• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.732-742
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• 2001

The concept of an agent has become important in computer science and has been applied to the number of application domains such electronic commerce and information retrieval. But, no one has proposed yet in software test. The test agent system applied the concept of an agent to software test is new test tool. It consists of the User Interface Agent. the Test Case Selection & Testing Agent and the Regression Test Agent. Each of these agents, with their intelligent rules, carry out the tests autonomously by empolying the object-oriented test processes. This system has 2 advantages. Firstly since the tests are carried our autonomously, it minimizes tester interference and secondly, since redundant-free and consistent effective test cases are intellectually selected, the testing time is reduced while the fault detection effectiveness improves. In this paper, by actually showing the testing process being carried out autonomously by the 3 agents that form the TAS, we show that the TAS minimizes tester interference. By also carrying out the 4 different types of experiments on the RE-Rule, CTS-Rule, overall TAS experiment, and the fault-detection effectiveness experiment on the RE-Rule, we show the cut-down on the testing time and improvement in the fault detection effectivity.

• Journal of electromagnetic engineering and science
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• v.12 no.1
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• pp.107-114
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• 2012

Microwave imaging (MI) is one of the most promising and attractive new techniques for earlier breast cancer detection. Microwave tomography (MT) realizes configuration of a multistatic multiple-input multiple-output system and reconstructs dielectric properties of the breast by solving a nonlinear inversion scattering problem. In this paper, we describe ETRI 3D MT system with 3D MI reconstruction program and demonstrate its robustness through some examples of the image reconstruction.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.179-193
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• 2016

In power quality monitoring, voltage fluctuation is one of the power quality problems, which cause light flickers. To determine the flicker severity, the flicker meter concept was developed in an IEC 61000-4-15 standard. Generally, voltage fluctuations are described as an amplitude modulation(AM). The flicker meter of IEC 61000-4-15 as an international standard for flicker measurement recommends square demodulation method to detect flicker signals from voltage fluctuation signals. This paper suggests a new effective method using filter bank to detect and estimate flicker signals, which do not need square demodulation. For the accurate detection of flicker signals, the filter bank is designed with a full consideration of the spectrum characteristics of voltage fluctuation signals described as AM. The frequency and magnitude of the detected flicker signals are estimated using recursive method. Computer simulations were performed on synthesized signals to prove validity of the proposed method.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.153-166
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• 2003

This study presents the time-divided forecasting model to integrate evolutionary optimization algorithm and change point detection based on artificial neural networks (ANN) for the prediction of (Korea) stock price index. The genetic algorithm(GA) is introduced as an evolutionary optimization method in this study. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as optimal or near-optimal change point groups, and to use them in the forecasting of the stock price index. The proposed model consists of three phases. The first phase detects successive change points. The second phase detects the change-point groups with the GA. Finally, the third phase forecasts the output with ANN using the GA. This study examines the predictability of the proposed model for the prediction of stock price index.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.3
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• pp.721-730
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• 1996

In this paper, a new test criterion for binary decision problems is proposed. The integrated power flunction over a parameter interval is first itroduced as an extension of the power function. The concept of the most integrated powerful (MIP) test based on the integrated power function is then introduced. The MIP criterion is to masimize the value of the integrated power function in any paricular parameter interval. As an applicationof the MIP test, the known signal detection problem is considered. The test statistic of the MIP detector for known signals is obtained and an approximation to the MIP test statistic is also considered.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.257-268
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• 2006

To determine the faults of ceramic cup it is proposed to use pseudo reverberation time concept estimated by impact test in room. Schroeder curves estimated from impact test for a cup with small crack and without one are utilized to estimate pseudo reverberation time. Pseudo reverberation times are compared and discussed according to a sort of impact hammers and impact points and also boundary conditions. As a result. proposed method is proved to be very useful to detect the existence of faults for candidate cups.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1925-1931
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• 2011

This paper suggests an approach to the subtle concept, "good", through the fuzzy logic and the design of the Canny edge detector of Gray scale images based on the rules of fuzzy anisotropic diffusion. The Canny edge detection algorithms design is to divide the gray levels into pixels and then calculate the diffusion coefficients at each pixel of non-edgy regions. Based on this processing, we present the Canny edge detector implementing fuzzy logic and comparing the results to other existing methods. The proposed approach is the narrow dynamic range of the gray-level image Sharpening the edge detection and has the advantage.

• Journal of Sensor Science and Technology
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• v.20 no.3
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• pp.178-186
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• 2011

Line tracking is a well defined method of mobile robot navigation. It is simple in concept, technically easy to implement, and already employed in many industrial sites. Among several different line tracking methods, magnetic sensing is widely used in practice. In comparison, vision-based tracking is less popular due mainly to its sensitivity to surrounding conditions such as brightness and floor characteristics although vision is the most powerful robotic sensing capability. In this paper, a vision-based robust path line detection technique is proposed for the navigation of a mobile robot assuming uncontrollable surrounding conditions. The technique proposed has four processing steps; color space transformation, pixel-level line sensing, block-level line sensing, and robot navigation control. This technique effectively uses hue and saturation color values in the line sensing so to be insensitive to the brightness variation. Line finding in block-level makes not only the technique immune from the error of line pixel detection but also the robot control easy. The proposed technique was tested with a real mobile robot and proved its effectiveness.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.141-150
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• 2009

This paper proposes a system that can automatically write singing voices to music notes. First, the system uses Stabilized Diffusion Equation(SIDE) to divide the song to a series of syllabic parts based on pitch detection. By the song segmentation, our method can recognize the sound length of each fragment through clustering based on genetic algorithm. Moreover, this study introduces a concept called 'Relative Interval' so as to recognize interval based on pitch of singer. And it also adopted measure extraction algorithm using pause data to implement the higher precision of song transcription. By the experiments using 16 nursery songs, it is shown that the measure recognition rate is 91.5% and DMOS score reaches 3.82. These findings demonstrate effectiveness of system performance.