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

作者： Liu, Shiwei Xue, Sheng Li, Zhongyang Lanzhou Univ Technol Sch Elect Engn & Informat Engn Lanzhou Peoples R China

ISBN: (纸本)9798400708305

Lithium-ion battery state of health (SOH) estimate is crucial for the safe and reliable operation of the lithium-ion Battery management system (BMS) [1]. An improved whale algorithm optimized kernel Extreme learning machine-based SOH estimation method (IWOA-KELM) is suggested to address the issue of the low estimate accuracy of conventional estimation methods. In order to improve convergence speed and optimization algorithm accuracy, an inverse learning technique can be used to create an initial population with high adaptive values. Secondly, the capabilities of local optimization and global search are balanced by the addition of a nonlinear convergence component. Finally, the system can eventually deviate from the local optimal solution and increase search precision by adding nonlinear time-varying adaptive weight coefficients. The kernel limit learning machine's variables were optimized using the suggested IWOA algorithm, and a battery SOH estimate model based on KELM was created. The suggested model was then tested using three NASA lithium-ion battery datasets and compared to WOA-KELM and KELM, showing that the method has good estimate accuracy and robustness and that the estimation error is stable within 3%.

关键词： improved whale optimization algorithm Lithium ion batteries Health status Kernel Extreme learning machine

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Research on Transformer Oil Kinematic Viscosity Detection Method Based on IWOA-RBF and Multi-frequency Ultrasonic Technology 3

Research on Transformer Oil Kinematic Viscosity Detection Me...

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3rd International Conference on Control and Robots (ICCR)

作者： Xin, Zhang Xin, Wang Jilin Elect Power Co Ltd Yanbian Power Supply Co Yanbian 133000 Jilin Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

ISBN: (纸本)9781728175621

In order to achieve rapid and accurate detection of transformer oil kinematic viscosity, a transformer oil kinematic viscosity detection model based on the improved whale algorithm (IWOA) optimized RBF neural network and multi-frequency ultrasonic detection technology is proposed. First of all, use the propagation characteristics of different frequencies of ultrasonic waves in transformer oil to measure the attenuation coefficient and other parameters of its propagation in transformer oil. Then, by improving the convergence factor and weight of the whale algorithm, the efficiency, accuracy, and global and local search capabilities of the whale algorithm are improved. Finally, IWOA is used to optimize the RBF neural network to improve the prediction accuracy, and then the IWOA-RBF training experiment samples are used to obtain the transformer oil kinematic viscosity detection model. Experimental results prove that the detection model has certain practicability.

关键词： transformer oil kinematic viscosity multi-frequency ultrasonic detection technology improved whale optimization algorithm RBF neural network

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Research on Electrospindle Vibration Prediction Model Based on improved whale algorithm to Optimize Convolutional Neural Network

Research on Electrospindle Vibration Prediction Model Based ...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Jing, Hou Jianzhou, Li Jinxiang, Pian School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350309614

During the high-speed operation of the mechanical electric spindle, due to the uneven distribution of rotor mass and long-term wear, the rotation deviates from the axis to produce vibration, resulting in low working accuracy, so the dynamic balance method is often used to reduce the impact caused by such reasons. Aiming at the problem that it is difficult for traditional electromechanical spindles to accurately adjust the vibration amplitude of spindle rotor in a short time in a long-term complex changing environment, a mechatronic spindle vibration prediction model based on the improved whale optimization algorithm (WOA) optimization convolutional neural network (CNN) is proposed. Experimental results show that the prediction model has higher accuracy for predicting electrospindle vibration, and the convergence speed is faster and more accurate. © 2023 IEEE.

关键词： convolutional neural networks improved whale optimization algorithm mechanical spindle vibration forecasting

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An event-triggered and dimension learning scheme WOA for PEMFC modeling and parameter identification

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ENERGY 2024年 305卷

作者： Sun, Zhe Wang, Yiwen Xie, Xiangpeng Yang, Qiming Bi, Yunrui Sun, Zhixin Nanjing Univ Posts & Telecommun Engn Res Ctr Post Big Data Technol & Applicat Jian Res & Dev Ctr Post Ind Technol State Posts Bur Internet Things Techno Nanjing 210023 Jiangsu Peoples R China Nanjing Inst Technol Sch Automat Nanjing Peoples R China

Aiming at the high dimension and complexity of parameters identification problem, a whale optimization algorithm based on event-triggered and dimension learning scheme (EDWOA) is presented, which is specifically designed for the proton exchange membrane fuel cell (PEMFC) model. Drawing inspiration from group optimization strategies, a novel dimensional learning method is introduced to enhance the dynamic search capabilities of the algorithm. To assess the efficacy of the proposed algorithm, benchmark function testing was conducted, and its fitness surpassed common heuristic algorithms on over 15 objective functions. The results clearly indicate that the EDWOA algorithm outperforms its counterparts in terms of global search performance. Its ability to navigate complex search spaces sets it apart from other algorithms. Finally, the proposed EDWOA algorithm is successfully applied to parameter identification in the PEMFC model. Through a comparative analysis with existing research findings, it was found that the identified PEMFC model exhibited a notable enhancement in fitness, ranging from 0.02 to 0.93. This underscores the effectiveness of the EDWOA algorithm in improving the performance and dynamic output of PEMFC models.

关键词： Proton exchange membrane fuel cell Parameter identification improved whale optimization algorithm

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Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Gated Recurrent Unit

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SENSORS 2024年第16期24卷 5394页

作者： Liu, Mian Wang, Zhiwu Jiang, Pingping Yan, Guozheng Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai 200240 Peoples R China

Piezoresistive pressure sensors have broad applications but often face accuracy challenges due to temperature-induced drift. Traditional compensation methods based on discrete data, such as polynomial interpolation, support vector machine (SVM), and artificial neural network (ANN), overlook the thermal hysteresis, resulting in lower accuracy. Considering the sequence-dependent nature of temperature drift, we propose the RF-IWOA-GRU temperature compensation model. Random forest (RF) is used to interpolate missing values in continuous data. A combination of gated recurrent unit (GRU) networks and an improved whale optimization algorithm (IWOA) is employed for temperature compensation. This model leverages the memory capability of GRU and the optimization efficiency of the IWOA to enhance the accuracy and stability of the pressure sensors. To validate the compensation method, experiments were designed under continuous variations in temperature and actual pressure. The experimental results show that the compensation capability of the proposed RF-IWOA-GRU model significantly outperforms that of traditional methods. After compensation, the standard deviation of pressure decreased from 10.18 kPa to 1.14 kPa, and the mean absolute error and root mean squared error were reduced by 75.10% and 76.15%, respectively.

关键词： temperature compensation piezoresistive pressure sensors gated recurrent unit improved whale optimization algorithm random forest

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Bus Arrival Time Prediction Based on the Optimized Long Short-Term Memory Neural Network Model With the improved whale algorithm

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JOURNAL OF ADVANCED TRANSPORTATION 2024年第1期2024卷

作者： Zhang, Bing Tang, Lingfeng Zhou, Dandan Liu, Kexin Xue, Yunqiang East China Jiaotong Univ Jiangxi Key Lab Comprehens Stereoscop Trafc Inform Nanchang 330013 Peoples R China Jintang Cty Transportat Dept Traff Engn Safety Tech Inspect Stn Chengdu 610499 Peoples R China Zhejiang Univ Urban Planning & Design Inst Co Ltd Hangzhou 310012 Peoples R China East China Jiaotong Univ Sch Transportat Engn Nanchang 330001 Peoples R China

Accurate prediction of bus arrival time is essential to achieve efficient bus dispatch and improve bus trip sharing rate. This article proposes using the improved whale optimization algorithm-long short-term memory (IWOA-LSTM) model to predict bus arrival times and improving the whale algorithm by optimizing the hyperparameters of the LSTM model, so that the advantages and disadvantages of the whale algorithm and the LSTM model can complement each other, thus enhancing the robustness of the model. Initially, the bus arrival process and its associated influencing factors are analyzed, with certain factors being quantified to serve as input features for the prediction model. After processing the GPS data of the No. 220 bus in Nanchang, Jiangxi, China, the proposed prediction model is analyzed and validated using an example and compared with other prediction models. The results show that the IWOA-LSTM prediction model has the best-fitting effect between the predicted values and actual values in all time periods. Its MAPE, RMSE, and MAE have been reduced by at least 9.47%, 12.77%, and 8.93%, respectively, and the overall R2 has been improved by at least 10.65%. These results indicate that the model has the best predictive performance.

关键词： bus arrival time prediction improved whale optimization algorithm IWOA-LSTM model public transportation

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A multi-phase integrated scheduling method for cloud remanufacturing systems

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ADVANCED ENGINEERING INFORMATICS 2024年第PartC期62卷

作者： Zhang, Wenkang Zheng, Yufan Ma, Yongsheng Ahmad, Rafiq Anhui Agr Univ Sch Engn Hefei 230036 Peoples R China Univ Alberta Dept Mech Engn Smart & Sustainable Mfg Syst Lab SMART LAB Edmonton AB T6G 1H9 Canada Xian Jiaotong Liverpool Univ Sch Intelligent Mfg Ecosyst Suzhou 215123 Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen 518055 Peoples R China

The cloud remanufacturing system embraces a series of interdependent remanufacturing macroscopic phases (RMAs) with intricate precedence relationships, increasing the complexity of task scheduling and resource allocation. Thus, the multi-phase integrated scheduling is necessary to manage remanufacturing tasks and optimize resources and capabilities effectively in the cloud environment. This research investigates the multiphase integrated scheduling problem for cloud remanufacturing system involving a series of RMAs including initial inspection, disassembly, reprocessing, reassembly, and final test. A mathematical model is created to explain the scheduling issue using the suggested cloud remanufacturing framework. Due to the high complexity of integrated scheduling, traditional meta-heuristic algorithms cannot be directly applied to solving the problem. Thus, an improved whale optimization algorithm (IWOA) incorporating the self-adaptive weighting and quadratic interpolation techniques is proposed for addressing the studied problem efficiently. A case study is designed and conducted, and the findings indicate that the IWOA is more effective than other methods in addressing the proposed complex scheduling issues with better accuracy, faster computation, and improved convergence efficiency.

关键词： Cloud remanufacturing Multi-phase integrated scheduling Remanufacturing system improved whale optimization algorithm Service searching and matching

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Prediction of Lost Circulation in Southwest Chinese Oil Fields Applying improved WOA-BiLSTM

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PROCESSES 2023年第9期11卷 2763页

作者： Liu, Xianming Jia, Wen Li, Zhilin Wang, Chao Guan, Feng Chen, Kexu Jia, Lichun Yangtze Univ Sch Mech Engn Jinzhou 434000 Peoples R China Chuanqing Drilling Engn Co Ltd Res Inst Drilling & Prod Engn Technol Guanghan 618300 Peoples R China

Drilling hazards can be significantly decreased by anticipating potential mud loss and then putting the right well control measures in place. Therefore, it is critical to provide early estimates of mud loss. To solve this problem, an enhanced WOA (whale optimization algorithm) and a BiLSTM (Bidirectional Long Short Term Memory) optimization based prediction model of lost circulation prior to drilling has been created. In order to minimize the noise in the historical comprehensive logging data, a wavelet filtering technique was first used. Then, according to the nonlinear Spearman rank correlation coefficient between mud loss and logging parameter values from large to small, seven characteristic parameters were preferred, and the sliding window was used to extract the relevant data. Secondly, the number of neurons in the first and second hidden layers, the maximum training time, and the initial learning rate of the BiLSTM model were optimized using the enhanced WOA method. The BiLSTM network was given the acquired superparameters in order to improve the model's ability to predict occurrences. Finally, the model was trained and tested using the processed data. In comparison to the LSTM model, BiLSTM model, and WOA-BiLSTM model, respectively, the improved WOA-BiLSTM early mud loss prediction in southwest Chinese oil fields suggested in this study beat the others, receiving 22.3%, 18.7%, and 4.9% higher prediction accuracy, respectively.

关键词： lost circulation prior to drilling prediction model correlation analysis improved whale optimization algorithm Bidirectional Long Short Term Memory

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Multi-objective ship path planning using non-dominant relationship-based WOA in marine environment

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OCEAN ENGINEERING 2022年 266卷

作者： Han, Qilong Yang, Xiao Song, Hongtao Du, Wei Harbin Engn Univ Sch Comp Sci & Technol Nantong St Harbin 150001 Heilongjiang Peoples R China Harbin Engn Univ Sch Power & Energy Engn Nantong St Harbin 150001 Heilongjiang Peoples R China

Marine environmental issues have become increasingly prominent in recent years, and ocean transportation has also received extensive attention. Relevant personnel is concerned about the minimum fuel consumption and the shortest sailing time, which are two contradictory goals. This paper describes the ship path problem model as a multi-objective optimization model considering fuel consumption and navigation time. The waves, wind speed, and wind direction in the marine meteorological environment significantly impact the ship's actual navigation, making it challenging to design a reasonable path. Ship path planning in the marine environment is an NP-Hard Problem. The paper applies an improved whale optimization algorithm to find Pareto solutions in the multi-objective optimization model considering marine meteorology. This paper discusses the performance of the enhanced algorithm. Compared with other advanced algorithms, the algorithm is applied to five benchmark multi-objective optimization functions. The test results show that the scheme can balance the ship's fuel consumption and sailing time to provide a reasonable candidate path for decision-makers.

关键词： Marine environmental Multi-objective optimization NP-Hard Problem improved whale optimization algorithm Pareto solutions

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Automated Sortie Scheduling optimization for Fixed-Wing Unmanned Carrier Aircraft and Unmanned Carrier Helicopter Mixed Fleet Based on Offshore Platform

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DRONES 2022年第12期6卷 375-375页

作者： Liu, Zixuan Han, Wei Wu, Yu Su, Xichao Guo, Fang Naval Aviat Univ Aviat Fdn Coll Yantai 264001 Peoples R China Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China Naval Aviat Univ Sch Aviat Operat & Support Yantai 264001 Peoples R China

Optimizing naval aircraft deck sortie scheduling is important for improving the sortie efficiency of naval aircraft groups. It is also an important link in realizing the automated scheduling of aircraft carriers. Firstly, a single-aircraft surface transit path library was constructed. This paper used the improved A* algorithm and the optimal control deck path planning algorithm to construct the transit path library between the deck parking position and take-off position, or between warmable and non-warmable parking positions. Secondly, a mathematical optimization model was constructed for the sortie scheduling of a mixed fleet of fixed-wing carrier aircraft and carrier helicopters. The model took the maximum sortie time of the fleet and taxiing time of the fleet of fixed-wing aircraft as the optimization objectives, and considered the process flow, spatial, and resource constraints. Thirdly, a discretized improved whale optimization algorithm (IWOA) was designed to solve the problem. To improve the optimization-seeking capability of the algorithm, we discretized the algorithm, optimized its coding method, and introduced pre-constraints, learning factors, parameter improvements, and population restart. Finally, we set up various cases and proposed a new strategy for simulation experiments: these simulations verified the validity of the model, the excellent optimization performance of the IWOA, and the superiority of the sortie strategy. The research in this study will help to implement automated aircraft carrier scheduling.

关键词： unmanned carrier aircraft mixed fleet sortie scheduling improved whale optimization algorithm

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