• The Science & Technology
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• v.33 no.9 s.376
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• pp.32-33
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• 2000

21세기의 화이트칼라는 아침에 출근하면 보안이 잘 된 지능형 문으로 걸어 들어와서 데스크탑의 가상조수가 그날의 스케줄을 큰 소리로 읽는 것을 듣느다. 그리고 지능형 의자에 앉아서 그날의 할 일을 음성인식장치 PC로 챙긴다. 평판스크린으로 된 벽의 영상과 데이터를 보면서... 멀리 있는 동료들과 얘기하려면 실물 그대로 입체 비디오 회의시스템에 불러낸다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.721-724
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• 2014

Ultrasonic sensor is used widely for many applications because low cost, simple structure, and low restriction. There are many difficulties to recognize an object by use an ultrasonic sensor, because of low resolution, poor direction, and measurement error. To improve the these problem, we use the various kinds of sensor arrangement methods, large amount of sensor, and change the arrangement pattern of sensor. In this paper, to obtain the most basic parameters for pattern recognition such as distance, dimension of the object, an angle of the object, we get the improved results by use the intelligent calculation algorithm based on Neuro-Fuzzy. This method use the multifarious output voltage of ultrasonic sensor by simple electronic circuit.

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.216-224
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• 2010

A RAM-based Neural Network(RNN) has the advantages of processing speed and hardware implementation. In spite of these advantages, it has a saturation problem, weakness of repeated learning and extract of a generalized pattern. To resolve these problems of RNN, the 3DNS model using cumulative multi discriminator was proposed. But that model does not solve the saturation problem yet. In this paper, we proposed a adaptive weight cumulative neural net(AWCNN) using the adaptive weight neuron (AWN) for solving the saturation problem. The proposed nets improved a recognition rate and the saturation problem of 3DNS. We experimented with the MNIST database of NIST without preprocessing. As a result of experimentations, the AWCNN was 1.5% higher than 3DNS in a recognition rate when all input patterns were used. The recognition rate using generalized patterns was similar to that using all input patterns.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.5
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• pp.482-494
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• 1992

In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.17-26
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• 1998

Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But for the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. The time-of-flight(TOF) method generally used for distance measurement can not distinguish small object patterns of plane, corner or edge. To resolve the problem, an increased number of the sensors in the forms of a linear array or 2-dimensional array of the sensors has been used. Also better resolution has been obtained by shifting the array in several steps using mechanical actuators. Also simple patterns are classified based on analyzing signal reflections. In this paper we propose a method of a sensor array system with improved capability in pattern distinction using electronic circuits accompanying the sensor array, and intelligent algorithm based on neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. A set of different return signals from neighborhood sensors is manipulated to provide enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• Journal of the Korea Concrete Institute
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• v.22 no.1
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• pp.19-27
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• 2010

Aging and severe environments are major causes of damage in reinforced concrete (RC) structures such as buildings and bridges. Deterioration such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for strengthening and rehabilitation of RC structures have been developed in the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to the conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of adaptive neuro-fuzzy inference system (ANFIS) in predicting behavior of circular type concrete column retrofitted with FRP. To construct training and testing dataset, experiment results for the specimens which have different retrofit profile are used. Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber and size of specimens are selected as input parameters to predict strength, strain, and stiffness of post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.382-382
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• 2011

최근의 극심한 기상이변으로 인하여 발생되는 유출량의 예측에 관한 사항은 치수 이수는 물론 방재의 측면에서도 역시 매우 중요한 관심사로 부각되고 있다. 강우-유출 관계는 유역의 수많은 시 공간적 변수들에 의해 영향을 받기 때문에 매우 복잡하여 예측하기 힘든 요소이며, 과거에는 추계학적 예측모형이나 확정론적 예측모형 혹은 경험적 모형 등을 사용하여 유출량을 예측하였으나 최근에는 인공신경망과 퍼지모형 그리고 유전자 알고리즘과 같은 인공지능기반의 모형들이 많이 사용되고 있다. 하지만 유출량을 예측하고자 할 때 학습자료 및 검정자료로써 사용되는 유출량은 수위-유량 관계곡선식으로부터 구하는 경우가 대부분으로 이는 이렇게 유도된 유출량의 경우 오차가 크기 때문에 그 신뢰성에 문제가 있을 것으로 판단된다. 따라서 본 논문에서는 수위를 직접 예측함으로써 이러한 오차의 문제점을 극복 하고자 한다. Neuro-Fuzzy 모형은 과거자료의 입 출력 패턴에서 정보를 추출하여 지식으로 보유하고, 이를 근거로 새로운 상황에 대한 해답을 제시하도록 하는 인공지능분야의 학습기법으로 인간이 과거의 경험과 훈련으로 지식을 축적하듯이 시스템의 입 출력에 의하여 소속함수를 최적화함으로서 모형의 구조를 스스로 조직화한다. 따라서 수학적 알고리즘의 적용이 어려운 강우와 유출관계를 하천유역이라는 시스템에서 발생된 신호체계의 입 출력패턴으로 간주하고 인간의 사고과정을 근거로 추론과정을 거쳐 수문계의 예측에 적용할 수 있을 것이다. 유전자 알고리즘은 적자생존의 생물학 원리에 바탕을 둔 최적화 기법중의 하나로 자연계의 생명체 중 환경에 잘 적응한 개체가 좀 더 많은 자손을 남길 수 있다는 자연선택 과정과 유전자의 변화를 통해서 좋은 방향으로 발전해 나간다는 자연 진화의 과정인 자연계의 유전자 메커니즘에 바탕을 둔 탐색 알고리즘이다. 즉, 자연계의 유전과 진화 메커니즘을 공학적으로 모델화함으로써 잠재적인 해의 후보들을 모아 군집을 형성한 뒤 서로간의 교배 혹은 변이를 통해서 최적 해를 찾는 계산 모델이다. 이러한 유전자 알고리즘은 전역 샘플링을 중심으로 한 수법으로 해 공간상에서 유전자의 개수만큼 복수의 탐색점을 설정할 뿐만 아니라 교배와 돌연변이 등으로 좁아지는 탐색점 바깥의 영역으로 탐색을 확장할 수 있기 때문에 지역해에 빠질 위험성이 크게 줄어든다. 따라서 예측과 패턴인식에 강한 뉴로퍼지 모형의 해 탐색방법을 유전자 알고리즘을 사용한다면 보다 정확한 해를 찾는 것이 가능할 것으로 판단된다. 따라서 본 논문에서는 선행우량 및 상류의 수위자료로부터 하류의 단시간 수위예측에 관해 연구하였으며, 이를 위해 유전자 알고리즘을 이용항여 소속함수를 최적화 시키는 형태의 Neuro-Fuzzy모형에 대하여 연구하였다.

• Archives of design research
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• v.14
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• pp.121-138
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• 1996

The purpose of this study is to develop a new architectural language and design strategies that would anticipate and incorporate new historical situations and new paradigms to understand the world. It consists of four sections as follows: First, it presents a new interpretation of space, human body, and movement that we find in modern art and tries to combine that new artistic insight with environmental design to provide a theoretical basis for performance-event architecture. Second, it conceives of architectural environment as a combination of space, movement, and probabilistic situations rather than a mere conglomeration of material. It also perceives the environment as a stage for performance and the act of designing as a performance. Third, in this context, man is conceived of as an organic system that responds to, interacts with, and adapts himself to his environment through self-regulation. By the same token, architecture should be a dynamic system that undergoes a constant transformation in its attempt to accommodate human actions and behaviors as he copes with the contemporary philosophy characterized by the principle of uncertainty, fast-changing society, and the new developments in technology. Fourth, the relativistic and organic view-point that constitutes the background for all this is radically different from the causalistic and mechanistic view that characterized the forms and functions of modernistic design. The present study places a great emphases on dematerialistic conception of environment and puts forth a disprogramming method that would accommodate interchangeability in the passage of time and the intertextuality of form and function. In the event, performance-event architecture is a strategy based on the systems world-view that would enable the recovery of man's autonomy and the reconception of his environment as an object of art.