Quality Prediction Model Based on Novel Elman Neural Network Ensemble by Lan Xu, Yuting Zhang

“…Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy.…”
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Task distribution offloading algorithm of vehicle edge network based on DQN by Haitao ZHAO, Tangwei ZHANG, Yue CHEN, Houlin ZHAO, Hongbo ZHU

“…In order to achieve the best balance between latency,computational rate and energy consumption,for a edge access network of IoV,a distribution offloading algorithm based on deep Q network (DQN) was considered.Firstly,these tasks of different vehicles were prioritized according to the analytic hierarchy process (AHP),so as to give different weights to the task processing rate to establish a relationship model.Secondly,by introducing edge computing based on DQN,the task offloading model was established by making weighted sum of task processing rate as optimization goal,which realized the long-term utility of strategies for offloading decisions.The performance evaluation results show that,compared with the Q-learning algorithm,the average task processing delay of the proposed method can effectively improve the task offload efficiency.…”
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Startup Drift Compensation of MEMS INS Based on PSO–GRNN Network by Songlai Han, Jingyi Xie, Jing Dong

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Analysis of traffic accident characteristics and recovery strategy of urban road network by Yuzhou Duan, Zhipeng Lin, Yulong Wang

“…When a traffic accident occurs in the edge area of the network, the average vehicle delay is reduced by 6.73%, 16.15%, and 16.84% under low, medium, and high traffic demand, respectively. …”
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Energy-efficient and fast method on storage and querying in wireless sensor networks by PENG Shao-liang, LI Shan-shan, PENG Yu-xing, LIAO Xiang-ke, XIAO Nong

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Boundary Recognition by Simulating a Diffusion Process in Wireless Sensor Networks by De Gu, Jishuai Wang, Ji Li

“…The simulation cases show the algorithm is well worked in WSN with average degree higher than 10. The boundary recognition could be very valuable for other algorithms dedicated to optimize the overall performance of WSN.…”
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Outage performance analysis of IRS-aided cognitive satellite-terrestrial network by Min LIN, Huaibo GUO, Xiaoyu LIU, Lue HAN, Miaomiao TAN, Lyuxi YANG

“…The outage performance of intelligent reflecting surface (IRS) aided cognitive satellite-terrestrial network was analyzed.Firstly, for the scenario where the point beam was enabled at the satellite to serve the earth station, and the IRS and non-orthogonal multiple access (NOMA) were deployed at the base station to expand the communication range and serve multiple users, respectively, under the condition that cognitive radio technology was employed to enable spectrum sharing between the satellite primary network and the terrestrial secondary network, an optimization problem was established to maximize the average signal-to-noise ratio of terrestrial users.Secondly, a low complexity IRS phase shift design was proposed by using channel state information based on the angle domain, and the phase shift matrix of IRS was obtained.Then, the closed-form expressions of outage probabilities of terrestrial users were derived when users were static and moving, respectively.Furthermore, the asymptotic outage probability formulas at high signal-to-noise ratio were also derived to analyze the system performance.Finally, the correctness of derivation and the influence of main parameters on outage performance were verified by computer simulations.…”
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Analysis of Offshore Pile–Soil Interaction Using Artificial Neural Network by Peiyuan Lin, Kun Li, Xiangwei Yu, Tong Liu, Xun Yuan, Haoyi Li

“…By strictly characterizing the probability cumulative function of the neural network model factors, a foundation is provided for reliability-based design. …”
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An Adaptive Finite Resolution Selection Scheme for RIS Assisted NOMA Networks by Shady M. Ibraheem, Mona Shokair, Mohamed E. Nasr, Nancy Alshaer

“…Furthermore, the impact of network parameters on the throughput and asymptotic average sum rate is discussed in both delay-limited and delay-tolerant transmission modes, respectively. …”
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Using PAT for Energy Recovery and Pressure Reduction in Water Distribution Networks by Hamed Mohammadi, Mahnaz Ghaeini-Hessaroeyeh, Ehsan Fadaei-Kermani

“…Moreover, the nodal pressures have been significantly reduced, and the average pressure of the network decreased by 58%.…”
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Image retrieval method based on data mining and deep residual network by Hongzhi Yuan, Wei Hu

“…To overcome the vanishing gradient problem of traditional residual networks in deep structures, an improved residual network structure was introduced in the study, and optimization was performed simultaneously by combining the spatial transformation network and the feature enhancement module. …”
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Dynamic Routing in Urban Transport Logistics under Limited Traffic Information by Viktor Danchuk, Oleksandr Hutarevych, Serhii Taraban

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Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network by Liming Li, Shubin Zheng, Chenxi Wang, Shuguang Zhao, Xiaodong Chai, Lele Peng, Qianqian Tong, Ji Wang

“…The proposed algorithm mainly includes improved You Only Look Once version 3 (YOLOv3) and the crack recognition network, where the crack recognition network includes two modules, the crack encoder-decoder network (CEDNet) and the crack residual refinement network (CRRNet). …”
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An Innovative Priority Queueing Strategy for Mitigating Traffic Congestion in Complex Networks by Ganhua Wu

“…Optimizing transportation in both natural and engineered systems, particularly within complex network environments, has become a pivotal area of research. …”
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Functional brain networks develop from a "local to distributed" organization. by Damien A Fair, Alexander L Cohen, Jonathan D Power, Nico U F Dosenbach, Jessica A Church, Francis M Miezin, Bradley L Schlaggar, Steven E Petersen

“…Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. …”
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Training Large Models on Heterogeneous and Geo-Distributed Resource with Constricted Networks by Zan Zong, Minkun Guo, Mingshu Zhai, Yinan Tang, Jianjiang Li, Jidong Zhai

“…Due to the lack of awareness of heterogeneous devices and network topologies, existing parallel training frameworks struggle to leverage mixed GPU resources across constrained networks effectively. …”
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Optimization of Temperature Schedule Parameters on Heat Supply in Power-and-Heat Supply Systems by V. A. Sednin, A. V. Sednin, M. L. Bogdanovich

“…The paper considers problems concerning optimization of a temperature schedule in the district heating systems with steam-turbine thermal power stations having average initial steam parameters. …”
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Integrating IT and OT for Cybersecurity: A Stochastic Optimization Approach via Attack Graphs by Gonzalo Martinez Medina, Krystel K. Castillo-Villar, Tanveer Hossain Bhuiyan

“…The defense strategies identified by our approach demonstrate that robust security protection can be achieved with optimal resource allocation, providing robust protection while minimizing implementation costs across the most critical vulnerabilities in the manufacturing network.…”
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Stock Price Pattern Prediction Based on Complex Network and Machine Learning by Hongduo Cao, Tiantian Lin, Ying Li, Hanyu Zhang

“…It is found that network topology characteristics, such as average degree centrality, average strength, average shortest path length, and closeness centrality, can identify periods of sharp fluctuations in the stock market. …”
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Particle swarm optimization with YOLOv8 for improved detection performance of tomato plants by Sarah M. Ayyad, Nada M. Sallam, Samah A. Gamel, Zainab H. Ali

“…The enhancements made to the original YOLO-v8 network have attained promising results. The experiments on the collection of different datasets reveal that the proposed model performs with the highest precision, recall, F1-score, and mean average precision (mAP) of 0.89, 0.9, 0.89, and 0.89 (mAP@0.5:0.95), respectively, exceeding other models. …”
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