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RUSLE/SDR for assessment of soil erosion by a new non-convex regression

EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-24页

作者： Ebrahimzadeh, Somayeh Alavipanah, Seyed Kazem Attarchi, Sara Mahboub, Vahid Motagh, Mahdi Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran Iran Golestan Univ Environm Hazard Res Inst Hydroinformat Res Grp Gorgan Iran Golestan Univ Fac Engn Dept Surveying Engn Aliabad Katoul Iran GeoForsch Zentrum Potsdam GFZ Haus E Sekt 2 1Telegrafenberg D-14473 Potsdam Germany GFZ German Res Ctr Geosci Helmholtz Ctr Potsdam Potsdam Germany Leibniz Univ Hannover Inst Photogrammetry & Geoinformat Fac Civil Engn & Geodet Sci Hannover Germany

In this research, by proposing an improved metaheuristic algorithm, it will be shown that the results of empirical soil erosion models can be more accurate by integrating with more advanced algorithms that can fill the limitations of previous methods. Modelling Revised Universal Soil Loss Equation (RUSLE) in combination with Sediment delivery ratio (RUSLE_SDR) in Taleghan watershed, located in the north of Iran, is the case study of this research. Two non-convex optimization methods are applied to construct the rainfall-runoff erosivity surface using rainfall data and Digital Elevation Model (DEM) instead of traditional interpolation methods since in fact it involves a non-convex target function due to existence of the random observed quantities which appear in all parts of the model. The first method to find the optimal solution is based on whale method as a metaheuristic approach. One of the main advantages of the whale is solving non-convex optimization problems where analytical methods may get stuck in a local minimum. As one may not have perfect information about the corresponding stochastic model, the second method is a new whale algorithm based on total least square- variance components estimation (TLS-VCE) in which variance matrices are estimated during the non-convex optimization. The rates of soil erosion and sediment yield are estimated by RUSLE_SDR using three methods: the ordinary least-squares (LS) method, the whale algorithm, and the proposed (VCE_whale) algorithm. The modeling results using the three methods are compared with sediment yield measured at the hydrometric station. The results show that the VCE_whale algorithm (with a relative error of 3.12%) performs better than either the whale method (with a relative error of 5.46%) or the LS method (with a relative error of 6.88%). The whale algorithm also performs better than the LS.

关键词： RUSLE Non-convex optimization whale algorithm Soil erosion Sediment yield TLS-VCE

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Energy-Saving Control Strategy for Multisleep States of CNC Machine Tool Considering Components Priority

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第2期71卷 1885-1895页

作者： Huang, Zixuan Li, Hongcheng Cao, Huajun Wen, Xuanhao Ge, Weiwei Wang, Qianyue Zhang, Qiongzhi Li, Chengchao Univ Chongqing State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China

As the representative energy-consuming equipment in the manufacturing industry, machine tools consume lots of energy during the nonmachining period, which leads to low energy efficiency. The existing strategies do not consider the startup sequence among components, which causes the control parameters to fail to run during the practical startup process, and this paper conducts energy-saving research to address the issues. The relationship of priority among components was used as the constraint to obtain an energy-saving control strategy for multisleep states of machine tools considering components priority. Furthermore, the improved whale algorithm was applied to solve the energy-saving control strategy of the machine tool, and the search and convergence ability of the algorithm was improved by introducing the chaotic convergence factor and adaptive weight coefficient. Finally, the energy-saving strategy was utilized with a vertical CNC machine tool as an experimental example to mill the end face of the randomly arriving workpieces. The results show that this changed algorithm has a better solution speed and effect than the ordinary whale algorithm and some other improved whale algorithms. Compared with the practical control process and other control strategies, the control strategy considering components priority can obtain more stable productivity and better energy-saving effect.

关键词： CNC machine tool components priority energy-saving control multisleep states whale algorithm

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Research on the MPPT of Photovoltaic Power Generation Based on Improved WOA and P&O under Partial Shading Conditions

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Energy Engineering 2024年第4期121卷 951-971页

作者： Jian Zhong Lei Zhang Ling Qin School of Electrical Engineering Nantong UniversityNantong226019China

Partial shading conditions(PSCs)caused by uneven illumination become one of the most common problems in photovoltaic(PV)systems,which can make the PV power-voltage(P-V)characteristics curve show *** maximum power point tracking(MPPT)methods have shortcomings in tracking to the global maximum power point(GMPP),resulting in a dramatic decrease in output *** order to solve the above problems,intelligent algorithms are used in ***,the existing intelligent algorithms have some disadvantages,such as slow convergence speed and large search ***,an improved whale algorithm(IWOA)combined with the P&O(IWOA-P&O)is proposed for the MPPT of PV power generation in this ***,IWOA is used to track the range interval of the GMPP,and then P&O is used to accurately find the MPP in that *** with other algorithms,simulation results show that this method has an average tracking efficiency of 99.79%and an average tracking time of 0.16 s when tracking ***,experimental verification is conducted,and the results show that the proposed algorithm has better MPPT performance compared to popular particle swarm optimization(PSO)and PSO-P&O algorithms.

关键词： Photovoltaic power generation maximum power point tracking whale algorithm perturbation and observation

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Optimal Polarization Design for Distributed MIMO Radar Networks: A Stackelberg Game Perspective

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 9332-9341页

作者： Jin, Biao Liu, Shujin Zhang, Zhenkai Lian, Zhuxian Xu, Zhaoyang Zhu, Xiaohua Jiangsu Univ Sci & Technol Zhenjiang 212100 Peoples R China China Shipbldg Ind Corp Res Inst 723 Yangzhou 225001 Peoples R China Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China

Distributed multiple-input multiple-output (MIMO) Radar networks have the potential to significantly enhance target detection performance by adjusting transmitting polarizations based on target scattering characteristics. However, the optimal design of transmitting antenna polarization presents a discrete optimization problem that necessitates a more efficient solution. This article proposes an optimal polarization design approach for distributed MIMO radar networks. First, we establish a functional relationship between the detection probability of distributed polarized MIMO radar and the polarization mode of the transmitting antennas. Subsequently, we formulate the polarization design problem as a Stackelberg game, where each transmitting antenna acts as a participant, and the detection probability serves as the utility function. To solve the Stackelberg equilibrium, we employ the iterative whale algorithm. Through numerical simulations, we demonstrate that our proposed method outperforms traditional approaches such as random selection and single polarization. Furthermore, the whale algorithm exhibits rapid convergence with low computational complexity.

关键词： Transmitting antennas MIMO radar Receiving antennas Games Radar antennas whales Object detection Distributed multiple-input multiple-output (MIMO) radar game theory polarization design Stackelberg game target detection whale algorithm

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Spectrum Sensing algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

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IEEE ACCESS 2023年 11卷 109307-109318页

作者： Dong, Qing Jilin Police Coll Publ Basic Teaching & Res Dept Changchun 130000 Peoples R China

As spectrum resources become increasingly scarce, efficient spectrum management is crucial in wireless communication. To address this challenge, a new spectrum sensing algorithm based on multi-objective optimization theory and fuzzy integral method has been developed. This innovative algorithm combines the whale algorithm and fuzzy integral algorithm, allowing for multi-objective optimization and interference decision-making. It overcomes limitations in existing multi-user spectrum sensing methods, such as low perception probability, poor detection accuracy, high false alarm probability, and computational complexity. Comparative studies show that the multi-objective whale algorithm achieves excellent optimization performance and fast convergence, reaching a throughput of 16.8 after 600 cycles. The collaborative spectrum sensing method, especially the one based on fuzzy integration, performs exceptionally well, particularly in high signal-to-noise ratio environments. This algorithm has significant application potential and effectively addresses spectrum resource management in wireless communication. It offers benefits such as improved spectrum utilization efficiency, scalability, and practicality, making valuable contributions to technological progress and efficiency enhancement in the field.

关键词： whale algorithm fuzzy integral method spectrum sensing multi objective optimization theory interference decision

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Sustainable and optimal design of Chinese herbal medicine supply chain network based on risk dynamic regulation mechanism

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SN APPLIED SCIENCES 2023年第6期5卷 1-22页

作者： Wu, Yao Liu, Weiwei Shenyang Univ Technol Sch Mech Engn Shenyang Peoples R China

We propose a robust fuzzy design model for a sustainable closed-loop supply chain network. The model is based on a risk dynamic regulation mechanism. In this way, we can solve the problem of sudden disruptions and uncertain demand in the supply chain of Chinese herbal medicines. We also develop a hybrid algorithm solution to solve the model and design a resilient supply chain network. The specific steps are as follows: (1) The risk dynamic regulation mechanism is created with strong risk resistance by considering the information sharing platform, facility defense, drying station scheduling, safety stock, and shared inventory. (2) Based on the dynamic risk regulation mechanism, we establish a sustainable Chinese herbal medicine supply chain network design model. Then, we use the robust fuzzy method and the epsilon constraint to deal with the uncertainty and integrate the model. (3) We introduce opposition-based learning, cosine convergence factor, and levy flight to the original whale and Grey wolf algorithms to obtain the Hybrid algorithm, which is used to solve the processed model. The results show the model and algorithm proposed in this paper have strong applicability and advantages in designing closed-loop supply chain networks for Chinese herbal medicine and provide references for relevant decision-makers.

关键词： Chinese herbal closed-loop supply chain network Risk dynamic regulation mechanism Robust fuzzy approach whale algorithm Grey wolf algorithm Opposition-based learning

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Tool Wear State Recognition Based on Multi-source Feature Fusion and Deep Learning 4

Tool Wear State Recognition Based on Multi-source Feature Fu...

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4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)

作者： Song, Ning Yu, Yuna Han, Tongtongn Xie, Guihua Mo, Desheng Li, Na Qilu Inst Technol Jinan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Aiming at the problems of single feature dimension and low accuracy in condition monitoring only from vibration characteristics and tool wear characteristics. In this paper, a tool wear state recognition method based on multi-source feature fusion and deep learning is proposed. The main innovation of our work lies in two aspects: firstly, the tool wear data is processed by multi-source feature fusion method, which effectively fuses multiple sensor signals and improves the efficiency and accuracy of data processing. Secondly, we optimize GRU (Gated Recursive Unit) model by combining whale algorithm for parameter optimization and add XGBoost to improve the prediction performance and robustness. Different milling wear experimental data sets are used to verify the recognition performance of the training model. The experimental results show that compared with the traditional methods, our proposed method has obvious advantages in tool wear state recognition.

关键词： tool wear multi-source feature fusion deep learning GRU whale algorithm

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Load Prediction of Composite Curing Equipment Cluster Based on Combined EEMD-WOA-LSTM Model 21

Load Prediction of Composite Curing Equipment Cluster Based ...

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21st IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Feng, Chunyu Li, Zhenyu Shao, Lei Li, Ji Li, Chao Liu, Hongli AVIC CAPDI Integrat Equipment Co Ltd Beijing Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin Peoples R China Tianjin Key Lab New Energy Power Convers Transmis Tianjin Peoples R China

ISBN: (纸本)9798350388084;9798350388077

For groups utilizing composite curing equipment, load forecasting is crucial in ensuring productivity, optimizing energy consumption, and reducing costs. This paper aims to enhance the accuracy of load forecasting through the utilization of artificial intelligence algorithms. Neural networks with long short-term memory (LSTM) are integrated into a combined model for short-term load forecasting, which is based on whale bionic optimization. The whale bionic algorithm is applied to improve parameter optimization accuracy and address the issue of local optimization within long-short-term memory neural networks. Multiple feature components are extracted from the source signal using empirical mode decomposition. The bionically optimized combined model utilizes each feature component as input for prediction, ultimately resulting in improved load prediction results. The enhanced combination model using whale bionics demonstrates reduced prediction error and greater accuracy when compared to the prediction results of RNN model, LSTM model, WOA-LSTM model, and EEMD-ARMA model.

关键词： Artificial intelligence Short term load forecasting Long short-term memory neural network whale algorithm Ensemble empirical mode decomposition

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"First Kilometer" Scheduling Task of Multiple Unmanned Aerial Vehicles Based on Multisource Heterogeneous Sensors

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SENSORS AND MATERIALS 2022年第8期34卷 3339-3358页

作者： Yang, Xiaohua Zhang, Miaohan Pan, Nan Chen, Shiyun Han, Yuhang Yunnan Power Grid Co Ltd Measurement Ctr Kunming 650051 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Yunnan Peoples R China

To solve the problem of " first-kilometer" distribution difficulties in rural areas, we propose a transportation method using unmanned aerial vehicles (UAVs) for delivery. The mountainous environment of Fengshan County in Guizhou is first simulated as the UAV delivery environment. A differential evolution strategy based on the improved whale optimization algorithm ( DEIWOA) combined with multisource heterogeneous sensors is then proposed to solve the UAV obstacle avoidance path. After the UAV's delivery path is planned using the DEIWOA algorithm, the multisource heterogeneous sensor is used to perform obstacle avoidance among multiple UAVs and path correction of UAVs in actual situations. Afterwards, to minimize the delivery cost of UAVs, a multi-UAV cargo delivery model is built with the optimization goal of minimizing the transportation cost and time window violation cost. This UAV scheduling model is solved using the proposed DEIWOA algorithm. Finally, simulations are performed to compare the proposed method with the cutting-edge algorithms. The obtained results show that the proposed DEIWOA algorithm can provide a better plan of the UAV path and reduce the cost of logistics scheduling. It can also provide support for UAV logistics and distribution in mountainous areas in actual situations.

关键词： UAVs path planning logistics scheduling whale algorithm multisource heterogeneous sensors

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Transformer Failure Diagnosis based on VNWOA-MRVM 3

Transformer Failure Diagnosis based on VNWOA-MRVM

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Zhou, Zilong Liu, Xin Wu, Tong Guo, Yufu Liu, Chunsheng Liu, Zeyang Yang, Biao Wang, Gang State Grid Jilin Electric Power Company Limited Construction Branch China Northeast Electric Power Design Institute Co. Ltd. of China Power Engineering Consulting Group China Changchun University of Technology China

ISBN: (纸本)9798350382426

This paper introduces a new methodology for diagnosing transformer failure to enhance diagnostic accuracy based on the combination of the Von Neumann whale Optimization algorithm (VNWOA) and the multi-categorical correlation vector machine (MRVM). Firstly, the whale algorithm is improved by using the principle of Von Neumann topology to increase the converge velocity and the optimization finding veracity of the whale algorithm by constructing a VN topology for each individual whale. Secondly, the VNWOA is applied to the MRVM arithmetic to find the optimal kernel features argument and penalty factor, and a diagnostic model of VNWOA-MRVM is advanced for failure diagnosis of the test data. Finally, the collected DGA data of 276 oil-immersed transformers are classified into exercise data and test data at a rate of 2: 1 for case analysis. After calculation, the accuracy of the proposed method can reach 95.65%, which is improved by 5.95%, 3.56%, 4.76% and 2.37% compared with SVM, PSO-SVM, M-RVM and PSO-RVM methods, respectively. © 2023 IEEE.

关键词： multi-classification correlation vector machine transformer fault diagnosis von neumann topology whale algorithm

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