Distributed Authentication of Power Grid Safety and Stability Control Terminals Based on DHT and Blockchain by Yening LAI, Ke FENG, Tongwei YU, Wang WANG, Guanjun TANG

“…The authentication scheme is systematically implemented, and experiments are performed on its average authentication latency and average terminal storage cost, which has verified the feasibility of the proposed scheme and its advantages in spatial-temporal efficiency. …”
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The inversion of the single-stage hydraulic fracture parameters for shale gas well based on the shut-in pressure-drop data: a case of shale gas well in Changning N209 well area by ZHAO Yulong, CUI Qianchen, GAO Shangjun, CHEN Man, ZHENG Chuntao, ZHANG Liehui

“…Accurate evaluation of hydraulic fracture network parameters is an important prerequisite for activities such as the evaluation of fracturing effect, fracturing process optimization and productivity prediction after hydraulic fracturing. …”
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Improved biosynthesis and characteristics of silver nanoparticles using marine endophytic fungi exposed to hypo-osmotic stress by Miguel A. M. Oliveira, Maria L. Calado, Sara F. C. Guerreiro, Juliana R. Dias, Maria J. Campos, Patrick G. Murray, Sara C. Novais

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MIMO-ISAC Precoding Design Toward Random Signals by Fan LIU, Shihang LU, Zihao CHEN

“…Integrated Sensing And Communications (ISAC) based on reusing random communication signals within the existing network architecture may drastically reduce implementation costs, thereby accelerating the integration of sensing functionalities into current communication networks. …”
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KERF: a knowledge-enhanced relearning framework for tailings pond detection from high-resolution remote sensing images by Bingjie Liu, Wei Wu, Hao Wu, Ailong Ma, Shaohua Hu, Nianchun Du, Xuan Cui, Junyang Xie

“…An augmented feature pyramid network and multi-stage detectors are introduced to generate proposals with feature enhancement and location optimization. …”
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Block-Based Adaptive Compressed Sensing by Using Edge Information for Real-Time Reconstruction by V. Pavitra, V. B. S. Srilatha Indira Dutt

“…The recovery time for compressively sensed images, whether they are natural or medical, is real-time, with an average duration of around 50-65 milliseconds. The algorithm is also used in conjunction with Content Aware Scalable deep compressed sensing Network (CASNET) to get learned matrix for measurements on encoder side and pretrained model for reconstruction on decoder side. …”
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Steel Surface Defect Detection Technology Based on YOLOv8-MGVS by Kai Zeng, Zibo Xia, Junlei Qian, Xueqiang Du, Pengcheng Xiao, Liguang Zhu

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MSFNet3D: Monocular 3D Object Detection via Dual-Branch Depth-Consistent Fusion and Semantic-Guided Point Cloud Refinement by Rong Yang, Zhijie You, Renhui Luo

“…Our contributions are threefold: (1) We introduce a dual-branch network to optimize depth maps and propose a multi-scale channel spatial attention module (MS_CBAM). …”
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Effectiveness Evaluation of Signal Coordination Based on Spatially Sparse Trajectory Data by Yuxuan Sun, Chunhui Yu, Wanjing Ma

“…Signal coordination is an effective measure to improve the traffic efficiency of urban road networks, and network partition is an important part of it. …”
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Splitting of Traffic Flows to Control Congestion in Special Events by Ciro D'Apice, Rosanna Manzo, Luigi Rarità

“…The obtained results have been tested by simulations of urban networks. Decongestion effects are also confirmed estimating the time a car needs to cross a fixed route on the network.…”
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Machine learning assisted design of Fe-Ni-Cr-Al based multi-principal elements alloys with ultra-high microhardness and unexpected wear resistance by Ling Qiao, Jingchuan Zhu, Junya Inoue

“…Generalized Regression Neural Network (GRNN) showed high accuracy to construct the composition-microhardness model and was used for microhardness prediction and composition optimization. …”
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Novel quality of service-oriented Spark job scheduler by HE Yulin, MO Peiheng, Fournier-Viger Philippe, HUANG Zhexue

“…In the scheduling environment, training methods based on absolute deep <italic>Q</italic>-network and a combination of proximal policy optimization and generalized advantage estimation were implemented, enabling DRL agent to adaptively learn the characteristics of different types of jobs as well as the characteristics of dynamic and bursty cluster environments. …”
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A novel quality of service-oriented Spark job scheduler by HE Yulin, MO Peiheng, Fournier-Viger Philippe, HUANG Zhexue

“…In the scheduling environment, training methods based on absolute deep <italic>Q</italic>-network and a combination of proximal policy optimization and generalized advantage estimation were implemented, enabling DRL agent to adaptively learn the characteristics of different types of jobs as well as the characteristics of dynamic and bursty cluster environments. …”
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Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method by Rencheng Fang, Tao Zhou, Baohua Yu, Zhigang Li, Long Ma, Tao Luo, Yongcai Zhang, Xinqi Liu

“…Firstly, the model incorporates the good point set initialization, triangle wandering strategy and adaptive t-distribution strategy to obtain the Modified Dung Beetle Optimization Algorithm (MDBO), secondly, it uses MDBO to optimize the weights and thresholds of the BP neural network, and lastly, the optimized BP neural network is used as a weak learner for Adaboost. …”
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Research on Health Monitoring of Flying-Swallow-Typed Tied Arch Bridge Based on PSO-GRNN Algorithm by Tianpeng Zhang, Long Liu, Pengfei Ji

“…The prediction model is built in accordance with the general regression neural network (GRNN), and the parameters of the GRNN model are optimized using particle swarm optimization (PSO) to build the PSO-GRNN prediction model, with the aim of modifying the finite element model. …”
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Charging Management of Electric Vehicles on Loading Capability of Distribution System Equipment, Voltage Quality, and Energy Loss by Monte Carlo Simulation and Linear Programming by Somporn Sirisumrannukul

“…To mitigate the overloading of the system components, a coordinated charging (also known as smart charging) model formulated as a linear programming problem is proposed with the objective of maximizing the total amount of energy consumption by EVs and the sum of all individual final states of charge (SoCs), and minimizing the sum of the absolute deviation of individual SoCs from the overall average SoC. The optimization problem is subject to equipment capability loading and planning criteria constraints with low, medium, and high EV penetration levels. …”
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Model Predictive Control With Reinforcement Learning-Based Speed Profile Generation in Racing Simulator by Min-Seong Kim, Tae-Hyoung Park

“…Model Predictive Control (MPC) is a widely used optimal control strategy, particularly effective in managing complex constraints. …”
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Noise Currents Measurement Method for Rail Vehicles Based on Broad Learning System by HAN Ran, LI Jie, LIAN Cheng, ZHANG Kun

“…Moreover, the testing accuracy of this method is significantly better than existing methods: weighting method, BP neural network, particle swarm optimization and artificial bee colony, the effectiveness of the proposed testing method has been verified.…”
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Multimodal diffusion framework for collaborative text image audio generation and applications by Junhua Wang, Ouya Zhang, Yuan Jiang

“…Our approach implements cross-modal mutual guidance and consistency optimization to ensure semantic coherence across generated modalities. …”
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Deep learning-based prediction of multi-level just noticeable distortion by Haifeng XU, Hongkui WANG, Haibing YIN, Chuqiao CHEN

“…Visual just noticeable distortion (JND) directly reflects the sensitivity of the human visual system to visual signal noise, and is widely used in image and video processing.Aiming at the multilevel prediction problem of video JND threshold, it was transformed into the prediction problem of satisfied user ratio (SUR) curve, and a feature fusion-based SUR curve prediction model was proposed.The model was mainly divided into key frame extraction module, feature extraction and fusion module, and SUR score regression module.In the key frame extraction module, according to the visual perception mechanism, the spatial-temporal domain perception complexity was proposed and used as the video key frame judgment index.In the feature extraction and fusion module, a multi-scale dense residual network was proposed based on dense residual block (RDB) to realize image feature extraction and multi-scale fusion.The experimental results show that the proposed SUR curve prediction model is overall better than the existing models in terms of JND prediction accuracy and reduces the time cost by 8.1% on average in terms of operational efficiency.Meanwhile, the model can also be used to predict other layers of JND thresholds, which can be directly applied to video multilevel perceptual coding optimization.…”
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