• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.63-65
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• 1993

전통적 의사결정 이론에 입각한 기존의 다기준 의사결정 모형은 명확하게 정의된 문제에 대해서 실함수로 표현된 사전의 선호정보에 의하여 모호함이 없이 확실한 선호의 판별을 산출하는 true-criterion 모형이다. 그러나 현실적인 의사결정 환경하에서 선호정보가 사전에 명확하게 하나의 실함수로 얻어지기는 매우 어렵다. 이는 곧 선호의 불확실성(fuzziness)이나 선호판별을 할 수 없는 비교불가능성(incomparability)등이 있을 수 있음을 의미한다. 1980년대 이후의 다기준의사결정 이론에 대한 연구는 불명확한 문제의 정형화나 선호의 불확실성을 인정하고, 이를 fuzzy 이론을 이용하여 모형의 설정에 반영하고 있다. 심지어는 선호관계의 비추이성(intransitivity)이나 비교불가능성까지도 인정하는 등 모형의 강건성(robustness)을 고려하는 연구가 활발하게 이루어지고 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.20-28
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• 2012

We define a language $\mathcal{RS}$, a subclass of the scheduling language $\mathcal{RS}V$ (resource constrained project scheduling with variant processes). $\mathcal{RS}$ involves the determination of the starting times for ground activities of a project satisfying precedence and resource constraints, in order to minimize the total project duration. In $\mathcal{RS}$ ground activities and two structural symbols (operators) 'seq' and 'pll' are used to construct activity-terms representing scheduling problems. We consider three different variants for formalizing the $\mathcal{RS}$-scheduling problem, the optimizing variant, the number variant and the decision variant. Using the decision variant we show that the problem $\mathcal{RS}$ is $\mathcal{NP}$-complete. Further we show that the optimizing variant (or number variant) of the $\mathcal{RS}$-problem is computable in polynomial time iff the decision variant is computable in polynomial time.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.261-264
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• 2002

In this study, we used a 16-bit microprocessor, 80C196KC for a control part in order to develop a multi-functional wheel-chair system, and implemented a joy-stick to control this system. For the complete system, we used a commercial electromotive wheelchair as a basic plant, and applied an encoder to get the rotating number of the motor to transfer data to the MCU to control the motor. We used PWM (Pulse Width Modulation) method to control the wheel-chair motor where a H-bridge circuit was configured. We used the fuzzy control algorithm for the operation of DC motor, which was attached to the electromotive wheelchair and manipulated following the change of the joystick position while a user was controlling the joystick. He also could control the speed and direction of DC motor as well as control position information.

• Bulletin of the Korean Mathematical Society
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• v.33 no.4
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• pp.565-585
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• 1996

In 1976, Caristi [1] established a celebrated fixed point theorem in complete metric spaces, which is a very useful tool in the theory of nonlinear analysis. Since then, several generalizations of the theorem were given by a number of authors: for instances, generalizations for single-valued mappings were given by Downing and Kirk [4], Park [11] and Siegel [13], and the multi-valued versions of the theorem were obtained by Chang and Luo [3], and Mizoguchi and Takahashi [10].

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.99-100
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• 2006

One of the fundamental problems in sensor networks is the deployment of sensor nodes. The Fuzzy C-Means(FCM) clustering algorithm is proposed to determine the optimum location and minimum number of sensor nodes for the specific application space. We performed a simulation using two dimensional L shape model. The actual length of the L shape model is about 100m each. We found the minimum number of 15 nodes are sufficient for the complete coverage of modeled area. We also found the optimum location of each nodes. The real deploy experiment using 15 sensor nodes shows the 95.7%. error free communication rate.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.246-254
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• 2002

In this paper auto-tuning scheme of PID controller based on the reference model has been studied for a Process control system by immune algorithm. Up to this time, many sophisticated tuning algorithms have been tried in order to improve the PID controller performance under such difficult conditions. Also, a number of approaches have been proposed to implement mixed control structures that combine a PID controller with fuzzy logic. However, in the actual plant, they are manually tuned through a trial and error procedure, and the derivative action is switched off. Therefore, it is difficult to tune. Since the immune system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (Parallel Distributed Processing) network to complete patterns against the environmental situation. Simulation results reveal that reference model basd tuning by immune network suggested in this paper is an effective approach to search for optimal or near optimal process control.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.93-100
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• 2007

The mobile robots to rescue a life in a disaster area and to explore planets demand high mobility as well as recognition of the environment. To avoid unknown obstacles exactly in unknown environment, accurate sensing is required. This paper proposes a sensor fusion to recognize unknown obstacles accurately by using low-cost sensors. Ultrasonic sensors and infrared sensors are used in this paper to avoid obstacles. If only one of these sensors is used alone, it is not useful fer the mobile robots to complete their tasks in the real world since the surrounding environment in the real world is complex and composed of many kinds of materials. So infrared sensor may not recognize transparent or reflective obstacles and ultrasonic sensor may not recognize narrow obstacles, far example, columns of small diameter. Therefore, I selected six ultrasonic sensors and five infrared sensors to detect obstacles. Then, I fused ultrasonic sensors with infrared sensors in order that both advantages and disadvantages of each sensor are utilized together. In fusing sensors, fuzzy algorithm is used to cope with the uncertainties of each sensor. TAMRY which is terrain-adaptive mobile robot is used as the mobile robot for experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.449-449
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• 2000

Many researchers have studied architectures for non-Neumann's computers because of escaping its bottleneck. To avoid the bottleneck, a neuron-based computer has been developed. The computer has only neurons and their connections, which are constructed of the learning. But still it has information processing facilities, and at the same time, it is like as a simplified brain to make inference; it is called "neuron-computer". No instructions are considered in any neural network usually; however, to complete complex processing on restricted computing resources, the processing must be reduced to primitive actions. Therefore, we introduce the instructions to the neuron-computer, in which the most important function is implications. There is an implication represented by binary-operators, but general implications for multi-value or fuzzy logics can't be done. Therefore, we need to use Lukasiewicz' operator at least. We investigated a neuron-computer having instructions for general implications. If we use the computer, the effective inferences base on multi-value logic is executed rapidly in a small logical unit.

• Computers and Concrete
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• v.16 no.3
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• pp.399-414
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• 2015

Nowadays, fiber reinforced polymer (FRP) composites are widely used for rehabilitation, repair and strengthening of reinforced concrete (RC) structures. Also, recent advances in concrete technology have led to the production of high strength concrete, HSC. Such concrete due to its very high compression strength is less ductile; so in seismic areas, ductility is an important factor in design of HSC members (especially FRP strengthened members) under flexure. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and multiple regression analysis are used to predict the curvature ductility factor of FRP strengthened reinforced HSC (RHSC) beams. Also, the effects of concrete strength, steel reinforcement ratio and externally reinforcement (FRP) stiffness on the complete moment-curvature behavior and the curvature ductility factor of the FRP strengthened RHSC beams are evaluated using the analytical approach. Results indicate that the predictions of ANFIS and multiple regression models for the curvature ductility factor are accurate to within -0.22% and 1.87% error for practical applications respectively. Finally, the effects of height to wide ratio (h/b) of the cross section on the proposed models are investigated.