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Finite Element Analysis and optimization of the Rotational Stiffness of Semi-Rigid Base Connection under Simultaneous Moment and Tension

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BUILDINGS 2024年第7期14卷 2166页

作者： Nawar, Mahmoud T. El-Zohairy, Ayman Alaaser, Ahmed G. Hamdy, Osman Prince Sultan Univ Coll Engn Engn Management Dept Riyadh 11586 Saudi Arabia Zagazig Univ Struct Engn Dept Zagazig 44519 Egypt Texas A&M Univ Commerce Dept Engn & Technol Commerce TX 75429 USA Zagazig Higher Inst Engn & Technol Dept Civil Engn Zagazig 44519 Egypt

The base connection is flexible, not fully pinned/fixed, implying a nonlinear moment-rotation relationship. This deviates from a linear response, where rotation is not directly proportional to the applied moment. Numerical investigations using the commercial software ABAQUS were conducted to analyze the steel base plate connections. The finite element (FE) models were verified against previous experimental results. Moreover, numerical findings of a comprehensive parametric investigation were conducted. The studied connections were examined with different configurations, including variations in the diameter, spacing, and number of the anchor bolts;the thickness of the base plate;and the applied axial force. The current study aims to use numerical results combined with the whale optimization algorithm (WOA) and classical genetic algorithm (GA) to derive a formulation for the moment-rotation (M-theta r) relationship. The distinctive aspect of this formulation is that it aims to simulate the nonlinear rotational behavior exhibited by flexible base connections under combined moment and tension loads, while also considering various parameters such as bolt number/diameter and plate thickness. The findings indicate that the WOA is capable of obtaining an optimal equation for accurately simulating the M-theta r relationship. This underscores the ability of the WOA to effectively address the complexity of the problem and provide a reliable equation for predicting the rotational behavior of such connections. Consequently, the WOA method can be utilized to calculate the rotational stiffness at H/150, offering valuable support for engineering design processes.

关键词： flexible base connection tension force whale optimization algorithm flexural stiffness genetic algorithm rotational stiffness

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Speed Sensorless Method of Bearingless Synchronous Reluctance Motor Based on WOA-Elman Neural Network 7

Speed Sensorless Method of Bearingless Synchronous Reluctanc...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Xu, Enxiang Zhao, Ruijie Jiangsu Univ Natl Res Ctr Pumps Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9798350375794;9798350375800

In the control of bearingless motors, it is essential to install sensors for detecting rotor position and speed signals. However, the conventional mechanical sensor installation increases motor size and costs, limiting its operational scope at high speeds. In order to realize the ultra-high speed, low cost, and practical operation of bearingless synchronous reluctance motor, a novel rotor position and speed estimation scheme base on the Elman neural network is proposed and designed. The Elman neural network's initial weights and thresholds are optimized using the whale optimization algorithm, which improved the prediction accuracy of the method. Subsequently, utilizing Matlab/Simulink software and an experiment platform, a bearingless synchronous reluctance motor control system is built to validate the proposed theory. Simulation and experimental results collectively demonstrate the correctness, anti-interference ability and feasibility of the proposed methodology.

关键词： bearingless motor whale optimization algorithm Elman neural network sensorless

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Optimizing Faculty Workloads and Room Utilization using Heuristically Enhanced WOA

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第11期13卷 554-562页

作者： Austero, Lea D. Sison, Ariel M. Matias, Junrie B. Medina, Ruji P. Technol Inst Philippines Grad Programs Quezon City Philippines Emilio Aguinaldo Coll Sch Engn & Technol Manila Philippines Caraga State Univ Coll Comp & Informat Sci Butuan City Philippines

The creation and generation of schedules that are free of conflicts manually every academic semester present higher education institutions with a duty that is laborious and demanding of their resources. The course timetabling optimization, as an education timetabling problem, is a popular example of an NP-hard combinatorial problem. Numerous attempts have been made over the course of the past few decades to find a solution to this problem, but no one has yet developed a foolproof approach that can examine all alternatives to find the best method. The promising swarm-based optimization algorithm called whale optimization algorithm was heuristically enhanced in the present study and is called HEWOA. It was designed as a solution to the course timetabling problem discussed in the current study. HEWOA was able to generate an efficient timetable for the large dataset of 1700 events for an average time of 14.92 seconds only, with an average generation of 7.2 and a best time of 8.38 seconds. These results reveal that the performance of HEWOA was better than that of various hybrids of the Genetic algorithm that was compared in the present study.

关键词： Heuristics mutation optimization swarm timetabling whale optimization algorithm

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Short-Term Power Load Forecasting Study Based on IWOA Optimized CNN-BiLSTM 20th

Short-Term Power Load Forecasting Study Based on IWOA Optimi...

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20th International Conference on Intelligent Computing (ICIC)

作者： Mei, Tongda Si, Zhanjun Yan, Jing Lu, Lifeng Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China

ISBN: (纸本)9789819755776;9789819755783

In order to improve the prediction accuracy of short-term power load, a CNN-BiLSTM-Attention short-term power prediction model based on the hybrid strategy Improved whale optimization algorithm (IWOA) is proposed. The model first combines convolutional neural network (CNN) and bidirectional long and short-term memory network (BiLSTM) to fully extract the spatio-temporal features of the load data itself. Then, the time-attention (TPA) mechanism is introduced to automatically assign corresponding weights to the BiLSTM hidden layer states to distinguish the importance of different temporal load sequences. Meanwhile, in order to solve the problems of the temporal model's parameter difficult to choose and the whale algorithm's poor global search ability, which is prone to fall into the local optimum, the Tent chaotic mapping is used to optimize the whale algorithm. " to optimize the whale algorithm (IWOA), and better search for optimization of model parameters. The proposed method achieves 97.82% prediction accuracy and is compared with LSTM, BiLSTM and CNN-BiLSTM prediction models. The experimental results show that the proposed method has higher prediction accuracy and can provide a reliable basis for power system planning and stable operation.

关键词： whale optimization algorithm Convolutional Neural Network Long Short-Term Memory Network Attention Mechanism Electricity Load Forecasting

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Research on the efficient performance algorithm of fragmented blockchain for industrial Internet of Things applications 7

Research on the efficient performance algorithm of fragmente...

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7th International Conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Yin, Yanbo Zhao, Jianming Wang, Tianyu Tong, Weiyan Shenyang Univ Technol Sch Chem Proc Automat Liaoyang Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang Peoples R China

ISBN: (纸本)9798400708305

This article addresses the challenge of optimizing performance in a fragmentation blockchain system for industrial Internet of things (IIOT) applications. It introduces an enhanced version of the traditional whale optimization algorithm (WOA) known as the Chaotic whale optimization algorithm. This enhanced algorithm is designed to optimize the parameters of the fragmentation blockchain system with the goal of maximizing real-time memory utilization in the fragmentation nodes while minimizing block consensus time. The proposed method identifies optimal blockchain system parameters within diverse network environments, taking into account factors such as node computing power and inter-node transmission rates. To mitigate the issue of local optima inherent in traditional algorithms, the Chaotic whale optimization algorithm transforms the optimization problem in a more intuitive manner. This algorithm strikes a fine balance between diversity and convergence. The effectiveness of this method is assessed through simulation experiments, with results indicating its superior performance compared to alternative approaches. Overall, the approach presented in this article significantly reduces consensus time within the fragmentation blockchain system and enhances real-time memory utilization in the fragmented blocks.

关键词： industrial Internet of Things whale optimization algorithm consensus time real-time utilization of node memory

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A hybrid IMFO-fuzzy algorithm for performance enhancement of microgrid under uncertainty via DGs integration and reconfiguration

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期43卷 8397-8415页

作者： Periasamy, Madhumathi Kaliannan, Thenmalar Vivekanandha Coll Engn Women Autonomous Dept EEE Elaiyampalayam Tamil Nadu India

Microgrids (MGs) are distributed generation and distribution systems that include distributed generation (DG) units, energy storage systems (ESSs), distributed reactive sources (DRSs), and resilient loads that can operate in either connected or isolated modes. When dealing with uncontrolled DGs such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES), MGs planners have a difficult time making decisions. The work proposed in this paper addresses three interconnected works: (i) the implementation of a rigorous hybrid optimization approach for reconfiguration and DGs placement;(ii) the performance investigation under uncertain behavior of RES-based DGs and demand;and (iii) performance enhancement realization through the replacement of hybrid DGs for RES-based DGs. An Improved Moth Flame optimization (IMFO), which is a multi-objective optimization method, has been linked with fuzzy logic in order to handle multiple objectives in an efficient manner. These objectives include the minimization of voltage deviation, the reduction of generation cost, and the reduction of loss. The quality of the power, the amount of money saved by consumers, and the benefits to the Distribution System Operator (DSO) might all be improved with the help of a hybrid algorithm. This research is also extended to address the uncertainties of RES-based DGs by replacing hybrid DGs in the most optimal locations. IEEE 33 bus RDS is used to test radial distribution system (RDS) microgrids. For validation purposes, uses 24-hour load patterns to mimic WES and PVES' 24-hour load dispatching behavior. The research findings clearly demonstrate the advantages of microgrids over traditional architectures. The hybrid DG requires an average generating cost of 185.33 $/kW in order to produce 100 kW of power throughout the day with significantly reduced emissions.

关键词： Renewable energy sources radial distribution system optimization whale optimization algorithm power loss

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POT-Net: solanum tuberosum (Potato) leaves diseases classification using an optimized deep convolutional neural network

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IMAGING SCIENCE JOURNAL 2022年第6期70卷 387-403页

作者： Kiran Pandiri, D. N. Murugan, R. Goel, Tripti Sharma, Nishant Singh, Aditya Kumar Sen, Soumya Baruah, Tonmoy Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar Assam India Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar 788010 Assam India

Plant disease phenotyping using technology assures a promising step in sustainable agriculture. Solanum Tubrosum (Potato) is a highly cultivated vegetable plant, which is affected by fungal pathogens that leads to Early Blight and Late Blight diseases. Continuous monitoring of plant disease is a challenging task in identifying diseased leaves. This paper proposes a novel deep Convolutional Neural Network (CNN) for classifying the potato disease by reducing the computational time of learnable parameters using image phenotyping. A meta-heuristic algorithm known as the whale optimization algorithm (WOA) is used in training to optimize the hyperparameters of the proposed CNN network. Potato disease is classified using optimized CNN called POT-Net, and the performance is analyzed using performance metrics: precision, recall, F1-score, and accuracy of each class. The performance POT-Net is compared with pre-trained DL and optimized algorithms using performance metrics and it is better than the state-of-the-art models, with an accuracy of 99.12%.

关键词： Potato leaves classification deep learning convolutional neural network whale optimization algorithm fungal pathogens Early Blight Late Blight

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Improved whale algorithm for Economic Load Dispatch Problem in Hydropower Plants and Comprehensive Performance Evaluation

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WATER RESOURCES MANAGEMENT 2022年第15期36卷 5823-5838页

作者： Yang, Kun Yang, Kan Hohai Univ Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Peoples R China

A novel method for economic load dispatch problem (ELDP) based on improved whale optimization algorithm(IWOA) is presented, and the optimization performance of IWOA in ELDP was evaluated comprehensively. The search mechanism is modified to improve the ability of the algorithm to jump out of the local optimal. The adaptive nonlinear inertia weight is introduced to improve the convergence speed of the algorithm. A limited mutation mechanism is proposed to improve the convergence of the algorithm. The evaluation indicator of calculation time and calculation accuracy was established. Taking 26 units of the Three Gorges Hydropower Station as an example, limited adaptive genetic aigorithm (LAGA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and improved whale optimization algorithm (IWOA) were used to solve ELDP. The result shows that IWOA is superior to other algorithms in calculation results of various heads and loads. The calculation accuracy of IWOA was better than WOA when the number of units turned on was more than 6. The analysis results of IWOA and DP show that the calculation time of IWOA is better than that of DP when the number of units turned on is more than 6. The IWOA and the evaluation indicators proposed in this paper provide a new way for solving ELDP of large hydropower stations.

关键词： Economic load dispatch problem whale optimization algorithm Adaptive inertia weight Limited mutation mechanism Evaluation indicator

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A hybrid robust system considering outliers for electric load series forecasting

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APPLIED INTELLIGENCE 2022年第2期52卷 1630-1652页

作者： Yang, Yang Tao, Zhenghang Qian, Chen Gao, Yuchao Zhou, Hu Ding, Zhe Wu, Jinran Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Coll Automat Nanjing Peoples R China Queensland Univ Technol Sch Comp Sci Brisbane Qld Australia Queensland Univ Technol Sch Math Sci Brisbane Qld Australia

Electric load forecasting has become crucial to the safe operation of power grids and cost reduction in the production of power. Although numerous electric load forecasting models have been proposed, most of them are still limited by poor effectiveness in the model training and a sensitivity to outliers. The limitations of current methods may lead to extra operational costs of a power system or even disrupt its power distribution and network safety. To this end, we propose a new hybrid load-forecasting model, which is based on a robust extreme-learning machine and an improved whale optimization algorithm. Specifically, Huber loss, which is insensitive to outliers, is proposed as the objective function in extreme learning machine (ELM) training. In addition, an improved whale optimization algorithm is designed for the robust ELM training, in which a cellular automaton mechanism is used to enhance the local search. To verify our improved whale optimization algorithm, some experiments were then conducted based on seven benchmark test functions. Due to the enhancement of the local search, the improved optimizer was around 7% superior to the basic. Finally, our proposed hybrid forecasting model was validated by two real electric load datasets (Nanjing and New South Wales), and the experimental results confirmed that the proposed hybrid load-forecasting model could achieve satisfying improvements in both datasets.

关键词： Outilers whale optimization algorithm Cellular automata Load forecasting Robust regression

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3D location optimization of multiple Unmanned Aerial Vehicle Base Stations in next-generation cellular networks and a new meta-heuristic approach

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2021年第3期36卷 1468-1482页

作者： Ozdag, Recep Van Yuzuncu Yil Univ Muhendislik Fak Bilgisayar Muhendisligi Bolumu TR-65080 Van Turkey

The use of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs) in wireless communication is emerging as an effective technique that should be used for the services planned in the next-generation cellular networks or in disaster situations. Currently, effective 3D placement of BS mounted on UAVs or Drones (Drone-BS), also called Low-altitude Air Platforms, in the defined area significantly increases the Quality of Service (QoS) of wireless communication. This study has been aimed to be performed dynamic deployments (location optimization) of Drone-BSs randomly distributed in the 3D plane in order to optimally cover the users in the urban environment according to the Air-to-Ground (ATG) model. Using new approaches based on the Electromagnetism-Like (EML) algorithm and the whale optimization algorithm (WOA), which are widely used in the literature and are meta-heuristic, it was planned to be covered the maximum number of users on the ground by multiple Drone-BSs. In addition, Extensive-interval Optimal Fitness Search algorithm (EOFSA-EML) and Discrete-interval Optimal Fitness Search algorithm (DOFSA-EML) approaches were developed based on the EML algorithm. Based on comparison metrics for multiple Drone-BSs distributed by EOFSA-EML, DOFSA-EML, and Pure-WOA, it has been found that EML-based algorithms achieve optimal results compared to Pure-WOA.

关键词： Cellular networks aerial base station multiple Drone-BS deployment electromagnetism-like algorithm whale optimization algorithm

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