• Journal of Welding and Joining
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• v.28 no.5
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• pp.99-105
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• 2010

Inverter DC resistance spot welding process has been very widely used for joining such as automotive body sheet metal. Because the lobe area of DC welding is larger than AC welding and DC welding has low electrode wear. So the use of Inverter DC resistance spot welding process has been further increased. And the application of high tensile steel is growing for light weight vehicle. To improve the weldability of high strength steel, the development of Inverter DC resistance spot welding system is more conducted. However, Inverter DC resistance spot welding system has a few problems. Current waveform is unstable and the expulsion has been occurred by characteristics of steel. In this study, inverter DC resistance spot welding system was made. And Fuzzy control algorithm was applied for constant current. The genetic algorithm was applied to optimize the fuzzy scaling factors, in order to optimize the fuzzy control.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.2
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• pp.75-82
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• 2010

The economical efficiency and easy ship building have enabled to spread FRP ships in the shipbuilding field. As waste FRP ships have been thrown away at a river or within a harbour, this matter has become issues. For the improvement of this matter, the melting technique and system of waste FRP ships was developed. But, Decision making was required for a location plan of the melting system of waste FRP ships. It's recognized that the location decision of this system is difficult due to the dependence on technical, economical, environmental factors. In this paper, we survey the primary factors of location-economic, life-environment, infrastructure and make up a question for the experts. We also calculate the important weight and related weight using Fuzzy AHP, Limiting probability method and discuss on the calculation results on the proposed sites.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.719-726
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• 2020

Education policy is a dynamic process featuring social development trends. The world countries have focused their education program on empowering the learners for future life and work. This paper aims to assess the higher education curriculum based on a survey of 280 students, employers, alumni, and lecturers in both social sciences and natural sciences in Ho Chi Minh City, Vietnam. The fuzzy decision-making method, namely the Fuzzy Extent Analysis Method (F-EAM), was applied to measure the relative weight of each parameter. Seven factors under the curriculum development have been put in the ranking. Input with emphasis on foreign language was the highest priority in curriculum development, given the expected demand of the labor market. Objective and learning outcome and teaching activities ranked second and third, respectively. The traditional triangle of teaching content, methodology, and evaluation and assessment are still proven their roles, but certain modifications have been defined in the advanced curriculum. Teaching facilities had the least weight among the seven dimensions of curriculum development. The findings are helpful for education managers to efficiently allocate scarce resources to reform the curriculum to bridge the undergraduate quality gap between labor supply and demand, meeting the dynamic trends of social development.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.692-702
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• 2006

In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.55-62
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• 2009

This paper proposes a decision method of fuzzy weights by utilizing degrees of contribution in order to classify insect footprint patterns having difficulties to classify species clearly. Insect footprints revealed delicately in the form of scattered spots since they are very small. Therefore it is not easy to define shape of footprints unlike other species, and there are lots of noises in the footprint patterns so that it is difficult to distinguish those from correct data. For these reasons, the extracted feature set has obvious feature values with some uncertain feature values, so we estimate weights according to degrees of contribution. If the one of feature values has distinct difference enough to decide a class among other classes, high weight is assigned to make classification. A calculated weight determines the membership values by fuzzy functions and objects are classified into the class having a superior value.atu present experimental resultseighrontribution. Iinsect footprints with noises by the proposed method.

• Journal of Korean Society of Steel Construction
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• v.9 no.4 s.33
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• pp.579-588
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• 1997

During the construction of cable stayed bridge, errors are always caused by various reasons, accumulated and amplified through the complex construction steps. It is likely that the undesirable stress distribution of members and the large deflection of the bridge different from design values come out The adjustment of cables during construction is absolutely indispensable to correct the stress distribution of the members and the geometrical configuration of the bridge. In the conventional method, weight coefficients are used to consider the difference of units between cable forces and girder deflections during the optimization process of cable adjustment. However, it is not easy to determine weight coefficients and the adjustment must be repeated several times with the time consuming process of the determination of new weight coefficients in case that errors are out of design allowable limits. In this paper, fuzzy linear regression analysis is applied to the cable adjustment to overcome those problems. In the application of fuzzy linear regression analysis method the designer's intention and the design allowable limits can be formulated in the form of the constraints of the linear optimization problem. Therefore, the cable adjustment in construction site can be carried out with the fuzzy linear regression analysis more rapidly than with the convetional method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.301-314
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• 2016

Juels and Sudan's fuzzy vault scheme has been applied to various researches due to its error-tolerance property. However, the fuzzy vault scheme does not consider the difference between people's preferences, even though the authors instantiated movie lover' case in their paper. On the other hand, to make secure and high performance face authentication system, Nyang and Lee introduced a face authentication system, so-called fuzzy face vault, that has a specially designed association structure between face features and ordinary fuzzy vault in order to let each face feature have different weight. However, because of optimizing intra/inter class difference of underlying feature extraction methods, we can easily expect that the face authentication system does not successfully decrease the face authentication failure. In this paper, for ensuring the flexible use of the fuzzy vault scheme, we introduce the bucket structure, which differently implements the weighting idea of Nyang and Lee's face authentication system, and three distribution functions, which formalize the relation between user's weight of preferences and system implementation. In addition, we suggest a matchmaker scheme based on them and confirm its computational performance through the movie database.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.62-68
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• 1998

In this paper, a speed controller considering the effects of parameter variations and external disturbance for induction motor driver is designed. An proportional plus integral(P1) fuzzy controller is designed to match desired speed tracking specification. Then a robust controller using Fuzzy Weight matrix are designed that in order to reduce the effect of parameter variations caused by external disturbance. The desired speed tracking control performance of the driver is preserved under wide operating range, and also good speed performance is confirmed by the computer simulation.