Fault diagnosis model of rolling bearings based on the M-YOLO network by NING Shaohui, ZHANG Shaopeng, WU Yukun, DU Yue, FAN Xiaoning

“…The rolling bearing is taken as the research object, and the fault diagnosis algorithm with two-dimensional signal as the input is studied, and the fault diagnosis model of rolling bearing based on M-YOLO network is constructed for the problems of multi-condition fault diagnosis, small data sample, and long model training time.MethodsFirstly, the mosaic data augmentation method was used to enrich the samples to improve the interference of unbalanced data on the diagnostic results. …”
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Hardware-software simulator for modeling indoor temperature and humidity processes by О.M. Yevseienko

“…As a result, algorithms that model temperature-humidity processes in the building are described, and a list of software and input-output signals of the control object is compiled, which allows modeling its state. …”
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A Hybrid Deep Learning Model for UAV Path Planning in Dynamic Environments by Junchi Zhang, Yanning Xian, Xun Zhu, Hongtao Deng

“…However, the commonly used Rapidly-exploring Random Tree (RRT) algorithm for UAV path planning often generates suboptimal paths that require extensive post-processing to improve. …”
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Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots by Jianping Sun, Weian Yang, Xin Meng, Jun Peng, Zhaoping Tang

“…The method considers the differences in non-probabilistic reliability of robot end positions in different workspace ranges and uses KLFCM clustering combined with a genetic algorithm to perform a two-stage hierarchical partitioning optimization. …”
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Modeling and Control of Permanent Magnet Synchronous Motor Based Electric Vehicle by Rajesh G., K. Sebasthirani

“…The research focuses on the design and optimization of Fractional Order PID (FOPID) controllers, leveraging Genetic Algorithm (GA) and Hybrid Reinforcement Genetic Algorithm-Recursive Backpropagation Learning (GA-RBL) techniques to enhance tuning performance. …”
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Bayesian Uncertainty Quantification of Reflooding Model With PSO–Kriging and PCA Approach by Ziyue Zhang, Dong Li, Nianfeng Wang, Meng Lei

“…To improve the process of best estimate plus uncertainty (BEPU) for nuclear safety assessment and calibration of thermal–hydraulic models for error reduction, inverse uncertainty quantification (IUQ) is proposed in recent years to quantify the uncertainty of model parameters in reactor program. …”
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Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies by Denesh Sooriamoorthy, Aaruththiran Manoharan, Siva Kumar Sivanesan, Soon Kian Lun, Alexander Chee Hon Cheong, Sathish Kumar Selva Perumal

“…Metaheuristic algorithms such as Particle Swarm Optimization (PSO) are explored extensively for MPPT applications. …”
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MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF). by Hasim Khan, Ahmed Ibrahim Alutaibi, Ghanshyam G Tejani, Sunil Kumar Sharma, Ahmad Raza Khan, Fuzail Ahmad, Seyed Jalaleddin Mousavirad

“…Furthermore, an effective feature selection method is proposed using the Dipper- grey wolf optimization (DGWO) algorithm to improve the performance of the proposed model. …”
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Quality Prediction Model Based on Novel Elman Neural Network Ensemble by Lan Xu, Yuting Zhang

“…In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. …”
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Optimal control strategy based on artificial intelligence applied to a continuous dark fermentation reactor for energy recovery from organic wastes by Kelly Joel Gurubel Tun, Elizabeth León-Becerril, Octavio García-Depraect

“…This strategy has demonstrated to be a viable algorithm for real-time control applications. First, experimental data from continuous dark fermentation are modeled using support vector machine (SVM) algorithm for response prediction and then, optimization algorithms are employed to identify the key parameters that enhance H2 production. …”
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A graph-based sensor recommendation model in semantic sensor network by Yuanyi Chen, Yihao Lin, Peng Yu, Yanyun Tao, Zengwei Zheng

“…We use the improved fast non-dominated sorting algorithm to obtain the local optimal solutions of sensor data set, and we apply the simple additive weight algorithm to characterize and sort local optional solutions. …”
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Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China by Yanna Gao, Hong Dong, Liujun Hu, Fanhong Zeng, Yuqun Gao, Zhuonan Huang, Shaohua Wang, Shaohua Wang

“…To solve this model at large scale, a deep reinforcement learning (DRL) algorithm is designed and implemented. …”
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LightGBM hybrid model based DEM correction for forested areas. by Qinghua Li, Dong Wang, Fengying Liu, Jiachen Yu, Zheng Jia

“…To overcome these shortcomings, this paper cites an improved SSA search algorithm that incorporates the ingestion strategy of the FA algorithm to increase the diversity of solutions and global search capability, the Firefly Algorithm-based Sparrow Search Optimization Algorithm (FA-SSA algorithm) is introduced. …”
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Towards precision agriculture: metaheuristic model compression for enhanced pest recognition by Sana Parez, Norah Saleh Alghamdi, Tahir Mahmood, Waseem Ullah, Muhammad Attique Khan, Taha Houda, Naqqash Dilshad

“…The core of this approach integrates InceptionV3 as a backbone feature extractor to capture rich and discriminative features, enhanced further using a channel attention (CA) mechanism for feature refinement. To reduce model complexity and improve deployment feasibility, a metaheuristic optimization algorithm was incorporated that significantly reduces computational overhead without compromising performance. …”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…Using S-G smoothing filtering (S-G), multivariate scattering correction (MSC), standard normal transformation (SNV), second derivative (SD), reciprocal logarithm (LR) and continuum removal (CR) to preprocess the data, the spectral characteristics and their correlation with water content were analyzed. In order to further improve the prediction ability of the model, the competitive adaptive reweighting method (CARS) was used to optimize the characteristic band, and a prediction model was established by combining random forest regression (RFR), least squares support vector regression (LSSVR) and particle swarm optimization least squares support vector regression (PSO-LSSVR). …”
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Human fall direction recognition in the indoor and outdoor environment using multi self-attention RBnet deep architectures and tree seed optimization by Awais Khan, Jung-Yeon Kim, Chomyong Kim, Muhammad Attique Khan, Hyojin Shin, Jiyoung Woo, Yunyoung Nam

“…To optimize feature selection and improve classification performance while reducing computational costs, we employed the tree seed algorithm on the self-attention features of 7-RBNet and 9-RBNet self-attention models. …”
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Management of the business system of the enterprise by O. Grosheleva, M. Ivanova, O. Usatenko

“…An optimization model has been built, which allows to determine the share of expenditures for certain communication activities in the overall budget structure. …”
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Evaluation method of e-government audit information based on big data analysis by Jingui He, Hansi Ya

“…Furthermore, a parallel PSO-RF algorithm combining Particle Swarm Optimization (PSO) and Random Forest (RF) is designed to enhance classification performance and computational efficiency. …”
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Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam by Yeong-Nam Jeon, Jae-ha Ko

“…The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. …”
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LGN-YOLO: A Leaf-Oriented Region-of-Interest Generation Method for Cotton Top Buds in Fields by Yufei Xie, Liping Chen

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