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Drive-by spatial offset detection for high-speed railway bridges based on fusion analysis of multi-source data from comprehensive inspection train 认领 引用
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作者 Chuang Wang Jiawang Zhan +4 位作者 Nan Zhang Yujie Wang Xinxiang Xu Zhihang Wang Zhen Ni 《Railway Engineering Science》 EI 2026年第1期128-148,共21页
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ... The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges. 展开更多
关键词 High-speed railway bridge Drive-by inspection Spatial offset Multi-source data fusion Deep learning
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High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data 认领 引用
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作者 Qiuhua TANG Ningning LI +4 位作者 Yujie ZHANG Zhipeng DONG Yongling ZHENG Jingjing BAO Jingyu ZHANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第1期99-108,共10页
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications... Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs. 展开更多
关键词 Wuzhizhou Island marine remote sensing coastal mapping multi-spectral remote sensing shallow water reef seabed sediment classification benthic habitat mapping multi-source data fusion random forest(RF)
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A Survey of Key Technologies for Multi-source Heterogeneous Data in Intelligent Manufacturing 认领 引用
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作者 Minghao Zhu Pengfei Yang +2 位作者 Bo Gao Xuehan Li Letian Wang 《Instrumentation》 2026年第1期26-39,共14页
Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large v... Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large volumes of multi-source heterogeneous data across all stages of the product life cycle.However,current data utilization methods remain simplistic,and the goal of leveraging multi-source heterogeneous data to drive manufacturing value has yet to be fully realized.To address this issue,this study first defines the concept and characteristics of multi-source heterogeneous data in intelligent manufacturing,based on an analysis of its relationship with industrial big data.Then,integrating principles from data science,a technological framework for multi-source heterogeneous data is proposed.The key technologies involved in each stage of data processing are investigated,and typical applications of such data in intelligent manufacturing are discussed.Finally,this paper analyzes the challenges and future development directions of multi-source heterogeneous data processing in intelligent manufacturing.The goal is to provide theoretical and technical support for integrating intelligent manufacturing with data science. 展开更多
关键词 intelligent manufacturing multi-source heterogeneous data feature fusion data system technological framework
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Optimizing Energy Efficiency in Tunnel Boring Machine Rock Breaking via Multi-source Data Fusion 认领 引用 被引量：1
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作者 Xiaojun Yan Wencong Qi +4 位作者 Chuan Qu Minghui Ma Shanglin Liu Qian Zhang Xuesong Cheng 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第4期481-493,共13页
The era of big data has profoundly transformed mechanics research,with data-driven approaches playing a vital role in modeling and optimization.This study focuses on tunnel boring machine(TBM),where the thrust-torque ... The era of big data has profoundly transformed mechanics research,with data-driven approaches playing a vital role in modeling and optimization.This study focuses on tunnel boring machine(TBM),where the thrust-torque ratio is a key determinant of their tunneling energy efficiency.However,due to the complexity of experiments and the testing requirements,obtaining sufficient high-quality data under varying geological conditions remains a major challenge in optimizing the tunneling energy efficiency of TBM.To address this,multi-cutter rotary cutting machine experiments and numerical simulations were conducted on 22 different rock types.Comprehensive datasets of normal and rolling forces were systematically collected.Using specific energy(SE)as the rock-breaking efficiency metric,we integrated physical and numerical data through a CatBoost-based fusion framework.The predictive model was initially trained on simulation data to capture the relationships among penetration,uniaxial compressive strength,tensile strength,and SE,and was subsequently fine-tuned with experimental data to develop the final fused model.Compared to models trained solely on experimental or simulated data,the fused model reduced RMSE by 37.1%and 58.6%,respectively,and improved R2by 19.0%and 44.6%,thereby enhancing both prediction accuracy and generalization capability.Furthermore,Bayesian optimization was employed to minimize SE and identify the optimal penetration.The results indicate that as rock strength increases,the optimal penetration decreases,while the corresponding minimal SE increases.These findings provide theoretical and engineering insights for improving TBM energy efficiency and parameter optimization,while establishing a robust data fusion framework for mechanical data analysis. 展开更多
关键词 Multi-source data fusion CatBoost Tunnel boring machine Multi-cutter rock-breaking Energy efficiency
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Monitoring of agricultural drought based on multi-source remote sensing data in Heilongjiang Province,China 认领 引用
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作者 Chenfa Jiang Changhui Ma +4 位作者 Sibo Duan Xiaoxiao Min Youzhi Zhang Dandan Li Xia Zhang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第4期1716-1730,共15页
Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Curre... Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Currently,in-situ drought monitoring based on weather stations and based on remote sensing data has limitations,including infrequent updates,limited coverage,and low accuracy.This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province,China.We developed multi-source composite drought indices(MCDIs)at various timescales(3,6,9,and 12 months)by integrating precipitation,land surface temperature,soil moisture,and vegetation indices.Utilizing remote sensing data from various sources,we calculated a series of single drought indices,which are the precipitation condition index,soil moisture condition index,vegetation condition index,and temperature condition index.These are then integrated into MCDIs using a multivariable linear regression approach.The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index(SPEI)than single drought indices.When examining the correlation between different MCDIs and the affected area of crops and major grain production,MCDI-9 showed the highest correlation with the affected area of crops,while MCDI-12 showed the highest correlation with grain production.This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring,gradually spreading from the Greater Khingan Mountains region to the southeastern plains.The drought gradually alleviates during the summer,ending by the autumn harvest period.Therefore,the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions. 展开更多
关键词 agricultural drought spatio-temporal monitoring multi-source remote sensing data SPEI Heilongjiang Province
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Temporal and spatial variations in evapotranspiration on the northern slope of the Kunlun Mountains based on multi-source datasets 认领 引用
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作者 YuanYuan Zhang MingJun Zhang +2 位作者 ShiQin Xu CunWei Che QinQin Du 《Research in Cold and Arid Regions》 CAS CSCD 2026年第1期59-70,共12页
By combining eight types of evapotranspiration datasets,the spatial and temporal variations in the evapotranspiration(ET)on the northern slope of the Kunlun Mountains were analyzed in uninhabited areas that lack obser... By combining eight types of evapotranspiration datasets,the spatial and temporal variations in the evapotranspiration(ET)on the northern slope of the Kunlun Mountains were analyzed in uninhabited areas that lack observational data.The order of the average annual ET was ERA5_Land(312.32 mm/a)>CR(239.80 mm/a)>MOD16STM(211.87 mm/a)>GLADS(119.02 mm/a)>ETM(111.88 mm/a)>EB-ET(109.90 mm/a)>GLEAM(100.84 mm/a)>MERRA-2(100.81 mm/a).The ET value from the ERA5_Land dataset was three times higher than that of the other five datasets.The ET values of the CR and MOD16STM datasets were twice that of the other five datasets.In terms of time,the correlation coefficient between the GLEAM and MERRA-2 datasets was the highest(R?0.82).In terms of space,GLDAS and MERRA-2 had the highest multi-year average ET correlation coefficient(R?0.80).The reduction in spatial scale resulted in clear differences in the multi-year average ET correlations among different products in the same region.In terms of time,the average annual ET of the basins on the northern slope of the Kunlun Mountains exhibited an overall increasing trend for all data sources,and the overall annual average change in the study area estimated by the eight datasets was 1.09 mm/a.The most rapid rates of increase were obtained from GLDAS(1.38 mm/a)and GLEAM(1.38 mm/a).In the CR,ERA5_Land,GLEAM,GLDAS,MERRA-2,ETM,MOD16STM,and EB-ET datasets,46.46%,41.47%,87.30%,40.30%,49.10%,47.13%,57.16%,and 45.12%of the watersheds,respectively,showed a significantly increasing trend.The ET value of the Yarkand River Basin showed a significantly increasing trend for all eight data sources.The results of this study provide a scientific reference for the allocation of water resources on the northern slope of the Kunlun Mountains. 展开更多
关键词 North slope basin of Kunlun Mountains Evapotranspiration Multi-source data Climate change
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CCDNN:A Novel Deep Learning Architecture for Multi-Source Data Fusion 认领 引用
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作者 Zhiwen Chen Siwen Mo +4 位作者 Haobin Ke Steven X.Ding Zhaohui Jiang Chunhua Yang Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期555-567,共13页
With the rapid development of Industrial 4.0 and Industrial Internet of Things,the data collection with multisource has significantly improved.How to effectively fuse these data for various engineering applications is... With the rapid development of Industrial 4.0 and Industrial Internet of Things,the data collection with multisource has significantly improved.How to effectively fuse these data for various engineering applications is still an open and challenge issue.To this end,we propose the canonical correlation guided deep neural network(CCDNN),a novel deep learning architecture,to learn a correlated representation for multi-source data fusion.Unlike the linear canonical correlation analysis(CCA),kernel CCA and deep CCA,in the proposed method,the optimization formulation is not restricted to maximize correlation,instead we make canonical correlation as a constraint,which preserves the correlated representation learning ability and focuses more on the engineering tasks endowed by optimization formulation,such as reconstruction,classification and prediction.Furthermore,to reduce the redundancy induced by correlation,a redundancy filter is designed.We illustrate its data fusion ability via correlated representation learning and superior performance on various engineering tasks.In experiments on MNIST dataset,the results show that CCDNN has better reconstruction performance in terms of mean squared error and mean absolute error than deep CCA and deep canonically correlated autoencoders(DCCAE).Also,we present the application of the proposed network to industrial fault diagnosis and remaining useful life cases for the classification and prediction tasks accordingly.The proposed method demonstrates approving performance in both tasks when compared to existing methods.Extension of CCDNN to much more deeper with the aid of residual connection is also presented in Appendix. 展开更多
关键词 Canonical correlation analysis(CCA) correlated representation learning deep learning fault diagnosis multi-source data fusion remaining useful life
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Blockchain-Based Transparent Certificateless Data Integrity Auditing with Enhanced Tag Security 认领 引用
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作者 Chao Zhang Weidong Zhong +6 位作者 Xu An Wang Weiwei Jiang Ziteng Wang Miao Tian Jianhong Ling Hangjiang Du Yunhui Duan 《Computers, Materials & Continua》 SCIE EI 2026年第8期948-976,共29页
The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing(RDIA)technologies.However,traditional schemes rely on trusted third-party auditors(TPAs),lea... The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing(RDIA)technologies.However,traditional schemes rely on trusted third-party auditors(TPAs),leading to potential collusion and single-point failure vulnerabilities.The integration of blockchain alleviates these issues through decentralization and transparency,yet existing blockchain-based certificateless auditing schemes still suffer from security flaws in the tag generation phase.Addressing the tag forgery vulnerability in Miao et al.’s scheme,which stems from the absence of random parameters in the hash function input,this paper proposes a lightweight enhancement mechanism:incorporating a random factor into the hash input during tag generation to ensure dynamic unforgeability of tags.While retaining the efficiency advantages of the original framework,the improved scheme achieves resistance against tag forgery,proof forgery,and collusion attacks under the Computational Diffie-Hellman(CDH)and Discrete Logarithm(DL)hardness assumptions,validated through rigorous formal proofs.Experimental performance analysis demonstrates that the proposed enhanced scheme introduces negligible computational overhead,providing a secure,practical,and transparent auditing solution for multi-cloud storage environments. 展开更多
关键词 Blockchain tag security certificateless cryptography data integrity auditing cloud storage
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Secret Sharing-Based Reversible Data Hiding for Enhanced Audio Data Security across Multiple Genres 认领 引用
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作者 Mohammad Muzayyin Amrulloh Tohari Ahmad Royyana Muslim Ijtihadie 《Computers, Materials & Continua》 SCIE EI 2026年第8期2219-2240,共22页
The rapid development of digital technology has facilitated data exchange and communication,while simultaneously increasing security threats such as data theft and manipulation.As personal data is highly confidential,... The rapid development of digital technology has facilitated data exchange and communication,while simultaneously increasing security threats such as data theft and manipulation.As personal data is highly confidential,effective protection mechanisms are required in the digital era.Audio steganography hides secret messages(payload)within audio signals;however,many existing approaches rely on a single stego-audio output,which can lead to information leakage during storage or transmission if the file is intercepted.This vulnerability allows an attacker to more easily reconstruct the steganographic scheme from a single output.To address this limitation,this study proposes a secret-sharing-based audio steganography method in which the payload is divided into multiple parts prior to embedding,thereby providing an additional protection mechanism.Nevertheless,the use of secret sharing may degrade stego-audio quality during embedding,as indicated by lower Peak Signal-to-Noise Ratio(PSNR)values.To mitigate this issue,a linear interpolation technique is incorporated to optimize the quality of the stego-audio.The proposed method focuses on three main aspects:improving protection by embedding messages in multiple parts,maintaining embedding capacity without introducing additional stego outputs,and improving audio quality through interpolation-based optimization.Experimental results show that the proposed approach improves stego-audio quality by approximately 6.95%in PSNR compared with several previous studies,while maintaining relatively high PSNR under the evaluated experimental conditions.Statistical evaluation using Normalized Correlation(NC)and entropy measurements indicates consistent payload reconstruction with limited statistical variation after embedding.Overall,the method contributes to an audio steganography scheme with a balanced trade-off between security,capacity,and audio quality. 展开更多
关键词 Audio steganography cybersecurity ICT infrastructure information security reversible data hiding secret sharing
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Research on Security Protection Technologies for Generative Artificial Intelligence and Data Privacy 认领 引用 被引量：1
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作者 Yiran Zhai 《Journal of Electronic Research and Application》 2026年第2期58-64,共7页
The widespread application of generative artificial intelligence has raised profound concerns about data privacy.This study systematically analyzed the multidimensional privacy and security risks hidden by this techno... The widespread application of generative artificial intelligence has raised profound concerns about data privacy.This study systematically analyzed the multidimensional privacy and security risks hidden by this technology throughout the entire lifecycle of data,covering key aspects such as data collection,model training,and content generation.The study further sorted out and explored the current mainstream data privacy and security protection technology system,advocating the construction of a multi-level defense plan covering data,models,outputs,and system architecture.The continuous evolution of technology and the synergy of institutional norms have become the key to balancing technological innovation and privacy protection. 展开更多
关键词 Generative AI Data privacy Security risk Security protection
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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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Practices and Insights of Scientific Data Security Grading Management Based on the Entire Life Cycle 认领 引用
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作者 Yu Zhai Yong Song 《Journal of Electronic Research and Application》 2026年第3期163-168,共6页
In the era of big data,scientific data has become a strategic resource for national scientific and technological innovation and economic and social development,and the importance of its security management has become ... In the era of big data,scientific data has become a strategic resource for national scientific and technological innovation and economic and social development,and the importance of its security management has become increasingly prominent.Based on the theory of the entire life cycle management of scientific data,this paper deeply discusses the core connotation of data security grading management,and systematically analyzes the prominent problems existing in the current scientific data security management in terms of system connection,process coverage,technology adaptation,and rights protection.On this basis,the paper constructs a practical path of scientific data security grading management covering six stages:data planning,collection,storage,use,sharing,and destruction,and puts forward targeted implementation strategies.Research shows that scientific data security grading management based on the entire life cycle is not only a technical issue but also a systematic project involving system design,organizational collaboration,and cultural cultivation.It has important theoretical value and practical enlightenment for improving data governance capabilities and promoting the orderly opening and sharing of scientific data. 展开更多
关键词 Scientific data Entire life cycle Data grading Security management Data governance
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Research on Rapid Update and Accuracy Improvement of DOM Based on Multi-source Aerial Data Fusion 认领 引用
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作者 GAN Yuting 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期042-045,共4页
As a core geospatial data product, Digital Orthophoto Map (DOM) plays a key role in many fields such as urban planning, disaster assessment, engineering surveying and mapping, and topographic mapping. With the increas... As a core geospatial data product, Digital Orthophoto Map (DOM) plays a key role in many fields such as urban planning, disaster assessment, engineering surveying and mapping, and topographic mapping. With the increasing requirements of various industries for the timeliness and accuracy of DOM, the traditional single-data-source update method can hardly meet the actual needs. Aiming at this problem, this paper deeply studies the rapid update and accuracy improvement methods of DOM driven by multi-source aerial data fusion. The research shows that multi-source aerial data fusion technology can effectively improve the efficiency and accuracy of DOM update, providing reliable support for the efficient application of geospatial data. 展开更多
关键词 multi-source aerial data data fusion Digital Orthophoto Map (DOM) rapid update accuracy optimization
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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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Monitoring track irregularities using multi-source on-board measurement data 认领 引用 被引量：1
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作者 Qinglin Xie Fei Peng +4 位作者 Gongquan Tao Yu Ren Fangbo Liu Jizhong Yang Zefeng Wen 《Railway Engineering Science》 EI 2025年第4期746-765,共20页
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co... Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models. 展开更多
关键词 Track irregularities Vehicle accelerations On-board monitoring Multi-source data Deep learning
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IDCE:Integrated Data Compression and Encryption for Enhanced Security and Efficiency 认领 引用 被引量：1
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作者 Muhammad Usama Arshad Aziz +2 位作者 Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期1029-1048,共20页
Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive da... Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field. 展开更多
关键词 Chaotic maps security data compression data encryption integrated compression and encryption
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A Lightweight IoT Data Security Sharing Scheme Based on Attribute-Based Encryption and Blockchain 认领 引用 被引量：2
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作者 Hongliang Tian Meiruo Li 《Computers, Materials & Continua》 SCIE EI 2025年第6期5539-5559,共21页
The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili... The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure. 展开更多
关键词 Edge blockchain CP-ABE data security sharing IoT
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Data Inference:Data Security Threats in the AI Era 认领 引用
19
作者 Zijun Wang Ting Liu +2 位作者 Yang Liu Enrico Zio Xiaohong Guan 《Engineering》 SCIE EI CSCD 2025年第9期29-33,共5页
1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf h... 1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era. 展开更多
关键词 data security threats data security threat artificial intelligence ai era artificial intelligence data inference data inference dinf advanced professional threat
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Data Security and Privacy for AI-Enabled Smart Manufacturing 认领 引用
20
作者 Xin Wang Daniel E.Quevedo +3 位作者 Dongrun Li Peng Cheng Jiming Chen Youxian Sun 《Engineering》 SCIE EI CSCD 2025年第9期34-39,共6页
1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands ... 1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands for greater flexibility and efficiency.The integration of advanced information technology facilitates smart manufacturing(SM),which optimizes production,management,and supply chains[1]. 展开更多
关键词 smart manufacturing data security smart manufacturing sm which ai enabled digital transformation advanced information technology privacy
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