Horizontal Control System for Maglev Ruler Based on Improved Active Disturbance Rejection Controller by Gengyun Tian, Chunlin Tian, Jiyuan Sun, Shusen Diao

“…Initially, a mathematical model is meticulously established based on the principles of magnetic circuits and dynamics. …”
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IMPROVEMENT OF PASSENGER SERVICE PROCESSES OF DEPARTING FLIGHT BASED ON PROJECT MANAGEMENT METHODS by S. A. Kropiventseva

“…The article considers the formalization of the service technology of departing passengers into network model, which is a good basis for improving the service technology, monitoring the performance that determines the process duration and service optimization in cost and resources. …”
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Two-stage iterative data transmission scheduling algorithm for satellite Internet integrating communication and remote sensing by LI Hai, LI Yongjun, ZHANG Taijiang, LI Yu, ZHAO Shanghong, WANG Jie, HAN Yue

“…Simulation results demonstrate that the proposed TSIDTS algorithm significantly improves data transmission profits compared with the same type data transmission scheduling algorithms, and confirm that the inter-satellite communication link can significantly improve the timeliness of remote sensing data downloading.…”
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Use of Machine Learning Algorithms to Predict Almen (Shot Peening) Intensity Values of Various Steel Materials by Murat İnce, Hatice Varol Özkavak

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Enhancing Cardiopulmonary Resuscitation Quality Using a Smartwatch: Neural Network Approach for Algorithm Development and Validation by Gaurav Rao, David W Savage, Gabrielle Erickson, Nathan Kyryluk, Pawan Lingras, Vijay Mago

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The optimization path of agricultural industry structure and intelligent transformation by deep learning by Xingchen Pan, Jinyu Chen

“…Subsequently, a hybrid optimization method is designed, combining the Genetic Algorithms with particle swarm optimization to improve the model’s global search capability and local convergence speed. …”
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The use of artificial neural network algorithms to enhance tourism economic efficiency under information and communication technology by Yun Tian, Xiaojie Tang

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Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms by Gökhan Ekinci, Harun Kemal Ozturk

“…These findings provide practical insights for optimizing wind energy forecasting models, which can improve energy trading strategies, enhance grid stability, and support informed decision making in renewable energy investments. …”
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Research on Life Attenuation of Lithium-Ion Batteries Based on IC Curves and Voltage Hybrid Evaluation Algorithm by Xiaorong Huang, Wanwei Wang, Zhijun Guo, Xiliang Dai, Maoquan Ye, Danyu Tian

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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management by Mahmoud Badee Rokaya Mahmoud, Dalia Ismaeil Ibrahim Hemdan, Samah Hazzaa Alajmani, Raneem Yousif Alyami, Ghada Elmarhomy, Hassan Hashim, El-Sayed Atlam

“…This study presents an artificial intelligence-based optimization framework that improves forecasting accuracy, computational speed, and real-time adaptability. …”
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Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents by Lida Barba, Nibaldo Rodríguez, Cecilia Montt

“…Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. …”
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Structural optimization and performance evaluation of a sugarcane leaf mulching machine by Weihua Huang, Shuo Wang, Chang Ge, Lijiao Wei, Dongjie Du, Zhaojun Niu, Ming Li, Zhenhui Zheng

“…Field tests under typical post-harvest conditions (leaf moisture content of 31.8 %, representing the average humidity of sugarcane leaves in tropical regions) demonstrated that the optimized machine achieved a pick-up rate of 98.4 % and a mulching rate of 94.4 % (≤20 cm), reflecting improvements of 0.8 % and 7.1 % over the previous design, respectively. …”
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A location-constrained crowdsensing task allocation method for improving user satisfaction by Huihui Chen, Bin Guo, Zhiwen Yu, Liming Chen

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Binary program taint analysis optimization method based on function summary by Pan YANG, Fei KANG, Hui SHU, Yuyao HUANG, Xiaoshao LYU

“…Taint analysis is a popular software analysis method, which has been widely used in the field of information security.Most of the existing binary program dynamic taint analysis frameworks use instruction-level instrumentation analysis methods, which usually generate huge performance overhead and reduce the program execution efficiency by several times or even dozens of times.This limits taint analysis technology’s wide usage in complex malicious samples and commercial software analysis.An optimization method of taint analysis based on function summary was proposed, to improve the efficiency of taint analysis, reduce the performance loss caused by instruction-level instrumentation analysis, and make taint analysis to be more widely used in software analysis.The taint analysis method based on function summary used function taint propagation rules instead of instruction taint propagation rules to reduce the number of data stream propagation analysis and effectively improve the efficiency of taint analysis.For function summary, the definition of function summary was proposed.And the summary generation algorithms of different function structures were studied.Inside the function, a path-sensitive analysis method was designed for acyclic structures.For cyclic structures, a finite iteration method was designed.Moreover, the two analysis methods were combined to solve the function summary generation of mixed structure functions.Based on this research, a general taint analysis framework called FSTaint was designed and implemented, consisting of a function summary generation module, a data flow recording module, and a taint analysis module.The efficiency of FSTaint was evaluated in the analysis of real APT malicious samples, where the taint analysis efficiency of FSTaint was found to be 7.75 times that of libdft, and the analysis efficiency was higher.In terms of accuracy, FSTaint has more accurate and complete propagation rules than libdft.…”
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A Method for Solving LiDAR Waveform Decomposition Parameters Based on a Variable Projection Algorithm by Ke Wang, Guolin Liu, Qiuxiang Tao, Luyao Wang, Yang Chen

“…First, using a variable projection algorithm, we separated the linear (amplitude) and nonlinear (center position and width) parameters in the Gaussian function model; the linear parameters are expressed with nonlinear parameters by the function. …”
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Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System by T. O. Ting, Ka Lok Man, Eng Gee Lim, Mark Leach

“…This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune Q and R parameters of the KF. …”
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Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm by Fang Liu, Xiaoli Liu, Changyou Yin, Hongrong Wang

“…For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. …”
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Ant Colony Optimization Using Common Social Information and Self-Memory by Yoshiki Tamura, Tomoko Sakiyama, Ikuo Arizono

“…Ant colony optimization (ACO), which is one of the metaheuristics imitating real ant foraging behavior, is an effective method to find a solution for the traveling salesman problem (TSP). …”
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Research on Recommendation Model Based on Multi-round Dialogue of Large Language Model by CHANG Baofa, CHE Chao, LIANG Yan

“…Compared with the optimal comparison baseline algorithm on the three datasets, the average increase in HR is 10.53% and the average increase in NDCG is 5.10%, which proves the effectiveness of the model.…”
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Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN by Peihao Tang, Zhen Li, Xuanlin Wang, Xueping Liu, Peng Mou

“…Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. …”
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