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检索条件"主题词=Whale Optimization Algorithms"

共 403 条记录，以下是211-220 订阅

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Hybrid Solar Energy System with AI-Based Predictive

Hybrid Solar Energy System with AI-Based Predictive

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： V. Nijanthan C. Shobana Bharathi Department of Electrical and Electronics Engineering Excel Engineering College Namakkal India Department of Power Electronics & Drives Excel Engineering College Namakkal India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Energy Management Techniques play an important role in innovative microgrid systems primarily dependent on Sustainable Energy Sources (SES) power generation. The irregular weather conditions, low power backup, managing the combination of ESS, and ensuring the distribution generation's reliability and low power quality. Methods: To solve these limitations, this study proposes a novel hybrid optimization technique that combines the Symbiotic Search Algorithm (SSA) and whale optimization Algorithm (WOA) for managing Renewable Energy (RE) sources. The proposed system integrates hybrid wind Photovoltaic and Wind energy systems with an advanced Hybrid Energy Storage System (HESS) that includes Battery Energy Storage (BES) and SC technology in a Microgrid (MG). Renewable energy sources ensure power balance by addressing variations in load demand and fluctuations in renewable energy generation. The output testing and validation are facilitated through control parameters; the SSA-WOA-based microgrid offers an experimental framework for bright grid power quality.

关键词： Symbiosis Photovoltaic systems Renewable energy sources Wind energy Power quality Microgrids Solar energy whale optimization algorithms Batteries Wind turbines

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A Hybrid Method for Improving SOC Prediction in Lithium-Ion Batteries by Combining EDNN with WOA

A Hybrid Method for Improving SOC Prediction in Lithium-Ion ...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： V.Indumathi R.Gopalakrishnan C.Vijayakumar A.Pavithran M.Kamalesh M.Balaji Faculty of Mechatronics Engineering K S Rangasamy College of Technology Namakkal Tamil Nadu India Faculty of Electrical and Electronics Engineering K S Rangasamy College of Technology Namakkal Tamil Nadu India K S Rangasamy College of Technology Namakkal Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The safe and effective functioning of lithium-ion (Li-ion) batteries depends on accurate State of Charge (SOC) prediction. In order to increase the accuracy of SOC estimation, this study suggests a unique hybrid model that combines the whale optimization Algorithm (WOA) with Enhanced Deep Neural Networks (EDNN). Complex, non-linear connections between battery characteristics including voltage, current, temperature, and time are well captured by the EDNN. WOA ensures improved generalization and quicker convergence by optimizing the EDNN hyperparameters. Real-world battery datasets are used to validate the model, and metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to assess performance. According to the results, the EDNN-WOA hybrid model improves reliability under a range of operating situations by drastically lowering prediction errors when compared to conventional machine learning techniques. This method shows how cutting-edge deep learning and metaheuristic optimization approaches can be combined to improve the performance and longevity of Li-ion batteries and aid in the creation of intelligent battery management systems.

关键词： Lithium-ion batteries Measurement Vehicle-to-grid Accuracy Battery management systems Predictive models whale optimization algorithms Real-time systems State of charge Root mean square

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Low Complexity Parameter Estimation Approach for OTFS-Based ISAC System

Low Complexity Parameter Estimation Approach for OTFS-Based ...

引用

IEEE Conference on Wireless Communications and Networking

作者： Jianyu Zhu Jing Liang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Given that Orthogonal time frequency space (OTFS) modulation provides high robustness to Doppler shifts, its integrated sensing and communications (ISAC) has attracted more attention. In this work, we investigate the fractional delay-Doppler (DD) estimation problem for the OTFS-based ISAC system. We establish a two-dimensional (2D) correlation structure with superimposed multiple frames and propose an efficient Maximum Likelihood algorithm based on multi-frame superposition (ML-MFS) that significantly reduces computational complexity. To further improve the estimation accuracy in multi-target scenarios, we develop an estimation method based on the whale optimization algorithm, named WOA-MFS. Simulation results demonstrate that both algorithms markedly reduce complexity, with the WOA-MFS demonstrating superior estimation accuracy.

关键词： Time-frequency analysis Accuracy Parameter estimation Correlation Simulation Two-dimensional displays Estimation Integrated sensing and communication Robustness whale optimization algorithms

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Research on Noise Suppression and Signal Enhancement in Electricity Price Anomaly Detection

Research on Noise Suppression and Signal Enhancement in Elec...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Xinru Chen Sa Zhao State Grid Shanghai South Power Supply Company Shanghai China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

As the key economic index of electricity market, electricity price is directly affected by the change of supply and demand balance. However, there are often various noises in the actual electricity market data, which makes the detection of electricity price anomalies difficult. Through effective noise suppression and signal enhancement, this paper proposes a method called PRO-WOA, which combines whale optimization algorithm and wavelet transform to achieve noise suppression and signal enhancement in power price anomaly detection. Taking the actual power trading data of a province in 2022 as the research object, we conducted comparative experiments on different models. The results show that PRO-WOA performs well in forecasting accuracy and real-time performance, significantly outperforming other comparison models.

关键词： Wavelet transforms Accuracy Electricity Biological system modeling Noise reduction Predictive models Electricity supply industry Real-time systems whale optimization algorithms Anomaly detection

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Technological Innovations in Human Resource Management: Using Cloud-Edge Computing

Technological Innovations in Human Resource Management: Usin...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Laxman Narayanrao Renapure Sagar Raghunath Powar Muskan Gupta Shaik Mastan Vali Haider Alabdeli S. B. Sawant Faculty of Management Navsahyadri Group of Institutions Pune India Karmveer Hire Arts Science Commerce and education College Kolhapur India Mandsaur University India Department of MBA Malla Reddy Engineering College and Management Sciences Hyderabad India Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq Bharati Vidyapeeth Deemed to be University Solapur India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Utilizing cloud-edge technological advances offers substantial opportunities for resource administration, effectiveness, and financial savings. These characteristics have promoted consumer and enterprise usage of the smart integrated cloud-edge model in the Internet of Things (IoT). In integrated cloud-edge computation, among the major difficulties is human resource management (HRM). Choosing the best capabilities depending on Quality of Service (QoS) considerations is a crucial and significant problem in IoT settings as software and hardware assets in the edge context are allotted for reacting to human demands. In the sense that system effectiveness rises and reaction duration falls with appropriate resource choosing, allotment, and surveillance, HRM may be viewed as an NP issue. This work uses the whale Optimized Technique in cloud-edge computation to offer an optimal framework for the HRM issue. According to empirical outcomes, the suggested framework outperformed the additional meta-heuristic techniques in two distinct situations: shortest response duration, allocation expenses, and growing proportion of HRs allotted.

关键词： Cloud computing Technological innovation Surveillance Metaheuristics Quality of service Motors whale optimization algorithms Resource management Internet of Things Human resource management

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Cost-Efficient Task Scheduling in Cloud-Fog Systems using Hybrid Algorithm

Cost-Efficient Task Scheduling in Cloud-Fog Systems using Hy...

引用

Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Sumit Bansal Bhim Sain Singla Himanshu Aggarwal Department of CSE Thapar Institute of Engineering and Technology Patiala Punjab India Department of Computer Science Punjabi University Neighbourhood Campus Ralla Punjab India Department of Computer Science Punjabi University Patiala Punjab India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Workflow scheduling in cloud-fog environment aims to optimize task allocation, execution time, and resource management. The complexity and dynamic nature of tasks and resources make finding an optimal workflow scheduling algorithm challenging. To address these limitations, this study introduces a Hybrid Particle Swarm optimization Algorithm-whale optimization Algorithm (HPWOA). While Particle Swarm optimization (PSO) struggles with slow convergence and high-dimensional search spaces, whale optimization Algorithm (WOA) faces difficulties in irregular search spaces and inconsistent convergence rates. By integrating PSO and WOA, HPWOA mitigates local optimization issues and enhances optimisation efficiency. The primary objective of this work is to minimise Total Execution Cost (TEC). To validate HPWOA, a comparative analyses against PSO, WOA, and HPWOA across three scientific workflows with increasing test cases has been performed. The results demonstrate that the proposed algorithm is 1.92% better than PSO and 9.60% better than WOA in terms of TEC. The paper highlights HPWOA's effectiveness in improving workflow scheduling and identifying the optimal workflow configurations for cloud-fog environments.

关键词： Costs Scheduling algorithms Heuristic algorithms Dynamic scheduling whale optimization algorithms Resource management Particle swarm optimization optimization Faces Convergence

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Interleaved Boost Converter with Optimized MPPT Approach for Renewable Energy Based Propulsion System of EV

Interleaved Boost Converter with Optimized MPPT Approach for...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Cinu S Robin J. Raji MD Yaseen N. Jothippriya Md Mujahid Irfan I. Shivasankkar Faculty of Electrical Engineering Bethlahem Institute of Engineering Karungal Department of Electrical and Electronics Engineering Bharath Institute of Higher Education and Research Chennai India Department of Electrical and Electronics Engineering Anurag University Hyderabad Telangana India Department of Electrical and Electronics Engineering Erode Sengunthar Engineering College Perundurai Erode India Department of Electrical and Electronics Engineering SR University Warangal Telangana India Department of Electrical and Electronics Engineering Sri Manakula Vinayagar Engineering College Puducherry India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Human civilization is based on transportation, which is essential to social advancement, economic growth, and global connectivity. While fossil fuel vehicles have been the backbone of transportation for over a century, their drawbacks have become increasingly evident in terms of environmental sustainability and public health. Therefore, a sustainable and efficient way of addressing these issues is to include Renewable Energy Sources (RESs), also known as green energy sources or alternative energy sources, into the network architecture. Therefore, this paper proposes a renewable energy - based propulsion system for Electric Vehicles (EVs) using Interleaved Boost Converter (IBC) and an optimized MPPT approach. The Photovoltaic (PV) system provides power that is allowed to IBC, and it works to current sharing between stages, which reduces the thermal stress on individual components, improving reliability. Additionally, by continuously adapting to fluctuations in surroundings, an MPPT based on whale optimization Algorithm (WOA) tracks the maximum power point more precisely. After converting the incoming DC power, the 3-phase Voltage Source Inverter (VSI) provides AC to the three-phase source. A Brush Less DC (BLDC) motor is achieved by the power use of a PI controller. The simulation is implemented in MATLAB Simulink. In contrast to WOA techniques, the results indicate that the proposed system offers excellent performance and improves the overall system efficiency.

关键词： Photovoltaic systems Voltage source inverters Software packages Transportation Propulsion Electric vehicles whale optimization algorithms Public healthcare Thermal stresses Stress

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An Adversarial Sample Generation Method Based on the Balance Between Aggression and Concealment

An Adversarial Sample Generation Method Based on the Balance...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Xinyuan Su Sinong Quan Zhihao Cai Weize Meng Shiqi Xing State Key Laboratory of CEMEE College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

Aggression and concealment in adversarial sample generation present a fundamental trade-off. To achieve an optimal balance, this paper introduces input transformation and gradient stabilization strategies into the pre-convergence adversarial sample generation framework, proposing the Stabilized Momentum with Input Transformation and Preconvergence FGSM (SPMI-FGSM) method. A measurement model for aggression and concealment is constructed, and the non-dominated sorting whale optimization algorithm is employed to solve the model, generating a set of adversarial samples that satisfy the dominance relationship. The optimal equilibrium point parameters are determined using a Constant False Alarm Rate (CFAR) detector to quantify critical conditions. Experimental results demonstrate that the proposed method reduces model recognition accuracy to 2.03 % (attack condition) while maintaining a structural similarity index (SSIM) of at least 0.519 (concealment condition), with a corresponding perturbation factor of 0.09, achieving an optimal balance between attack and concealment.

关键词： Seminars Accuracy Systematics Perturbation methods Detectors whale optimization algorithms Indexes Information technology optimization Sorting

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Soft Sensing of Distillation Systems Based on Data Reconstruction and Optimized LSTM Model

Soft Sensing of Distillation Systems Based on Data Reconstru...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： DeCheng Fu HaiSheng Chen Chemical Systems Engineering Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

The data driven soft sensing method has remarkable effects on predicting the variables of complex systems such as the distillation process. However, it has high requirements for the accuracy of soft sensing. In this work, a soft sensing algorithm based on the combination of CEEMDAN and the optimized LSTM model is proposed. Firstly, the HSIC algorithm is used to select auxiliary variables. Then, CEEMDAN is utilized to perform modal decomposition on the noisy data, so as to obtain measurement data in different frequency bands and complete data reconstruction. Finally, the optimized WOA-LSTM model is constructed. Through the verification of practical simulation comparison experiments, it is verified by different quantitative indicators that the proposed soft sensing method has high prediction accuracy and stability.

关键词： Accuracy Soft sensors Stability criteria Predictive models Prediction algorithms Data models whale optimization algorithms Frequency measurement Noise measurement Long short term memory

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Research on Intelligent Layout of Indoor Floor Plans based on Gated Recurrent Units

Research on Intelligent Layout of Indoor Floor Plans based o...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Qi Zhang Sichuan Vocational College of Culture and Communication Chengdu China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

With the swift progression of intelligent automation, its applications in architecture and interior design have significantly evolved, leading to more efficient and intelligent solutions for floor plan layout generation. Traditional methods for indoor floor planning rely on heuristic or rule-based approaches, which often fail to optimize space utilization, accessibility, and functional requirements effectively. Existing AI-powered approaches, including convolutional neural networks (CNNs) and deep reinforcement learning, have shown significant potential but face challenges in capturing long-term dependencies and dynamic adjustments in complex layouts. To overcome these challenges, we propose a novel approach that leverages Gated Recurrent Units (GRU) to model spatial relationships and sequential dependencies in floor plan generation. Additionally, we introduce the whale optimization Algorithm (WOA) to enhance the efficiency of layout optimization by improving space utilization, room positioning, and traffic flow. The proposed method is evaluated using a benchmark dataset of architectural floor plans, achieving a layout coherence score of 94.3%, space optimization efficiency of 92.7%, and computational speed improvement of 28.5% compared to existing deep learning-based approaches. The deployment of this system can significantly enhance the automation and intelligence of indoor architectural design, offering a practical and efficient solution for architects and urban planners.

关键词： Intelligent automation Layout Refining Manuals whale optimization algorithms Real-time systems Computational efficiency Planning Floors optimization

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