• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1203-1208
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• 2016

For young children who are not spontaneous or not accurate in verbal communication of their emotions and experiences, drawing is a good means of expressing their status in mind and thus drawing analysis with chromatics is a traditional tool for art therapy. Recently, children enjoy digital drawing via painting tools thus there is a growing needs to develop an automatic digital drawing analysis tool based on chromatics and art therapy theory. In this paper, we propose such an analyzing tool based on dominant color analysis. Technically, we use ART2 clustering and fuzzy logic to understand the fuzziness of subjects' status of mind expressed in their digital drawings. The frequency of color usage is fuzzified with respect to the membership functions. After applying fuzzy logic to this fuzzified central vector, we determine the dominant color and supporting colors from the digital drawings and children's status of mind is then analyzed according to the color-personality relationships based on Alschuler and Hattwick's historical researches.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.3-10
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• 2017

Counting Bloom filters (CBFs) have been popularly used in many network algorithms and applications for the membership queries of dynamic sets, since CBFs can provide delete operations, which are not provided in a standard 1-bit vector Bloom filter. However, because of the counting functions, a CBF can have overflows and accordingly false negatives. CBFs composed of 4-bit counters are generally used, but the 4-bit CBF wastes memory spaces by allocating 4 bits for every counter. In this paper, we propose a simple alternative of a 4-bit CBF named ternary Bloom filter (TBF). In the proposed TBF structure, if two or more elements are mapped to a counter in programming, the counters are not used for insertion or deletion operations any more. When the TBF consumes the same amount of memory space as a 4-bit CBF, it is shown through simulation that the TBF provides a better false positive rate than the CBF as well as the TBF does not generate false negatives.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.421-434
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• 2018

Decision-makers have different and sometimes conflicting goals with utilities in operating dam reservoirs. As repeated interactions exist between decision-makers in the long-term, and the utility of each decision-making organization is affected not only by its selected strategy, but also by other rivals' strategies; selecting and prioritizing optimum strategies from a decision maker's point of view are of great importance while interacting with others. In this paper, a model based on a fuzzy set theory, for determining the priority of decision-makers' strategies in optimal qualitative-quantitative operation management of dam reservoir is presented. The fuzzy priority matrix is developed via defining membership functions of a fuzzy set for each decision maker's strategies, so that all uncertainties are taken into account. This matrix includes priorities assigned to possible combination for other decision makers' strategies in bargaining with each player's viewpoint. Here, the 15-Khordad Dam located in the central part of Iran, suffering from low water quality, was studied in order to evaluate the effectiveness of the model. Then, the range of quality of water withdrawal agreed by all decision-makers was determined using the prioritization matrix based on fuzzy logic. The results showed that the model proposed in the study had high effectiveness model.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.1
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• pp.102-108
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• 2010

The T-S fuzzy model of a ship is made from the nonlinear extension of Nomoto's 2nd-order model as the previous step before designing of the fuzzy type autopilot to consider the design specifications and the economic efficiency. The T-S fuzzy model is considered as a design variable of the heading angular velocity of ship. The linear models will be combined as "IF-THEN" fuzzy rules after get in this one area of the linear model(sub-system) by change of the heading angular velocity of a ship. The dynamic characteristic of a ship with the parameters of linear models and fuzzy membership functions are estimated to match by using the model adjustment technic with input/output data and a RCGA.

• Journal of Korean Society of Transportation
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• v.23 no.8 s.86
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• pp.171-179
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• 2005

This paper presents the algorithm using fuzzy inference that preestimates each link speed changed by different kinds of road situations. The elements we are considered are time zone, rainfall probability information and lane control information. This paper is consists of three parts. First of all we set up the fuzzy variables, and preestimate link speed changed by various road situations. For this process, we build the membership functions for each fuzzy variable and establish the fuzzy inference relations to find how fuzzy variables influence on link speed. Second, using backtracking method, we search the shortest route influenced by link speed changed by fuzzy inference. Third, we apply this algorithm to hypothetical network and find the shortest path. As a result, it is shown that this algorithm choose appropriate roundabout path according to the changing road situations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.3
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• pp.644-653
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• 1998

In resistance spot weld, the assurance of weld quality has been a long-standing problem. Since the weld nuggets if resustance spot welding form between the workpieces, visual detection of defects in usually impossible. Welding quality of resistance spot welding can be verified by non destructive and destructive inspections such as X-Ray inspection and testing of weld strength. But these tests, in addition to being time-consuming and costly, can entail risks due to sampling basis. The purpose of this study is the development of the monitoring system based on fuzzy inference, aimed at diagonosis of quality in resistance spot welding. The fuzzy inference system consists of fuzzy input variables, fuzzy membership functions and fuzzy rules. For inferring the welding quality(strength), the experimental data of the spot welding were acquired in various welding conditions with the monitoring system designed. Some fuzzy input variables-maximum, slop and difference values of electrode movement signals-were extracted from the experimental data. It was confirmed that the fuzzy inference values of strength have a .${\pm}$5% error in comparison with actual values for the selected welding conditions(9-10.5KA, 10-14 cycle, 250-300 $kg_f$). This monitoring system can be useful in improving the quality assurance and reliability of the resistance spot welding process.

• 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.

• Economic and Environmental Geology
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• v.40 no.3 s.184
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• pp.307-318
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• 2007

The occurrence of geothermal water has high correlates highly with fossil geothermal system. A fuzzy logic based data integration is applied for geothermal potential mapping in the Southern Gyeongsang Basin which is distributed in the regional fossil geothermal system. Several data sets are related with the origin and distribution of fossil geothermal system, such as the geological map, the density of lineaments, the aerial survey map of magnetic intensity, the map of hydrothermal alteration, the distribution density of hydrothermal mines, which were collected as thematic maps for the integration. Fuzzy membership functions for all thematic maps were compared to the locations of the spa hot springs, which were used as ground-truth control points. After integrating all thematic maps, the results of gamma operator (${\gamma}=0.1$) was showed the highest success rate, and new geothermal potential zone is prospected in some area.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.