• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.132-140
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• 2019

In this paper, we propose a classification method for radar signals depending on the type of threat by applying machine learning to parameter data of radar signals. Currently, the army uses a library of mapping relations between the parameters and the types of threat to recognize threat signals. This approach has certain limitations when classifying signals and recognizing new types of threat or types of threat that do not exist in the current libraries. In this paper, we propose an automatic radar signal classification method depending on the type of threat that uses only parameter data without a library. A convolutional neural network is used as the classifier and machine learning is applied to train the classifier. The proposed method does not use a library, and hence, can classify threat signals that are new or do not exist in the current library.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.279-283
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• 2004

In general, triangular shape filters are used in the filter bank when we extract MFCC feature parameters from the spectrum of the speech signal. A different approach, which uses specific filter shapes in the filter bank that are optimized to the spectrum of training speech data, is proposed by Lee et al. to improve the recognition rate. A principal component analysis method is used to get the optimized filter coefficients. Using a large amount of 4-digit telephone speech database, in this paper, we get the MFCCs based on the PCA-optimized filter bank and compare the recognition performance with conventional MFCCs and direct weighted filter bank based MFCCs. Experimental results have shown that the MFCC based on the PCA-optimized filter bank give slight improvement in recognition rate compared to the conventional MFCCs but fail to achieve better performance than the MFCCs based on the direct weighted filter bank analysis. Experimental results are discussed with our findings.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.193-196
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• 2007

본 논문은 관측기 기반 시스템에 대한 강인 디지털 재설계 방안을 제안한다. 디지털 재설계란, 기존의 안정화된 연속시간 플랜트와 이산 시간에서 설계된 디지털 제어기와의 상태 접합 및 안정도 분석을 통해 전체 시스템을 재구성 하는 것을 말한다. 그리고 전 역적 접근을 위한 방안으로서 문제를 볼록 최적화 관점으로 변환 후, 에러가 가질 수 있는 놈의 영역을 최소화 하여 상태 접합을 이루고자 하였다. 본 논문에서는 관측기 기반 시스템에 대한 디지털 재설계를 목표로 하되, 추가적인 파라미터 불확실성을 고려한 강인 디지털 재설계를 구성하게 된다. 파라미터 불확실성은 이산화 과정에서 구조적 형태가 변화하기 때문에, 이를 고려하여 주어진 식을 선형 행렬 부등식 형태로 나타내게 된다. 이 조건들을 통해 디지털 재설계의 상태 접합 및 안정도가 유도 가능하다는 것을 본 논문에서 증명하게 된다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9B
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• pp.1073-1081
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• 2011

An URL parameter can hold some information that is confidential or vulnerable to illegitimate tampering. We propose Link-E-Param(Link with Encrypted Parameters) to protect the whole URL parameter names as well as their values. Unlike other techniques concealing only some of the URL parameters, it will successfully discourage attacks based on URL analysis to steal secret information on the Web sites. We implement Link-E-Param in the form of a servlet filter to be deployed on any Java Web server by simply copying a jar file and setting a few configuration values. Thus it can be used for any existing Web application without modifying the application. It also supports numerous encryption algorithms to choose from. Experiments show that our implementation induces only 2~3% increase in user response time due to encryption and decryption, which is deemed acceptable.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.231-236
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• 2014

The transit time of contrast agents and the parameters of time-intensity curves in ultrasonography are important factors to diagnose various diseases of a digestive organ. We have implemented an automatic parametric imaging method to overcome the difficulty of the diagnosis by naked eyes. However, the micro-bubble noise and the respiratory motions may degrade the reliability of the parameter images. In this paper, we introduce an optimization technique based on MRF(Markov Random Field) model to enhance the quality of the parameter images, and present an image tracking algorithm to compensate the image distortion by respiratory motions. A method to extract the respiration periods from the ultrasound image sequence has been developed. We have implemented the ROI(Region of Interest) tracking algorithm using the dynamic weights and a momentum factor based on these periods. An energy function is defined for the Gibbs sampler of the image enhancement method. Through the experiments using the data to diagnose liver lesions, we have shown that the proposed method improves the quality of the parametric images.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.10-18
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• 2010

This paper presents a optimization of sigmoid activation function parameter using genetic algorithms and pattern recognition analysis in input space of two spirals benchmark problem. To experiment, cascade correlation learning algorithm is used. In the first experiment, normal sigmoid activation function is used to analyze the pattern classification in input space of the two spirals problem. In the second experiment, sigmoid activation functions using different fixed values of the parameters are composed of 8 pools. In the third experiment, displacement of the sigmoid function to determine the value of the three parameters is obtained using genetic algorithms. The parameter values applied to the sigmoid activation functions for candidate neurons are used. To evaluate the performance of these algorithms, each step of the training input pattern classification shows the shape of the two spirals.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.5
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• pp.372-378
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• 2009

With the increase in capacity of photovoltaic generation systems, studies are being actively conducted to improve system efficiency. In order to develop the high performance photovoltaic power system it is required to understand the physical characteristics of the solar cell. However, solar cell models have a non-linear form with many parameters entangled and conventional methods suggested to extract the parameters of the solar cell model require some kind of assumptions, which accompanies the calculation errors, thereby lowering the accuracy of the model. Therefore, in this paper a novel method is proposed to calculate the ideality factor and reverse saturation current of the solar cell from the I-V curve measured and announced by solar cell manufacturers, derive the ideal I-V curve, and then extract the series and shunt resistances value from the difference between the ideal and measured I-V curve. Also, validity of the proposed method is demonstrated by calculating the correlation between I-V curve based on modeling parameters and I-V curve actually measured through least squares method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.176-177
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• 2008

이 논문에서는 $0.35{\mu}m$ 공정으로 제작된 MOSFET의 고주파 동작 특성을 분석하였다. Multi-finger 형태인 게이트 폭의 길이 변화에 따른 특성 변화를 BSIM3v3 모델과 외부 기생 파라미터를 포함한 lumped element를 이용해 모델링을 하였다. 또한 Multi-finger 게이트 구조에서 게이트 finger 수의 증가에 따라 생기는 특성 변화를 각각의 구조에 따라 추출된 주요 기생 파라미터의 변화를 통해 분석하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.215-218
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• 1996

ATM망은 실제로 이용 가능한 대역폭 이상을 할당하는 통계적 다중화(statistical multiplexing) 기법을 사용하므로 망을 통한 트래픽 흐름을 적절히 관리하지 못하면 혼잡(congestion), 셀 손실, 망의 성능 저하 등을 야기하게 된다. 이러한 상황을 예방하고 셀의 도착 시간 버스트(burstiness)를 줄이며 셀 손실 특성을 개선하여 망의 성능을 증가시키기 위하여, 트래픽의 형태 제어 방법을 제안한다. 트래픽 형태 제어 파라미터 값의 역전파 신경망을 적용하여 예측되며, 이 예측된 값들에 의해 형태 제어 방법을 수행한다. 제안된 형태 제어 기법의 성능은 Poisson 트래픽 입력에 대한 컴퓨터 시뮬레이션에 의해 얻어지며, 멀티플렉서에서의 최대 버퍼 크기를 측정하여 성능을 평가하였다.