• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.167-170
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• 2007

동일한 집단에 속하는 개체를 다른 집단에 속하는 개체로부터 구별할 수 있는 염기의 특징을 해당 집단의 시그너쳐라고 한다. 학습 데이터는 두 집단에 속하는 염기서열들이고, 염기서열에 대한 시그너쳐는 개체를 다른 집단과 구별할 수 있는 위치의 염기들로 구성된 서열이다. 제안한 방법에서는 각 집단에 대해서 위치별로 염기의 발생빈도를 계산하고, 가장 발생빈도가 높은 염기를 결정한 다음, 다른 집단의 대응 위치에서 해당 염기의 빈도를 계산하여, 빈도차이가 지정한 분류임계값 이상이면, 해당 위치의 염기를 시그너쳐를 구성하는 특징으로 간주한다. 시그너쳐를 대한 임의의 염기서열에 대한 부합정도는 시그너쳐에 속하는 염기의 학습집단에서의 상대빈도값을 가중치로 하여 계산한다. 임의의 염기서열이 특정 집단에 속하는지 판단하기 위해서는 해당 집단의 시그너쳐에 대한 부합정도를 계산하게 되는데, 부합정도가 얼마이상이 되어야 해당 집단에 속하는 것으로 간주할지 기준이 되는 임계값을 엄밀도 임계값이라고 한다. 엄밀도 임계값은 학습 데이터 집합에 대해서 주어진 시그너쳐에 대한 엄밀도 임계값이 민감도와 특이도를 최대로 하는 것을 선택한다. 제안한 방법을 구현한 바이오인포매틱스 도구를 개발하여, 한국형 HIV-1 바이러스 시그너쳐 추출에 적용하여 분류특성이 우수한 시그너쳐를 추출할 수 있음을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.510-515
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• 2001

Generally, there are (1)Words for conversation (2)Tone (3)Pitch (4)Formant frequency (5)Speech speed, etc as the element for emotional recognition from speech signal. For human being, it is natural that the tone, vice quality, speed words are easier elements rather than frequency to perceive other s feeling. Therefore, the former things are important elements fro classifying feelings. And, previous methods have mainly used the former thins but using formant is good for implementing as machine. Thus. our final goal of this research is to implement an emotional recognition system based on pitch, formant, speech speed, etc. from speech signal. In this paper, as first stage we foun specific features of feeling angry from his words when a man got angry.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.3-12
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• 1995

A controller utilizing fuzzy logic is developed to control the speed of a motor in a washing machine by choosing an appropriate phase. Due to the hardship imposed on obtaining a result from a relation established for inputs, present speed and present rate of speed, and ouput, a phase, of the system that can be tested against an experimental result, it is impossible to apply a genetic algorithm to fine-tune the fuzzy logic controller. To avoid this difficulty, a proper assumption that the parameters of an if-part of a primary fuzzy logic controller have a functional relationship with an error between computed values and experimental ones in made. Setting up of a fuzzy relationship between the parameters and the errors is then achieved through experimentally obtained data. Genetic Algorithm is then applied to this secondary fuzzy logic controller to verify the fuzzy logic. In the verification process, the primary fuzzy logic controller is used in obtaining experimental results. In this way the kind of difficulty in obtaining enough experimental values used to verify the fuzzy logic with genetic algorithm is gotten around. Selection of the parameters that would produce the least error when using the secondary fuzzy logic controller is done with applying genetic algorithm to the then-part of the controller. In doing so the optimal values for the parameters of the if-part of the primary fuzzy logic controller are assumed to be contained. The experimental result presented in the paper validates the assumption.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.315-319
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• 2001

A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide e8rly w, ul1ings in rolling mill. Because dynamics of rolling mill is non-linear. This paper shows a chaotic behaviour of vibration signal in rolling mill using embedding method. Phase plane and Poincare map, FFT and histogram of vibration signal in rolling mill are implemented by qualitative analysis and Fractal dimension, Lyapunov exponent are presented by quantitative analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.3
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• pp.20-32
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• 1991

This study seeks a way a way of dealing with unformatted natural language considering fuzzy set theory. The goal of the study is to establish a framework of an effective language understanding system that is linked to language classification system This study has found that languate understanding is strongly influenced by the language classification. The understanding of language. This study shows that the precision of language classification depends upon the way of how the language is classified in advance. In this study, a fuzzy logic was used to improve the precision of language classification. It was considered that the fuzzy logic might be albe to distinctively classify nuatural language texts into pretinent homogenious groups where contents of the language were identical. Accordingly, in the study, it was expected that classification of language were precisely classified by the fuzzy logic. An experimentalsystems was designed to evaluate the performane of a natural language understanding system that was connected to a fuzzy language classification system. Finally, the experiment suggests that a successful language understanding should require an real time interaction between mem andmachine fuzzy provious language classification.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.97-104
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• 2015

An 'associative cube', a class of auto-associative memories, is revisited here, in which training data and hidden orthogonal basis functions such as wavelet packets or Fourier kernels, are combined in the weight cube. This weight cube has hidden units in its depth, represented by a three dimensional cubic structure. We develop an unsupervised incremental learning mechanism based upon the adaptive least squares method. Training data are mapped into orthogonal basis vectors in a least-squares sense by updating the weights which minimize an energy function. Therefore, a prescribed orthogonal kernel is incrementally assigned to an incoming data. Next, we show how a decoding procedure finds the closest one with a competitive network in the hidden layer. As noisy test data are applied to an associative cube, the nearest one among the original training data are restored in an optimal sense. The simulation results confirm robustness of associative cubes even if test data are heavily distorted by various types of noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.712-717
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• 2008

In swarm robot systems, each robot must act by itself according to the its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. By distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.262-267
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• 2013

The recognition of human emotional state is one of the most important components for efficient human-human and human- computer interaction. In this paper, four emotions such as fear, disgust, joy, and neutral was a main problem of classifying emotion recognition and an approach of visual-stimuli for eliciting emotion based on physiological signals of skin conductance (SC), skin temperature (SKT), and blood volume pulse (BVP) was used to design the experiment. In order to reach the goal of solving this problem, half-against-half (HAH) multi-class support vector machine (SVM) with Gaussian radial basis function (RBF) kernel was proposed showing the effective techniques to improve the accuracy rate of emotion classification. The experimental results proved that the proposed was an efficient method for solving the emotion recognition problems with the accuracy rate of 90% of neutral, 86.67% of joy, 85% of disgust, and 80% of fear.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.2
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• pp.127-137
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• 2012

This study focuses on the optimal washing conditions for dry cleaning recommended fabrics to minimize dimensional changes using wet cleaning. We suggest water-based alternatives to a perchloroethylene based cleaning process. Wool and rayon fabrics were laundered under various washing conditions and then air-dried for 24hrs. All specimens were extended after spinning and shrunk after drying. This is probably because the fibers were swollen and extended by wetting. The wool fabrics were shown to be acutely influenced by washing temperature and mechanical force. The optimal washing conditions for wool fabric to minimize the dimensional change implied a normal washing temperature and minimized mechanical force. For rayon specimens, dimensional change by a hand wash showed a remarkable decrease compared with a machine wash. Rayon fabric seemed to be influenced by the quantity of water contained in the fabric after spinning and washing time. Therefore, the desirable washing conditions for rayon fabric are to reduce the time required for washing and to increase the spin speed.