• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.9
• /
• pp.189-193
• /
• 2013

In this paper, in order to predict and prevent electrical fires that occur in the power system, we measured the informations of electric power, and then proposed a system to predict the electrical fire using these informations. To this end, we analyzed the correlations for over-current, overload and overheating. These states are caused by the grounding current and the leakage current, and are the main causes of an electrical fire. Use these correlations to derive the derivative of the fuzzy rules for membership function. The designed algorithm was simulated by utilizing the informations of the actual power of the switchgear-panel.

• Korean Journal of Construction Engineering and Management
• /
• v.1 no.1 s.1
• /
• pp.53-62
• /
• 2000

This study proposes FREES(Fuzzy Risk Evaluation Expert System) for analyzing and evaluating risks occurring during the construction process. The feasibility of this system model was tested by virtual scenario. For the development of the model, at first, risk breakdown structure was established based on risks identified in the existing researches, that is quantitative and qualitative. FREES can reflect human cognition process in the risk analysis and evaluation by adopting artificial intelligence fuzzy theory, differentiating the existing quantitative analysis model. The FREES can be applied to all the project phases from planning to operation & maintenance stage.

• Journal of Internet Computing and Services
• /
• v.10 no.2
• /
• pp.179-186
• /
• 2009

This paper presents an approach to classify normal and Ventricular Tachycardia/Ventricular Fibrillation(VT/VF) from the Creighton University Ventricular Tachyarrhythmia DataBase(CUDB) using the neural network with weighted fuzzy membership functions(NEWFM). In the first step, wavelet transform is used for producing input values which are used in the next step. In the second step, two numbers of input features are extracted by phase space reconstruction method and peak extraction method using coefficients produced by wavelet transform in the previous step. NEWFM classifies normal and VT/VF beats using two numbers of input features, and then the accuracy rate is 90.13%.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.29 no.1
• /
• pp.77-83
• /
• 2005

In collision avoidance problem of a ship, collision risk model is usually set up using the interview results fron experts who sit on a simulator by varying parameters, in which DCPA and TCPA are commonly used. This method, however, has the weakness in that not only it is expensive but also it shows different results depending on the inerviewees and other navigational parameters. In this study, a fuzzy inference system is designed based on own ship's maneuverability verified fron simulation instead of interviewing navigators. The time and distance corresponding to the collision risk value on which avoidance maneuver should be started are set to the minimum marginal time at which own ship starts maneuvering and the minimum marginal distance suggested by marine traffic rules respectively. This system can be recorfigured as a nonlinearity-strengthened one by increasing the number of fuzzy membership functions.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.4
• /
• pp.23-28
• /
• 2011

Ultrasound images for the abdominal muscles are complicated enough to have difficulty in interpreting their results. For better interpretation, magnifying the original image is necessary but its magnified image could be deteriorated and suffer from information loss. Thus, in this paper, we propose a magnifying method that reduces the gap between the original image and the magnified one in quality using a fuzzy method with weights for its brightness and interpolation. The proposed method extracts information of pixels in magnified image that have most similar characteristics of the original one by applying fuzzy membership function. In the process, the difference in the brightness between pixels of the magnified image and the original one using bilinear interpolation method and the weight value using the interpolation from multiplied values of four pixels are supplied to the fuzzy membership function. In this experiment, the proposed method reduces the cloudy phenomenon appears commonly compared to the bilinear interpolation method among those qualitative issues of image interpretation.

• Journal of Advanced Navigation Technology
• /
• v.13 no.6
• /
• pp.901-906
• /
• 2009

This paper proposes a call admission control(CAC) method for wireless networks, which is based on the upper bound of a possibility distribution of handoff calls dropping rates. The possibility distribution is estimated in a fuzzy inference and a learning algorithm in neural network. The learning algorithm is considered for tuning the membership functions(then parts)of fuzzy rules for the inference. The fuzzy inference method is based on a weighted average of fuzzy sets. The proposed method can avoid estimating excessively large handoff calls dropping rates, and makes possibile self-compensation in real time for the case where the estimated values are smaller than real values. So this method makes secure CAC, thereby guaranteeing the allowed CDR. From simulation studies we show that the estimation performance for the upper bound of call dropping rate is good, and then handoff call dropping rates in CAC are able to be sustained below user's desired value.

• Journal of KIISE:Software and Applications
• /
• v.33 no.8
• /
• pp.692-702
• /
• 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 Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.15 no.4
• /
• pp.14-22
• /
• 2001

In this paper, we design the Adaptive Neuro-Fuzzy Precompensator(ANFP) for the suppression of low-frequency oscillation and the improvement of system stability. Here, ANFP is designed to compensate the conventional Power System Stabilizer(PSS). This design technique has the structural merit that is easily implemented by adding ANFP to an existing PSS. Firstly, the Fuzzy Precompensator with Loaming ability is constructed and is directly learned from the input and output data of the generating unit. Because the ANFP has the property of learning, fuzzy rules and membership functions of the compensator can be automatically tuned by teaming algorithm Loaming is based on the minimization of the ems evaluated by comparing the output of the ANFP and a desired controller. Case studies show the 7posed schema can be provided the good damping of the power system over the wide range of operating conditions and improved dynamic performance of the system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.5
• /
• pp.1013-1018
• /
• 2007

In general, there are many sensors for fuel injection control such as an air flow sensor, an air intake temperature sensor, a cooling water temperature sensor, a throttle position sensor, and a motor position sensor. In this paper, we proposed a method for controlling the amount of fuel consumption in cars using fuzzy control technique by temperature change of an air intake temperature sensor and air-fuel ratio, the ratio of air and fuel mixture. In the proposed method, the amount of fuel injection is controlled by fuzzy membership functions and fuzzy inference rules established for air-fuel ratio, air intake temperature, and final fuel compensation, after computing air-fuel values using each amount of air intake and each amount of fuel injection. We verified that the proposed method is more efficient than conventional methods in fuel injection control from the results of the simulation program.

• Korean Journal of Remote Sensing
• /
• v.21 no.2
• /
• pp.99-111
• /
• 2005

A fuzzy logic based data integration was applied for geothermal potential mapping in Jeju Island. Several data sets, such as geological map, the density of drainage system, the distribution density of cinder cones, density of lineaments, aerial survey map for total magnetic intensity and total gamma ray, were collected as thematic map for the integration. Fuzzy membership function for all thematic maps were compared to the locations of the spa, which were used as ground-truth control points. The older geology, the lower density of drainage, cinder cones and lineaments, and the lower intensity of magnetic and gamma ray were showed the higher fuzzy membership function values, respectively. After integrating all thematic maps, the results of gamma operator with the gamma value of 0.75 was the highest success rate, and new geothermal potential zone is prospected in western north part of Jeju Island.