed method uses a fuzzy neural network(FNN) with the back-Propagation loaming algorithm. The U decides road images good or bad with respect to visibility of lane marks on road images. Most input parameters to the FNN are extracted from an edge distribution function(EDF), a function of edge histogram constructed by edge phase and norm. The shape of the EDF is deeply correlated to the visibility of lane marks of road image. Experimental results obtained by simulations with real images taken by various lighting and weather conditions show that the proposed method was quite successful, providing decision-making of noisiness with about 99%.