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bacterial foraging Optimization Based Radial Basis Function Neural Network (BRBFNN) for Identification and Classification of Plant Leaf Diseases: An Automatic Approach Towards Plant Pathology

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IEEE ACCESS 2018年 6卷 8852-8863页

作者： Chouhan, Siddharth Singh Kaul, Ajay Singh, Uday Pratap Jain, Sanjeev Shri Mata Vaishno Devi Univ Dept Comp Sci & Engn Katra 182320 India Madhav Inst Sci & Technol Dept Appl Math Gwalior 474005 India

The contribution of a plant is highly important for both human life and environment. Plants do suffer from diseases, like human beings and animals. There is the number of plant diseases that occur and affects the normal growth of a plant. These diseases affect complete plant including leaf, stem, fruit, root, and flower. Most of the time when the disease of a plant has not been taken care of, the plant dies or may cause leaves drop, flowers, and fruits drop. Appropriate diagnosis of such diseases is required for accurate identification and treatment of plant diseases. Plant pathology is the study of plant diseases, their causes, procedures for controlling and managing them. But, the existing method encompasses human involvement for classification and identification of diseases. This procedure is time-consuming and costly. Automatic segmentation of diseases from plant leaf images using soft computing approach can be reasonably useful than the existing one. In this paper, we have introduced a method named as bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases automatically. For assigning optimal weight to radial basis function neural network we use bacterial foraging optimization that further increases the speed and accuracy of the network to identify and classify the regions infected of different diseases on the plant leafs. The region growing algorithm increases the efficiency of the network by searching and grouping of seed points having common attributes for feature extraction process. We worked on fungal diseases like common rust, cedar apple rust, late blight, leaf curl, leaf spot, and early blight. The proposed method attains higher accuracy in identification and classification of diseases.

关键词： bacteria foraging algorithm image segmentation plant diseases radial basis function neural network soft computing

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A hybrid evaluation model for flight performance based on bacterial foraging and Elman network

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AEROSPACE SCIENCE AND TECHNOLOGY 2016年 55卷 392-399页

作者： Wu, Qi Chen, Xi Ren, He Wei, Chuanfeng Zhang, Wenxin Liu, Dongnan Xie, Hongcai Chen, Mingjian Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Civil Aircraft Hlth Monitor Shanghai Peoples R China CAST Inst Manned Space Syst Engn Human Space Flight Syst Engn Div Beijing Peoples R China

The evaluation of the flight performance via multiple physiological signals is an important problem in the field of flight safety. A hybrid prediction model is proposed to dispose multiple physiological signals with high dimension in this paper. Main contribution of our model is that of a novel bacterial foraging algorithm (BF) to optimize Elman neural network, which can perform parallel search and escape local minimum easily, and provide better prediction accuracy of the flight performance. Other bio-inspired algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and chaotic GA are also used to optimize the unknown parameters of the Elman network. Experimental results indicate that the proposed hybrid model based on BF algorithm and Elman network is well suited for the evaluation and prediction of the flight performance. Compared with the other public algorithms, the BF can easily identify the unknown parameters of the established models and has better optimization capability. (C) 2016 Elsevier Masson SAS. All rights reserved.

关键词： Multiple physiological signal bacteria foraging algorithm Elman network Flight performance

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Web Facilitated Anthracnose Disease Segmentation from the Leaf of Mango Tree Using Radial Basis Function (RBF) Neural Network

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WIRELESS PERSONAL COMMUNICATIONS 2020年第2期113卷 1279-1296页

作者： Chouhan, Siddharth Singh Singh, Uday Pratap Jain, Sanjeev Shri Mata Vaishno Devi Univ Dept Comp Sci & Engn Katra 182320 Jammu & Kashmir India Shri Mata Vaishno Devi Univ Dept Math Katra 182320 Jammu & Kashmir India

In this article, we present an automatic web facilitated leave disease segmentation system for mango tree using a neural network (NN). The proposed system compromised of four major steps. First, the real-time images of the mango leaves are acquired using the digital camera enabled with the web. Second, the images are preprocessed and features are extracted using a scale-invariant feature transform method. Third, the training of the NN is optimized with bacterial foraging optimization algorithm using the most dissimilar features. Finally, the radial basis function NN is used for the extraction of the diseased region from the mango leave images. The experimental results validated the high-level accuracy of the proposed system for the segmentation of anthracnose (fungal) disease obtaining an average Specificity = 0.9115 and Sensitivity = 0.9086. A comparison with other states of art methods is also presented, and some relevant future developments are also offered. This presented system is intuitive, user-friendly and is being developed to be espoused in precision agriculture.

关键词： bacteria foraging algorithm Computer vision Plant pathology Radial basis function neural network Scale-invariant feature transform Soft computing

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MBFA algorithm based optimization of tungsten carbide alloy wire cut machining process

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年第1期17卷 307-329页

作者： Nouri, Hossein Islamic Azad Univ Dept Mech Engn Sanandaj Branch POB 618 Sanandaj Iran

Wire cut machining process is one of the methods of producing tungsten carbide parts, which due to its poor machining capability, optimal machining with traditional methods is not possible. Material removal rate and surface roughness are the most important output indicators of the wire cut process. In this research, the wiring machining process on tungsten carbide alloy has been modeled and optimized. Mathematical modeling has been used to establish an accurate relationship between input parameters and process outputs. In this regard, first, the necessary data were collected by conducting experiments, designed by the Design of Experiment Technique by Taguchi method. The validity of these models was assessed using statistical hypothesis tests and analysis of variance. After determining the appropriate models, the process optimization is performed using the Multi-Objective bacteria foraging algorithm and the table of optimizations for combining the input parameters is presented. Finally, a validation experiment was performed with one of the combinations of the obtained optimal table and the results of this experiment were compared with the results obtained through optimization. The results obtained show a good correlation between the proposed model than the experimental data, making it possible to validate the methodology developed. Furthermore, The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as NSGA-II. The results lie in favor of better performance of the proposed algorithm.

关键词： Wire cut machining Surface roughness Modeling Optimization bacteria foraging algorithm

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Modelling and voltage control of the solar-wind hybrid micro-grid with optimized STATCOM using GA and BFA

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2020年第3期23卷 576-584页

作者： Bakir, Hale Kulaksiz, Ahmet Afsin Konya Tech Univ Dept Elect & Elect Engn Konya Turkey

Electricity generation from the wind and solar photovoltaic (PV) systems are highly dependent upon weather conditions. Their intermittent nature leads to fluctuations in their output. Therefore, the need for rapid compensation for energy transmission and distribution systems is increasingly important. Static Synchronous Compensator (STATCOM) can be adopted for reactive power compensation and for decreasing the voltage fluctuation caused by the system and renewable energy sources. This study presents modelling of a Solar PV-Wind Hybrid Micro-grid and the increase of the stable operating limit of the system in case of the incorporation of STATCOM is examined. The major contribution of this paper is the optimization of gain parameters of four PI controllers in STATCOM control circuit based on genetic algorithms (GA) and bacteria foraging algorithm (BFA) and therefore obtaining better responses and voltage stability in terms of nonlinear nature of solar-wind hybrid micro-grid. The Simulink models of the system architecture include a wind turbine model, a solar PV power system model and a STATCOM. It is certified that the voltage fluctuation at the end of the bus bar is reduced by 8% using conventional PI controller, by 10% for GA-based PI controller, and by 15% for BFA based PI controller under variable load. The results obtained by GA and BFA-based optimization of PI controllers are compared with that of the conventional controller and better results attained. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.

关键词： Voltage control bacteria foraging algorithm PV-wind hybrid system Static synchronous compensator Genetic algorithm

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Design of PI controller for UPFC by using bacteria foragin optimization algorithm to enhance power system transient stability 3

Design of PI controller for UPFC by using bacteria foragin o...

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3rd International Conference on Electrical and Control Technologies

作者： Lesani, Hamid Jazebi, Saeed Baghaee, Hamid Reza Univ Tehran Tehran Iran Amirkabir Univ Technol Tehran Iran

ISBN: (纸本)9781634397971

Various control strategies have been reported to control UPFC in different operating conditions. PI regulators used for UPFC suffer from the inadequacies of providing suitable control for transient stability enhancement over a wide range of system operating conditions. A new scheme to control UPFC using bacteria foraging algorithm (BFA) is proposed to improve the efficiency of UPFC PI controller in damping power system oscillations. BFA is applied to UPFC conventional PI controller in order to optimize its performance under different operating conditions. The effectiveness of the proposed on-line PI controller is demonstrated using simulations of a multi-machine power system.

关键词： UPFC power system stability inter-area oscillations bacteria foraging algorithm

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The Use of BFA Simulation to Evaluate Reliability of Composite System Case study: Calculation of Muarakarang-Gandul 1 Composite System (islanding system which part of Jawa-Bali-Madura grid in Indonesia)

The Use of BFA Simulation to Evaluate Reliability of Composi...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Nugraha, Herry Sinisuka, Ngapuli I. Arifianto, Yudi Darwis, Sutawanir Inst Teknol Bandung PLN Bandung Indonesia Inst Teknol Bandung Sch Elect Engn & Informat Bandung Indonesia Inst Teknol Bandung Fac Math & Nat Sci Bandung Indonesia

ISBN: (纸本)9781479935611

optimization of bacteria foraging algorithm (BFA) has been widely implemented in power engineering. This algorithm is emulated from escherichia coli bacteria's ability in finding nutrients in the human body. This research focuses on the implementation of BFA to calculate reliability indices of power system such as Loss of Load Probability (LOLP), Loss of Load Expectation (LOLE) and Expected Energy Not Supplied (EENS). The positions of each bacterium describe the status of the generation system and the resulting fitness value is its probability. The generation system states were visited included a failure state (state which causes load curtailment or loss of load), furthermore some generation systems are configured by block of units which are identically. It is opportunity in this research to improve BFA calculation methodology by considering probability of a system state with identical combinations. New approach of reliability calculation of composite system will be proposed. When the high probability failure states are obtained, the reliability indices can be calculated accurately. To simulate the methodology, case study of BFA and Genetic algorithms (GA) calculation of Muarakarang-Gandul 1 Composite System (islanding system which part of Jawa-Bali-Madura grid in Indonesia) will be applied.

关键词： BFA bacteria foraging algorithm reliability generation system power system LOLE EENS

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Parameter Estimation of Dielectric Response Circuit Using Improved Mathematical Model

Parameter Estimation of Dielectric Response Circuit Using Im...

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IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD)

作者： Zhang, Tao Li, Xiao Qin Zuo, Qian Zhang, Bin Liu, Ya Qin China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

ISBN: (纸本)9781467381079

Return voltage method can assess water content in transformer pressboard by dominant time constant. However, the data of return voltage method (RVM) takes for a long measuring time. The parameters of dielectric response based on extended Debye circuit model can reflect oil-paper insulation state. To shortcut measuring time, therefore, it is necessary to estimate equivalent circuit parameter and calculate return voltage characteristic quantity. This paper established improved mathematical model using return voltage peak, peak time and initial slope characteristics to solve dielectric response equivalent circuit parameters. And then improved bacteria foraging algorithm is used for solving the problem. The improved mathematical model can decrease sampling data of return voltage. The result shows that the dominant time constant calculated and measured return voltage curve have good consistency, which can accurately diagnose the transformer oil-paper insulation status. It illustrates that the improved method proposed in this paper is feasible and effective.

关键词： transformer oil-paper insulation return voltage method parameter identification bacteria foraging algorithm

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Design of Robust Fractional Order Lead-Lag controller using a Hybrid Dynamic GA-BF algorithm for Power System Oscillation Damping 5

Design of Robust Fractional Order Lead-Lag controller using ...

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5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT)

作者： Abdulkhader, Haseena Kuttom Parambil Natl Inst Technol Calicut Dept Elect Engn Calicut Kerala India

ISBN: (纸本)9781665432726

This paper deals with the design and analysis of a fractional-order robust lead-lag compensator based damping controller to enhance the dynamic stability of power system having disturbance uncertainty. First, the fractional order lead-lag controller is optimized as the Power System Stabilizer (PSS) by transforming the design problem to an optimization problem in which, the objective function is the Integral of the Time-Weighted Absolute Error (ITAE), and the constraints are the parameter limits. The parameter optimization is done by using a hybrid met heuristic algorithm. Furthermore, the stability of the closed loop fractional-order power system is discussed, and a sufficient condition for stability analysis is proved. The robustness and stability performance of the proposed controller have been ensured through extensive simulations and evaluating some performance indices on the test system under various disturbance scenario, operating points and system parameter variations.

关键词： Fractional order controller bacteria foraging algorithm Power system dynamic stability

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Detection of Dementia from Brain Tissues Variation in MR Images Using Minimum Cross-Entropy Based Crow Search algorithm and Structure Tensor Features 8th

Detection of Dementia from Brain Tissues Variation in MR Ima...

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8th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Priyanka, N. Ahana Kavitha, G. Madras Inst Technol Dept Elect Engn Chennai 600044 Tamil Nadu India

ISBN: (纸本)9789811500350;9789811500343

Dementia causes cognitive dysfunction and deterioration of brain. Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) are most common forms of dementia. Globally, it is estimated that about 47 million people are affected by dementia. Various researches suggest that AD and MCI share a number of equally severe cognitive deficits, but the pathophysiology has not yet been addressed in a comprehensive way. An attempt is made to observe the prognosis difference in these disorders and to analyze the tissue variation in T1-weighted MR brain images. Samples used in this analysis are obtained from IXI, MIRIAD, and ADNI 2 database. Initially, skull stripping is carried out using Robust Brain Extraction Tool (ROBEX), Brain Extraction Tool (BET), and Brain Surface Extractor (BSE). Further, segmentation of brain tissues is performed using multilevel minimum cross-entropy based bacteria foraging algorithm (BFO) and Crow Search algorithm (CSA). Various geometric features and Structure Tensor (ST) features are extracted from White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) for normal, MCI, and AD to observe the structural changes. The result shows that ROBEX performs better delineation of brain. Minimum cross-entropy based CSA achieves better segmentation than BFO based on similarity measures and computation time. Further, ST features extracted from the brain tissues are able to show anatomical variation effectively than geometric features. It is identified from the ANOVA test that structure tensor features of GM shows better variation to discriminate normal, MCI, and AD images. Hence, this framework could be used to differentiate normal, MCI, and AD images such as cognitive disorders effectively.

关键词： Dementia bacteria foraging algorithm Crow search algorithm Structure tensor

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