• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1089-1099
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• 2015

This paper proposes vision-based collision risk estimation method for an unmanned surface vehicle. A robust image-processing algorithm is suggested to detect target obstacles from the vision sensor. Vision-based Target Motion Analysis (TMA) was performed to transform visual information to target motion information. In vision-based TMA, a camera model and optical flow are adopted. Collision risk was calculated by using a fuzzy estimator that uses target motion information and vision information as input variables. To validate the suggested collision risk estimation method, an unmanned surface vehicle experiment was performed.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.4
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• pp.135-149
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• 1994

Demand forecasting is to estimate the demand of customers for products and services. Since the future is uncertain in nature, it is too difficult for us to predict exactly what will happen. Therefore, when the forecasting is performed upon the uncertain future, it is realistic to estimate the value of demand as an interval or a fuzzy number instead of a crisp number. In this paper, we propose a demand forecasting method using the standard back-propagation algorithm and then we extend the method to the case of interval inputs. Next, we demonstrate that the proposed method using the interval neural networks can represent the fuzziness of forecasting values as intervals. Last, we propose a demand forecasting method using the transformed input variables that can be obtained by taking account of the degree of influence between an input and an output.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.132-137
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• 1993

In this paper, a fish drying process control system is proposed, which predicts the proper change with time in weight of the material fish and the drying conditions in advance, based on the performance of skilled worker. In order to implement a human expertise into an automated fish drying process control system, an experimental analysis is made and a model for the process is built. The proposed system divided into two procedures: The procedure before drying and the one during drying. The procedure before drying is for the prediction of necessary drying time. To estimate the necessary drying time, first, the proper change in weight for the product is obtained by using fuzzy reasoning. The condition part of the production rule consists of the factors of fish body and the expected degree of dryness. Kext, the necessary drying time is obtained by regression models. The variables employed in the models are the factors, inferred change in weight and drying conditions. The model for the procedure during drying is also proposed for more accurate estimation, which is described by a system of linear-differential equations.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.2
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• pp.16-34
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• 1991

It has been widely accepted that expert systems must reason from multiple sources of information that is to some degree evidential - uncertain, imprecise, and occasionally inaccurate - called evidential information. Evidence theory (Dempster/Shafet theory) provides one of the most general framework for representing evidential information compared to its alternatives such as Bayesian theory or fuzzy set theory. Many expert system applications require evidence to be specified in the continuous domain - such as time, distance, or sensor measurements. However, the existing evidence theory does not provide an effective approach for dealing with evidence about continuous variables. As an extension to Strat's pioneeiring work, this paper provides a new combination rule, a new method for mass function transffrmation, and a new method for rendering joint mass fuctions which are of great utility in evidence theory in the continuous domain.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.25-35
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• 2002

This paper presents a heuristic algorithm that a mobile robot avoids obstacles using optical flow. Using optical flow, the mobile robot can easily avoid static obstacles without a prior position information as well as moving obstacles with unknown trajectories. The mobile robot in this paper is able to recognize the locations or routes of obstacles, which can be detected by obtaining 2-dimensional optical flow information from a CCD camera. It predicts the possibilities of crash with obstacles based on the comparison between planned routes and the obstacle routes. Then it modifies its driving route if necessary. Driving acceleration and angular velocity of mobile robot are applied as controlling variables of avoidance. The corresponding simulation test is performed to verify the effectiveness of these factors. The results of simulation show that the mobile robot can reach the goal with avoiding obstacles which have variable routes and speed.

• International Journal of Safety
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• v.11 no.1
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• pp.6-10
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• 2012

This paper presents a study on the analysis for reducing the number of false alarms in fire detection system. In order to intelligent algorithm fuzzy logic is adopted in developing fire detection system to reduce false alarm. The intelligent fire detection algorithm compared and analyzed the fire and non-fire signatures measured in circuits simulating flame fire and smoldering fire. The algorithm has input variables obtained by fire experiment with K-type thermocouple and optical smoke sensor. Also triangular membership function is used for inference rules. And the antecedent part of inference rules consists of temperature and smoke density, and the consequent part consists of fire probability. A fire-experiment is conducted with paper, plastic, and n-heptane to simulate actual fire situation. The results show that the intelligent fire detection algorithm suggested in this study can more effectively discriminate signatures between fire and similar fire.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.794-796
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• 1999

엄격한 시간 제약성에 의해 특성화되는 실시간 전력 시스템의 성능 및 신뢰도를 평가하기 위해서 퍼지-랜덤 변수가 포함된 이산 사건 모델 및 확장된 path-space 기법을 제시한다. 실시간 시스템의 정확성은 출력의 논리적 결과 뿐 아니라 반응시간에도 의존하므로, 본 논문에서는 실시간 전력 시스템의 성능 및 신뢰도를 유연하게 평가하기 위해서 퍼지-랜덤 변수에 의해 적절하게 변형된 상태 오토마타를 제시하고 몇가지 수치 예제를 제시함으로써 제안한 기법의 효용성을 검증한다.

• International Journal of Automotive Technology
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• v.3 no.3
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• pp.89-94
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• 2002

This paper presents an architecture for distributed control systems and its underlying methodological framework. Ideas and concepts of distributed systems, artificial intelligence, and soft computing are merged into a unique architecture to provide cooperation, flexibility, and adaptability required by knowledge processing in intelligent control systems. The distinguished features of the architecture include a local problem solving capability to handle the specific requirements of each part of the system, an evolutionary case-based mechanism to improve performance and optimize controls, the use of linguistic variables as means for information aggregation, and fuzzy set theory to provide local control. A distributed traffic control system application is discussed to provide the details of the architecture, and to emphasize its usefulness. The performance of the distributed control system is compared with conventional control approaches under a variety of traffic situations.

• Korean System Dynamics Review
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• v.1 no.1
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• pp.103-131
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• 2000

If a system such as a port has a large boundary and complexity, and the system's substance is considered as a black box, forecast accuracy will be very low. Futhermore various components in a port exert significant influence on each other. To copy with these problem the form of structure models were introduced by using SD method. The Competitive Ports Model had several sub-systems consisting of each Unit Port models, and each Unit Port model was made by quantitative, qualitative factors and their feedback loops. The fact that all components of one port have influence on the components of the other ports should be taken into account to construct Competitive Port Models. However, with the current approach that is impossible, and in this paper therefore, models were simplified by HFP adapted to integrate level variables of unit port models. Although many studies on modelling of port competitive situation have been conducted, both theoretical frame and methodology are still very weak. In this study, a new algorithm called ESD(Extensional System Dynamics) for the evaluation of port competition was presented, and applied to simulate port systems in northeast asia.