• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.965-980
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• 2011

In this paper, the use of conditional Fuzzy C-Means (CFCM) aimed at estimation of desired states of an unknown digital communication channel is investigated for blind channel equalization. In the proposed CFCM, a collection of clustered centers is treated as a set of pre-defined desired channel states, and used to extract channel output states. By considering the combinations of the extracted channel output states, all possible sets of desired channel states are constructed. The set of desired states characterized by the maximal value of the Bayesian fitness function is subsequently selected for the next fuzzy clustering epoch. This modification of CFCM makes it possible to search for the optimal desired channel states of an unknown channel. Finally, given the desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In a series of simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The experimental studies demonstrate that the performance (being expressed in terms of accuracy and speed) of the proposed CFCM is superior to the performance of the existing method exploiting the "conventional" Fuzzy C-Means (FCM).

• Proceedings of KOSOMES biannual meeting
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• 2006.05a
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• pp.59-64
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• 2006

In this study, the risk of SRRs was assessed upon the scale of the damage of marine accidents. For the risk assessment, inner-outer dependence methods and special knowledge-based fuzzy logic were introduced. Also, in order to calculate the importance of assessment value in this study, a max min composition method was used for fuzzy logic based on the principle of fuzzy extension and the centroid of gravity method was used for non-fuzzy formation. In order to produce the importance of assessment items, the inner-outer dependence methods were used for assessment items, and markov analysis method was used for the importance of the final comprehensive assessment. As a result, the risk of SRR of Tongyoung and Yeosu was proven relatively higher, thus, it needs to have more rescue ships and rescue devices for relieving the risk in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.12 no.3 s.26
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• pp.219-224
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• 2006

In this study, the risk of SRRs was assessed upon the scale of the damage of marine accidents. For the risk assessment, inner-outer dependence methods and special knowledge-based fuzzy logic were introduced. Also, in order to calculate the importance of assessment value in this study, a max-min composition method was used for fuzzy logic based on the principle of fuzzy extension and the centroid of gravity method was used for non-fuzzy formation. In order to produce the importance of assessment items, the inner-outer dependence methods were used for assessment items, and markov analysis method was used for the importance of the final comprehensive assessment. As a result, the risk of SRR of Tongyoung, Mokpo and Yeosu was proven relatively higher, thus, it needs to have more rescue ships and rescue devices for relieving the risk in the future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.34-39
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• 2004

Huffman coding which has been used in many data compression algorithms is a popular data compression technique used to reduce statistical redundancy of a signal. It has been proposed that the Huffman algorithm can decode efficiently using characteristics of the Huffman tables and patterns of the Huffman codeword. We propose a new Huffman decoding algorithm which used a multi way tree search and present an efficient hardware implementation method. This algorithm has a small logic area and memory space and is optimized for high speed decoding. The proposed Huffman decoding algorithm can be applied for many multimedia systems such as MPEG audio decoder.

• Steel and Composite Structures
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• v.47 no.4
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• pp.513-521
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• 2023

The goal of this work is to create a new and improved GWO (Grey Wolf Optimizer), the so-called Robot GWO (RGWO), for dynamic and static target tracking involving multiple robots in unknown environmental conditions. From applying ourselves with the Gray Wolf Optimization Algorithm (GWO) and how it works, as the name suggests, it is a nature-inspired metaheuristic based on the behavior of wolf packs. Like other nature-inspired metaheuristics such as genetic algorithms and firefly algorithms, we explore the search space to find the optimal solution. The results also show that the improved optimal control method can provide superior power characteristics even when operating conditions and design parameters are changed.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.61-70
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• 2004

It is difficult to find an appropriate web site because exponentially growing web contains millions of web documents. Personalization of web search can be realized by recommending proper web sites using user profile but more efficient method is needed for estimating preference because user's evaluation on web contents presents many aspects of his characteristics. As user profile has a property of non-linearity, estimation by classifier is needed and combination of classifiers is necessary to anticipate diverse properties. Structure adaptive self-organizing map (SASOM) that is suitable for Pattern classification and visualization is an enhanced model of SOM and might be useful for web mining. Fuzzy integral is a combination method using classifiers' relevance that is defined subjectively. In this paper, estimation of user profile is conducted by using ensemble of SASOM's teamed independently based on fuzzy integral and evaluated by Syskill ＆ Webert UCI benchmark data. Experimental results show that the proposed method performs better than previous naive Bayes classifier as well as voting of SASOM's.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• Proceedings of KOSOMES biannual meeting
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• 2004.11a
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• pp.83-88
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• 2004

This paper suggests on evaluation of risk level for damage of marine accidents in SRRs. This paper intoduces a concept of fuzzy logic with the plenty of related literature riview, fuzzy measure t-seminormed fuzzy integral and in the Korean. SRRs of RCC and RSC. The methodology of this paper is max$\cdot$min composition of fuzzy extensive principle, defuzzifiation is centroid of gravity methods. And final evaluation value using t-seminormed fuzzy integral. At the result, the evaluation of risk level is especially over Serious for marine accident of Mokpo, Tongyoung, Busan SRRs. This paper recommends tint many Rescue Vessels and Equipments need to the reduction of risk level about those.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.85-98
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• 1998

This paper proposes a new design method of neuro-FLC by the Lamarckian co-adaptation scheme that incorporates the backpropagation learning into the GA evolution in an attempt to find optimal design parameters (fuzzy rule base and membership functions) of application-specific FLC. The design parameters are determined by evolution and learning in a way that the evolution performs the global search and makes inter-FLC parameter adjustments in order to obtain both the optimal rule base having high covering value and small number of useful fuzzy rules and the optimal membership functions having small approximation error and good control performance while the learning performs the local search and makes intra-FLC parameter adjustments by interacting each FLC with its environment. The proposed co-adaptive design method produces better approximation ability because it includes the backpropagation learning in every generation of GA evolution, shows better control performance because the used COG defuzzifier computes the crisp value accurately, and requires small workspace because the optimization procedure of fuzzy rule base and membership functions is performed concurrently by an integrated fitness function on the same fuzzy partition. Simulation results show that the Lamarckian co-adapted FLC produces the most superior one among the differently generated FLCs in all aspects such as the number of fuzzy rules, the approximation ability, and the control performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.333-338
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• 2005

This paper presents the target object search algorithm under Dynamic Programming (DP) in the Tree-type maze. We organized an experimental environment with the concatenation of Y-shape diverged way, small mobile robot, and a target object. By the principle of optimality, the backbone of DP, an agent recognizes that a given whole problem can be solved whether the values of the best solution of certain ancillary problem can be determined according to the principle of optimality. In experiment, we used two different control algorithms: a left-handed method and DP. Finally we verified the efficiency of DP in the practical application using our real robot.