Dynamic cache allocation routing strategy of Internet of things satellite node based on traffic prediction by Weidong WANG, Cheng WANG, Huiwen WANG, Pin XU

“…Aiming at the routing problem of low earth orbit (LEO) Internet of things (IoT) satellite systems,a dynamic cache allocation routing strategy based on traffic prediction for IoT satellite nodes was proposed.Firstly,the space-time characteristics of traffic distribution in the LEO coverage area were analyzed,and an end-to-end traffic prediction model was proposed.Then,according to the traffic prediction result,a dynamic cache allocation routing strategy was proposed.The satellite node periodically monitored the traffic load of the inter-satellite link,dynamically allocated the cache resources of each inter-satellite link between the neighboring nodes.The cache allocation process was divided into two phases,initialization and system operation.At the same time,the traffic offload and packet forwarding strategy when the node was congested was proposed.By comparing the queuing delay and the forwarding delay,it was determined whether the data packet needs to be rerouted.The simulation results show that the proposed routing strategy effectively reduces the packet loss rate and average end-to-end delay,and improves the traffic distribution in the whole network.…”
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Research on resource allocation algorithm of centralized and distributed Q-learning in machine communication by Yunhe YU, Jun SUN

“…Under the premise of ensuring partial machine type communication device (MTCD)’s quality of service (QoS) requirements, the resource allocation problem was studied with the goal of maximizing system throughput in the massive machine type communication (mMTC) scenario.Two resource allocation algorithms based on Q-learning were proposed: centralized Q-learning algorithm (team-Q) and distributed Q-learning algorithm (dis-Q).Firstly, taking into account MTCD’s geographic location and multi-level QoS requirements, a clustering algorithm based on cosine similarity (CS) was designed.In the clustering algorithm, multi-dimensional vectors that represent MTCD and data aggregator (DA) were constructed, and MTCDs can be grouped according to the CS value between multi-dimensional vectors.Then in the MTC network, the team-Q learning algorithm and dis-Q learning algorithm were used to allocate resource blocks and power for the MTCD.In terms of throughput performance, team-Q and dis-Q algorithms have an average increase of 16% and 23% compared to the dynamic resource allocation algorithm and the greedy algorithm, respectively.In terms of complexity performance, the dis-Q algorithm is only 25% of team-Q algorithm and even below, the convergence speed is increased by nearly 40%.…”
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An effective scheduling in data centres for efficient CPU usage and service level agreement fulfilment using machine learning by Rohit Daid, Yogesh Kumar, Yu-Chen Hu, Wu-Lin Chen

“…The proposed research utilises the neural network and linear regression analysis to perform the classification and compares the performance for the efficient CPU usage.…”
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Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine by Yanhua Zhao, Kai Zhang, Aojun Guo, Fukang Hao, Jie Ma

“…The support vector machine (SVM) model demonstrates superior predictive performance, evidenced by its R² value nearing one and a notable reduction in RMSE 1.123 and 1.573 less than the long short-term memory network (LSTM) and BP neural network (BPNN) models, respectively. …”
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Impact of Extreme Rainstorms on Accessibility of Urban Medical Services and Layout Method of Emergency Points: A Case Study of the Shenzhen River Basin by YANG Guang, LIU Yongqiang, LIU Guoqing, LU Wenxiu, FAN Ziwu, LI Suyun, XIAO Zhiming

“…By constructing coupled models of watershed hydrology, one-dimensional hydrodynamics of river network and pipe network, and regional two-dimensional hydrodynamics, it evaluated the urban flood inundation risk under extreme rainstorm conditions and comparatively analyzed the impact of flood inundation on the accessibility of medical services. …”
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Evaluating the Efficacy of Deep Learning Models for Identifying Manipulated Medical Fundus Images by Ho-Jung Song, Ju-Hyuck Han, You-Sang Cho, Yong-Suk Kim

“…Comparatively, five ophthalmologists achieved lower average scores on manipulated data: sensitivity of 0.71, precision of 0.61, F1-score of 0.65, and AUC of 0.822. (4) Conclusions: This study presents the possibility of addressing and preventing problems caused by manipulated medical images in the healthcare field. …”
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Energy Efficiency of Substation Buildings Based on Low Power Analysis Technology by Jingbo Song, Chen Chen, Liang Zhang, Han Yao, Kai Li, Jinfeng Zhang

“…To promote environmentally friendly and low-carbon growth of buildings, accelerate building energy efficiency renovation, and solve the substation energy consumption problem, the study proposes to analyse substation buildings with wireless sensor network low power technology. …”
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Assessing the Potential of the Strategic Formation of Urban Platoons for Shared Automated Vehicle Fleets by Senlei Wang, Gonçalo Homem de Almeida Correia, Hai Xiang Lin

“…However, delays lead to longer travel times for the travelers with the platoon leaders, similar to what people experience while traveling in highly congested networks when platoon formation does not happen. Moreover, the platoon delay increases as the volume of AMoD requests decreases; in the case of an AMoD system serving only 20% of the commuter trips (by private cars in the case-study city), the average platoon delays experienced by these trips increase by 25%. …”
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Online Calibration Method of LiDAR and Camera Based on Fusion of Multi-Scale Cost Volume by Xiaobo Han, Jie Luo, Xiaoxu Wei, Yongsheng Wang

“…The online calibration algorithm for camera and LiDAR helps solve the problem of multi-sensor fusion and is of great significance in autonomous driving perception algorithms. …”
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Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment by Rui Liu, Yuanyuan Jia, Xiangqian He, Zhe Li, Jinhua Cai, Hao Li, Xiao Yang

“…Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. …”
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Multi-label software requirement smells classification using deep learning by Ashagrew Liyih Alem, Ketema Keflie Gebretsadik, Shegaw Anagaw Mengistie, Muluye Fentie Admas

“…After executing numerous experiments with different parameters in the Bi-LSTM, LSTM, and GRU models, we obtained 90.3%, 89%, and 88.7% of F1-score macro averages with ELMo, respectively. Therefore, Bi-LSTM achieved a greater F1-score macro average than the other algorithms.…”
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Optimized DINO model for accurate object detection of sesame seedlings and weeds by Yong Wang, ShunFa Xu, ZhenYuan Ye, KongHao Cheng

“…To overcome the high complexity and low detection accuracy limitations of the original DINO model for this problem, the backbone network was replaced with MobileNet V3, the SENet attention mechanism and neck structure were optimized, and the H-Swish6 activation function was introduced to suit edge devices. …”
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Outage-Constrained Beamforming for Two-Tier Massive MIMO Downlink with Pilot Reuse by Guozhen Xu, An Liu, Wei Jiang, Haige Xiang, Wu Luo

“…Hence, a new kind of multitier networks which combine massive MIMO macro cells with a secondary tier of small cells is proposed to resolve the contradiction of large network coverage and high data rate. …”
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Facial Expression Recognition-You Only Look Once-Neighborhood Coordinate Attention Mamba: Facial Expression Detection and Classification Based on Neighbor and Coordinates Attention... by Cheng Peng, Mingqi Sun, Kun Zou, Bowen Zhang, Genan Dai, Ah Chung Tsoi

“…In studying the joint object detection and classification problem for facial expression recognition (FER) deploying the YOLOX framework, we introduce a novel feature extractor, called neighborhood coordinate attention Mamba (NCAMamba) to substitute for the original feature extractor in the Feature Pyramid Network (FPN). …”
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EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR by Jiajun Dun, Hai Yang, Shixin Yuan, Ying Tang

“…In response to the shortcomings of existing detection methods in identifying tiny defects and model efficiency, this study innovatively constructed the EER-DETR detection framework: firstly, a feature reconstruction module WDBB with a differentiable branch structure was introduced to significantly enhance the feature retention ability for fine cracks and other small targets; secondly, an adaptive feature pyramid network EHFPN was innovatively designed, which achieved efficient integration of multi-level features through a dynamic weight allocation mechanism, reducing the model complexity by 9.7% while maintaining detection accuracy, solving the industry problem of “precision—efficiency imbalance” in traditional feature pyramid networks; finally, an enhanced upsampling component was introduced to effectively address the problem of detail loss that occurs in traditional methods during image resolution enhancement. …”
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Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2 by Yifan Bu, Hao Liu, Hongda Li, Bryan Gilbert Murengami, Xingwang Wang, Xueyong Chen

“…The original ShuffleNet V2 network was modified by replacing the ReLU activation function with the Mish activation function to alleviate the neuron death problem. …”
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Lightweight Football Motion Recognition and Intensity Analysis Using Low-Cost Wearable Sensors by Qian Xie, Ning Jin, Shanshan Lu

“…The multitasking single-layer long short-term memory (LSTM) network with 32 neural units can achieve the accuracy of 0.8372, F1 score of 0.8172, mean average precision (mAP) of 0.7627, and mean absolute error (MAE) of 0.6117, while the multitasking single-layer LSTM network with 64 neural units can achieve the accuracy of 0.8407, F1 score of 0.8132, mAP of 0.7728, and MAE of 0.5966.…”
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Accelerating Streaming Subgraph Matching via Vector Databases by Liuyi Chen, Yi Ding, Xushuo Tang, Fangyue Chen, Siyuan Gong, Xu Zhou, Zhengyi Yang

“…Graphs are widely used in applications such as social network analysis, bioinformatics, and recommendation systems to represent relationships and complex dependencies. …”
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Model Predictive Control With Reinforcement Learning-Based Speed Profile Generation in Racing Simulator by Min-Seong Kim, Tae-Hyoung Park

“…To address these challenges, MPC problems are often simplified, and parameters are manually tuned through iterative adjustments—a process that is both time-consuming and labor-intensive. …”
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HURST STATISTICS (R/S-ANALYSIS) INTHESTUDY OF CLIMATIC VARIABLES by A. A. Tashilova

“…The use of long-term data of average, maximum and minimum surface air temperature of 20 meteorological stations of different climatic southern Russia (according to the state observational network of Roshydromet of the North Caucasian Directorate of the Hydrometeorological Service) is used. …”
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