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A hybrid Multifarious Clustering algorithm for the Analysis of Memmogram Images

Journal of Computer and Communications 2019年第12期7卷 136-151页

作者： T. Velmurugan E. Venkatesan PG and Research Department of Computer Science D. G. Vaishnav College Chennai India PG Department of Computer Science R. V. Government College Chengalpattu India.

A number of clustering algorithms were used to analyze many databases in the field of image clustering. The main objective of this research work was to perform a comparative analysis of the two of the existing partitions based clustering algorithms and a hybrid clustering algorithm. The results verification done by using classification algorithms via its accuracy. The perfor-mance of clustering and classification algorithms were carried out in this work based on the tumor identification, cluster quality and other parameters like run time and volume complexity. Some of the well known classification algorithms were used to find the accuracy of produced results of the clustering algorithms. The performance of the clustering algorithms proved mean-ingful in many domains, particularly k-Means, FCM. In addition, the proposed multifarious clustering technique has revealed their efficiency in terms of performance in predicting tumor affected regions in mammogram images. The color images are converted in to gray scale images and then it is processed. Finally, it is identified the best method for the analysis of finding tumor in breast images. This research would be immensely useful to physicians and radiologist to identify cancer affected area in the breast.

关键词： Medical Images hybrid Clusteing algorithm K-Means algorithm Fuzzy C Means algorithm Classification algorithms

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Model predictive control and improved low-pass filtering strategies based on wind power fluctuation mitigation

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Journal of Modern Power Systems and Clean Energy 2019年第3期7卷 512-524页

作者： Yushu SUN Xisheng TANG Xiaozhe SUN Dongqiang JIA Zhihuang CAO Jing PAN Bin XU Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190China Sinopec Oilfield Service Shengli Corporation Tarim Branch Company Korla 841000China State Grid Beijing Electric Power Company Electric Power Research Institute Beijing 100075China State Grid Anhui Electric Power Company Hefei 230000China

The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage(ES) to mitigate wind power fluctuation. We studied the strategy of smoothing wind power fluctuation and the strategy of hybrid ES power distribution. Firstly, an effective control strategy can be extracted by comparing constant-time low-pass filtering(CLF), variable-time low-pass filtering(VLF), wavelet packet decomposition(WPD), empirical mode decomposition(EMD) and model predictive control algorithms with fluctuation rate constraints of the identical grid-connected wind power. Moreover, the mean frequency of ES as the cutoff frequency can be acquired by the Hilbert Huang transform(HHT), and the time constant of filtering algorithm can be obtained. Then, an improved low-pass filtering algorithm(ILFA) is proposed to achieve the power allocation between lithium battery(LB) and supercapacitor(SC), which can overcome the over-charge and over-discharge of ES in the traditional low-pass filtering algorithm(TLFA). In addition, the optimized LB and SC power are further obtained based on the SC priority control strategy combined with the fuzzy control(FC) method. Finally, simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system.

关键词： Wind power fluctuation mitigation Control strategies Model predictive control algorithm hybrid energy storage(ES) Improved low-pass filtering algorithm(ILFA) Fuzzy control(FC) Strategy

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An aero-engine life-cycle maintenance policy optimization algorithm: Reinforcement learning approach

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Chinese Journal of Aeronautics 2019年第9期32卷 2133-2150页

作者： Zhen LI Shisheng ZHONG Lin LIN School of Mechatronics Engineering Harbin Institute of Technology

An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is adopted to improve the aero-engine operational reliability. Thus,the long service-life and the hybrid maintenance strategy should be considered synchronously in aero-engine maintenance policy optimization. This paper proposes an aero-engine life-cycle maintenance policy optimization algorithm that synchronously considers the long service-life and the hybrid maintenance strategy. The reinforcement learning approach was adopted to illustrate the optimization framework, in which maintenance policy optimization was formulated as a Markov decision process. In the reinforcement learning framework, the Gauss–Seidel value iteration algorithm was adopted to optimize the maintenance policy. Compared with traditional aero-engine maintenance policy optimization methods, the long service-life and the hybrid maintenance strategy could be addressed synchronously by the proposed algorithm. Two numerical experiments and algorithm analyses were performed to illustrate the optimization algorithm in detail.

关键词： Aero-engine hybrid strategy Maintenance policy Optimization algorithm Reinforcement learning

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Towards Energy Efficient Shape Rolling:Roll Pass Optimal Design and Case Studies

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Chinese Journal of Mechanical Engineering 2019年第3期32卷 78-88页

作者： Kan Huang Bin Huang Lei Fu Kazem Abhary School of Civil Engineering Changsha University of Science and TechnologyChangsha 410114China School of Engineering University of South AustraliaAdelaideSouth Australia 5095Australia

Shape rolling is widely employed in the production of long workpieces with appropriate cross-section profiles for other industrial applications. In the development of shape rolling systems, roll pass design (RPD) plays an essential role on the quality control of products, service life of rolls, productivity of rolling systems, as well as energy consumption of rolling operations. This study attempts to establish a generic strategy based on hybrid modeling and an improved genetic algorithm, to support the optimizations of RPD and shape rolling operations at a systematic perspective. Objectives include improving the quality and efficiency of RPD, reducing energy consumption of shape rolling, as well as releasing the demands on costly trails and expert knowledge in RPD. hybrid modeling based on cross-disciplinary knowledge is developed to overcome the limitations of isolated single-disciplinary models. And conventional genetic algorithm is improved for the implementation of optimal design. Targeting to integrate empirical data and published reliable solutions into optimizations, a parameters estimation method is proposed to transfer the initially misaligned models into a uniform pattern. A tool based on the Matlab platform is developed to demonstrate the optimal design operations, with case studies involved to validate the proposed methodology.

关键词： Roll pass optimal design hybrid modelling Genetic algorithm Parameters estimation

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A Novel Cascaded PID Controller for Automatic Generation Control Analysis With Renewable Sources

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1438-1451页

作者： Aurobindo Behera Tapas Kumar Panigrahi PrakashK·Ray Arun Kumar Sahoo Department of Electrical and Electronics Engineering International Institute of Information Technology BhubaneswarBhubaneswar 751003India Department of Electrical Engineering Parala Maharaja Engineering CollegeBiju Patnaik University of TechnologyRourkela 769015India Department of Electrical Engineering College of Engineering and TechnologyBiju Patnaik University of TechnologyRourkela 769015India

Present day power scenarios demand a high quality uninterrupted power supply and needs environmental issues to be addressed. Both concerns can be dealt with by the introduction of the renewable sources to the existing power system. Thus, automatic generation control(AGC) with diverse renewable sources and a modified-cascaded controller are presented in the ***, a new hybrid scheme of the improved teaching learning based optimization-differential evolution(hITLBO-DE) algorithm is applied for providing optimization of controller parameters. A study of the system with a technique such as TLBO applied to a proportional integral derivative(PID), integral double derivative(IDD) and PIDD is compared to hITLBO-DE tuned cascaded controller with dynamic load *** suggested methodology has been extensively applied to a 2-area system with a diverse source power system with various operation time non-linearities such as dead-band of, generation rate constraint and reheat thermal units. The multi-area system with reheat thermal plants, hydel plants and a unit of a wind-diesel combination is tested with the cascaded controller scheme with a different controller setting for each area. The variation of the load is taken within 1% to 5% of the connected load and robustness analysis is shown by modifying essential factors simultaneously by± 30%. Finally, the proposed scheme of controller and optimization technique is also tested with a 5-equal area thermal system with non-linearities. The simulation results demonstrate the superiority of the proposed controller and algorithm under a dynamically changing load.

关键词： Automatic generation control(AGC) cascaded controller hybrid improved teaching learning based optimization-differential evolution(hITLBO-DE) algorithm integral of square time multiplied by square error(ISTSE)

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Multi-objective optimal operation of hybrid AC/DC microgrid considering source-network-load coordination

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Journal of Modern Power Systems and Clean Energy 2019年第5期7卷 1229-1240页

作者： Peng LI Miaomiao ZHENG School of Electrical and Electronic Engineering North China Electric Power UniversityBaoding 071003China

Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives,the adjustment of load demand curves is carried out considering the demand side response(DSR)on the load *** complementary utilization of renewable energy between AC area and DC area is achieved to meet the load demand on the source *** the network side,the hybrid AC/DC microgrids purchase electricity from the power grid at the time-of-use(TOU)price and sell the surplus power of renewable energy to the power grid for *** improved memetic algorithm(IMA)is introduced and applied to solve the established mathematical *** promotion effect of the proposed source-network-load coordination strategies on the optimal operation of hybrid AC/DC microgrid is verified.

关键词： Source-network-load coordination Optimal operation Improved memetic algorithm hybrid AC/DC microgrid Complementary utilization

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Application of several optimization techniques for estimating TBM advance rate in granitic rocks

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Journal of Rock Mechanics and Geotechnical Engineering 2019年第4期11卷 779-789页

作者： Danial Jahed Armaghani Mohammadreza Koopialipoor Aminaton Marto Saffet Yagiz Geotropik-Center of Tropical Geoengineering Faculty of Civil EngineeringUniversiti Teknologi MalaysiaSkudai81310Malaysia Faculty of Civil and Environmental Engineering Amirkabir University of TechnologyTehran15914Iran Environmental & Green Technology Department Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala LumpurMalaysia School of Mining and Geosciences Nazarbayev UniversityNur-Sultan010000Kazakhstan

This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of *** this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in *** properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for ***,to estimate the advance rate of TBM,two new hybrid optimization techniques,*** artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic ***,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for *** evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized *** values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best *** is concluded that both the optimization techniques,***-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.

关键词： Tunnel boring machines (TBMs) Advance rate hybrid optimization techniques Particle swarm optimization (PSO) Imperialist competitive algorithm (ICA)

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A Comprehensive State-of-the-Art Survey on the Transmission Network Expansion Planning Optimization algorithms

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IEEE ACCESS 2019年 7卷 123158-123181页

作者： Ude, Nnachi Gideon Yskandar, Hamam Graham, Richards Coneth Tshwane Univ Technol ZA-0001 Pretoria South Africa ESIEE Paris F-93162 Noisy Le Grand France

Long term planning in power transmission network expansion provides a well ordered and profitable extension of power equipment and facilities to meet the expected electric energy demand with an allowable degree of reliability. However, high quality and improved reliability in energy supply have to be balanced with the available funds. The need to expand transmission network can never be over emphasized. Transmission Network Expansion Planning (TNEP) is a periodical measure that must be carried out due to dynamic societies that attract extra energy demands. It is highly important to minimize the network reinforcement and operational costs while satisfying the increase in demand imposed by technical and economic conditions over the planning horizon. Several optimization algorithms for TNEP problems have been developed and applied over the past decades. This paper presents a comprehensive state-of-the-art survey on the TNEP optimization algorithms. The approach of this paper is in the area of highlights of the various available TNEP algorithms, their applications, viability, computational complexities and drawbacks, which can aid in the identifications of the proper methods that can yield an optimal solution to TNEP problem.

关键词： algorithm hybrid meta-heuristics optimization techniques power network expansion planning power system transmission network expansion

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Wind farm water area path planning algorithm based on A* and reinforcement learning 5

Wind farm water area path planning algorithm based on A* and...

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5th International Conference on Transportation Information and Safety (ICTIS)

作者： Zha, Tianqi Xie, Lei Chang, Jiliang Wuhan Univ Technol Intelligent Transport Syst Res Ctr Wuhan Peoples R China

ISBN: (纸本)9781728104898

In recent years, the scale of offshore wind farms is increasing because of the high efficiency and pollution-free wind power resources. However, the introduction of many facilities in the corresponding wind farm sea area has led to the increasing difficulty of ship navigation. Therefore, it is very important to plan safe and efficient driving path according to the corresponding starting and ending points for the navigation of ships in the increasing wind farm area. In this paper, a path planning algorithm based on the hybrid method of A* algorithm and reinforcement learning is proposed, which can plan an effective collision avoidance path for the sea area of wind farm. Then the method is used to simulate the ship's path planning in a wind farm, which proves the feasibility of the method. Finally, it shows that the method has universal reference significance for ship navigation in the wind farm waters.

关键词： ship plan path hybrid algorithm

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Convergence Theorem of hybrid Iterative algorithm for Equilibrium Problems and Fixed Point Problems of Finite Families of Uniformly Asymptotically Nonexpansive Semigroups

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Advances in Pure Mathematics 2014年第6期4卷 244-252页

作者： Hongbo Liu Yi Li School of Science Southwest University of Science and Technology Mianyang China

Throughout this paper, we introduce a new hybrid iterative algorithm for finding a common element of the set of common fixed points of a finite family of uniformly asymptotically nonexpansive semigroups and the set of solutions of an equilibrium problem in the framework of Hilbert spaces. We then prove the strong convergence theorem with respect to the proposed iterative algorithm. Our results in this paper extend and improve some recent known results.

关键词： hybrid Iterative algorithm Uniformly Asymptotically Nonexpansive Semigroups Equilibrium Problem Common Fixed Point

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