• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.653-665
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• 2010

In this study, we introduce a neurofuzzy approach to the design of fuzzy controllers. The development process exploits key technologies of Computational Intelligence (CI), namely, genetic algorithms (GA) and neurofuzzy networks. The crux of the design methodology deals with the selection and determination of optimal values of the scaling factors of fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out. Next, we form a nonlinear mapping for the scaling factors, which are realized by GA-based neurofuzzy networks by using a fuzzy set or fuzzy relation. The proposed approach is applied to control nonlinear systems like the inverted pendulum. Results of comprehensive numerical studies are presented through a detailed comparative analysis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• Journal of Communications and Networks
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• 제15권6호
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• pp.601-605
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• 2013

The performance of M-ary turbo coded synchronous, fast frequency-hopping spread spectrum multiple-access (FHSS-MA) networks with M-ary frequency shift keying (MFSK) and noncoherent detection is analyzed under Rayleigh fading. Results indicate that M-ary turbo codes dramatically enhance the performance of FHSS-MA networks using MFSK compared to binary turbo codes.

• Journal of Communications and Networks
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• 제18권4호
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• pp.551-558
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• 2016

In multifiber all-optical networks, optical amplifiers are used for amplifying multiple optical signals with different wavelengths in fibers. An optical amplifier operates when any of lightpaths passes through it. Therefore, it should simultaneously amplify as many lightpaths as possible for efficiently utilizing its power. This paper proposes a dynamic lightpath establishment scheme considering the use efficiency of the optical amplifiers and the depletion of the wavelength resources in multifiber all-optical networks. The proposed scheme provides a routing and wavelength assignment strategy that reduces both the power consumption of the optical amplifiers and the blocking probability of the lightpath establishment. Through simulation experiments, we demonstrate the effectiveness of the proposed scheme.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2358-2361
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• 2001

In recent years there have been increasing interests in real-time object tracking with image information. Since image information is affected by illumination, this paper presents the real-time object tracking method based on neural networks which have robust characteristics under various illuminations. This paper proposes three steps to track the object and the fast tracking method. In the first step the object color is extracted using neural networks. In the second step we detect the object feature information based on invariant moment. Finally the object is tracked through a shape recognition using neural networks. To achieve the fast tracking performance, this paper first has a global search of entire image and tracks the object through local search when the object is recognized.

• 전기학회논문지P
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• 제64권2호
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• pp.50-56
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• 2015

This paper presents a smart monitoring technique for photovoltaic power systems by using wire and wireless communication networks in which the RS-232/484 and the Zigbee communication networks are inherently established respectively. In the proposed monitoring systems, environmental data sequences and the output power measured by sensors in photovoltaic systems are transferred to PC systems via two communication networks. We made electronic hardware boards for sensors and communication networks to construct its real-time monitoring system and carry out experiments for demonstrating reliability of the proposed monitoring system.

• Journal of the Korean Physical Society
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• 제73권12호
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• pp.1889-1894
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• 2018

An innovative small-scale piezoelectric energy harvester has been proposed to gather wind energy. A conventional horizontal-axis wind power generation has a low generating efficiency at low wind speed. To overcome this weakness, we designed a piezoelectric windmill optimized at low-speed wind. A piezoelectric device having high energy conversion efficiency is used in a small windmill. The maximum output power of the windmill was about 3.14 mW when wind speed was 1.94 m/s. Finally, the output power and the efficiency of the system were compared with a conventional wind power system. This work will be beneficial for the piezoelectric energy harvesting technology to be applied to the real world such as wireless sensor networks (WSN).

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.483-487
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• 2005

We introduce a new architecture of hetero-hybridized feed-forward neural networks composed of fuzzy set-based polynomial neural networks (FSPNN) and polynomial neural networks (PM) that are based on a genetically optimized multi-layer perceptron and develop their comprehensive design methodology involving mechanisms of genetic optimization and Information Granulation. The construction of Information Granulation based HFSPNN (IG-HFSPNN) exploits fundamental technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks, and genetic algorithms(GAs) and Information Granulation. The architecture of the resulting genetically optimized Information Granulation based HFSPNN (namely IG-gHFSPNN) results from a synergistic usage of the hybrid system generated by combining new fuzzy set based polynomial neurons (FPNs)-based Fuzzy Neural Networks(PM) with polynomial neurons (PNs)-based Polynomial Neural Networks(PM). The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being tuned by using Genetie Algorithms throughout the overall development process. However, the new proposed IG-HFSPNN adopts a new method called as Information Granulation to deal with Information Granules which are included in the real system, and a new type of fuzzy polynomial neuron called as fuzzy set based polynomial neuron. The performance of the IG-gHFPNN is quantified through experimentation.

• 한국지능시스템학회논문지
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• 제14권5호
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• pp.660-665
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• 2004

In this study, a new architecture and comprehensive design methodology of genetically optimized Multi-layer Fuzzy Neural Networks (gMFNN) are introduced and a series of numeric experiments are carried out. The gMFNN architecture results from a synergistic usage of the hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gMFNN. The consequence part of the gMFNN is designed using PNN. The optimization of the FNN is realized with the aid of a standard back-propagation learning algorithm and genetic optimization. The development of the PNN dwells on the extended Group Method of Data Handling (GMDH) method and Genetic Algorithms (GAs). To evaluate the performance of the gMFNN, the models are experimented with the use of a numerical example.

• Journal of Communications and Networks
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• 제7권2호
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• pp.207-221
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• 2005

The proliferation of wireless local area networks (WLANs) offering high data rate in hot spot area have spurred the demand for possible WLANs and third-generation (3G) cellular network integration solutions as the initiative step towards 4G systems. This paper provides a novel architecture for seamless location-aware integration of WLANs into 3G cellular networks and also an analysis for the efficient handover techniques. We introduce location as a key context in secure roaming mechanism for context-aware interworking in 4G systems. The fast secure roaming with location-aware authentication is implemented at an entity called location-aware service (LAS) broker that utilizes the concepts of direction of user and pre-warming zone. The location-ware interworking architecture supports seamless roaming services among heterogeneous wireless networks including WLANs, wireless metropolitan area networks (WMANs), and 3G cellular networks. This paper also includes a description of procedures needed to implement efficient mobility and location management. We show how the LAS broker with pre-warming and context transfer can obtain significant lower latency in the vertical handover.