• 한국해양공학회지
• /
• 제18권2호
• /
• pp.33-38
• /
• 2004

All adaptive neural network controller has been developed for a model of an underwater vehicle. This controller combines a radial basis neural network and sliding mode control techniques. No prior off-line training phase is required, and this scheme exploits the advantages of both neural network control and sliding mode control. An on-line stable adaptive law is derived using Lyapunov theory. The number of neurons and the width of Gaussian function should be chosen carefully. Performance of the controller is demonstrated through computer simulation.

• 한국전자파학회논문지
• /
• 제13권8호
• /
• pp.834-842
• /
• 2002

임의 모양의 완전도체의 전자파 산란을 해석하기 위하여 모멘트 방법을 이용하였다. GID(Graphic Interface Design)툴을 이용하여 설계된 금속 도체의 표면은 서로 다른 모양의 삼각패치로 모델링 되었다. 도체 표면전류는 삼각패치의 벡터 기저함수로 확장되었다. 관측점과 전원점이 동일 삼각패치에 위치할 때, 삼각패치의 특이점 적분은 Duffy 방법을 이용하여 특이점이 없는 적분으로 변환이 가능하였고, 변환된 특이점 항들은 1차원의 가우시안 구적법을 이용하여 간단하게 계산되어질 수 있었다. 특이점을 제외한 적분들은 2차원 가우시안 구적법으로 계산되었다.

• 한국소음진동공학회논문집
• /
• 제16권11호
• /
• pp.1149-1157
• /
• 2006

Donoho et al. suggested a wavelet thresholding denoising method based on discrete wavelet transform. This paper proposes an improved denoising method using a new thresholding function based on translation-invariant wavelet for underwater acoustic measurement. The conventional wavelet thresholding denoising method causes Pseudo-Gibbs phenomena near singularities due to the lack of translation-invariant of the wavelet basis. To suppress Pseudo-Gibbs phenomena, a denoising method combining a new thresholding function based on the translation-invariant wavelet transform is proposed in this paper. The new thresholding function is a modified hard-thresholding to each node according to the discriminated threshold so as to reject unknown external noise and white gaussian noise. The experimental results show that the proposed method can effectively eliminate noise, extract characteristic information of radiated noise signals.

• 제어로봇시스템학회논문지
• /
• 제19권4호
• /
• pp.364-371
• /
• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

• 한국멀티미디어학회논문지
• /
• 제10권10호
• /
• pp.1284-1294
• /
• 2007

본 논문에서는 개선된 퍼지 클러스터(Modified Fuzzy C-Means: MFCM) 알고리즘을 이용하여 블라인드 비선형 채널등화기를 구현하였다. 이를 위해 제안된 MFCM은 기존의 유클리디언 거리 값 대신 Bayesian Likelihood 목적함수(fitness function)를 이용하여 채널의 출력으로 수신된 데이터들로부터 비선형 채널의 최적의 채널 출력 상태 값(optimal channel output states)을 추정한다. 이렇게 구해진 채널 출력 상태 값들로 비선형 채널의 이상적 채널 상태(desired channel states) 벡터를 구성하고 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용하여 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우스 잡음을 추가한 데이터를 사용하여 하이브리드 유전자 알고리즘 (genetic algorithm(GA) merged with simulated annealing (SA): GASA)과 그 성능을 비교하였으며, 제안된 MFCM을 이용한 등화기가 GASA를 사용한 것 보다 상대적으로 정확도와 속도 면에서 우수함을 보였다.

• 한국방재안전학회논문집
• /
• 제6권2호
• /
• pp.1-8
• /
• 2013

우리나라 전체 재해 60%이상인 태풍과 같은 바람재난으로부터 구조물이나 외장재가 안전과 사용성 측면에서 설계되려면 내풍설계과정에서 기본풍속, 설계속도압, 풍하중 등 많은 파라미터들이 요구된다. 본 논문에서는 최근 2003년부터 2012년까지의 10년 동안 년최대풍속이 발생한 날의 풍속으로부터 확률과정과 확률분포, 통계적 성질 등을 알아보기 위하여 8개의 대표지점을 여수, 인천, 서울, 청주, 원주, 대구, 속초, 울릉도로 선정했다. 선정된 각 지점에 대한 최근 10년 동안의 풍속자료는 기상청으로부터 획득했다. 각 지점의 획득한 풍속자료는 우리나라를 직접 통과하면서 영향을 미친 태풍과 통과는 안했지만 간접 영향을 미친 태풍, 년최대순간풍속과 년최대평균퐁속이 같은 날 등을 고려 90개의 앙상블 중 선별된 33개의 모집단에 대한 풍속자료의 확률과정 및 확률분포의 특성을 비교 검토하였다.

• 대한조선학회논문집
• /
• 제52권1호
• /
• pp.88-95
• /
• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권7호
• /
• pp.3076-3092
• /
• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• 한국음향학회지
• /
• 제13권3호
• /
• pp.16-24
• /
• 1994

음성신호에 내재한 배경잡음을 제거하는 단일입력 적응잡음제거 시스템을 구성하였다. 기존 방법에서는 프레임 단위로 분석된 음성신호의 피치 정보를 이용하여 적응여파기의 기준신호를 얻는데 비해 제안된 방법에서는 매 샘플마다 지연 정보를 추정하여 기준신호를 만든다. 입력되는 음성신호로부터 매 샘플시간마다 지연 정보를 구하기 위하여 일반적인 자기상관 함수와 평균절대차 함수로부터 재귀적 자기상관함수와 재귀적 평균절대차함수를 유도하였다. 정규화된 최소평균자승(NLMS) 적응알고리듬을 사용하는 단일입력 잡음제거 시스템에 제안된 지연추정 방법을 적용하여 백색 가우시안 잡음에 왜곡된 음성에 대해 음성개선 실험을 하였으며, 기존 방법과의 성능비교 실험을 하였다. 제안된 방법에 의한 음성개선이 기존 방법보다 음질 및 SNR면에서 더 좋은 결과를 보였다.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제49권5호
• /
• pp.269-277
• /
• 2000

We propose two new types multi-stage blind clustering equalizers for QAM demoulation, which are called a complex classification algorithm(CCA) and a radial basis function algorithm(RBFA). The CCA uses a clustering technique based on the joint gaussian probability function and computes separately the real part and imaginary part for simple implementation as well as less computation. In order to improve the performance of CCA, the Dual-Mode CCA(DMCCA) incorporates the CCA tap-updating mode with the decision-directed(DD) mode. The RBFA reduces the number of cluster centers through three steps using the classification technique of RBF and then updates the equalizer taps for QAM demodulation. Test results on 16-QAM confirm that the proposed algorithms perform better the conventional multi-state equalizers in the senses of SER and MSE under multi-path fading channel.