• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계 학술대회논문집(수송기계편)
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• pp.57-60
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• 2005

Modal parameters of a structure are the important factor to control the missile maneuver. In general, a dummy structure is used for the modal test of missile structure instead of the real warhead and propellant because there may be the danger of a explosion by the electric shock of test equipment, such as the exciter and the power amplifier. However, the modal testing of a real missile structure is required to acquire the modal parameters and to analyze the missile performance accurately. The new test system and technique are developed to get rid of the danger and secure the safety during the testing. This test system is made of with the computer network system and controlled remote from test site. Using His new test system, the modal test of real missile structure is performed successfully and its validity is proven.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.193-207
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• 2001

When designing a test set, we need to consider constraints on items that are deemed important by item developers or test specialists. The constraints are essentially on the components of the test domain or abilities relevant to a given test set. And so if the test domain could be represented in a more refined form, test construction would be made in a more efficient way. We assume that relationships among task abilities are representable by a causal model and that the item response theory (IRT) is not fully available for them. In such a case we can not apply traditional item selection methods that are based on the IRT. In this paper, we use entropy as an uncertainty measure for making inferences on task abilities and developed an optimal item selection algorithm which reduces most the entropy of task abilities when items are selected from an item pool.

• International journal of advanced smart convergence
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• 제6권4호
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• pp.73-79
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• 2017

The Convolutional Neural Network (CNN) has shown an excellent performance in computer vision task. Applications of CNN include image classification, object detection in images, autonomous driving, etc. This paper will evaluate the performance of CNN model with ReLU and SELU as activation function. The evaluation will be performed on four different choices of hyperparameter which are initialization method, network configuration, optimization technique, and regularization. We did experiment on each choice of hyperparameter and show how it influences the network convergence and test accuracy. In this experiment, we also discover performance improvement when using SELU as activation function over ReLU.

• Journal of Information Processing Systems
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• 제2권3호
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• pp.183-188
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• 2006

A RMON agent system, which locates on a subnet, collects the network traffic information for management by retrieving and analyzing all of the packets on the subnet. The RMON agent system can miss some packets due to the high packet analyzing overhead when the number of packets on the subnet is huge. In this paper, we have developed a light-weight RMON agent system that can handle a large amount of packets without packet loss. Our RMON agent system has also been designed such that its functionality can be added dynamically when needed. To demonstrate the dynamic reconfiguration capability of our RMON agent system, a simple port scanning attack detection module is added to the RMON agent system. We have also evaluated the performance of our RMON agent system on a large network that has a huge traffic. The test result has shown our RMON agent system can analyze the network packets without packet loss.

• 한국군사과학기술학회지
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• 제4권2호
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• pp.189-201
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• 2001

For a design of multi-layer armor, the extensive full scale or sub-scale penetration test data are required. In generally, the collection of penetration data is in need of time-consuming and expensive processes. However, the application of numerical or analytical method is very limited due to poor understanding about penetration mechanics. In this paper, we have developed a neural network analyzer which can be used as a design tool for a new armor. Calculation results show that the developed neural network analyzer can predict relatively exact penetration depth of a new armor through the effective analysis of the pre-existing penetration database.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권11호
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• pp.780-785
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• 2004

In this paper, we proposed a ventricular fibrillation detection algorithm based on wavelet transform and artificial neural network. we selected RR intervals, the 6th and 7th wavelet coefficients(D6, D7) as features for classifying ventricular fibrillation. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference and fuzzy-neural network. MIT-BIH Arrhythmia database, Creighton University Ventricular Tachyarrhythmia database and MIH-BIH Malignant Ventricular Arrhythmia database were used as test and learning data. Among the algorithms, the proposed algorithm showed that the classification rate of normal and abnormal beat was sensitivity(%) of 96.10 and predictive positive value(%) of 99.07, and that of ventricular fibrillation was sensitivity(%) of 99.45. Finally. the proposed algorithm showed good performance compared to two other methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.21-23
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup rates when control rod and boron were adjusted in load following operations. Data of UCN 3 were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and seems to be utilized as a handy tool for the use of a plant simulation.

• 한국전기전자재료학회논문지
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• 제21권1호
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• pp.73-78
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• 2008

A resistor network model for the horizontal AlInGaN LED was investigated, The parameters of the proposed model are extracted from the test dies and $350{\mu}m$ LED, The center of the P-area is the optimal position of a P-electrode by the simulation using the model. Also the optimal chip size of the LED for the new target current was investigated, Comparing the simulation and fabrication result, the errors for the forward voltage and the light power are average 0,02 V, 8 % respectively, So the proposed resistor network model with the linear forward voltage approximation and the exponential light power model are useful in the simulation for the horizontal AlInGaN LED.

• 산업경영시스템학회지
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• 제35권1호
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• pp.10-15
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• 2012

This paper considers an economic design problem of a tree-based network which is a kind of computer network. This problem can be modeling to be an objective function to minimize installation costs, on the constraints of spanning tree and maximum traffic capacity of sub tree. This problem is known to be NP-hard. To efficiently solve the problem, a tabu list coupled genetic algorithm approach is proposed. Two illustrative examples are used to explain and test the proposed approach. Experimental results show evidence that the proposed approach performs more efficiently for finding a good solution or near optimal solution in comparison with a genetic algorithm approach.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1997년도 제25회 춘계학술대회
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• pp.25-32
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• 1997

The neural network was applied to identify wear debris generated from the lubricated machine moving surface. The wear test was carried out under different experimental conditions. In order to describe characteristics of debris of various shapes and sizes. The four parameter(50% volumetric diameter, aspect, roundness and reflec- tivity) of wear debris are used as inputs to the network and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network.