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Integrating Two-Tier optimization algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles

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IEEE ACCESS 2025年 13卷 6820-6833页

作者： Alotaibi, Moneerah Alohali, Manal Abdullah Mahmood, Khalid Alhashmi, Asma A. Alqurni, Jehad Saad Alotaibi, Sultan Refa Alzahrani, Ahmad A. Issaoui, Imene Shaqra Univ Coll Sci & Humanities Dawadmi Dept Comp Sci Shaqra 11961 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Informat Syst Abha 61421 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 91431 Saudi Arabia Imam Abdulrahman bin Faisal Univ Coll Educ Dept Educ Technol Dammam 31441 Saudi Arabia Saudi Elect Univ Coll Comp & Informat Dept Comp Sci Riyadh 11673 Saudi Arabia Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Mecca 21955 Saudi Arabia Qassim Univ Appl Coll Unit Sci Res Buraydah 51452 Saudi Arabia

Industrial development has changed vehicles of traditional into autonomous vehicles (AVs). AVs play a significant role since they are measured as a vital module of smart cities. The AV is an advanced automobile efficient in preserving secure driving by evading collisions formed by drivers. In contrast with traditional vehicles, which are fully coordinated and functioned by humans, AVs gather information regarding the exterior environment utilizing sensors to guarantee secure navigation. AVs decrease environmental effects since they regularly employ electricity to function rather than fossil fuel, thus diminishing greenhouse gasses. However, AVs might be exposed to cyber-attacks, causing dangers to human life. Machine learning (ML) and deep learning (DL) based anomaly recognition has progressed as a new study track in autonomous driving. ML and DL-based anomaly detection scholars have focused on improving accuracy as a typical classification task without aiming at mischievous information. This article develops an improved whale optimization algorithm-based feature selection using explainable artificial intelligence for robust anomaly detection (IWOAFS-XAIAD) technique in autonomous driving. The major aim of the IWOAFS-XAIAD technique is an endwise XAI structure to construe and imagine the anomaly recognition classifications prepared by AI models securing autonomous driving systems. Initially, the IWOAFS-XAIAD technique utilizes the Z-score data normalization method to convert input data into a compatible layout. Besides, the IWOAFS-XAIAD technique employs an improved whale optimization algorithm (IWOA)-based feature subset selection to pick an optimum set of features. An attention mechanism with the CNN-BiLSTM (CNN-BiLSTM-A) model is employed for anomaly detection and classification. Moreover, the catch-fish optimization algorithm (CFOA) selects the hyperparameters connected to the CNN-BiLSTM-A model. Finally, utilizing the SHAP XAI method, the IWOAFS-XAIAD technique

关键词： Anomaly detection Artificial intelligence Feature extraction Autonomous vehicles Accuracy Transportation Classification algorithms whale optimization algorithms Safety Data models INDEX TERMS Explainable artificial intelligence anomaly detection improved whale optimization algorithm autonomous vehicles catch fish optimization

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optimization and control for a novel uncoupled regenerative braking system of three-axle electric bus

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JOURNAL OF CLEANER PRODUCTION 2025年 490卷

作者： Yang, Kun Zhang, Zhenwen Wang, Jie Ma, Chao Tan, Di Shao, Changjiang Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China Weichai Power Co Ltd Weifang 261043 Peoples R China

The regenerative braking system is an important device to reduce the energy consumption of new energy vehicles. The accuracy of the coupled braking force is an important factor affecting the braking energy recovery efficiency. Firstly, a novel configuration for uncoupled regenerative braking system based on stacked check valve (SC-valve) and linear exhaust solenoid valve (LES-valve) is proposed, and it is called SCLES-URBS. The Sequential Quadratic Programming (SQP) algorithm and orthogonal experiments are employed to optimize the SC-valve and LES-valve, respectively. The parameters of ABS solenoid valve (ABS-valve) are optimized, and the regulation characteristics of ABS-valve are verified by bench test. Secondly, the improved whale optimization algorithm (WOA) is employed to distribute three-axle braking force. The recognition algorithm for braking strength based on BP neural network is studied. The validation platform is built based on AMESim, MATLAB/ Simulink and cSPACE. Finally, the effect for braking energy recovery is verified by CCBC and the bus condition in Shandong Province, China. The coupled braking torque can follow demand braking torque accurately. Compared with the no braking energy recovery and coupled regenerative braking system under different loads, the efficiency of energy-saving is improved by 33.77% and 19.06%, respectively.

关键词： Uncoupled regenerative braking system Parameters optimization improved whale optimization algorithm Validation platform Energy-saving

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Multi-model fusion short-term power load forecasting based on improved WOA optimization

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第12期19卷 13399-13420页

作者： Ji, Xiaotong Liu, Dan Xiong, Ping State Grid Hubei Elect Power Co Ltd Wuhan 430077 Peoples R China State Grid Hubei Elect Power Res Inst Wuhan 430077 Peoples R China

The high accuracy of short-term power load forecasting has a pivotal role in helping power companies to construct reasonable production scheduling plans and avoid resource waste. In this paper, a multi-model short-term power load prediction method based on Variational mode decomposition (VMD)-improved whale optimization algorithm (IWOA)-wavelet temporal convolutional network (WTCN)-bidirectional gated recurrent unit (BiGRU)-attention and CatBoost model fusion is proposed. First, VMD was employed to decompose the load data into different intrinsic mode functions. Second, a WTCN was utilized to extract the load data features, and multi-dimensional feature factors were integrated into the BiGRU network for model training. Moreover, an attention mechanism was added to enhance the influence degree of important information. The WTCN-BiGRU-attention model is improved by the WOA algorithm to optimize the hyperparameters of the network. Finally, the model was fused with the predicted data of the CatBoost network by the mean absolute percentage error-reciprocal weight (MAPE-RW) algorithm to construct the best fusion model. Compared with other forecasting models, the proposed multi-model fusion method has higher accuracy in short-term power load forecasting using the public data set for an Australian region.

关键词： temporal convolutional networks gated recurrent networks improved whale optimization algorithm catboost network power load forecasting

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Developing mathematical model and optimization algorithm for designing energy efficient semi-automated assembly line

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106768-106768页

作者： Zhang, Beikun Xu, Liyun Zhang, Jian Tongji Univ Inst Adv Mfg Technol Shanghai 201800 Peoples R China

In this study, a new extension of the semi-automated assembly line designing problem is presented. The main difference between this paper and existing work is that the energy consumption, smoothness index and total cost are all taken into consideration. The tasks can be performed by more than one format in the semi-automated assembly line, which means the operation time and energy consumption of tasks depend on the allocated stations. To obtain the trade-off among three objectives. First, a mathematical model with constraints is provided. Then, an improved whale optimization algorithm (IWOA) is presented, the chaotic maps, variable neighborhood search (VNS) and mutation are adopted to enhance the algorithm performance. In order to demonstrate the effectiveness of the algorithm and employed mechanisms, numerical comparison experiments are accordingly conducted. The results show that the specific designed algorithm outperforms five other well-known algorithms in solving the energy-oriented semi-automated assembly line designing problem. At last, the managerial insights are provided based on the sensitivity analysis. And a real case derives from car floor assembly system is used for validation of the proposed model and algorithm.

关键词： Semi-automated assembly line Assembly line balancing problem Energy efficient manufacturing improved whale optimization algorithm

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A novel state of charge estimation method of lithium-ion batteries based on the IWOA-AdaBoost-Elman algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第4期46卷 5134-5151页

作者： Li, Huan Wang, Shunli Islam, Monirul Bobobee, Etse Dablu Zou, Chuanyun Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

Lithium-ion (Li-ion) battery is a very complex nonlinear system. The data-driven state of charge (SOC) estimation method of Li-ion battery avoids complex equivalent circuit modeling and parameter identification, which can describe the nonlinearity of the battery more directly and accurately. To address the problems of low generalization ability, local miniaturization, low prediction accuracy, and insufficient dynamics in the prediction process of a single feedforward neural network, an IWOA-AdaBoost-Elman algorithm-based SOC estimation method for Li-ion batteries is proposed. The method introduces an improved whale optimization algorithm (IWOA) to continuously optimize the nonlinear weights of the Elman neural network during the iterative process. Using the AdaBoost algorithm, multiple weak IWOA-Elman predictors are recombined into one strong SOC estimator by successive iterations. The combined strong predictor has strong generalization ability, estimation accuracy, and dynamic characteristics. To verify the rationality of the model, the SOC estimation is performed under dynamic operating conditions. The experimental results show that the proposed method is more accurate and stable compared with other optimization models. In addition, the proposed method can overcome the effects of different discharge multipliers, different ambient temperatures, and different aging cycles on SOC estimation. Both theoretical and experimental results show that the IWOA-AdaBoost-Elman algorithm provides a new way for the SOC estimation of Li-ion batteries.

关键词： AdaBoost Elman neural network improved whale optimization algorithm Lithium-ion battery state of charge

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Multi-step ahead wind speed prediction based on a two-step decomposition technique and prediction model parameter optimization

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ENERGY REPORTS 2022年 8卷 6086-6100页

作者： Wang, He Xiong, Min Chen, Hongfeng Liu, Sumei Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN USA

Accurate and reliable wind speed prediction is essential for the exploitation and utilization of the wind energy. In this paper, a novel hybrid multi-step wind speed prediction model is proposed based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), robust local mean decomposition (RLMD), and improved whale optimization algorithm (IWOA), and long-term and short-term memory network (LSTM). CEEMDAN is utilized to decompose the wind speed series into a number of intrinsic mode functions, and the RLMD is used to the second step decompose the most non-stationary intrinsic mode function into a series of product functions. After the two-step decomposition, a group of new subsequences is formed. The long-term and short-term memory network (LSTM) prediction model is constructed for every subsequence and an improved whale optimization algorithm (IWOA) is used to optimize key parameters affecting the prediction performance of the LSTM model. And at last, the subsequence prediction results are superimposed to provide the final prediction results. The effectiveness and advancement of the proposed hybrid model is verified by employing wind speed data from two different wind farms. And according to the experimental results and comparison, it can be found that the proposed model can provide better performance than the seven compared models. (c) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Wind speed prediction Two-step decomposition Complete ensemble empirical mode decompositionwithadaptivenoise Robust local mean decomposition Long and short term memory neural network improved whale optimization algorithm

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Dynamic synchronization optimization of beef atmosphere packaging system

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JOURNAL OF FOOD ENGINEERING 2024年 380卷

作者： Li, You Zhang, Luwei Wu, Di Li, Xinxing Zhang, Xiaoshuan Liu, Xingxing China Agr Univ Coll Engn Beijing 100083 Peoples R China China Agr Univ Sanya Inst Sanya 572025 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing Lab Food Qual & Safety Beijing Peoples R China China Agr Univ Beijing 100083 Peoples R China

Modified atmosphere packaging (MAP) of beef is a key tool to ensure its post-production shelf life. However, it is still a technical challenge to realize intelligent, dynamic, modified-atmosphere packaging of beef products on the production line to improve production and packaging efficiency. In this paper, we propose a dynamic and synchronized modified atmosphere packaging optimization strategy for beef based on admittance control. The purpose of synchronized, accurate, and efficient modified-atmosphere packaging of beef is achieved. Firstly, particle swarm optimization was designed to optimize the conductance parameters, which did not achieve good optimization results, so a whale optimization algorithm based on inertia weights as well as teaching strategy improvement was designed for optimization, and simulation analysis was carried out. The most important parts of the packaging process were also tested, such as the effect of synchronous following motion, the stability and ability to block interference, the accuracy of the Cartesian Coordinate Robot's repeated positioning, and the system's overall operation performance. The results show that the synchronous following error of the system is +/- 1%, and after the optimization of the improved whale optimization algorithm, the conduction model is reduced in both overshooting amount and regulation time, which improves the performance of the closed-loop following motion model. And the beef dynamic modified atmosphere packaging system, with the application of the algorithm, can complete the modified atmosphere packaging work stably, accurately, continuously, and efficiently.

关键词： Beef preservation Modified atmosphere packaging Synchronized following Admittance control improved whale optimization algorithm

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Sliding Mode Predictive Active Fault-tolerant Control Method for Discrete Multi-faults System

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2021年第3期19卷 1228-1240页

作者： Yang, Pu Liu, Zhangxi Li, Dejie Zhang, Zhiqing Wang, Zixin Nanjing Univ Aeronaut & Astronaut Coll Automat Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Jiangsu Peoples R China

For discrete systems with sensor and actuator failures, this paper proposes an observer that can estimate both sensor failures and actuator failures and designs a sliding mode predictive fault-tolerant control method based on an improved whale optimization algorithm. First, a proportional-integral observer that can observe actuator fault and sensor fault is designed to estimate the value of faults, which greatly improves the work efficiency. After that, a global sliding mode surface is designed as a prediction model, so that the initial state of the system is located on the sliding mode surface to avoid the instability of the sliding mode approaching the process. The reference trajectory of a power function with uncertainty and disturbance compensation is designed to reduce the bad influence of uncertainty and disturbance on the system and suppress chattering greatly. Meanwhile, in the rolling optimization part, an improved whale optimization algorithm(IWOA) is designed to optimize the control law. Finally, the simulation results on the four-rotor helicopter simulation platform show the practicability and superiority of the algorithm.

关键词： Fault tolerant control four-rotor helicopter improved whale optimization algorithm multiple faults observer sliding mode prediction method

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Sliding mode prediction fault-tolerant control method for multi-delay uncertain discrete system with sensor fault

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第7期43卷 1519-1530页

作者： Yang, Pu Liu, Zhangxi Li, Dejie Jiang, Bin Zhu, Jiaqi Nanjing Univ Aeronaut & Astronaut Coll Automat Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Natl Key Lab Sci & Technol Helicopter Transmiss Nanjing Peoples R China

In this paper, we design a novel sliding mode prediction fault-tolerant control algorithm for multi-delays discrete uncertain systems with sensor fault. The global sliding surface is designed to replace the traditional linear sliding surface as a predictive model to ensure the global robustness of the system. For sensor fault and sliding mode buffeting, a power-dependent function reference trajectory with fault compensation is designed to attenuate chattering and achieve better stability. In the process of rolling optimization, an improved whale optimization algorithm is developed. On the premise of obtaining good convergence speed and accuracy, the optimization process can avoid falling into the local minimum value and solve the problem of premature convergence. Finally, the comparison experiments on the quad-rotor simulation platform prove the rationality and superiority of the algorithm.

关键词： Sliding mode prediction algorithm sensor fault multi-delays fault tolerant control improved whale optimization algorithm

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A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2024年第16期31卷 23951-23967页

作者： Jia, Zihao Zhang, Qin Shi, Bowen Xu, Congchao Liu, Di Yang, Yihong Xi, Beidou Li, Rui Guilin Univ Technol Sch Environm Sci & Engn Guilin 541004 Peoples R China Minist Ecol & Environm China Nucl & Radiat Safety Ctr Beijing 100082 Peoples R China Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing 100012 Peoples R China China Univ Geosci Beijing Sch Water Resources & Environm Beijing 100083 Peoples R China

Accurate prediction of the groundwater level (GWL) is crucial for sustainable groundwater resource management. Ecological water replenishment (EWR) involves artificially diverting water to replenish the ecological flow and water resources of both surface water and groundwater within the basin. However, fluctuations in GWLs during the EWR process exhibit high nonlinearity and complexity in their time series, making it challenging for single data-driven models to predict the trend of groundwater level changes under the backdrop of EWR. This study introduced a new GWL prediction strategy based on a hybrid deep learning model, STL-IWOA-GRU. It integrated the LOESS-based seasonal trend decomposition algorithm (STL), improved whale optimization algorithm (IWOA), and Gated recurrent unit (GRU). The aim was to accurately predict GWLs in the context of EWR. This study gathered GWL, precipitation, and surface runoff data from 21 monitoring wells in the Yongding River Basin (Beijing Section) over a period of 731 days. The research results demonstrate that the improvement strategy implemented for the IWOA enhances the convergence speed and global search capabilities of the algorithm. In the case analysis, evaluation metrics including the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) were employed. STL-IWOA-GRU exhibited commendable performance, with MAE achieving the best result, averaging at 0.266. When compared to other models such as Variance Mode Decomposition-Gated Recurrent Unit (VMD-GRU), Ant Lion Optimizer-Support Vector Machine (ALO-SVM), STL-Particle Swarm optimization-GRU (STL-PSO-GRU), and STL-Sine Cosine algorithm-GRU (STL-SCA-GRU), MAE was reduced by 18%, 26%, 11%, and 29%, respectively. This indicates that the model proposed in this study exhibited high prediction accuracy and robust versatility, making it a potent strategic choice for forecasting GWL changes in the context of

关键词： Groundwater level Deep learning STL decomposition improved whale optimization algorithm Ecological water replenishment

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