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RISS 인기검색어
- 검색키워드
- 주제어 : GA-based optimization (검색결과 6 건)
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Outage Analysis and Optimization for Four-Phase Two-Way Transmission with Energy Harvesting Relay
( Guanyao Du ),( Ke Xiong ),( Yu Zhang ),( Zhengding Qiu ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.10
This paper investigates the outage performance and optimization for the four-phase two-way transmission network with an energy harvesting (EH) relay. To enable the simultaneous information processing and energy harvesting at the relay, we firstly propose a power splitting-based two-way relaying protocol (PSTWR). Then, we discuss its outage performance theoretically and derive an explicit expression for the system outage probability. In order to find the optimal system configuration parameters such as the optimal power splitting ratio and the optimal transmit power redistribution factor, we formulate an outage-minimized optimization problem. As the problem is difficult to solve, we design a genetic algorithm (GA) based algorithm for it. Besides, we also investigate the effects of the power splitting ratio, the power redistribution factor at the relay, and the source to relay distance on the system outage performance. Finally, extensive simulation results are provided to demonstrate the accuracy of the analytical results and the effectiveness of the GA-based algorithm. Moreover, it is also shown that, the relay position greatly affects the system performance, where relatively worse outage performance is achieved when the EH relay is placed in the middle of the two sources.
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A Deep Learning Based Breast Cancer Classification System Using Mammograms
Meenalochini G.,Ramkumar S. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.4
An automatic breast cancer detection and classifi cation system plays an essential role in medical imaging applications. But accurate disease identifi cation is one of the complicated processes due to the existence of noisy contents and irrelevant structure of the original images. In conventional works, various medical image processing techniques have been developed for accurately classifying the types of breast cancer. Still, it confronts diffi culties due to the aspects of increased complexity in computations, error values, false positives, and misclassifi cation outputs. Hence, this research work proposes to develop an optimization-based classifi cation system for the breast cancer identifi cation system. Here, the Gaussian fi ltering and Adaptive Histogram Equalization (AHE) techniques are utilized for preprocessing the original mammogram images by eliminating the noisy contents and enhancing the contrast of an image. Then, the Markov Random Adaptive Segmentation (MRAS) technique is employed for detecting the boundary region based on the random value selection. To make the classifying procedure easier, the set of features is optimally extracted from the segmented region with the help of a Genetic Algorithm (GA). In which, the global best fi tness value is estimated by using the crossover, mutation, and selection operations. Finally, the Convolutional Neural Network (CNN) classifi cation technique is utilized for categorizing the image as to whether normal or abnormal with its type. The entire performance analysis of the suggested model is validated and compared using multiple measures during the evaluation. In the proposed method GA performs feature selection and prunes unnecessary features. The major goal is to improve the classifi cation performance while reducing the number of features used. The proposed system GA-CNN provides improved performance results with a reduced error rate.The suggested GA-CNN increases accuracy (98.5), sensitivity (99.38), and specifi city values (98.4) as compared to the existing technique by eff ectively identifying the classed label.
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Genetic Algorithm과 Expert System의 결합 알고리즘을 이용한 직구동형 풍력발전기 최적설계
김상훈(Shang-Hoon Kim),정상용(Sang-Yong Jung) 한국조명·전기설비학회 2010 조명·전기설비학회논문지 Vol.24 No.10
In this paper, the optimal design of a wind generator, implemented with the hybridized GA(Genetic Algorithm) and ES(Expert System), has been performed to maximize the AEP(Annual Energy Production) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, to solve the problem of calculation iterate, ES finds the superior individual and apply to initial generation of GA and it makes reduction of search domain. Meanwhile, for effective searching in reduced search domain, it propose Intelligent GA algorithm. Also, it shows the results of optimized model 500[㎾] wind generator using hybridized algorithm and benchmark result of compare with GA.
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( Guanyao Du ),( Ke Xiong ),( Yu Zhang ),( Zhengding Qiu ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.2
Energy harvesting (EH) and network coding (NC) have emerged as two promising technologies for future wireless networks. In this paper, we combine them together in a single system and then present a time switching-based network coding relaying (TSNCR) protocol for the two-way relay system, where an energy constrained relay harvests energy from the transmitted radio frequency (RF) signals from two sources, and then helps the two-way relay information exchange between the two sources with the consumption of the harvested energy. To evaluate the system performance, we derive an explicit expression of the outage probability for the proposed TSNCR protocol. In order to explore the system performance limit, we formulate an optimization problem to minimize the system outage probability. Since the problem is non-convex and cannot be directly solved, we design a genetic algorithm (GA)-based optimization algorithm for it. Numerical results validate our theoretical analysis and show that in such an EH two-way relay system, if NC is applied, the system outage probability can be greatly decreased. Moreover, it is shown that the relay position greatly affects the system performance of TSNCR, where relatively worse outage performance is achieved when the relay is placed in the middle of the two sources. This is the first time to observe such a phenomena in EH two-way relay systems.
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TLBO-CS 알골리즘을 이용한 독립형 하이브리드 에너지시스템의 최적설계에 관한 연구
조재훈(Jae-Hoon Cho),홍원표(Won-Pyo Hong) 한국조명·전기설비학회 2018 조명·전기설비학회논문지 Vol.32 No.5
In this paper, a new design optimal economical sizing of a Hybrid PV/WT/Diesel/Battery energy system is presented in order to assist the designers to take into consideration both the economical and ecological aspects. The reliability and cost of a hybrid system are two main criteria for designing a stand-alone hybrid system. The aim of this design is minimization of total annual cost(TAC) considered loss power supply probability and annual fuel cost of diesel generation system. A hybrid Teaching-Learning-Based Optimization algorithm is used for choosing the optimal number and type of units and the Total Annual Cost and Loss of Power Supply Probability are taken as the multi-objective functions of the proposed optimization algorithm. The effectiveness of the proposed method is verified using Matlab software and the simulation results confirm that the proposed method is more efficient than conventional methods and a feasible solution for stand-alone applications at the remote location.
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