• Proceedings of the IEEK Conference
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• 2001.06b
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• pp.309-312
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• 2001

A primitive gas classification system which can classify limited species of gas was designed and simulated. The 'electronic nose' consists of an array of 4 metal oxide gas sensors with different selectivity patterns, signal collecting unit and a signal pattern recognition and decision Part in PLD(programmable logic device) chip. Sensor array consists of four commercial, tin oxide based, semiconductor type gas sensors. BP(back propagation) neutral networks with MLP(Multilayer Perceptron) structure was designed and implemented on CPLD of fifty thousand gate level chip by VHDL language for processing the input signals from 4 gas sensors and qualification of gases in air. The network contained four input units, one hidden layer with 4 neurons and output with 4 regular neurons. The 'electronic nose' system was successfully classified 4 kinds of industrial gases in computer simulation.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.1
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• pp.46-57
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• 2022

In this paper, we proposed the DFMEA Implementation Method for safety design of Weapon System. First, we presented the process for DFMEA. And then, the case analysis of OOO missile was performed in accordance with the process presented. After defining the system requirements of OOO missile, failure definition scoring criteria was set. In order to clarify the definition of failure, the failure was classified into safety, reliability, maintainability and others. After performing the function analysis, the relationship matrix analysis was performed to identify the failure mode according to the function without omission. After clarifying the failure classification, mode of failure, cause of failure and effect were analyzed to calculate the severity, occurrence and detection values. After the action priority was judged, the recommended action according to the failure classification was identified for the determined action priority. The results of this study can be used as a relevant basis for the design reflection and resource re-allocation of stakeholders.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.4
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• pp.5-24
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• 2012

The aim of this study is 1) to analyse the historical development and current condition of the subject based classification(SBC), 2) to clarify the function and to categorize the specific kind of SBC, for its conceptualization and the development plan. For this purpose, almost 30 cases, for the period 1937 through now, were analyzed concerning their terms used in the names and the specific kinds as SBC. In addition, the analysis was made regarding how the SBC fulfill the selected main functions as a classificatory scheme and how SBC is inter-related with the other knowledge organization systems(KOS) such as classification and subject heading. Based on the above analysis, the conclusion addressed that SBC could be defined in consideration of the detailed function, type, information environment, and interconnection among the KOS, and suggested the future development plan of SBC as a classification scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.417-422
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• 1998

This paper presents a method of object classification from dynamic image based on fuzzy inference algorithm which is suitable for low speed such as, conveyor, uninhabited transportation. At first, by using feature parameters of moving object, fuzzy if - then rule that can be able to adapt the wide variety of surroundings is developed. Secondly, implication function for fuzzy inference are compared with respect the proposed algorithm. Simulation results are presented to testify the performance and applicability of the proposed system.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.96-98
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• 2003

1. Standard and regulations for functional food evaluation cases form overseas (1) Japan For food function indication, Food Nutrition Improvement Act was amended in September 1991 and they managed functional food after setting specific health food in one of classification of special functional foods. For manification of raw material usage, the classification of health functional foods was performed by their application on: the control of internal organ status, cholesterol, blood pressure, mineral absorption, and prevention of dental caries. (omitted)

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.220-224
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• 2000

A new auscultation system for the detection of breath sound form trachea was developed in house. Small size microphone(panasonic pin microphone) was encapsuled in a housing for resonant effect, and hardware for the sound detection was fabricated. Pulmonary function test results were compared with the parameters extracted from frequency spectrum of breath sound obtained from the developed system. Results showed that the peak frequency and relative ratio of integral values between low(80∼400Hz) and high(400∼800Hz) frequency ranges revealed the significant differences. Developed system could be used for distinguishing normal subject and the patients who have pulmonary disease.

• Aerospace Engineering and Technology
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• v.7 no.2
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• pp.38-45
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• 2008

It is challenging to build the integrated information system for a large scale cooperative R & D project. To develop the aircraft program which especially has several leading agencies and is supported by many demestic/foreign participating companies, the common data flow in harmony is the core factor to achieve a development goal. For this, the development are carried out maintaining the existing management systems of agencies and companies. As a first step, the standard for the common data information and the classification category of technical data are defined. Second, the work flow standards are also set. Based on the foundation, the efficient technical data management system are built including the function of storage, inquiry, revision, link, approval, submission, etc.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.11-18
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• 2019

One of the key objectives of control systems is to maintain a system in a specific stable state. To achieve this goal, a variety of control techniques can be used and it is often uses a feedback control method. As known this kind of control methods requires mathematical model of the system. This article presents seesaw unstable system with two propellers which are controlled without use of a mathematical model instead. The goal was to control it using training data. For system control we use a logistic regression technique which is one of machine learning method. We tested our controller on the real model created in our laboratory and the experimental results show that instability of the seesaw system can be fixed at a given angle using the decision boundary estimated from the classification method. The results show that this control method for structural equilibrium can be used with relatively more accuracy of the decision boundary.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.664-672
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• 2009

Recently, studies on utilizing tensor expression on image data analysis and processing have been attracting much interest. The purpose of this study is to develop an efficient system for classifying image patterns by using second order tensor expression. To achieve the goal, we propose a data generation model expressed by class factors and environment factors with second order tensor representation. Based on the data generation model, we define a function for measuring similarities between two images. The similarity function is obtained by estimating the probability density of environment factors using a matrix normal distribution. Through computational experiments on a number of benchmark data sets, we confirm that we can make improvement in classification rates by using second order tensor, and that the proposed similarity function is more appropriate for image data compared to conventional similarity measures.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.10-17
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• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.