• Science of Emotion and Sensibility
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• v.5 no.3
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• pp.11-20
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• 2002

There are Two new models developed for objective evaluation of fabric color patterns by applying a multiple regression analysis and an adaptive foray-rule-based system. The physical features of fabric color patterns are extracted through digital image processing and the emotional features are collected based on the psychological experiments of Soen[3, 4]. The principle physical features are hue, saturation, intensity and the texture of color patterns. The emotional features arc represented thirteen pairs of adverse adjectives. The multiple regression analyses and the adaptive fuzzy system are used as a tool to analyze the relations between physical and emotional features. As a result, both of the proposed models show competent performance for the approximation and the similar linguistic interpretation to the Soen's psychological experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1350-1353
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• 1993

A fuzzy control system typically requires“tuning,”or adjuctment of the parameters defining its linguistic variables. Automating this process amounts to applying a second“metacontrol”layer to drive the controller and plant to desired performance levels. Current methods of automated tuning rely on a single crisp numeric functional to evaluate control system performance. A generalization of Box's complex algorithm allows more realistic tuning based on lexicographic aggregation of multiple ordinal scales of performance, such as effectiveness and efficiency. The method is presented and illustrated using a simple inverted pendulum control system.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.683-688
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• 2008

The emotional information processing is to simulate and recognize human sensibility, sensuality or emotion, to realize natural and harmonious human-machine interface. This paper proposes an emotion-based image retrieval method. In this method, user can choose a linguistic query among some emotional adjectives. Then the system shows some corresponding representative images that are pre-evaluated by experts. Again the user can select a representative one among the representative images to initiate traditional content-based image retrieval (CBIR). By this proposed method any CBIR can be easily expanded as emotion-based image retrieval. In CBIR of our system, we use several color and texture visual descriptors recommended by MPEG-7. We also propose a fuzzy similarity measure based on Choquet integral in the CBIR system. For the communication between system and user, a relevance feedback mechanism is used to represent human subjectivity in image retrieval. This can improve the performance of image retrieval, and also satisfy the user's individual preference.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.203-207
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• 1998

In this paper, it is shown how Fuzzy Pattern Matching can be applied to diagnosis of the most common faults of Rotating Machinery. The whole diagnosis process has been divided in three steps : Fault Detection, Fault Isolation and Fault Identification, whose possible results are described by linguistic patterns. Diagnosis will consist in obtaining a set of matching indexes that indexes that express the compatibility of the fuzzified features extracted from the measured vibration signals, with the knowledge contained in the corresponding patterns.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.135-138
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• 1997

In this paper, we proposed effective modeling method to nonlinear complex system. Fuzzy Neural Network(FNN) was used as basic model. FNN was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, we used FNN which was proposed by Yamakawa. The FNN used Simple Inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. This structure has better property than other structure at learning speed and convergence ability. But it has difficulty at definition of membership function. We used Hard c-Mean method to overcome this difficulty. To evaluate proposed method. We applied the proposed method to waste water treatment process. We obtained better performance than conventional model.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.866-871
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• 1991

The multi-layer neural network possesses the desirable characteristics of parallel distributed processing and learning capacity, by which the uncertain variation of the parameters in the dynamically complex system can be handled adoptively. However the error back propagation algorithm that has been utilized popularly in the learning procedure of the mulfi-Jayer neural network has the significant limitations in the real application because of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manipulator.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.66-74
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• 2004

In this paper the authors presents a new method, named FR-SERVQUAL, of evaluating perceived service quality in Public Sectors, using triangle fuzzy numbers and semantic differential scale. By conventional quantification methods, it is not easy to express the notion of a linguistic variables and customers' subjective judgements. In contrast to the conventional PZB methods which express the customers' perception of quality as a function of gap between the expected and perceived service, this paper suggests to use the ratio of the two. Through an application example, this paper shows that the current FR-SERVQUAL approach provides a more realistic way of measuring service quality compared to existing methods.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.165-168
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• 1994

The paper deals with the fuzzy PI control of a switched reluctance motor drive. Fuzzy algorithm based on linguistic rules describing the operator's control strategy is applied to speed control of the SRM. Simulation and experimental results show that performance of the furry PI controller is superior to that of conventional PI controller. In particular the robustness of the system is improved.

• Journal of Ocean Engineering and Technology
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• v.21 no.6
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• pp.115-119
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• 2007

Inference rules of the knowledge base, generated by experts or optimization, may be often inconsistent and incomplete. This paper suggests a systematic and automatic method which extracts inference rules not from experts' subject but from data. First, input/output linguistic variables are partitioned into several properties by the fuzzy equalization algorithm and each combination of their properties comes to premise of inference rule. Then, the conclusion which is the mast suitable for the premise is selected by evaluating consistent measure. This method, automatically from data, derives inference rules from experience. It is shown through application that extracts new inference rules between hull dimensions and hull performance.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.101-111
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• 1993

Many processes such as machining, injection-moulding and metal-forming are usually operated by hydraulic servo-systems. The dynamic characteristics of these systems are complex and highly non-linear and are often subjected to the uncertain external disturbances associated with the processes. Consequently, the conventional approach to the controller design for these systems may not guarantee accurate tracking control performance. An effective neuro-fuzzy controller is proposed to realize an accurate hydraulic servo-system regardless of the uncertainties and the external disturbances. For this purpose, first, we develop a simplified fuzzy logic controller which have multidimensional and unsymmetric membership functions. Secondly, we develop a neural network which consists of the parameters of the fuzzy logic controller. It is show that the neural network has both learning capability and linguistic representation capability. The proposed controller was implemented on a hydraulic servo-system. Feedback error learning architecture is adopted which uses the feedback error directly without passing through the dynamics or inverse transfer function of the hydraulic servo-system to train the neuro-fuzzy controller. A series of simulations was performed for the position-tracking control of the system subjected to external disturbances. The results of simulations show that regardless of inherent non-linearities and disturbances, an accuracy tracking-control performance is obtained using the proposed neuro-fuzzy controller.