• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.403-412
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• 1993

This paper presents the progress of the development of a nondestructive technique for the classification of normal, septicemic , and cadaver poultry carcasses by the Instrumentation and Sensing Laboratory at Beltsville, Maryland, U.S.A. The Sensing technique is based on the diffuse reflectance spectroscopy of poultry carcasses.

• Proceedings of the KIEE Conference
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• summer
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• pp.587-589
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• 2004

In this paper analytical pattern classification of TRV waves created by double-frequency synthetic test circuit was proposed. According to the classified patterns of the TRV wave, calculation methods of 3 reference lines and 4 parameters characterizing the TRV wave wire proposed. These methods can be utilized to optimize test facility and to standardize test quality.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.182-188
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• 2000

High-frequency electric resistance welding (HERW) technique is one of the most productive manufacturing method currently available for pipe and tube production because of its high welding speed. In this process, a heat input is controlled by skilled operators observing color and shape of bead but such a manual control can not provide reliability and stability required for manufacturing pipes of high grade quality because of a variety of bead shapes and noisy environment. In this paper, in an effort to provide reliable quality inspection, we propose a neural network-based method for classification of bead shape. The proposed method utilizes the structure of Kohonen network and is designed to learn the skill of the expert operators and to provide a good solution to classify bead shapes according to their welding conditions. This proposed method is implemented on the real pipe manufacturing process, and a series of experiments are performed to show its effectiveness.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.737-744
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• 2000

This paper resorts to rock mass rating and rock mass quality to draw value based on the evaluation of rock and to draw interrelation formula in relation to rock mass quality, A comparative analysis was given of survey values reported in the existing documents. This paper has tried to find out the relationship between RMR and Q System for the sake of choosing rational reinforcing patterns and of the safety of tunnels. The results run as follow: RMR=8.251n(Q)+43.83. This paper has also tried to find out the relationship between RMR and Q System by using Fuzzy Approximate Reasoning Concept. We suggest that those in charge should not depend on a single system only after evaluating the classification of rocks, and compare one result with another for the good of keeping track of the condition of base rocks in a better way.

• Journal of Korean Society for Quality Management
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• v.22 no.2
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• pp.143-153
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• 1994

Cluster analysis is a primitive technique in which no assumptions are made concerning the data structure. And the number of groups is known a priori discriminant analysis provides an information how well N individuals are classified into their own groups. In this study, clustering, which is any partition of a collection of data points, generated by the application of eight hierarchical clustering methods was re-classified by discriminant analysis. Then correct classification ratios were obtained for the application of discriminant analysis through each clustering method and the direct application of discriminant analysis. By comparing the correct classification ratios, the applicability of cluster analysis and discriminant analysis considered.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.910-912
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• 1995

This paper proposes an effective algorithm for automatic classification of power quality disturbances(PQD), where wavelet theory is utilized for the detection of PQD, and three neural networks such as MLP, RBF, MLP-Class are combined in parallel to classify PQD. To demonstrate the performance of the proposed system, simulation results are provided.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.9
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• pp.71-78
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• 2010

The existing capacitor-testers could only judge the condition of capacitor is normal or abnormal, and could not inform the real characteristics of the tested capacitors. In this research, it has designed the real-time observation of the new capacitor in charge and discharge tester for showing capacitor conditions and classification through wide voltages by regular cycle-tests. And proposed the quality classification algorithm of capacitors for more simple and practical, and approve them by test.

• Journal of Digital Contents Society
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• v.4 no.2
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• pp.181-189
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• 2003

The requirement of distributed multimedia applications is the need for sophisticated QoS(quality of service) management. In terms of distributed multimedia systems, the most important catagories for quality of service are a timeless, volume, and reliability In this paper, we discuss a method for increasing reliability through fault tolerance. We describe the design and implementation of the ECA running on distributed multimedia environment. ECA is a system is able to classify automatically a software error based on distributed multimedia. This papaer explains a performance analysis of an error classification system running on distributed multimedia environment using the rule-based DEVS modeling and simulation techniques. In DEVS, a system has a time base, inputs, states, outputs, and functions.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.302-306
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• 2004

When we include environment side safety, environmental material's reliability technology and study for the application method, the evidence supporting the investment of R&D person and financial. Clearly, the most important task in electrical and electronics company's product soldering process the probability of heavy metals exclude is to identify the mechanisms by which they may take place. Therefore, this study emphasis on the application environmental material classification and reliability technology.