LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement by Zhipeng Fan, Yayun Wu, Wei Liu, Ming Chen, Zeguo Qiu

“…Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. …”
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Research of Real Time Optimization of Gear for DCT Vehicle Under the Ramp by Ding Hua, Xu Cong

“…The value of slope is identified based on the EKF algorithm and the dynamics model of ramp. On the basis of ramp identification model and traditional shift schedule,a real time online optimization of dual clutch transmission（ DCT） gear is presented based on fuzzy control method. …”
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Method for Increasing the Energy Efficiency of the Gear Teeth Cutting Process by Smoothing the Cutting Force Variation by Gabriel Radu Frumusanu, Mihail Bordeanu, Florin Susac

“…The available solutions for energy optimization in cutting processes mentioned are the improvement of manufacturing equipment, the optimization of processes, and appropriate production scheduling. …”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Non-Vertical Well Trajectory Design Based on Multi-Objective Optimization by Xiaowei Li, Yu Li, Yang Wu, Zhaokai Hou, Haipeng Gu

“…By introducing multi-granularity reference vector generation and an information entropy-guided search direction adaptation mechanism, the performance of the algorithm in the complex target space is improved, and the three-stage wellbore trajectory is optimized. …”
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Application of Improved WOA in Hammerstein Parameter Resolution Problems under Advanced Mathematical Theory by Lu Zhao, Jiangjun Liu, Yuan Li

“…In response to the fact that whale optimization algorithms are prone to falling into local optima and the identification of important Hammerstein models ignores the issue of noise outliers in actual industrial environments, this study improves the whale algorithm and constructs a Hammerstein model identification strategy for nonlinear systems under heavy-tailed noise using the improved whale algorithm. …”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA by Bingqi Jia, Haihong Pan, Lei Zhang, Yifan Yang, Huaxin Chen, Lin Chen

“…To overcome these challenges, a Lévy flight-enhanced improved whale optimization algorithm (LF-IWOA) was developed. …”
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Mechanism of Immune System Based Multipath Fault Tolerant Routing Algorithm for Wireless Sensor Networks by Hongbing Li, Qingyu Xiong, Weiren Shi, Liwan Chen, Xin Shi, Qiang Chen

“…Mechanism of immune system is applied to do the variation on the initial antibody population, namely, the multiple disjoint paths, to establish the final optimal transmission paths. Mathematical model is established to do the theoretical analysis on the performance of the algorithm. …”
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Sentiment Analysis of Consumer Acceptance of Honda’s Digital Marketing Strategy Using Lexicon-Based Algorithm by Bartolomius Dias, Asma’ Khoirunnisa’, Yosef Budiman, Setiyawami Setiyawami

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Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG–VSC Algorithm by Zhengyang Cao, Gang Chen

“…The algorithm not only optimizes multi-UAV formation control efficiency but also improves obstacle avoidance, attitude stability, and decision-making speed. …”
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High-Performance Mobility Simulation: Implementation of a Parallel Distributed Message-Passing Algorithm for MATSim by Janek Laudan, Paul Heinrich, Kai Nagel

“…This paper presents an architectural update to the MATSim traffic simulation framework, introducing a prototype that adapts the existing traffic flow model to a distributed parallel algorithm. The prototype is capable of scaling across multiple compute nodes, utilizing the parallel computing power of modern hardware. …”
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Set-Based Differential Evolution Algorithm Based on Guided Local Exploration for Automated Process Discovery by Si-Yuan Jing

“…Thirdly, a fine-grained evaluation technique is designed to assign score to each node in a process model, which is employed to guide the local exploration and improve the efficiency of the algorithm. …”
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Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability by Maxim Zhabinets, Benjamin Tyler, Martin Lukac, Shinobu Nagayama, Ferdinand Molnár, Michitaka Kameyama

“…The Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. …”
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Investigating the Performance of Open-Vocabulary Classification Algorithms for Pathway and Surface Material Detection in Urban Environments by Kauê de Moraes Vestena, Silvana Phillipi Camboim, Maria Antonia Brovelli, Daniel Rodrigues dos Santos

“…Our approach uses large language models (LLMs) to improve the accuracy of classifying different pavement types. …”
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Research on Adaptive Education Path Dynamic Programming Algorithm Based on Reinforcement Learning and Cognitive Graphs by Hongli Lou, Pin Yue, Jianwen Chen

“…By employing reinforcement learning algorithms, the system continuously refines its model based on learner feedback and past interactions. …”
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Optimal Computation Offloading Decisions Based on System Utility and Cost Balance by Bingxue Zhang, Xisheng Li, Jia You

“…Based on the new evaluation model, an objective function is constructed to minimize the system’s cost-utility ratio, and an improved particle swarm optimization algorithm with enhanced search capabilities is proposed to solve it by designing a computation offloading strategy. …”
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Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm by Dawei Qiu, Chang Liu, Yuangfeng Shang, Zixu Zhao, Jinlin Shi

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A algorithm for low light image enhancement in coal mine underground based on illumination constraints by Yuchen BAI, Zuohua MIAO, Houyou XU, Mengting WANG

“…Compared to the original model, it showed improvements of approximately 23.40%, 16.07%, and 20.45% in these metrics, demonstrating optimal image enhancement effectiveness.…”
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Improving SLICES crystal representation through CHGNet integration and parameter tuning by Bizhu Zhang, Kedeng Wu, Chang Zhang, Hang Xiao, Liangliang Zhu

“…To bridge this gap, we present an enhanced approach to the simplified line-input crystal-encoding system (SLICES) representation by incorporating the Crystal Hamiltonian Graph Neural Network (CHGNet) machine learning force field model. Comprehensive evaluations across multiple datasets (MP-20, MP21-40, and MOF) using different neighbor recognition algorithms (EconNN and CrystalNN) show that our approach outperforms the original in reconstructing structures with fewer than 20 atoms per unit cell, achieving up to a 1.34% improvement in reconstruction rate (from 92.55% to 93.89%). …”
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