• Computers and Concrete
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• v.15 no.1
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• pp.55-71
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• 2015

This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)-connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs of data even without knowing the complete physical knowledge of the system. The monotonicity property is then exploited as an additional qualitative information to obtain a meaningful SIRMs-connected FIS model. This method is then validated using results from test data of the literature. Several parameters, such as initial tendon depth to beam ratio; deviators spacing to the initial tendon depth ratio; and distance of a concentrated load from the nearest support to the effective beam span are considered. A computer simulation for estimating the stress increase in externally prestressed tendon, ${\Delta}f_{ps}$, is then reported. The contributions of this paper is two folds; (i) it contributes towards a new monotonicity-preserving data-driven FIS model in fuzzy modeling and (ii) it provides a novel solution for estimating the ${\Delta}f_{ps}$ even without a complete physical knowledge of unbonded tendons.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.276-279
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• 2005

A review of how the functions of two or more independent variables can be approximated by using fuzzy systems is provided in this paper. We start with an exact represention of a linear interpolation function of two independent variables by using a fuzzy system. Next, we describe how this function can be approximated by another fuzzy system with a lesser number or with a desired number of output fuzzy sets. Thus, a reduction of the storage needed is achieved by storing the fuzzy rules or equivalently the output fuzzy set numbers instead of storing the whole discrete function values. A description on how the cubic spl me interpolation function can be represented exactly by using the fuzzy system method is provided, along with a few examples where fuzzy rule tables with a size of 7$\times$7 provide a representation of the functions with relative errors of the order of $10^{2}$ or less.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.449-454
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• 2000

A new fuzzy logic control design algorithm suitable for multi-objective control problems is proposed based on the orthogonal array which is widely used for design of experiments in statistics and industrial engineering. The essence of the algorithm is to introduce Nth-certainty factor defined from the F-value of the ANOVA(analysis of variance) table, in order to effectively exclude the less confident rules. The proposed algorithm with multi-objective decision table(MODT) is found to be capable of the detection of inconsistency and the rule classification, reduction and modification. It is also shown that the algorithm can be successfully applied to the fuzzy controller design of an active magnetic bearing system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.685-691
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• 2008

The selection of classification boundaries in fuzzy rule- based classification systems is an important and difficult problem. So various methods based on learning processes such as neural network, genetic algorithm, and so on have been proposed for it. In a previous study, we pointed out the limitation of the methods and discussed a method for fuzzy partitioning in the overlapped region on feature space in order to overcome the time-consuming when the additional parameters for tuning fuzzy membership functions are necessary. In this paper, we propose a method to determine three types of classification boundaries(i.e., non-overlapping, overlapping, and a boundary point) on the basis of statistical information of the given dataset without learning by extending the method described in the study. Finally, we show the effectiveness of the proposed method through experimental results applied to pattern classification problems using the modified IRIS and standard IRIS datasets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.249-255
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• 2000

HRIV(Hybrid Rule-Interval Variation) method is presented to stabilize a class of nonlinear systems, where SMC(Sliding Mode Control) and ADC (ADaptive Control) schemes are incorporated to overcome the unstable characteristics of a conventional FLC(Fuzzy Logic Control). HRIV method consists of two modes: I-mode (Integral Sliding Mode PLC) and R-mode(RIV method). In I-mode, SMC is used to compensate for MAE(Minimum Approximation Error) caused by the heuristic characteristics of FLC. In R-mode, RIV method reduces interval lengths of rules as states converge to an equilibrium point, which makes the defined Lyapunov function candidate negative semi-definite without considering MAE, and the new uncertain parameters generated in R-mode are compensated by SMC. In RIV method, the overcontraction problem that the states are out of a rule-table can happen by the excessive reduction of rule intervals, which is solved with a dynamic modification of rule-intervals and a transition to I-mode. Especially, HRIV method has advantages to use the analytic upper bound of MAE and to reduce Its effect in the control input, compared with the previous researches. Finally, the proposed method is applied to stabilize a simple nonlinear system and a modified inverted pendulum system in simulation experiments.

• Journal of Welding and Joining
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• v.14 no.1
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• pp.57-67
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• 1996

With an expansion in automation of welding processes, emphasis has been shifted from other welding processes to the GMA welding. However, there is a problem with this process that the spatter occurs very frequently. In GMA welding, there are several types in the way of metal transfer from the electrode wire to the weld pool, which have a close relatonship with the spatter genetration. This study was concerned with the spatter occurring in the short-circuiting transfer. In welding with short-circuiting, the electromagnetic force formed by the welding current facilitatics the rupture of the metal bridge between the wire and workpiece and ensures the normal process of the welding process. However, the spatter can be genetrated from the droplet because of the upward magnetic force, when the droplet contacts with the weld pool. The passage of current through the bridge results in the accumulation of the thermal energy, which causes the bridge to explode in the final stage of short-circuiting, thus forming the spatter. Based on the above phenomena in conjunction with other experimental results published, the physical phenomenon related with the occurrence of spatter was modeled and the current waveform was investigated to reduce the spatter. Finally, the fuzzy rule based method was proposed to predict the time of short-circuiting and arcing in the metal transfer.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.460-465
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• 2007

In this paper, we propose a novel pattern classification method based on statistical properties of the given data and fuzzy-rough set to minimize the coupling problem of the rules. In the proposed method, statistical properties is used by a selection criteria for deciding a partition number of antecedent fuzzy sets, and for minimizing an coupling problem of the generated rules. Moreover, rough set is used as a tool to remove unnecessary attributes between generated rules from the numerical data. In order to verify the validity of the proposed method, we compared the classification results (i.e, classification precision) of the proposed with the conventional pattern classification methods on the Fisher's IRIS data. From experiment results, we can conclude that the proposed method shows relatively better performance than those of the classification methods based on the conventional approaches.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.792-798
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• 2007

In this paper, we propose a hierarchical fuzzy-rough classification method based on statistical information for maximizing the performance of pattern classification and reducing the number of rules without learning approaches such as neural network, genetic algorithm. In the proposed method, statistical information is used for extracting the partition intervals of antecedent fuzzy sets at each layer on hierarchical fuzzy-rough classification systems and rough sets are used for minimizing the number of fuzzy if-then rules which are associated with the partition intervals extracted by statistical information. To show the effectiveness of the proposed method, we compared the classification results(e.g. the classification accuracy and the number of rules) of the proposed with those of the conventional methods on the Fisher's IRIS data. From the experimental results, we can confirm the fact that the proposed method considers only statistical information of the given data is similar to the classification performance of the conventional methods.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.63-74
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• 1999

A sliding mode fuzzy control (SMFC) algorithm is presented for vibration of large structures. Rule-base of the fuzzy inference engine is constructed based on the sliding mode control, which is one of the nonlinear control algorithms. Fuzziness of the controller makes the control system robust against the uncertainties in the system parameters and the input excitation. Non-linearity of the control rule makes the controller more effective than linear controllers. Design procedure based on the present fuzzy control is more convenient than those of the conventional algorithms based on complex mathematical analysis, such as linear quadratic regulator and sliding mode control(SMC). Robustness of presented controller is illustrated by examining the loop transfer function. For verification of the present algorithm, a numerical study is carried out on the benchmark problem initiated by the ASCE Committee on Structural Control. To achieve a high level of realism, various aspects are considered such as actuator-structure interaction, modeling error, sensor noise, actuator time delay, precision of the A/D and D/A converters, magnitude of control force, and order of control model. Performance of the SMFC is examined in comparison with those of other control algorithms such as $H_{mixed 2/{\infty}}$ optimal polynomial control, neural networks control, and SMC, which were reported by other researchers. The results indicate that the present SMFC is an efficient and attractive control method, since the vibration responses of the structure can be reduced very effectively and the design procedure is simple and convenient.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.373-376
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• 2014

최근 모바일 기기 및 무선기기의 발달로 인하여 센서 네트워크가 다양한 분야에서 응용되고 있다. 따라서 센서에서 실시간으로 발생하는 스트리밍 데이터에서 이벤트를 감지하고 분석하는 것은 중요한 연구 분야로 부각되고 있다. 단순 이벤트의 발생 조건을 빠르게 판별하기 위해 비트맵 인덱스 기반 복합 이벤트 검출 기법 등 여러 가지 방법들이 사용되고 있지만, 아직까지 이기종 센서에서 발생하는 각기 다른 형태의 데이터를 융합하여 이벤트를 검출하는 복합 이벤트 처리에 대한 연구는 미비한 실정이다. 본 논문에서는 각기 다른 형태를 가지는 스트리밍 데이터에 멤버쉽 함수를 적용하여 퍼지화 함으로서 이기종 센서에서 발생하는 데이터를 융합 처리가능하며, Quine-Mccluskey 감축기법을 통하여 규칙의 신뢰도 및 속도가 향상된 의사결정을 하는 고속 이벤트 탐지기법을 제안한다.