An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm by Qingjie Xu, Yong Liu, Shuo Li

“…To address the shortcomings in action evaluation within VR simulation power training, this paper introduces a novel action recognition and evaluation method based on dynamic recognition of finger keypoints combined with an improved Dynamic Time Warping (DTW) algorithm. By constructing an action recognition model centered on hand keypoints, the proposed method integrates distance similarity and cosine similarity to account comprehensively for both numerical differences and directional consistency of action features. …”
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A Rapid Convex Programming Method for Air-to-Air Missile Trajectory Based on Improved Trust-Region Algorithm by Zeng Yuwen, Zhang Huijun, Liao Xueyang

“…Firstly, taking the parabola-trajectory of air-to-air missile in the longitudinal plane as the research object, considering the endpoint constraints, path constraints and state constraints, after linearizing and discretizing the original state equations and constraints, it establishes the standard convex optimization problem model. On this basis, this paper analyzes the traditional trust-region algorithm and proposes an improved algorithm. …”
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Implementing Low Latency and High Energy Efficiency Task Scheduling in MEC Systems Using Improved DDPG Algorithm by Lihong Zhao, Xiaomei Ding, Shuqin Wang

“…This paper presented a method that improves the Deep Deterministic Policy Gradient (DDPG) algorithm for better performance in MEC systems. …”
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Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data by Jiali Wang, Jia Chen

“…Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved Boosting algorithms were proposed, namely Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting Tree (XGBoost), and Gradient Boosting Decision Tree (CatBoost). …”
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Research on Collaborative Optimization Method of CCHP Regional Integrated Energy System Based on Improved Multivariate Universe Algorithm by Dahai Xu, Changle Yu, Wenwen Li, Su Zhang, Zhengda Li, Zhihui Qu, Pengtao Li, Xingfan Han

“…A case study conducted in a representative northern region yielded the following experimental results: When compared with both the traditional particle swarm algorithm and an improved version of it, the CCHP-type integrated energy system optimized using the enhanced multi-objective multiverse algorithm reduced operating costs by 7.98% and carbon dioxide emissions by 12%, relative to the original system. …”
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Short-Term Electricity Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Sparrow Search Algorithm–Convolutional Neural Network–Bidirectional Long Short-Term Memory Model by Han Qiu, Rong Hu, Jiaqing Chen, Zihao Yuan

“…Accurate power load forecasting plays an important role in smart grid analysis. To improve the accuracy of forecasting through the three-level “decomposition–optimization–prediction” innovation, this study proposes a prediction model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved sparrow search algorithm (ISSA), a convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM). …”
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Automatic Generation Technology of Safety Measures for Digital Substation Based on Improved Support Vector Machine by Yabing YAN, Xu CHU, Haolong XIAO, Wenwu LIANG, Hui LI, Zhenxing XIA

“…Firstly, construct a secondary circuit model and equipment model based on adjacency matrix, and further integrate the secondary security measure rule library to form a sample dataset; Secondly, support vector machines were used to classify secondary security measures, and bacterial foraging algorithms were introduced to optimize penalty factors and kernel parameters, effectively improving the training effectiveness of the automatic generation model for security measures; Finally, the effectiveness of the proposed method was verified through numerical examples.…”
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Improved Hybrid Fireworks Algorithm-Based Parameter Optimization in High-Order Sliding Mode Control of Hypersonic Vehicles by Xiaomeng Yin, Xing Wei, Lei Liu, Yongji Wang

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Flexible manipulator trajectory tracking based on an improved adaptive particle swarm optimization algorithm with fuzzy PD control by W. Sun, Y. Jin, K. Dai, Z. Guo, F. Ma

“…To address these challenges and improve the trajectory tracking performance of the manipulator, this paper focused on vibration suppression and trajectory planning for a two-link flexible manipulator and proposed a novel control method that integrates a modified adaptive particle swarm optimization algorithm (MAPSO) with fuzzy proportional–derivative (PD) control to achieve effective trajectory tracking. …”
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Research on linear solvability of network coding based cooperative recovery scheme by Jun YIN, Xueqi SHA, Lei WANG, Dengyin ZHANG, Yuwang YANG

“…The linear solvability of network coding based cooperative recovery/repair (CR) scheme was studied.Specifically, the solvability analysis model for network coding based CR scheme was established, the upper and lower bounds of the probability for any receiver to decode all original information under arbitrary order of Galois coding field were proposed and proved, and an on-line solvability judgement algorithm was designed by improvement of Gauss-Jordan algorithm.Numerical results validate the compactness of the proposed upper and lower bounds as well as the short-time decoding waiting delay of the improved Gauss-Jordan algorithm.Node deployment experiments show that the decoding complexity of the improved Gauss Jordan algorithm is reduced by 35% compared with the traditional Gauss algorithm.…”
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Research on Cold Chain Logistics Joint Distribution Vehicle Routing Optimization Based on Uncertainty Entropy and Time-Varying Network by Huaixia Shi, Yu Hong, Qinglei Zhang, Jiyun Qin

“…The solution combines simulated annealing strategies with genetic algorithms. It also uses the entropy mechanism to optimize uncertainties, improving global search performance. …”
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Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept by S. Prabakaran, S. Mary Praveena

“…These extracted features undergo the final prediction phase that is accomplished by the novel improved recurrent gated recurrent unit (IGRU), in which the parameter tuning of GRU is accomplished by the osprey optimization algorithm (OOA) with the consideration of error minimization as the major objective function. …”
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Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack by Shuqin Tu, Haoxuan Ou, Liang Mao, Jiaying Du, Yuefei Cao, Weidian Chen

“…In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of group-reared pigs to detect health problems and improve animal welfare promptly. We have developed the method named Pig-ByteTrack. …”
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Educational improvement through machine learning: Strategic models for better PISA scores. by Bilal Baris Alkan, Serafettin Kuzucuk, Şevki Yetkin Odabasi, Leyla Karakuş

“…The study found that the main factors influencing the success of students in countries that perform well in the PISA exam are essentially access to information technology, weekly hours of instruction in the subject, economic-social and cultural status, parents' occupation, level of metacognition, awareness of PISA, sense of competition and attitudes towards reading. New prediction models based on these variables were proposed. The proposed models will give a significant advantage to policy makers who want to improve their country's PISA score and implement appropriate education policies.…”
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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…Aiming at the problems of insufficient vehicle detection accuracy, high misdetection and omission rate, and heavy model computational burden caused by complex lighting conditions, target occlusion, and other factors in urban traffic scenarios, this paper proposes an improved lightweight detection network, ZZ-YOLO. …”
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Improving parking availability prediction in smart cities with IoT and ensemble-based model by Stéphane Cédric Koumetio Tekouabou, El Arbi Abdellaoui Alaoui, Walid Cherif, Hassan Silkan

“…The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity.…”
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Improved artificial protozoa optimizer: A new method for solar photovoltaic parameter estimation by Wenhao Lai, Duoduo Liu, Jialong Yang, Lei Guo, Weijin Qian, Jiaojiao Wu, Haifeng Zhou

“…The accuracy of parameters in solar cell models is helpful for optimizing the maximum power point tracking, which allows for an improvement in power generation efficiency. …”
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Comparison of mask R-CNN and YOLOv8-seg for improved monitoring of the PCB surface during laser cleaning by Lu Qiao, Markus Veltrup, Bernd Mayer

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Improvement in low-homology template-based modeling by employing a model evaluation method with focus on topology. by Wentao Dai, Tingrui Song, Xuan Wang, Xiaoyang Jin, Lizong Deng, Aiping Wu, Taijiao Jiang

“…To improve the performance of TBM methods for such targets, a novel model evaluation method was developed here, and named MEFTop. …”
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Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models by Haoyuan Chen, Emil Constantinescu, Vishwas Rao, Cristiana Stan

“…Abstract The Madden–Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns. In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty levels in the MJO forecasts directly. …”
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