• Journal of Electrical Engineering and Technology
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• 제10권2호
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• pp.634-640
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• 2015

This paper deals with the design of a microstrip line feed toppled T shaped microstrip patch antenna that gives dualband characteristics at 4 GHz and 6.73 GHz respectively. The simulation of proposed antenna geometry has been performed using method of moment based IE3D simulation software. A radial basis function neural network (RBFNN) is used for the estimation of bandwidth for dualband at 4 GHz and 6.73 GHz respectively. In RBFNN model, antenna parameters such as dielectric constant, height of substrate, and width are used as input and bandwidth of first and second band is considered as output of the network. To validate the RBFNN output, an antenna has been physically fabricated on glass epoxy substrate. The fabricated antenna can be utilized in S and C bands applications. RBFNN results are found in close agreement with simulated and experimental results.

• 대한전자공학회논문지TC
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• 제46권11호
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• pp.50-55
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• 2009

본 논문에서는 분포정수 회로합성을 이용하여 최적의 정전용량 흡수 능력을 갖는 분포증폭기를 설계한다. 증폭기를 구성하는 여파기의 전달함수는 저역통과 Chebyshev 근사로 합성하며, 이의 정전용량 흡수 능력은 최소 삽입손실(MIL)과 리플의 함수로 계산한다. 분포증폭기의 능동 소자는 S-퍼래미터를 이용하여 등가회로로 모델링하며, 이의 정전용량은 전달함수의 MIL과 리플을 적절히 조정함으로써 여파기 구조로 흡수한다. 이의 응용 예로써, 0.1~7.5GHz의 주파수 대역에서 약 12.5dB의 이득을 갖는 분포증폭기를 설계하며, 실험을 통하여 정전용량 흡수 능력을 고려한 분포정수 회로합성이 분포증폭기의 설계에 유용하게 이용될 수 있음을 입증한다.

• Journal of Welding and Joining
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• 제17권5호
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• pp.40-46
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• 1999

The sensors employed in the robotic are welding system must detect the changes in weld characteristics and produce the output that is in some way related to the change being detected. Such adaptive systems, which synchronise the robot arm and eyes using a primitive brain will form the basis for the development of robotic GMA(Gas Metal Arc) welding which increasingly higher levels of artificial intelligence. The objective of this paper is to realize the mapping characteristics of bead height through learning. After learning, the neural estimation can estimate the bead height desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead height with reasonable accuracy and guarantee the uniform weld quality.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• Structural Engineering and Mechanics
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• 제83권5호
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• pp.671-680
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• 2022

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R²) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

• Fibers and Polymers
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• 제7권4호
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• pp.344-351
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• 2006

This paper is intended to determine the optimal processing parameters applied to the dyeing procedure so that the desired color strength of a raw fabric can be achieved. Moreover, the processing parameters are also used for constructing a system to predict the fabric quality. The fabric selected is the nylon and Lycra blend. The dyestuff used for dyeing is acid dyestuff and the dyeing method is one-bath-two-section. The Taguchi quality method is applied for parameter design. The analysis of variance (ANOVA) is applied to arrange the optimal condition, significant factors and the percentage contributions. In the experiment, according to the target value, a confirmation experiment is conducted to evaluate the reliability. Furthermore, the genetic algorithm (GA) is combined with the back propagation neural network (BPNN) in order to establish the forecasting system for searching the best connecting weights of BPNN. It can be shown that this combination not only enhances the efficiency of the learning algorithm, but also decreases the dependency of the initial condition during the network training. Most of all, the robustness of the learning algorithm will be increased and the quality characteristic of fabric will be precisely predicted.

• 한국컴퓨터정보학회논문지
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• 제9권1호
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• pp.87-93
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• 2004

이동 무선 통신 네트워크를 설계할 때 기지국(BTS), 기지국 콘트롤러(BSC), 이동 교환국(MSC)의 위치는 매우 중요한 파라미터들이다. 기지국의 위치를 설계할 때는 여러 가지 복잡한 변수들을 잘 조합하여 비용이 최소가 되도록 설계해야 한다 이러한 문제를 해결하는데 필요한 알고리듬이 전역 최적화 알고리듬이며, 지금까지 전역 최적화 검색 기술로는 Random Walk, Simulated Annealing, Tabu Search, Genetic Algorithm이 사용되어 왔다. 본 논문은 이동 통신 시스템의 기지국, 기지국 콘트롤러, 이동 교환국의 위치 최적화에 위의 4가지 알고리듬들을 적용하여 각 알고리듬의 결과를 비교 분석하며 알고리듬에 의한 최적화 과정을 보여준다.

• 전기학회논문지
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• 제65권6호
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• pp.1060-1069
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• 2016

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1011-1016
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• 2020

고속 VANET(: Vehicular Ad Hoc Network)에서 네트워크 토폴로지의 급속한 변화는 라우팅 프로토콜 설계에 중요한 과제이다. 라우팅 프로토콜의 성능에 영향을 미치는 다음 홉 릴레이 노드를 선택하는 작업은 어려운 과정이다. VANET과 관련된 AODV(: Ad Hoc On-Demand Distance Vector)의 단점은 종단 간 지연 및 패킷 손실이다. 본 논문은 AODV 라우팅 프로토콜을 수정하여 방향 매개 변수와 2단계 필터링을 추가하여 RREQ(: Route Request) 및 RREP(: Route Reply) 메시지 수를 줄이는 AAODV(: Advanced AODV)기법을 제안한다. 제안된 AAODV는 패킷 손실을 줄이고 방향 매개 변수의 영향을 최소화 함으로서 패킷전달율이 증가하고, 종단 간 지연이 감소됨을 알 수 있다.

• 전자공학회논문지
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• 제53권6호
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• pp.29-36
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• 2016

별도의 RF 소스가 센서 노드에게 에너지를 공급하는 무선 수동형 센서 망은 배터리 없이 영원히 동작할 수 있는 망이다. 그러나 영원한 수명에 대한 기대와 달리 무선 수동형 센서 망은 아직 에너지의 희소성, 에너지 수신과 데이타 전송의 동시불가성, 자원 활용의 비효율성 등 많은 문제를 안고 있다. 본 논문에서는 패킷 상실에는 관대하지만 패킷의 적시 전달을 요구하는 서비스를 제공하는 무선 수동형 센서 망을 다룬다. 이러한 망에서 여러 센서 노드들이 하나의 싱크 노드에게 패킷들을 전달하도록 현실적 제약을 인식하여 framed and slotted ALOHA에 기초한 경합형 MAC 방식을 고려한다. 이어서 지리적으로 흩어져있는 센서 노드들이 전송한 패킷들이 경로 손실을 겪어 결국 capture 현상이 빚어질 때 MAC 방식이 얻을 수 있는 망전체 throughput을 조사한다. 특히 두 센서 노드만이 망에 있을 때 망 전체 throughput의 정확한 공식을 closed form으로 도출한다. 마지막으로 설계 parameter들을 조절하여 최대의 망 전체 throughput을 취하도록 경합형 MAC 방식을 최적화한다.