Research review on improving the efficiency of multimodal transportation based on technological solutions by M. I. Malyshev

“…Improving efficiency of multimodal transportation today is possible due to the optimization of the interaction system between the used modes of transport and transportation of goods. …”
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Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO by Salar Babaei, Mehran Khalaj, Mehdi Keramatpour, Ramin Enayati

“…Furthermore, a metaheuristic algorithm was employed to analyze the NP-Hardness of the model. …”
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A Survey on Machine Learning Enhanced Integrated Sensing and Communication Systems: Architectures, Algorithms, and Applications by Mikael Ade Krisna Respati, Byung Moo Lee

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Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue by LIU Yuan, ZHAO Jing, JIANG Guoping, XU Fengyu, LU Ningyun

“…Finally, balance L1 loss was used to optimize the loss function of the baseline algorithm and enhance the stability of the model during the process of detection. …”
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Software-defined networking QoS optimization based on deep reinforcement learning by Julong LAN, Xueshuai ZHANG, Yuxiang HU, Penghao SUN

“…To solve the problem that the QoS optimization schemes which based on heuristic algorithm degraded often due to the mismatch between parameters and network characteristics in software-defined networking scenarios,a software-defined networking QoS optimization algorithm based on deep reinforcement learning was proposed.Firstly,the network resources and state information were integrated into the network model,and then the flow perception capability was improved by the long short-term memory,and finally the dynamic flow scheduling strategy,which satisfied the specific QoS objectives,were generated in combination with deep reinforcement learning.The experimental results show that,compared with the existing algorithms,the proposed algorithm not only ensures the end-to-end delay and packet loss rate,but also improves the network load balancing by 22.7% and increases the throughput by 8.2%.…”
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Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization by Guozhu Song, Jian Wang, Rongting Ma, Yan Shi, Yaqi Wang

“…Initially, the Fasternet bottleneck in YOLOv8 is replaced with the c2f bottleneck, and the MLCA attention mechanism is added after the backbone network to construct the FastMLCA-YOLOv8 model for fruit stalk recognition. Subsequently, the optimized K-means algorithm, utilizing K-means++ for clustering centre initialization and determining the optimal number of clusters via Silhouette coefficients, is employed to segment the fruit stalk region. …”
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An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks by Zhifeng Ma, Zhanjun Hao, Zhenya Zhao

“…Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. …”
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Data-Driven Cooperative Localization Algorithm for Deep-Sea Landing Vehicles Under Track Slippage by Zhenzhuo Wei, Wei Guo, Yanjun Lan, Ben Liu, Yu Sun, Sen Gao

“…In this study, a data-driven cooperative localization algorithm with a velocity prediction model is proposed to improve the positioning accuracy of DSLV under track slippage. …”
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MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention by Zhao Liangjun, Ning Feng, Xi Yubin, Liang Gang, He Zhongliang, Zhang Yuanyang

“…In addition, the DenseASPP module is incorporated to enhance the model’s adaptability to ships of varying scales, improving its ca-pability to accommodate larger ships within lower model scales. …”
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A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion by Chao Yan, Tao Li, Yandong Gao, Shijin Li, Xiang Zhang, Xuefei Zhang, Di Zhang, Huiqin Liu

“…The major advantages of TLPU are as follows: 1) A high-resolution U-Net (HRU-Net) model trained on a dataset constructed according to InSAR interferometric geometry is utilized for the PhU for the first time, which effectively improves the performance of the DLPU. 2) TLPU utilizes the traditional PhU method to optimize the results of DLPU, addressing the issue of weak generalization ability of a single DLPU, while improving accuracy in areas with large-gradient changes. …”
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Efficient Approach for Brain Tumor Detection and Classification Using Fuzzy Thresholding and Deep Learning Algorithms by Nashaat M. Hussain Hassan, Wadii Boulila

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Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction by Pin‐Chun Huang

“…The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. …”
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Comprehensive Comparison and Validation of Forest Disturbance Monitoring Algorithms Based on Landsat Time Series in China by Yunjian Liang, Rong Shang, Jing M. Chen, Xudong Lin, Peng Li, Ziyi Yang, Lingyun Fan, Shengwei Xu, Yingzheng Lin, Yao Chen

“…These findings highlight the necessity of region-specific calibration and parameter optimization tailored to specific disturbance types to improve forest disturbance monitoring accuracy, and also provide a solid foundation for future studies on algorithm modifications and ensembles.…”
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Autonomous Greenhouse Cultivation of Dwarf Tomato: Performance Evaluation of Intelligent Algorithms for Multiple-Sensor Feedback by Stef C. Maree, Pinglin Zhang, Bart M. van Marrewijk, Feije de Zwart, Monique Bijlaard, Silke Hemming

“…For an optimal strategy, however, it is essential that control algorithms properly account for crop responses, which requires appropriate sensors, reliable data, and accurate models. …”
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Dual-objective optimization of prefabricated component logistics based on JIT strategy by Chunli Zhang, Jianbo Jiang, Chaoming Xia, Yan Fu, Jun Liu, Peng Duan

“…Its objectives are to reduce carbon emissions during logistics and enhance customer satisfaction. An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the model, offering enhanced solution diversity and local search capabilities. …”
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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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