• 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 Korean Society of Steel Construction
• /
• v.18 no.2
• /
• pp.147-160
• /
• 2006

GA-based fuzzy optimum design algorithm incorporated with the refined plastic hinge analysis method is presented in this study. In the refined plastic hinge analysis method, geometric nonlinearity is considered by using the stability functions of the beam-column members. Material nonlinearity is also considered by using the gradual stiffness degradation model, which considers the effects of residual stresses, moment redistribution through the occurence of plastic hinges, and the geometric imperfections of the members. In the genetic algorithm, the tournament selection method and the total weight of the steel frames. The requirements of load-carrying capacity, serviceability, ductility, and constructabil ity are used as the constraint conditions. In fuzzy optimization, for crisp objective function and fuzzy constraint s, the tolerance that is accepted is 5% of the constraints. Furthermore, a level-cut method is presented from 0 to 1 at a 0 .2 interval, with the use of the nonmembership function, to solve fuzzy-optimization problems. The values of conventional GA optimization and fuzzy GA optimization are compared in several examples of steel structures.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.3
• /
• pp.135-144
• /
• 2003

An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

• Journal of Digital Convergence
• /
• v.13 no.10
• /
• pp.287-293
• /
• 2015

This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

• Journal of Digital Convergence
• /
• v.14 no.4
• /
• pp.277-283
• /
• 2016

This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.

• Journal of KIISE:Software and Applications
• /
• v.32 no.1
• /
• pp.1-9
• /
• 2005

In this paper we propose a modified fuzzy min-max neural network model for pattern classification and discuss the usefulness of the model. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. Ft)r this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.1017-1020
• /
• 2000

In this paper, a CNN using 1-dimensional chaos circuits with controllable nonlinear functions is proposed. The proposed CNN consists of $\p{times}q$ chaos circuits which are called cell circuits. The nonlinear functions of the cell circuits can be controlled by employing fuzzy scheme. Thanks to the controllability of the nonlinear functions, the proposed circuit can adjust transition behavior of the CNN electronically. Furthermore, the chaotic behavior of the cell circuit which is a portion of the proposed CNN is simple since the cell circuit is a 1-dimensional chaos circuit. To confirm the validity of the circuit design, SPICE simulations were performed concerning the proposed CNN.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.348-350
• /
• 2004

This paper presents the scheme for reliability improvement by dispersed generation system (US) installation and operation in distribution systems. The objective functions such as power losses cost, operation cost of DGS, power buy cost and interruption cost are minimized for reliability improvement. The original objective functions and constraints are transformed into the equivalent multiple objective functions with fuzzy sets to evaluate their imprecise nature. The several indices for reliability evaluation are improved by dispersed generation system installation.

• Journal of Navigation and Port Research
• /
• v.32 no.6
• /
• pp.459-464
• /
• 2008

In this paper, we present the model-based fuzzy controller for container cranes which effectively performs set-point tracking control of trolley and anti-swaying control under system parameter and disturbance changes. The first part of this paper focuses on the development of Takagi-Sugeno (T-S) fuzzy modeling in a nonlinear container crane system. Parameters of the membership functions are adjusted by a RCGA to have same dynamic characteristics with nonlinear model of a container crane. In the second part, we present a design methodology of the model-based fuzzy controller. Sub-controllers are designed using LQ control theory for each subsystem in fuzzy model and then the proposed controller is performed with the combination of these sub-controllers by fuzzy IF-THEN rules. In the results of simulation, the fuzzy model showed almost similar dynamic characteristics compared to the outputs of the nonlinear container crane model. Also, the model-based fuzzy controller showed not only the fast settling time for the change in parameter and disturbance, but also stable and robust control performances without any steady-state error.