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FINE-TUNING MobileNetV3 WITH DIFFERENT WEIGHT OPTIMIZATION algorithmS FOR CLASSIFICATION OF DENOISED BLOOD CELL IMAGES USING CONVOLUTIONAL NEURAL NETWORK

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2024年第6期22卷 65-93页

作者： Dhas, M. Mohana Singh, N. Suresh Manonmaniam Sundaranar Univ Annai Velankanni Coll Dept Comp Sci Tirunelveli 627012 Tamil Nadu India Malankara Catholic Coll Dept Comp Applicat Mariagri Kaliyakkavilai India

Breast cancer remains a formidable global health concern, underscoring the urgency for advanced diagnostic methodologies. This research presents a multifaceted framework aimed at significantly enhancing breast cancer diagnosis through innovative approaches in image processing and machine learning. The proposed framework encompasses several key contributions. Firstly, a robust denoising strategy is implemented using Convolutional Neural Network encoderdecoder architecture, augmented with data augmentation techniques. This addresses the challenge of vanishing gradients through enhanced Rectified Linear Units based Convolutional Neural Network, enhancing the model's generalization capability. Subsequent to denoising, feature extraction is performed utilizing a fine-tuned MobileNetV3 model. The model's performance is optimized through Modified Rectified Linear Units and NRMSProp approaches, effectively eliminating undesired features and improving overall efficiency. Crucially, a novel feature selection process is introduced, leveraging the Artificial Hummingbird algorithm based on Manta Ray Foraging Optimization algorithm. This algorithm selectively identifies essential features from breast cancer images, significantly elevating classification accuracy. To validate the proposed framework, a comprehensive evaluation is conducted, comparing its performance with a hybrid of five different metaheuristic algorithms, including marine predators algorithm, Tunicate Swarm algorithm, Manta Ray Foraging Optimization algorithm, Arithmetic Optimization algorithm, and Jelly Fish optimization algorithm. Artificial Hummingbird algorithm based on Manta Ray Foraging Optimization algorithm emerges as the most effective among these algorithms, showcasing superior performance. The evaluation utilized the Breast Cancer Histopathological Image Classification dataset, resulting in an impressive classification accuracy of 99.51% for the proposed model.

关键词： convolutional neural network artificial hummingbird algorithm marine predators algorithm MobileNetV3 improved ReLU

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Fractional-Order Ultra-Local Model-Based Optimal Model-Free Control for 7-DOF iReHave Upper-Limb Exoskeleton

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第8期69卷 3510-3514页

作者： He, Dingxin Wang, Haoping Tian, Yang Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

This brief proposes a fractional-order ultra-local model-based optimal model-free control (FO-OMFC) for trajectory tracking of 7-DOF iReHave upper-limb exoskeleton. This scheme is composed of fractional-order model-free controller (FO-MFC) and marine predators algorithm (MPA)-based parameter optimizer. Different from the existing model-free control, FO-MFC is designed based on a novel fractional-order ultra-local model. Then, the FO-MFC incorporates fractional-order time-delay estimation (FOTDE) as well as fractional-order error convergence controller (FOECC). The referred FOTDE is used to eliminate the lumped disturbance of the controlled exoskeleton. And the FOECC guarantees the convergence of the exoskeleton's trajectory tracking error. After that, the stability of closed-loop system is demonstrated by using Lyapunov theorem. Moreover, to obtain better control performance, the MPA is employed to search for optimal parameters of the FO-MFC. Finally, the co-simulation of iReHave exoskeleton based on SolidWorks and MATLAB is realized, and the results compared with other controllers and results under measurement noise are given to show the effectiveness and superiority of the proposed strategy.

关键词： Model-free control fractional-order ultra-local model robust control marine predators algorithm

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Wind integrated optimal power flow considering power losses, voltage deviation, and emission using equilibrium optimization algorithm

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ENERGY ECOLOGY AND ENVIRONMENT 2022年第4期7卷 369-392页

作者： Amroune, Mohammed Univ Ferhat Abbas Setif 1 Dept Elect Engn Setif 19000 Algeria

In this paper, the recently developed optimization algorithm, namely equilibrium optimization (EO), will be utilized to solve the optimal power flow problem (OPF), combining stochastic wind power with conventional thermal power generators in the system. The objectives are to minimize generation costs, including those incurred in thermal and stochastic wind power generation, active power loss, voltage deviation, and emission. To evaluate the performance of the EO algorithm in the OPF problem, modified IEEE 30-bus and IEEE 57-bus test systems with stochastic wind power generators will be used. A comparative study will be performed to show the efficiency of the EO algorithm compared with other recently developed metaheuristic algorithms such as the marine predators algorithm (MPA), artificial ecosystem-based optimization (AEO), and slime mould algorithm (SMA), as well as with other well-known algorithms. Based on the obtained results, the EO algorithm offered the best results.

关键词： Optimal power flow Wind power Equilibrium optimization marine predators algorithm Artificial ecosystem-based optimization Slime mould algorithm

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Enhancing photovoltaic hosting capacity in distribution networks by optimal allocation and operation of static var compensators 57

Enhancing photovoltaic hosting capacity in distribution netw...

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57th International Universities Power Engineering Conference (UPEC) - Big Data and Smart Grids

作者： Ahmadi, Bahman Ceylan, Oguzhan Ozdemir, Aydogan Univ Twente Dept EEMCS Enschede Netherlands Marmara Univ Dept Elect & Elect Engn Istanbul Turkey Istanbul Tech Univ Dept Elect Engn Istanbul Turkey

ISBN: (纸本)9781665455053

This paper presents a novel two-stage optimization approach for the optimal sizing of static var compensators and their operational management to maximize the photovoltaic integration capacity of distribution systems. In the first stage, the optimal locations for fixed size photovoltaic (PV) systems are determined to minimize the sum of total voltage violations. In the second phase, the size of the PV units are resized and the optimal size, number, location, and operating strategy of the SVC units are determined to maximize PV hosting capacity. In both phases, the marine predators algorithm is used for the solution optimization equations. The performance of the proposed approach and solution method is validated on modified 33-node and 141node radial distribution networks. The results are discussed from the point of view of the maximum hosting capacity and compared with the Grey Wolf Optimization and Whale Optimization algorithms in terms of computational performance.

关键词： Distributed generation Distribution Networks marine predators algorithm Static VAR compensator

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A distributed order PID controller for load frequency control in a two-area diverse-unit power system 4

A distributed order PID controller for load frequency contro...

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4th International Conference on Electronics and Electrical Engineering Technology (EEET)

作者： Ioannis, Moschos Constantinos, Parisses Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece

ISBN: (纸本)9781450385169

Until recently the conventional PI and PID controllers have been widely used in the load frequency control (LFC) problem. However, in today's modern power system those controllers cannot cope effectively with the increasing complexity of the power system resulting in system performance degradation. To tackle this problem, this paper proposes the application of two distributed order PID (DOPID) controllers which cover the whole range of integrals and derivatives of order -1 to order 1. Those controllers comprise with numerous fractional integrators and differentiators. The gains of the proposed controllers are optimized using the newly introduced marine predators algorithm (MPA). The analysis of the controllers is conducted in a realistic two area interconnected power system having diverse sources of power generation including communication delay, HVDC link and a geothermal power plant (GTPP) in both areas. A performance evaluation is conducted among various controllers like PIDN and FOPID with the proposed controllers and it is found that the DOPID controllers exhibit the best dynamic performance. Moreover, the transient response of the system is tested under the presence of wind power generation (WTG).

关键词： Distributed order PID marine predators algorithm Load frequency control Geothermal power plant

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Design of a Wide-Area Damping Controller to Tolerate Multiple Permanent Communication Failures Using Bio-Inspired algorithms 15

Design of a Wide-Area Damping Controller to Tolerate Multipl...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： Bento, Murilo E.C. Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350314182

Inter-area oscillation modes resulting from the interconnection of large electrical systems cause operation problems and even blackouts if minimum damping rate requirements are not met. Recent and promising research shows that Wide-Area Damping Controllers (WADC) are capable of improving the damping rates of inter-area modes because they use data from remote locations of the system. However, the operation of the WADC can be compromised if multiple communication failures occur on the WADC communication channels because this contingency causes changes in control actions. This work proposes a procedure for the desired design of a WADC that meets the robustness requirements to multiple communication failures that may occur. Some bio-inspired algorithms were used, evaluated and discussed in the proposed procedure. The effectiveness of the method was evaluated and discussed on the IEEE 68-bus test system. The results show that it is possible to design a WADC up to a limit of possible permanent communication losses. Furthermore, different bio-inspired algorithms present different performances in solving the optimization problem. ©2023 IEEE.

关键词： Bat algorithm Cuckoo Search algorithm marine predators algorithm Small-Signal Stability Synchronized Phasor Measurement System Wide-Area Damping Control

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Evaluation of metaheuristic optimization algorithms for optimal allocation of surface water and groundwater resources for crop production

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AGRICULTURAL WATER MANAGEMENT 2023年 279卷

作者： Jain, Sonal Ramesh, Dharavath Trivedi, Munesh C. Edla, Damodar Reddy Engn Indian Inst Technol ISM Dept Comp Sci Dhanbad 826004 Jharkhand India Natl Inst Technol Dept Comp Sci & Engn Agartala 799046 India Natl Inst Technol Dept Comp Sci & Engn Farmagudi 403601 Goa India

Extensive variability in current climatic conditions necessitates the need for optimal planning of water resources to manage socio-economic and environmental requirements efficiently. The optimal allocation of surface water and groundwater is essential to maximize crop net return due to uncertainty in seasonal rainfall, groundwater, and surface water availability in each region. This study demonstrated a multi-objective model to maximize the crop net return and efficient management of water resources. The multi-objective model comprised three objective functions maximizing the crop net return, minimizing the water deficit, and maximizing the aquifer recharge. The model's practicability was analyzed through the case study of the Pennar-Palar-Cauvery link canal command in India. Three meta-heuristic approaches, particle swarm optimization (PSO), genetic algorithm (GA), and marine predators algorithm (MPA), were employed to solve the presented model. And their performance was evaluated using hypervolume and coverage metrics, indicating MPA's superiority in obtaining well-distributed Pareto-optimal solutions. Thus, decision-makers can choose the best feasible solution based on current resource availability and preferences. The model was executed considering different cropping area deviations (5-25%) in the existing cropping pattern and allowance of groundwater mining from 0% to 70%. The obtained cropping pattern utilizing MPA at 25% cropping deviation achieved an increased net return of 1327.05 million INR.

关键词： marine predators algorithm Water resource planning Conjunctive use Cropping pattern Irrigation planning Sustainability

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Energy management between electric vehicle charging stations and electric distribution system considering quality of service using IACSO-MPA approach

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第12期31卷

作者： Patil, Manoj Dhondiram Aush, Mithun Gourang Mahadik, Yogesh Vilas Kamble, SachinGorakh Patil, Santosh V. Kharade, Jyoti Mohan ADCET Dept Elect Engn Ashta India Chh Shahu Coll Engn Dept Elect Engn Aurangabad Maharashtra India Govt Polytech Malvan Dept Elect Engn Malvan Maharashtra India

In this manuscript, an energy management system (EMS) is proposed for the performance of Electric Vehicle Charging Station (EVCS) and Electric Distribution System (DS) using a hybrid approach. The proposed hybrid approach is the combined execution of Improved Artificial Cell swarm optimization (ACSO) and marine predators algorithm (MPA) called IACSO-MPA approach. The searching behaviour of ACSO is enhanced through crossover and mutation operator;hence it is named as IACSO. Here, the updating behaviour of the IACSO is enhanced by MPA. The major purpose of this work is to maximize the ability of DS for assigning charging plans to EVCS and minimizing the purchase cost. Based on this objective the proposed IACSO-MPA technique is established for analysing the energy management interactive process in EVCS and DS and also for locating equilibrium solution. The proposed system is activated in MATLAB/Simulink site, then the efficiency is compared to various existing techniques, viz Crow search optimization (CSO), BAT algorithm, Particle swarm optimization. Moreover, the performance evaluation of EVCS and distribution system (DS) is assessed using the proposed existing technique. The proposed technique achieves for the EVCS 1 is 621.73. EVCS 2 is 3576.90, EVCS 3 is 4441.72, and DS is 4391.26. The experimental results show that the integrated energy system (IES) costs can be decreased using 3.89% and gains linked by EVCS can be maximized at least 7.8%. Furthermore, the proposed algorithm can locate the optimal global solutions effectively with accurately over the existing techniques.

关键词： artificial cell swarm optimization distribution system electric vehicle electric vehicle charging station energy management system marine predators algorithm

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Multi-objective task scheduling method for cyber-physical-social systems in fog computing

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KNOWLEDGE-BASED SYSTEMS 2023年 280卷

作者： Abdel-Basset, Mohamed Mohamed, Reda Sallam, Karam M. Hezam, Ibrahim M. Zagazig Univ Fac Comp & Informat Zagazig 44519 Sharqiyah Egypt Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Univ Canberra Sch IT & Syst Canberra ACT 2601 Australia King Saud Univ Coll Sci Dept Stat & Operat Res Riyadh Saudi Arabia

Cyber-physical-social systems refer to new systems that involve a large number of Internet of Things (IoT) devices connected through an interconnected network. These systems gather data for various services such as transportation, energy, healthcare, etc. This data is then transmitted to the cloud layer for storage, processing, and access. To handle the significant workload offloaded from resource-constrained IoT devices without latency or bandwidth issues, a technology called fog computing has emerged as an intermediary layer between the cloud and IoT devices. The quality of service (QoS) provided by fog computing relies on efficient task scheduling to optimize energy consumption and maximum execution time. This task scheduling problem is known to be NP-hard and cannot be solved with high precision in a reasonable time using traditional techniques or existing metaheuristic-based task schedulers. Therefore, this study proposes a new multi-objective task scheduling approach based on a modified marine predators algorithm (MMPA) to simultaneously minimize energy consumption and make-span under the Pareto optimality theory. This variant is named the multi-objective MMPA (M2MPA). M2MPA enhances the performance of the MPA by incorporating the polynomial crossover operator to improve its exploration operator and the adaptive CF parameter to enhance its exploitation operator. The effectiveness of M2MPA is evaluated using eighteen tasks with different scales (small, large, and medium) and heterogeneous workloads assigned to two hundred fog devices with varying processing speeds. Finally, M2MPA is compared with six well-established techniques using various performance indicators, including carbon dioxide emission rate, flowtime, make-span, and energy consumption. The experimental findings demonstrate the substantial superiority of M2MPA over all competing algorithms across different performance indicators and the Wilcoxon rank-sum test. Quantitatively, M2MPA achieves an avera

关键词： Multi-objective marine predators algorithm Make-span Cyber-physical systems Fog computing

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A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

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PROCESSES 2024年第1期12卷 193页

作者： Du, Gang Sheng, Zhenming Liu, Jiaguo Gao, Yiping Xin, Chunqing Ma, Wentao NARI Grp Corp State Grid Elect Power Res Inst Nanjing 211106 Peoples R China NARI Technol Co Ltd Nanjing 211106 Peoples R China Natl Key Lab Risk Def Technol & Equipment Power Gr Nanjing 211106 Peoples R China Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China

The prediction of dissolved gas change trends in power transformer oil is very important for the diagnosis of transformer faults and ensuring its safe operation. Considering the time series and nonlinear features of the gas change trend, this paper proposes a novel robust extreme learning machine (ELM) model combining an improved data decomposition method for gas content forecasting. Firstly, the original data with nonlinear and sudden change properties will make the forecasting model unstable, and thus an improved variational modal decomposition (IPVMD) method is developed to decompose the original data to obtain the multiple modal dataset, in which the marine predators algorithm (MPA) optimization method is utilized to optimize the free parameters of the VMD. Second, the ELM as an efficient and easily implemented tool is used as the basic model for dissolved gas forecasting. However, the traditional ELM with mean square error (MSE) criterion is sensitive to the non-Gaussian measurement noise (or outliers). In addition, considering the nonlinear non-Gaussian properties of the dissolved gas, a new learning criterion, called extended maximum correntropy criterion (ExMCC), is defined by using an extended kernel function in the correntropy framework, and the ExMCC as a learning criterion is introduced into the ELM to develop a novel robust regression model (called ExMCC-ELM) to improve the ability of ELM to process mutational data. Third, a gas-in-oil prediction scheme is proposed by using the ExMCC-ELM performed on each modal obtained by the proposed IPVMD. Finally, we conducted several simulation studies on the measured data, and the results show that the proposed method has good predictive performance.

关键词： dissolved gas prediction extreme learning machine variational mode decomposition marine predators algorithm extended maximum correntropy criterion

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