• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.33-40
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• 2009

The critical lag of crosswalk pedestrians is an important parameter in analyzing traffic operation at unsignalized crosswalks, however there is few research in this field in Korea. The purpose of this study is to develop a model to estimate the critical lag. Among the elements which influence the critical lag, the age of pedestrians and the length of crosswalks, which have fuzzy characteristics, and the each lag which is rejected or accepted are collected on crosswalks of which lengths range from 3.5 m to 10.5 m. The values of the critical lag range from 2.56 sec. to 5.56 sec. The age and the length are divided to the 3 fuzzy variables each, and the critical lag of each case is estimated according to Raff's technique, so a total of 9 fuzzy rules are established. Based on the rules, an ANFIS (Adaptive Neuro-Fuzzy Inference System) model to estimate the critical lag is built. The predictability of the model is evaluated comparing the observed with the estimated critical lags by the model. Statistics of $R^2$, MAE, MSE are 0.96, 0.097, 0.015 respectively. Therefore, the model is evaluated to explain the result well. During this study, it is found that the critical lag increases rapidly over the pedestrian's age of 40 years.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2083-2090
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• 2008

In this paper, we propose a method for control of temperature and the direction of wind in an air-cooler using thermal images and fuzzy inference rules in order to achieve energy saving. In a simulation for controlling temperature, a thermal image is transformed to a color distribution image of $300{\times}400$ size to analyze the thermal image. A color distribution image is composed of R, G and B values haying temperature values of Red, Magenta, Yellow, Green, Cyan and Blue. Each color has a temperature value from $24.0^{\circ}C$ to $27.0^{\circ}C$ and a color distribution image is classified into height hierarchies from level 1 to level 10. The classified hierarchies have their peculiar color distributions and temperature values are assigned to each level by temperature values of the peculiar colors. The process for controlling overall balance of temperature and the direction of wind in an indoor space is as follows. Fuzzy membership functions are designed by the direction of wind, duration time, and temperature and height values of a color distribution image to calculate the strength of wind. After then, the strength of wind is calculated by membership values of membership functions.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.245-250
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• 2008

Psychology of color is a research field of psychology for studying human's behavior connected with color. Color carries symbolism and image while sharing psychological consensus with human. Each color has a respective image such as hope, passion, love, life, death, and so on. Peculiar stimuli by colors on these images have great influence on human's emotion and psychology. We therefore proposed a method for understanding human's state of emotion based on colors in this paper. In order to understand human's state of emotion, we analyzed color information used to model a room by a user and then described frequencies of each color as percent using fuzzy inference rules by membership values of fuzzy membership functions for colors used for modeling the room. When we applied the proposed color-based emotion analysis method to emotional state based on colors of Alschuler and Hattwick, we could see the proposed method is efficient.

• Clean Technology
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• v.23 no.1
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• pp.15-26
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• 2017

The recycling process of ELV consists of three phases: dismantling, shredding and ASR treatment. Dismantling is the collection of reusable parts and the most important phase. The types of dismantling system is diverse and each country has different characteristics. Therefore, the selection of a suitable ELV dismantling system for a target country is dependent on the characteristics of each country. But the characteristics of country data changes every year and is insufficient and ambiguous. In this study, fuzzy inference and modified QFD (Quality function deployment) methods are utilized to solve the problems. The fuzzification of characteristics data for each country, customized rules and decision of modified QFD matrix are developed, which is applied to sample countries.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.125-132
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• 2022

The Ministry of Environment recently analyzed the output data of 10 fine dust measuring stations and, as a result, announced that about 60% had an error that the existing atmospheric measurement concentration was higher. In order to accurately predict fine dust, the wind direction and measurement position must be corrected. In this paper, in order to solve these problems, fuzzy rules are used to solve these problems. In addition, in order to calculate the fine particulate sensation index actually felt by pedestrians on the street, a computer simulation experiment was conducted to calculate the fine particulate sensation index in consideration of weather conditions, temperature conditions, humidity conditions, and wind conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.571-576
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• 2002

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.200-205
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• 2005

The objectives of this study were to introduce the easiest and most proper applications of datamining in industrial processes. Applying datamining in manufacturing is very different from applying it in marketing. Misapplication of datamining in manufacturing system results in significant problems. Therefore, it is very important to determine the best procedure and technique in advance. In previous studies, related literature has been introduced, but there has not been much description of datamining applications. Research has not often referred to descriptions of particular examples dealing with application problems in manufacturing. In this study, a datamining roadmap was proposed to support datamining applications for industrial processes. The roadmap was classified into three stages, and each stage was categorized into reasonable classes according to the datamining purposed. Each category includes representative techniques for datamining that have been broadly applied over decades. Those techniques differ according to developers and application purposes; however, in this paper, exemplary methods are described. Based on the datamining roadmap, nonexperts can determine procedures and techniques for datamining in their applications.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.226-232
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• 2016

The research for the improvement in math and science scores is active by the brain exercises, stress reliefs, and emotion sensitized illuminations. This principle is based on the following facts that the most effective brain turns are supported with the circumstances not only when the brain wave should keep stability and comfort in science criticism, but also when minimized stress and comfortable illumination should be adjusted in solving math problem. In this paper, in order to effectively learn mathematics and science, the most optimized simulating tests in learning conditions are conducted by using a stress relief. However, depending on the users' tastes, the effectiveness on favorite music or colors therapy have no convergency but many differentiations. Therefore, in this paper, in order to solve this problem, the proposed optimal illumination and music therapy treatment using fuzzy inference method.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.