• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.931-940
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• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.52-65
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• 1997

This paper presents a design technique of self tuning fuzzy controller for load frequency control of power system. The proposed parameter self tuning algorithm of fuzzy controller is based on the gradient method using four direction vectors which make error between inference values of fuzzy controller and output values of the specially selected optimal controller reduce steepestly. Using input-output data pair obtained from optimal controller, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed gradient method. The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones for reductions of undershoot and steady-state load frequency deviation and for minimization of settling time.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.45-50
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• 2003

Recently, robots technique has been developed remarkably. Emotion recognition is necessary to make an intimate robot. This paper shows the simulator and simulation result which recognize or classify emotions by learning pitch pattern. Also, because the pitch is not sufficient for recognizing emotion, we added acoustic elements. For that reason, we analyze the relation between emotion and acoustic elements. The simulator is composed of the DRNN(Dynamic Recurrent Neural Network), Feature extraction. DRNN is a learning algorithm for pitch pattern.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.91-99
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• 2014

Although it is well documented that patients with cochlear implant experience hearing difficulties when processing their first language, very little is known whether or not and to what extent cochlear implant patients recognize segments in a second language. This preliminary study examines how Korean learners of English identify English segments in a normal hearing and cochlear implant simulation conditions. Participants heard English vowels and consonants in the following three conditions: normal hearing condition, 12-channel noise vocoding with 0mm spectral shift, and 12-channel noise vocoding with 3mm spectral shift. Results confirmed that nonnative listeners could also retrieve spectral information from vocoded speech signal, as they recognized vowel features fairly accurately despite the vocoding. In contrast, the intelligibility of manner and place features of consonants was significantly decreased by vocoding. In addition, we found that spectral shift affected listeners' vowel recognition, probably because information regarding F1 is diminished by spectral shifting. Results suggest that patients with cochlear implant and normal hearing second language learners would experience different patterns of listening errors when processing their second language(s).

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.443-448
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• 2018

Recently, Hybrid Electric Vehicle(: HEV) is in the spotlight to global warming caused by carbon dioxide and emission reduction. HEV consists of a combination of mechanical engine and electric motor system. The flow of energy required to drive a HEV depends on the driving conditions of the vehicle. In this paper, we study the contents of HEV education using real-time monitoring system. A real-time monitoring system consisting of hardware and virtual programs is used to simulate the overall operation of a HEV through simulations according to driving conditions and to explain how to learn through hardware.

• The Korean Journal of Emergency Medical Services
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• v.20 no.3
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• pp.21-35
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• 2016

Purpose: The purpose of the study was to investigate the continuous effect of advanced cardiovascular life support (ACLS) simulation education according to Felder-Silverman learning style. Methods: A self-reported questionnaire was completed by 94 students of emergency medical technology and nursing. There were 50 female students (53.2%) and 88 students (93.6%) had basic life support certification. The study instruments included knowledge, performance, and confidence. Data were analyzed using SPSS v. 20.0. Results: The learning style consisted of reflective type (51.1%), sensory type (76.6%), visual type (63.8%), and sequential type (64.9%). There was a significant difference in continuous effect on performance by learning type. Conclusion: It is necessary to identify the learning style of students before simulation education in order to maintain continuous effect of ACLS education.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.343-347
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• 2012

Non-linear equalization techniques using decision feedback structure are highly demanded for cancellation of intersymbol interferences occurred in severe channel environments. In this paper decision feedback structure is applied to the linear blind equalizer algorithm that is based on information theoretic learning and a randomly generated symbol set. At the decision feedback equalizer (DFE) the random symbols are generated to have the same probability density function (PDF) as that of the transmitted symbols. By minimizing difference between the PDF of blind DFE output and that of randomly generated symbols, the proposed DFE algorithm produces equalized output signal. From the simulation results, the proposed method has shown enhanced convergence and error performance compared to its linear counterpart.

• Journal of KIISE
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• v.42 no.11
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• pp.1423-1429
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• 2015

The Internet of Things (IoT) is a new concept associated with the future Internet, and it has recently become a popular concept to build a dynamic, global network infrastructure. However, the deployment of IoT creates difficulties in satisfying different Quality of Service (QoS) requirements and achieving rapid service composition and deployment. In this paper, we propose a new QoS control scheme for IoT systems. The Markov game model is applied in our proposed scheme to effectively allocate IoT resources while maximizing system performance. The results of our study are validated by running a simulation to prove that the proposed scheme can promptly evaluate current IoT situations and select the best action. Thus, our scheme approximates the optimum system performance.

• The KIPS Transactions:PartB
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• v.8B no.3
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• pp.231-236
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• 2001

일반적인 다층 신경망에서 가중치의 갱신 알고리즘으로 사용하는 오류 역전과 방식은 가중치 갱신 결과를 고정된(fixed) 한 개의 값으로 결정한다. 이는 여러 갱신의 가능성을 오직 한 개의 값으로 고정하기 때문에 다양한 가능성들을 모두 수용하지 못하는 면이 있다. 하지만 모든 가능성을 확률적 분포로 표현하는 갱신 알고리즘을 도입하면 이런 문제는 해결된다. 이러한 알고리즘을 사용한 베이지안 신경망 모형(Bayesian Neural Networks Models)은 주어진 입력값(Input)에 대해 블랙 박스(Black-Box)와같은 신경망 구조의 각 층(Layer)을 거친 출력값(Out put)을 계산한다. 이 때 주어진 입력 데이터에 대한 결과의 예측값은 사후분포(posterior distribution)의 기댓값(mean)에 의해 계산할 수 있다. 주어진 사전분포(prior distribution)와 학습데이터에 의한 우도함수(likelihood functions)에 의해 계산한 사후확률의 함수는 매우 복잡한 구조를 가짐으로 기댓값의 적분계산에 대한 어려움이 발생한다. 따라서 수치해석적인 방법보다는 확률적 추정에 의한 근사 방법인 몬테 칼로 시뮬레이션을 이용할 수 있다. 이러한 방법으로서 Hybrid Monte Carlo 알고리즘은 좋은 결과를 제공하여준다(Neal 1996). 본 논문에서는 Hybrid Monte Carlo 알고리즘을 적용한 신경망이 기존의 CHAID, CART 그리고 QUEST와 같은 여러 가지 분류 알고리즘에 비해서 우수한 결과를 제공하는 것을 나타내고 있다.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1693-1702
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• 1999

This paper proposes the use of an adaptive Wiener filter for edge-preserving image filtering. Images are partitioned into a set of blocks of pixels which is divided into five subsets of blocks according to their edge contents and orientations. Each subset of blocks is used to define a covariance matrix, from which a Wiener filter is derived. Five covariance matrices and Wiener filters are thus obtained. An image-block classifier using the five sets of covariance matrices of the class is designed to classify each incoming block of pixels according to its edge content in the presence of noise. Experimental results are included to verify the usefulness of the proposed method.