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Local corner smoothing based on deep learning for CNC machine tools

SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Jiang, Bai Sun, Rong Li, Ze-long Xu, Liang Liao, Huang Teng, Xiao-yan Li, Bing Harbin Engn Univ Coll Mech & Elect Engn Harbin 150006 Peoples R China China Ordnance Ind Grp Aviat Ammunit Res Inst Harbin 150000 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150006 Peoples R China

Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem. The optimization challenge is then resolved by an intelligent optimization algorithm. Considering the influence of population size and computational resources on intelligent optimization algorithms, a deep learning algorithm (the Double-ResNet Local Smoothing (DRLS) algorithm) is proposed to further improve optimization efficiency. The First-Double-Local Smoothing (FDLS) algorithm is used to optimize the positions of NURBS (Non-Uniform Rational B-Spline) control points, and the Second-Double-Local Smoothing (SDLS) algorithm is employed to optimize the NURBS weights to generate a smoother toolpath, thus allowing the cutting tool to pass through each local corner at a higher feedrate during the machining process. In order to ensure machining quality, geometric constraints, drive condition constraints, and contour error constraints are taken into account during the feedrate planning process. Finally, three simulations are presented to verify the effectiveness of the proposed method.

关键词： Local corner smoothing intelligent optimization algorithm Deep learning Feedrate planning

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Research on millet origin identification model based on improved parrot optimizer optimized regularized extreme learning machine

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 141卷

作者： Gao, Peng Wang, Na Lu, Yang Liu, Jinming Wang, Guannan Hou, Rui Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Heilongjiang Peoples R China

To achieve nondestructive identification of millet origins, near-infrared spectroscopy technology was employed to obtain the original spectral data of millet. By integrating the Parrot Optimizer (PO) with the Regularized Extreme Learning Machine (RELM), the model achieved an accuracy of 83.67 % in millet origin identification. To further enhance model performance, this study incorporated strategies such as chaotic mapping and adaptivity into PO, resulting in the Improved Parrot Optimizer (IPO). The IPO was then combined with RELM to construct the IPO-RELM model, which significantly improved the model's generalization capability and robustness. Experimental results demonstrated that the IPO-RELM model outperformed the RELM model, achieving an accuracy of 98.33 %, a precision of 98.49 %, a recall of 98.33 %, an F1 score of 98.41 %, and a Kappa coefficient of 98 %, representing respective improvements of 11.32 %, 7.92 %, 11.32 %, 9.62 %, and 13.90 % over the traditional RELM model. Furthermore, the performance of the IPO-RELM model was validated using two publicly available datasets, confirming its superiority over the conventional RELM model. Compared to the PO algorithm, the IPO algorithm exhibited enhanced global search and local optimization capabilities with faster convergence speed. The IPO-RELM model accurately and efficiently identified millet origin information, providing robust support for ensuring millet quality and safety, while also contributing to the protection of the uniqueness and market value of geographically indicated agricultural products.

关键词： Near-infrared spectroscopy Millet Parrot optimizer RELM Origin identification intelligent optimization algorithm

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Nonlinear coupled asymmetric stochastic resonance for weak signal detection based on intelligent algorithm optimization

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PROBABILISTIC ENGINEERING MECHANICS 2024年 78卷

作者： Ma, Shaojuan Liu, Yuan Ma, Xiaoyan Liu, Yantong North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China Ningxia Key Lab Intelligent Informat & Big Data Pr Yinchuan 750021 Ningxia Peoples R China

Stochastic resonance has been extensively studied for detecting weak signals. To improve the diagnostic ability of weak signals, a novel nonlinear coupled asymmetric stochastic resonance (NCASR) system is investigated in this paper. Firstly, the NCASR system is established by coupling the asymmetric bistable system with the monostable system. Next, the expressions for the steady-state probability density (SPD) function, the mean first passage time (MFPT) and the signal-to-noise ratio (SNR) of the proposed system are derived based on the adiabatic approximation theory. Furthermore, the impact of system parameters on the SPD, the MFPT and the SNR is analyzed. Then, by simulation experiments, we verify the effectiveness of detecting weak signals for the NCASR system with L & eacute;vy noise. Finally, the NCASR system optimized by Adaptive Weighted Particle Swarm optimization (AWPSO) algorithm is applied to detect the bearing fault signal. Compared with the optimized classical bistable stochastic resonance (CBSR) system, it is found that the detection performance of the NCASR system is superior to the CBSR system in detecting bearing fault signals.

关键词： Weak signal detection Stochastic resonance intelligent optimization algorithm Nonlinear coupled asymmetric system

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Research on Interlayer Recognition Based on intelligent optimization algorithms and Convolutional Neural Networks 12th

Research on Interlayer Recognition Based on Intelligent Opti...

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12th International Conference on Applications and Technologies in Information Security (ATIS)

作者： Pan, Shaowei Kang, Mingzhu Guo, Zhi Luo, Haining Xian Shiyou Univ Xian 710065 Shaanxi Peoples R China PetroChina Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China PetroChina Res Inst Explorat & Dev Tarim Oilfield Co Korla 841000 Xinjiang Peoples R China

ISBN: (纸本)9789811911668;9789811911651

Due to the particularity of interlayer logging curves in reservoirs, as well as the traditional interlayer identification has many problems, such as small amount of data, poor adaptability, low discrimination among interlayer types and strong subjectivity, an interlayer identification method has been proposed based on the intelligent optimization algorithm and Convolutional Neural Networks (CNN) in this paper. In this method, the parameters and structure of the model are optimized by genetic algorithm, particle swarm algorithm and artificial fish swarm algorithm, so that the network model can be adjusted based on the basic architecture and characteristics of the data set. The experimental results show that through the optimization process, three intelligent optimization algorithms are used to build the model structure, and the obtained CNN can avoid artificial selection of the model structure, and evolve a model that is more suitable for the data set, thereby reducing the number of model parameters, shortening the training time and improving the accuracy.

关键词： CNN Interlayer intelligent optimization algorithm

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Comparison of intelligent optimization algorithms for Crack Shape Reconstruction from ECT Signals

Comparison of Intelligent Optimization Algorithms for Crack ...

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IEEE International Conference on Mechatronics and Automation

作者： Zhang, Siquan Yang, Hefa Shanghai Univ Engn Sci Coll Air Transportat Shanghai Peoples R China Guangzhou Civil Aviat Coll Dept Mech Engn Guangzhou Peoples R China

ISBN: (纸本)9781424426928

intelligent optimization is a vibrant area of investigation, with some of the widely known and used approaches being Genetic algorithm, Ant Colony optimization and Particle Swarm optimization algorithm. An inversion] algorithm for the reconstruction of natural crack shape from eddy current testing signals is developed by using an artificial neural network based forward model and intelligent inversion algorithm. The true crack shapes and the measured eddy current testing signals are used to train the neural network. The parameters of the algorithms are modified, their effectiveness on crack shape inversion problem are compared and discussed. The reconstructed results verified the efficiency of neural network based forward model and the promising of intelligent optimization algorithms in crack shape inversion.

关键词： Eddy current testing Artificial neural network intelligent optimization algorithm Crack shape reconstruction

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A critical review on intelligent optimization algorithms and surrogate models for conventional and unconventional reservoir production optimization

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FUEL 2023年第1期350卷

作者： Wang, Lian Yao, Yuedong Luo, Xiaodong Adenutsi, Caspar Daniel Zhao, Guoxiang Lai, Fengpeng China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China NORCE Norwegian Res Ctr N-5008 Bergen Norway Kwame Nkrumah Univ Sci & Technol Fac Civil & Geoengn Dept Petr Engn Reservoir Simulat Lab Kumasi Ghana China Univ Geosci Sch Energy Resources Beijing 100083 Peoples R China

Aiming to find the most suitable development schemes of conventional and unconventional reservoirs for maximum energy supply or economic benefits, reservoir production optimization is one of the most essential challenges in closed-loop reservoir management. With the developments of artificial intelligence technologies during the past decades, both intelligent optimization algorithms and surrogate models have been adopted to solve reservoir production optimization problems for improved efficiency and/or accuracy in the final optimi-zation results. In this paper, a critical review of intelligent optimization algorithms and surrogate models applied to production optimization problems in conventional and unconventional reservoirs is conducted. It covers a few different topics within the target research area, ranging from the basic elements (optimization variables, objective function and constraints) that constitute a reservoir production optimization problem, to various intelligent optimization algorithms developed from different perspectives and for different types of optimization problems (e.g., with single or multiple objective functions), and intelligent surrogate models that are built based on different artificial intelligence technologies and for different application purposes. The particular issues of production optimization in unconventional reservoirs are highlighted, and future challenges and prospects within the area of reservoir production optimization are also discussed. It is our hope that this critical review may help attract more attention to intelligent optimization algorithms and surrogate models applied to pro-duction optimization problems in conventional and unconventional reservoirs, and promote research and activities within this area in the future.

关键词： Reservoir production optimization intelligent optimization algorithm Surrogate model Conventional reservoirs Unconventional reservoirs

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Application of intelligent optimization algorithms in the Distribution Network Planning and Evaluation Models

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Procedia Computer Science 2023年 228卷 1205-1214页

作者： Xin Du Jiali Huo Lin Chen Mingchang Wang Zhaoshun Wu Chengliang Zhang Hangzhou Power Supply Company of State Grid Zhejiang Electric Power Company Hangzhou 310000 Zhejiang China

In order to reasonably plan distribution network evaluation strategies and improve the efficiency and accuracy of power system planning, this study aims to explore the application of intelligent optimization algorithms in distribution network planning and evaluation. It includes the principle, method and model construction of intelligent optimization algorithm, and designs a distribution network planning evaluation model using Ant colony optimization algorithms combined with GIS (Geographic Information System). This article applies its example to the distribution network planning of a certain area, and the results show that compared with traditional algorithms, the intelligent optimization algorithm has a voltage value of 230.2V at the highest and 220.2V at the lowest; The highest load value is 2.75 Megawatt, and the lowest is 1.19 Megawatt; The longest running time is 8913ms, surpassing traditional algorithms in all aspects. This algorithm improves the voltage stability and load balance of the distribution network, and reduces operating time. This study has certain promotion and application value for the research and practical application of power system planning, which can improve the efficiency and accuracy of power system planning and provide scientific basis for the reliability and effectiveness of power supply.

关键词： Distribution Network Planning intelligent optimization algorithm Ant Colony algorithm Geographic Information System

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Software and hardware co-design and implementation of intelligent optimization algorithms

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Fu, Zonglin Chu, Shu-Chuan Watada, Junzo Huc, Chia-Cheng Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Yango Univ Coll Artificial Intelligence Fuzhou 330015 Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

In order to improve the operating efficiency of the algorithm, some intelligent optimization algorithms are considered to be implemented on hardware. However, the existing design scheme has the problem of poor versatility. Therefore, this paper proposes a general software-hardware co-design scheme of intelligent optimization algorithms. In the design scheme, the initialization module and fitness module of the algorithm are deployed on the Advanced RISC Machines (ARM) for execution to increase the flexibility of the program. The update module of the algorithm is deployed on the Field Programmable Gate Array (FPGA) for execution to realize the hardware acceleration. The data between ARM and FPGA is transferred through Advanced eXtensible Interface (AXI) bus. In this paper, the PSO, BA, WOA, GWO, CMAES and EO algorithms are implemented with the proposed design scheme. And the six algorithms are tested on thirteen benchmark functions of different types. The experimental results prove the feasibility of the design scheme. In addition, by comparing with software and other implementation methods in execution time, resource occupancy and convergence, the effectiveness and superiority of the proposed scheme are proved. (c) 2022 Elsevier B.V. All rights reserved.

关键词： intelligent optimization algorithm Advanced RISC machines Field programmable gate array Advanced eXtensible interface

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Improved Hargreaves Model Based on Multiple intelligent optimization algorithms to Estimate Reference Crop Evapotranspiration in Humid Areas of Southwest China

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ATMOSPHERE 2021年第1期12卷 15页

作者： Wu, Zongjun Cui, Ningbo Zhu, Bin Zhao, Long Wang, Xiukang Hu, Xiaotao Wang, Yaosheng Zhu, Shidan Northwest A&F Univ Minist Educ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Xian 712100 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610000 Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610000 Peoples R China Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Peoples R China Yanan Univ Coll Life Sci Yanan 716000 Peoples R China Chinese Acad Agr Sci State Engn Lab Efficient Water Use Crops & Disast Key Lab Dryland Agr Inst Environm & Sustainable Dev Agr Beijing 100000 Peoples R China

Reference crop evapotranspiration (ET0) is an important indicator for precise regulation of crop water content, irrigation forecast formulation, and regional water resources management. The Hargreaves model (HG) is currently recognized as the simplest and most effective ET0 estimation model. To further improve the prediction accuracy of the HG model, this study is based on the data of 98 meteorological stations in southwest China (1961-2019), using artificial bee colony (ABC), differential evolution (DE) and particle swarm optimization (PSO) algorithms to calibrate the HG model globally. The standard ET0 value was calculated by FAO-56 Penman-Monteith (PM) model. We compare the calculation accuracy of 3 calibrated HG models and 4 empirical models commonly used (Hargreaves, Priestley-Taylor, Imark-Allen and Jensen-Hais). The main outcomes demonstrated that on a daily scale, the calibrated HG models (R-2 range 0.74-0.98) are more accurate than 4 empirical models (R-2 range 0.55-0.84), and ET0-PSO-HG has the best accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with average R-2 of 0.83, 0.82 and 0.80, average RRMSE of 0.23 mm/d, 0.25 mm/d and 0.26 mm/d, average MAE of 0.52 mm/d, 0.53 mm/d and 0.57 mm/d, and average GPI of 0.17, 0.05, and 0.04, respectively;on a monthly scale, ET0-PSO-HG also has the highest accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with median R-2 of 0.96, 0.95 and 0.94, median RRMSE of 0.16 mm/d, 0.17 mm/d and 0.18 mm/d respectively, median MAE of 0.46 mm/d, 0.50 mm/d, and 0.55 mm/d, median GPI of 1.12, 0.44 and 0.34, respectively. The calibrated HG models (relative error of less than 10.31%) are also better than the four empirical models (relative error greater than 16.60%). Overall, the PSO-HG model has the most accurate ET0 estimation on daily and monthly scales, and it can be suggested as the preferred model to predict ET0 in humid regions in southwest China regions.

关键词： reference evapotranspiration intelligent optimization algorithm Hargreaves model Penman– Monteith model southwest humid region

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Dual-task coupled forecasting of wind speed and wind power generation based on multimodal spatiotemporal feature fusion

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Energy Reports 2025年 13卷 5993-6016页

作者： Hui Cui Yunlong He Jingyang Zhou Yi Pan Hongyu Long China Electric Power Research Institute Beijing 100192 China School of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China Chongqing key laboratory of operation and detection of power source network load and storage Chongqing University of Posts and Telecommunications Chongqing 400065 China

Accurate prediction of wind speed and wind power is crucial for optimizing wind farm operations and improving renewable energy utilization. This paper proposes a dual-task coupled prediction model integrating multi-source data, including wind speed, wind direction, temperature, humidity, pressure, and wind power. The model incorporates dual decomposition, multi-dimensional feature extraction, adaptive multi-time-window fusion, and error correction to enhance prediction accuracy and stability. First, Singular Spectrum Analysis (SSA) and Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) are applied for dual denoising, while 3D data tensors are constructed from multi-height wind speed and meteorological factors to capture spatiotemporal patterns. A multi-scale Convolutional Neural Network (CNN) feature extraction module is then used to explore complex interactions. In the prediction phase, a dual-task structure is designed with an improved loss function, while the Improved Snake optimization (ISO) algorithm adaptively optimizes the weighted fusion of multi-time-window predictions. Finally, a Variational Mode Decomposition-Gate Recurrent Unit (VMD-GRU) error correction mechanism refines residuals to further improve accuracy. Experiments on two datasets show that the proposed model significantly outperforms benchmarks across multiple indexes (MSE, MAE, MAPE, RMSE, R²), demonstrating its effectiveness. Taking Dataset 1 as an example, the wind speed forecasting results of the proposed method demonstrated improvements over the baseline model, with the MSE, MAE, MAPE, RMSE, and R² enhanced by 93.7 %, 85.3 %, 79.5 %, 81.5 %, and 35.9 %, respectively. Ablation studies confirm that combining multi-feature extraction, dual-task coupling, adaptive fusion, and error correction significantly enhances wind speed and wind power prediction, compared to the baseline model, improvements of 95.3 %, 81.4 %, 78.8 %, 79.1 %, and 24.4 % were achieved

关键词： Dual-Task Coupled Forecasting Spatiotemporal features intelligent optimization algorithm

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