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OT-ADG:Optimal-transport-driven adaptive data generation for WiFi sensing in airborne mobile networks 认领 引用
1
作者 Xinxin LU Zhiyi ZHOU +4 位作者 Jiancheng CHI Lei WANG Yunbo CHEN Xin HAN Ning CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期67-83,共17页
Airborne Mobile Networks(AMNs)require high-precision situational awareness in enclosed spaces for critical tasks like border surveillance and maritime monitoring.Compared to vision and wearable technologies,WiFi signa... Airborne Mobile Networks(AMNs)require high-precision situational awareness in enclosed spaces for critical tasks like border surveillance and maritime monitoring.Compared to vision and wearable technologies,WiFi signals in AMN environments are a promising sensing medium due to their non-intrusiveness,cost-effectiveness,and privacy benefits.However,the development of WiFi-based sensing in AMNs is hindered by the scarcity of high-quality large-scale data.While Data Augmentation(DA)can address this scarcity,traditional time-series DA may distort WiFi Channel State Information(CSI)'s time–frequency properties,and deep generative models suffer from mode collapse,spectral distortion,and high computational costs.To overcome these limitations,we propose OT-ADG,an adaptive data generation algorithm based on Optimal Transport(OT)theory to synthesize high-fidelity WiFi sensing samples.It dynamically models subcarrierspecific energy distributions and computes optimal transport plans using Sinkhorn's algorithm with entropy regularization.Furthermore,we present ViFi,the first large-scale and scenario-rich WiFi sensing dataset to fill the gap in AMN applications,encompassing 26 real-world scenarios with 20640 CSI samples and synchronized videos.Experimental results show OT-ADG's superior performance,with maximum improvements of up to 14.88%over baseline recognition methods,while outperforming existing DA approaches.Moreover,the robust performance of multiple WiFi-based HAR models validates the effectiveness of the ViFi dataset. 展开更多
关键词 Airborne mobile networks WiFi sensing Data augmentation Optimal transport Multi-modal dataset Data processing
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Physics-informed neural network for reconstruction of seismic data under compressed sensing sampling 认领 引用
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作者 Yin-Shuo Li Wen-Kai Lu +2 位作者 Xiao-Gang Huang Ji-Cai Ding Cao Song 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1829-1841,共13页
Seismic exploration is one of the most critical methodologies and the highest-cost expenditures in the pre-exploration.The main cost of seismic exploration is acquiring seismic data,which can be significantly reduced ... Seismic exploration is one of the most critical methodologies and the highest-cost expenditures in the pre-exploration.The main cost of seismic exploration is acquiring seismic data,which can be significantly reduced through compressed sensing(CS)techniques.Traditional and deep learning(DL)CS methods offer unprecedented opportunities for cost optimization while maintaining data fidelity.However,CS methods rely on random acquisition,which performs poorly when the seismic data are not randomly acquired.This manuscript proposes a novel physics-informed neural network(PINN)framework for reconstructing 3D seismic data acquired via down-sampling from Ocean Bottom Seismometer(OBS)observation systems.The compressed sensing acquisition system of seismic data contains two types of sparsity:1)2D random missing traces,2)Dual random missing of source lines and source points.The proposed method employed move-out(MO)transformations with multiple constant velocities to mitigate aliasing artifacts and improve reconstruction accuracy.Then,a pre-interpolation process is utilized for the MO-transformed seismic data groups.Additionally,a semblance evaluation mechanism dynamically assigns weights to each MO dataset,generating optimized,pre-interpolated seismic profiles.Finally,the PINN architecture integrates physical constraints to refine the reconstructed data.The experimental results demonstrate the superior reconstruction performance and computational efficiency of the proposed method compared with the state-of-the-art. 展开更多
关键词 Physics-informed neural network Reconstruction of 3-D seismic data Move-out transformation Alias suppression
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Distribution network data asset protection considering multiple topology and multi-dimensional knowledge graph 认领 引用
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作者 Junfeng Yang Li Liu +5 位作者 Nawaraj Kumar Mahato Luhan Li Jiaxuan Yang Gangjun Gong Jun Lu Chao Yang 《Global Energy Interconnection》 EI CSCD 2026年第3期585-610,共26页
The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions,exacerbating security risks,such as unauthorized access,data tampering,an... The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions,exacerbating security risks,such as unauthorized access,data tampering,and forgery.In response to these challenges,this study introduces a novel framework that enhances the protection of data assets.It incorporates a multi-dimensional knowledge graph(MDKG)to refine access control and overcome current limitations by integrating a comprehensive set of data asset attributes,roles,policies,and permissions.This approach fosters the development of a nuanced and adaptable access-control mechanism.Furthermore,the framework integrates multiple topology(MTP)for holistic security risk detection,leveraging attention mechanisms,and cross-fusion to adapt to the dynamic data security landscape.Empirical evaluations affirm the effectiveness of MDKG-based access control,whereas comparative experiments demonstrate the superiority of the MTP-based security risk model over existing models.The framework was proven to be effective in countering security risks.This study provides innovative perspectives on data asset protection and establishes a solid foundation for the advancement of smart grid technology. 展开更多
关键词 Distribution network Data asset protection Security access control Multi-dimensional knowledge graph Multiple topology Security risk detection
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A 3D Object Recovery Framework for Enhancing In-Vehicle Network Resilience to Data Tampering Attack 认领 引用
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作者 Gangtao Han Yurui Chen +3 位作者 Song Wang Enqing Chen Lingling Li Gaofeng Pan 《Computers, Materials & Continua》 SCIE EI 2026年第6期2418-2436,共19页
The integrity of perception data transmitted over in-vehicle networks is important for the safety of autonomous driving.However,legacy protocols like the Controller Area Network(CAN)bus which lacks essential security ... The integrity of perception data transmitted over in-vehicle networks is important for the safety of autonomous driving.However,legacy protocols like the Controller Area Network(CAN)bus which lacks essential security features make In-Vehicle Networks(IVNs)vulnerable to data tampering attacks.Current research typically focuses on detecting the attack itself but ignores the information recovery from the missing data,leading to an unsafe autonomous driving system.To address the issue,we propose a 3D object recovery framework to recover the missing data caused by the tampering attack that occurred in in-vehicle networks.The proposed framework exploits both temporal and spatial context for the 3D object recovery,where a temporal branch is designed to learn the coordinate offsets of 3D objects based on historical data from previous frames,while a spatial branch employs information from the adjacent views of the attacked objects to locate the recovered objects from the overlapped regions in the current frame.By integrating the temporal and spatial clues,the framework effectively recovers the missing objects from the resting ones,thereby enhancing the immunity of in-vehicle networks for the tampering attack.Extensive experiments on the nuScenes dataset demonstrate that the proposed framework significantly improves 3D object detection performance under the attack when compared to the method without recovery.Additionally,the recovery performance becomes better as the attack intensity increases,highlighting the framework’s robustness in high-risk scenarios.The source will be available upon publication. 展开更多
关键词 3D object recovery data tampering attack in-vehicle networks autonomous driving security temporal-spatial context
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Reconstruction of bridge‑sensor data and detection of structural damage based on gradient‑coupled autoencoder and fully connected network 认领 引用
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作者 DUAN Yuanfeng DING Pengyao +1 位作者 DUAN Zhengteng CHENG J.J.Roger 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期1-11,共11页
A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data comp... A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data compression‑reconstruction and structural damage identification.Under the condition where 40% of the sensor nodes are missing,the model successfully reconstructs the full sensor network with an R2 of 0.916 and normalized root mean square error(NRMSE)of 0.0288.Even under significant noise contamination with an SNR of 12 dB,the model maintains strong reconstruction performance,achieving a R2 of 0.910 and NRMSE of 0.0253.Forty‑six structural damage scenarios were simulated using the scaled bridge model.The accuracy of spatial localization and quantification of the damage severity using the framework exceeds 99.3%.The proposed framework reduces the training time by 54.4%and iteration counts by 45.5% compared to conventional two‑stage machine learning approaches,demonstrating the efficiency of gradient‑coupled optimization. 展开更多
关键词 structural health monitoring machine learning data compression damage identification convolutional neural network fully connected neural network gradient‑coupled mechanism
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A Convolutional Neural Network-Based Deep Support Vector Machine for Parkinson’s Disease Detection with Small-Scale and Imbalanced Datasets 认领 引用
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作者 Kwok Tai Chui Varsha Arya +2 位作者 Brij B.Gupta Miguel Torres-Ruiz Razaz Waheeb Attar 《Computers, Materials & Continua》 SCIE EI 2026年第1期1410-1432,共23页
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d... Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested. 展开更多
关键词 Convolutional neural network data generation deep support vector machine feature extraction generative artificial intelligence imbalanced dataset medical diagnosis Parkinson’s disease small-scale dataset
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Methods for correcting mud-logging lithology profi le using well logging data based on neural networks 认领 引用
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作者 Jin Li-zuan Sun Yu-hong +2 位作者 Li Hui Dai Zi-shan Wang Cheng-fang 《Applied Geophysics》 SCIE CSCD 2026年第2期710-719,870,共10页
This study presents a method to correct the lithology of mud-logging profile with logging data based on neural network,which aims to solve the problems of time-consuming,high labor intensity and great infl uence of hu... This study presents a method to correct the lithology of mud-logging profile with logging data based on neural network,which aims to solve the problems of time-consuming,high labor intensity and great infl uence of human factors in the process of traditional lithology correction of mud-logging profi le.Firstly,the lithology of mud-logging profi le is processed by digital technology and converted into digital curve which is consistent with the logging sampling interval,and the logging lithology curve is calculated by using the optimal logging method.Then,combining automatic depth-correction technology with manual correction methods,the lithology of mud-logging profi le is corrected for depth.On the basis of lithology depth-correction of mudlogging profile,the multi-layer perceptron(MLP)neural network is used to learn logging data and realize accurate identification of multiple lithologies,so as to construct a high-precision logging profile lithology curve and provide accurate basis for lithology correction of mud-logging profile.The effectiveness and accuracy of the proposed method are verifi ed by practical application cases.The corrected lithology of mudlogging profi le is highly consistent with the lithology of logging profi le,which provides a solid foundation for subsequent geological interpretation,reservoir evaluation and oil and gas resource assessment.This study not only improves the effi ciency of mud-logging data processing,but also ensures that the needs of exploration and exploitation work are met in a timely manner,which has important theoretical signifi cance and application value. 展开更多
关键词 logging data neural network data fusion mud-logging profi le lithology correction
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CTSO-DRNN:Energy-Aware Delay Prediction and Optimized Data Aggregation in IoT-Based Wireless Sensor Networks 认领 引用
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作者 Reshma Siyal Jun Long +3 位作者 Muhammad Asim Mudasir Ahmad Wani Kashish Ara Shakil Sajid Shah 《Computers, Materials & Continua》 SCIE EI 2026年第7期1808-1823,共16页
The rapid growth of the Internet of Things(IoT)has led to dense wireless sensor networks(WSNs)deployed in critical applications such as smart cities,industrial monitoring,and healthcare.However,energy constraints,unpr... The rapid growth of the Internet of Things(IoT)has led to dense wireless sensor networks(WSNs)deployed in critical applications such as smart cities,industrial monitoring,and healthcare.However,energy constraints,unpredictable communication delays,and inefficient data aggregation remain significant challenges that limit network reliability and operational lifespan.Traditional approaches often fail to balance delay minimization with energy efficiency,especially in large-scale or dynamic networks.To address these issues,this study proposes CTSO-DRNN,a novel framework that integrates Chronological Tangent Search Optimization(CTSO)with a Deep Recurrent Neural Network(DRNN)for accurate delay prediction and optimized data aggregation.The framework constructs Link Delay-Distance(LDD)trees to guide hierarchical communication and leverages CTSO to optimize the DRNN for predicting network delays,enabling adaptive scheduling and energy-aware operation.Experimental findings from simulated WSNs comprising 100 to 250 nodes indicate that the CTSO-DRNN approach decreases the average communication delay by roughly 28%to 60%,increases link lifetime by 8%to 30%,and reduces routing distance by 14%to 25%when compared to various leading-edge techniques across diverse network densities.These improvements highlight the framework’s ability to maintain low latency,prolong network operation,and enhance overall energy efficiency. 展开更多
关键词 Internet of Things(IoT) wireless sensor networks deep recurrent neural network chronological tangent search optimization data aggregation
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A Missing Data Complement Method Based on 3D Convolutional Neural Network and CGAN for a Distribution Network 认领 引用
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作者 Kewen Li Xiaoyong Yu +1 位作者 Shifeng Ou Jueming Pan 《Energy Engineering》 EI 2026年第9期132-156,共25页
The increasing integration of renewable energy sources(e.g.,wind and solar power)into distribution grids and the development of new,source-grid-load-storage coordinated power systems have led to a substantial expansio... The increasing integration of renewable energy sources(e.g.,wind and solar power)into distribution grids and the development of new,source-grid-load-storage coordinated power systems have led to a substantial expansion in the volume of situational awareness data in the distribution networks.Moreover,the transmission of low-voltage distribution measurement data via a power line carrier(PLC)is often susceptible to packet loss and,consequently,data gaps.To address these issues,this paper proposes a data completion method using a conditional generative adversarial network(CGAN)integrated with a three-dimensional convolutional neural network(3D-CNN).This approach leverages the ability of CNNs to extract and fuse multidimensional spatiotemporal features and the power of GANs(generative adversarial networks)for data augmentation.Firstly,a 3D-CNN is trained to establish a mapping between the spatiotemporal context of the measured data and the target missing data.Secondly,a CGAN is practicing via adversarial training to establish a data completion model for the distribution networks.Finally,the simulations of the IEEE 14-bus and 33-bus systems demonstrate the proposed approach's performance improvement in the distribution networks compared with that of conventional methods in terms of root mean square error,spatiotemporal correlation,and maximum volatility amplitude,which are the typical measuring metrics. 展开更多
关键词 Distribution network data estimation 3D convolutional neural network conditional generative adversarial networks spatiotemporal correlation
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An Optimized Ensemble Learning Framework for Energy Efficiency Assessment in Low-Voltage Distribution Networks Using Multi-Source Data Integration 认领 引用
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作者 Yujie Shi Guoxing Wu +2 位作者 Qingwei Wang Xieli Fu Wenfeng Yang 《Energy Engineering》 EI 2026年第9期350-375,共26页
This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from ... This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from smart meters,SCADA systems,meteorological stations,and network topology databases,employing advanced feature engineering to extract 89 essential predictors from 147 initial features.Three gradient boosting algorithms-Random Forest,XGBoost,and LightGBM-are combined through an elastic net stacking strategy with Bayesian hyperparameter optimization.The stacking ensemble achieved superior performance with an MAE of 118.4 kWh,an RMSE of 164.2 kWh,an MAPE of 3.98%,and an R2of 0.952,representing 16.8%improvement over individual models.SHAP analysis provided model interpretability,identifying temperature,historical consumption,and temporal features as the primary drivers of efficiency.The framework demonstrated robust performance under data quality degradation and successfully generalized across diverse network configurations.Field implementation yielded an 8.3%reduction in distribution losses(95%CI:7.2%-9.4%,p<0.0001),34%decrease in transformer failure rates(95%CI:28%-40%,p=0.003),and 12%-15%operational cost reduction.The framework's ability to provide accurate predictions from 15 min to 24 h ahead while maintaining computational efficiency enables proactive distribution network management,supporting the transition toward efficient and sustainable power systems. 展开更多
关键词 Ensemble learning energy efficiency assessment low-voltage distribution networks multi-source data integration SHAP analysis
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Research on the Key Role of Data Assets in Operation and Maintenance Retrospective Analysis Under Rail Transit Network Security Control 认领 引用
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作者 Jianyong Huang 《Journal of Electronic Research and Application》 2026年第6期59-66,共8页
This paper takes the national railway ticketing system as the research object,and studies the role of data assets in the operation and maintenance retrospective analysis under the network security control situation.Co... This paper takes the national railway ticketing system as the research object,and studies the role of data assets in the operation and maintenance retrospective analysis under the network security control situation.Comprehensively sort out the overall scheme of network security operation and maintenance of the railway passenger ticket system,and focus on the significance of data assets with asset accounts as the core in operation and maintenance management,including important links such as asset modeling,status monitoring,log correlation,and fault tracing.Based on this premise,this paper studies the data collection,correlation analysis,and retrospective analysis technology for security operation and maintenance,and explains the supporting significance of the data asset entity model and relationship model to improve the efficiency of fault location and security event analysis.Research and summarize the practical experience of data asset management,operation,and maintenance,and provide a reference for data asset management of other major information infrastructures in network security operation and maintenance. 展开更多
关键词 Railway ticketing system Network security Data assets Operation and maintenance traceback
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Technical Security Vulnerabilities and Protection Strategies for Seismic Monitoring Data Transmission Networks 认领 引用
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作者 YANG Xiaofeng 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期026-031,共6页
The seismic monitoring data transmission network is the core infrastructure for emergency management departments to carry out seismic monitoring, early warning and emergency response. Its safe and stable operation is ... The seismic monitoring data transmission network is the core infrastructure for emergency management departments to carry out seismic monitoring, early warning and emergency response. Its safe and stable operation is directly related to the safety of people's lives and property and regional social stability. Combined with the actual seismic monitoring work in Botou City, based on the local base station equipment configuration and network operation status, this paper systematically analyzes the existing technical security vulnerabilities in the transmission link, terminal equipment, network management and environmental adaptation of the current seismic monitoring data transmission network. In line with the requirements of the 14th Five-Year Plan for the upgrading of the seismic backbone network and industry security specifications, targeted and implementable protection strategies are proposed to strengthen the coordinated connection between technical and management protection, avoiding the listing of construction and project plans. It provides theoretical and practical support for the Emergency Management Bureau of Botou City to optimize the network security system and improve risk prevention and control capabilities, ensuring the real-time, accuracy and security of monitoring data. 展开更多
关键词 Botou City seismic monitoring data transmission network technical security vulnerabilities protection strategies emergency management
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Installation of the Automatic Noise Monitoring Network for Urban Functional Zones and Data Application 认领 引用
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作者 DUAN Lili 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期066-070,共5页
Noise pollution is one of the major environmental pollution issues that harm people's quality of life. According to the "China Noise Pollution Prevention and Control Report (2024)" released by the Minist... Noise pollution is one of the major environmental pollution issues that harm people's quality of life. According to the "China Noise Pollution Prevention and Control Report (2024)" released by the Ministry of Ecology and Environment, in 2023, the number of public complaints about noise disturbances received by departments such as ecology and environment and public security in cities at or above the prefecture level nationwide (excluding municipalities directly under the central government, sub-provincial cities, and cities with independent planning status) reached approximately 5.7million cases, representing an increase of over 7%. To better address the increasingly severe noise pollution problem, the Ministry of Ecology and Environment issued the "Ten Measures for Sound Management" —the "14th Five-Year Plan for Noise Pollution Prevention and Control Action" —in 2023, explicitly proposing the comprehensive establishment of a regional automatic monitoring system for sound environmental quality: by January 1, 2025, all cities where provincial, autonomous region, or municipal governments are located, as well as cities with independent planning status, shall have established regional automatic monitoring stations for sound environmental quality connected to national and provincial platforms. After 2026, all cities at or above the prefecture level nationwide should participate in this automatic monitoring system. 展开更多
关键词 Functional area acoustic environment Automatic noise monitoring Network configuration Data utilization Disturbance level Voiceprint recognition
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DSP-free coherent receivers in frequency-synchronous optical networks for next-generation data center interconnects 认领 引用 被引量：2
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作者 Lei Liu Feng Liu +2 位作者 Cheng Peng Bo Xue William Shieh 《Advanced Photonics Nexus》 CSCD 2025年第3期141-148,共8页
Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communi... Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communication has evolved into an increasingly prominent area of research in recent years.Here,we demonstrate DSP-free coherent optical transmission by analog signal processing in frequency synchronous optical network(FSON)architecture,which supports polarization multiplexing and higher-order modulation formats.The FSON architecture that allows the numerous laser sources of optical transceivers within a data center can be quasi-synchronized by means of a tree-distributed homology architecture.In conjunction with our proposed pilot-tone assisted Costas loop for an analog coherent receiver,we achieve a record dual-polarization 224-Gb/s 16-QAM 5-km mismatch transmission with reset-free carrier phase recovery in the optical domain.Our proposed DSP-free analog coherent detection system based on the FSON makes it a promising solution for next-generation,low-power,and high-capacity coherent data center interconnects. 展开更多
关键词 digital signal processing-free data center interconnect frequency synchronous optical network analog signal processing
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A Modified Deep Residual-Convolutional Neural Network for Accurate Imputation of Missing Data 认领 引用 被引量：1
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作者 Firdaus Firdaus Siti Nurmaini +8 位作者 Anggun Islami Annisa Darmawahyuni Ade Iriani Sapitri Muhammad Naufal Rachmatullah Bambang Tutuko Akhiar Wista Arum Muhammad Irfan Karim Yultrien Yultrien Ramadhana Noor Salassa Wandya 《Computers, Materials & Continua》 SCIE EI 2025年第2期3419-3441,共23页
Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attentio... Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data. 展开更多
关键词 Data imputation missing data deep learning deep residual convolutional neural network
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Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks 认领 引用 被引量：4
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作者 Afshin Tatar Manouchehr Haghighi Abbas Zeinijahromi 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第1期106-125,共20页
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist... The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications. 展开更多
关键词 Deep learning(DL) Image analysis Image data augmentation Convolutional neural networks(CNNs) Geological image analysis Rock classification Rock thin section(RTS)images
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Dynamic Collaborative Data Download in Heterogeneous Satellite Networks 认领 引用 被引量：1
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作者 Wu Qi Li Xintong Zhu Lidong 《China Communications》 SCIE EI CSCD 2025年第2期26-46,共21页
Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic... Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic nature of LEO satellites leads to limited and rapidly varying contact time between them and Earth stations(ESs),making it difficult to timely download massive communication and remote sensing data within the limited time window.To address this challenge in heterogeneous satellite networks with coexisting geostationary-earth-orbit(GEO)and LEO satellites,this paper proposes a dynamic collaborative inter-satellite data download strategy to optimize the long-term weighted energy consumption and data downloads within the constraints of on-board power,backlog stability and time-varying contact.Specifically,the Lyapunov optimization theory is applied to transform the long-term stochastic optimization problem,subject to time-varying contact time and on-board power constraints,into multiple deterministic single time slot problems,based on which online distributed algorithms are developed to enable each satellite to independently obtain the transmit power allocation and data processing decisions in closed-form.Finally,the simulation results demonstrate the superiority of the proposed scheme over benchmarks,e.g.,achieving asymptotic optimality of the weighted energy consumption and data downloads,while maintaining stability of the on-board backlog. 展开更多
关键词 backlog stability data download heterogeneous satellite networks Lyapunov optimization power allocation
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Data Gathering Based on Hybrid Energy Efficient Clustering Algorithm and DCRNN Model in Wireless Sensor Network 认领 引用
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作者 Li Cuiran Liu Shuqi +1 位作者 Xie Jianli Liu Li 《China Communications》 SCIE EI CSCD 2025年第3期115-131,共17页
In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clu... In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay. 展开更多
关键词 clustering data gathering DCRNN model network lifetime wireless sensor network
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A Hierarchical-Based Sequential Caching Scheme in Named Data Networking 认领 引用
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作者 Zhang Junmin Jin Jihuan +3 位作者 Hou Rui Dong Mianxiong Kaoru Ota Zeng Deze 《China Communications》 SCIE EI CSCD 2025年第5期48-60,共13页
Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently r... Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH). 展开更多
关键词 hierarchical router named data networking sequential caching
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Optimal Secure Control of Networked Control Systems Under False Data Injection Attacks:A Multi-Stage Attack-Defense Game Approach 认领 引用
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作者 Dajun Du Yi Zhang +1 位作者 Baoyue Xu Minrui Fei 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第4期821-823,共3页
Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by de... Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels. 展开更多
关键词 designing defense strategy networked control systems ncss alterations energy networked control systems false data injection attacks fdias strategywhile false data injection attacks optimal secure control identifying attack strategymaintaining
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