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Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting data Augmentation Method

Computers, Materials & Continua 2024年第4期79卷 927-941页

作者： Le Zou Linsong Hu Yifan Wang Zhize Wu Xiaofeng Wang Anhui Provincial Engineering laboratory of big data Technology Application forUrban Infrastructure School ofArtificial Intelligence and Big DataHefei UniversityHefei230601China Institute of Applied Optimization School of Artificial Intelligence and Big DataHefei UniversityHefei230601China

Depth estimation is an important task in computer vision.Collecting data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth information.Therefore,data augmentation is crucial for this task.Existing data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge features.In this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth method.This method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge features.To validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation algorithms.Additionally,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed algorithm.Experimental results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data augmentationmethods.On the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to 0.351.On the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

关键词： Perpendicular depth estimation data augmentation

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Blockchain-Based Key Management Scheme Using Rational Secret Sharing

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Computers, Materials & Continua 2024年第4期79卷 307-328页

作者： Xingfan Zhao Changgen Peng Weijie Tan Kun Niu State Key laboratory of Public big data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Guizhou big data Academy Guizhou UniversityGuiyang550025China

Traditional blockchain key management schemes store private keys in the same location,which can easily lead to security issues such as a single point of failure.Therefore,decentralized threshold key management schemes have become a research focus for blockchain private key protection.The security of private keys for blockchain user wallet is highly related to user identity authentication and digital asset security.The threshold blockchain private key management schemes based on verifiable secret sharing have made some progress,but these schemes do not consider participants’self-interested behavior,and require trusted nodes to keep private key fragments,resulting in a narrow application scope and low deployment efficiency,which cannot meet the needs of personal wallet private key escrow and recovery in public blockchains.We design a private key management scheme based on rational secret sharing that considers the self-interest of participants in secret sharing protocols,and constrains the behavior of rational participants through reasonable mechanism design,making it more suitable in distributed scenarios such as the public blockchain.The proposed scheme achieves the escrow and recovery of personal wallet private keys without the participation of trusted nodes,and simulate its implementation on smart contracts.Compared to other existing threshold wallet solutions and keymanagement schemes based on password-protected secret sharing(PPSS),the proposed scheme has a wide range of applications,verifiable private key recovery,low communication overhead,higher computational efficiency when users perform one-time multi-key escrow,no need for trusted nodes,and personal rational constraints and anti-collusion attack capabilities.

关键词： Blockchain smart contract rational secret sharing key management

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Deformation monitoring of long-span railway bridges based on SBAS-InSAR technology

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Geodesy and Geodynamics 2024年第2期15卷 122-132页

作者： Lv Zhou Xinyi Li Yuanjin Pan Jun Ma Cheng Wang Anping Shi Yukai Chen College of Geomatics and geoinformation Guilin University of TechnologyGuilin 541004China Hubei Luo Jia laboratory Wuhan 430079China Ecological Spatiotemporal big data Perception Service laboratory Guilin 541004China School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China China Railway Siyuan Survey and Design Group Co. Ltd.Wuhan 430063China

The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property.The interferometric synthetic aperture radar(In SAR)technology has the advantage of high accuracy in bridge deformation monitoring.This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets(SBAS)In SAR technology and Sentinel-1A data.We analyzed the deformation results combined with bridge structure,temperature,and riverbed sediment scouring.The results are as follows:(1)The Ganjiang Super Bridge area is stable overall,with deformation rates ranging from-15.6 mm/yr to 10.7 mm/yr(2)The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span,which conforms to the typical deformation pattern of a cable-stayed bridge.(3)The sediment scouring from the riverbed cause the serious settlement on the bridge’s east side compared with that on the west side.(4)The bridge deformation negatively correlates with temperature,with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature.The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

关键词： SBAS-InSAR Long-span railway bridge Deformation monitoring Bridge structure Time series deformation

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Benefit of aerosol reduction to winter wheat during China's clean air action:A case study of Henan Province

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Journal of Environmental Sciences 2024年第7期141卷 90-101页

作者： Yang Bai Pengfei Zhao Xueyang Chen Lijun Wang Wenjuan Chang Jianzhong Guo Jiayao Wang College of Geography and Environmental Science Henan UniversityKaifeng 475004China Henan Industrial Technology Academy of Spatio-Temporal big data Henan UniversityZhengzhou 475000China Henan Technology Innovation Center of Spatio-Temporal big data Henan UniversityZhengzhou 450046China Key laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475000China

A strongly declining aerosol radiative effect has been observed in China since 2013 after implementing the clean air action,yet its impact on wheat(Triticum aestivum L.)production remains unclear.We use satellite measures and a biophysical crop model to assess the impact of aerosol-induced radiative perturbations on winter wheat production in the agricultural belt of Henan province from 2013 to 2018.After calibrating parameters with the extended Fourier Amplitude Sensitivity Test(EFAST)and the generalized likelihood uncertainty estimation(GLUE)method,the DSSAT CERES-Wheat model was able to simulate crop biomass and yield more accurately.We found that the aerosol negatively impacted wheat biomass by 21.87%and yield by 22.48%from 2006 to 2018,and the biomass effects from planting to anthesis were more significant compared to anthesis to maturity.Due to the strict clean air action,under all-sky conditions,the surface solar shortwave radiation(SSR)in 2018 increased by about 7.08%over 2006-2013 during the wheat growing seasons.As a result of the improvement of crop photosynthesis,winter wheat biomass and yield increased by an average of 5.46%and 2.9%,respectively.Our findings show that crop carbon uptake and yield will benefit from the clean air action in China,helping to ensure national food and health security.

关键词： Haze pollution Clean air action Aerosol-induced radiative perturbation Wheat production DSSAT model

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A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

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Computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key laboratory of big data Network Security Detection and Analysis Qiqihar UniversityQiqiharChina

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge.The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s performance.In the present study,a new model,referred to as UD-VLD(Unbalanced dataset-VAE-LSTM-DRN),was proposed to address above problem.The proposed model is an encrypted traffic identification model for handling unbalanced datasets.The encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets.The enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related issues.Subsequently,the data is classified and recognized.The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced datasets.The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset.The UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC curve.The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced datasets.Furthermore,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic classification.and can serve as a novel solution for encrypted traffic identification.

关键词： data enhancement LSTM deep residual network VAE

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Robust and Trustworthy data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

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Computers, Materials & Continua 2024年第2期78卷 2159-2179页

作者： Jinyang Yu Xiao Zhang Jinjiang Wang Yuchen Zhang Yulong Shi Linxuan Su Leijie Zeng School of Computer Science Northwestern Polytechnical UniversityXi’an710072China MIIT Key laboratory of big data Storage and Management Northwestern Polytechnical UniversityXi’an710072China National Engineering laboratory for Integrated Aero-Space-Ground-Ocean big data Application Technology Northwestern Polytechnical UniversityXi’an710072China

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy sharing.Conventional distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high latency.To address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage systems.This architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and access.The on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw data.The off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data operations.We provide a unified access interface for user-friendly system interaction.Extensive testing validates the system’s reliability and stable performance.The proposed approach significantly enhances storage capacity compared to standalone blockchain systems.Rigorous reliability tests consistently yield positive outcomes.With average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

关键词： On-chain and off-chain collaboration blockchain distributed storage system hyperledger fabric IPFS cluster

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Changes in Spring Snow Cover over the Eastern and Western Tibetan Plateau and Their Associated Mechanism

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Advances in Atmospheric Sciences 2024年第5期41卷 959-973页

作者： Fangchi LIU Xiaojing JIA Wei DONG Key laboratory of Geoscience big data and Deep Resource of Zhejiang Province School of Earth SciencesZhejiang UniversityHangzhouZhejiang 310058China

The spring snow cover(SC)over the western Tibetan Plateau(TP)(TPSC)(W_TPSC)and eastern TPSC(E_TPSC)have displayed remarkable decreasing and increasing trends,respectively,during 1985–2020.The current work investigates the possible mechanisms accounting for these distinct TPSC changes.Our results indicate that the decrease in W_TPSC is primarily attributed to rising temperatures,while the increase in E_TPSC is closely linked to enhanced precipitation.Local circulation analysis shows that the essential system responsible for the TPSC changes is a significant anticyclonic system centered over the northwestern TP.The anomalous descending motion and adiabatic heating linked to this anticyclone leads to warmer temperatures and consequent snowmelt over the western TP.Conversely,anomalous easterly winds along the southern flank of this anticyclone serve to transport additional moisture from the North Pacific,leading to an increase in snowfall over the eastern TP.Further analysis reveals that the anomalous anticyclone is associated with an atmospheric wave pattern that originates from upstream regions.Springtime warming of the subtropical North Atlantic(NA)sea surface temperature(SST)induces an atmospheric pattern resembling a wave train that travels eastward across the Eurasian continent before reaching the TP.Furthermore,the decline in winter sea ice(SIC)over the Barents Sea exerts a persistent warming influence on the atmosphere,inducing an anomalous atmospheric circulation that propagates southeastward and strengthens the northwest TP anticyclone in spring.Additionally,an enhancement of subtropical stationary waves has resulted in significant increases in easterly moisture fluxes over the coastal areas of East Asia,which further promotes more snowfall over eastern TP.

关键词： snow cover Tibetan Plateau long-term changes spring

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Improving Federated Learning through Abnormal Client Detection and Incentive

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 383-403页

作者： Hongle Guo Yingchi Mao Xiaoming He Benteng Zhang Tianfu Pang Ping Ping College of Computer and Information Hohai UniversityNanjing211100China Key laboratory ofWater big data Technology of Ministry ofWater Resources Hohai UniversityNanjing211100China

data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

关键词： Federated learning abnormal clients incentive credit score abnormal score detection

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key laboratory of big data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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Visible-frequency nonvolatile reconfigurable Janus metasurfaces for dual-wavelength-switched and spin-asymmetric holograms

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Photonics research 2024年第2期12卷 356-368页

作者： HUAN YUAN ZHEQIANG ZHONG BIN ZHANG College of Electronics and Information Engineering Sichuan UniversityChengdu 610064China big data Analysis and Fusion Application Technology Engineering laboratory of Sichuan Province Chengdu 610065China

Janus metasurface holography with asymmetric transmission characteristics provides new degrees of freedom for multiplexing technologies.However,earlier metasurfaces with asymmetrical transmission faced limitations in terms of tunability and multifunctionality.In this study,we propose a metasurface color holographic encryption scheme with dynamic switching and asymmetric transmission at visible frequencies using a low-loss nonvolatile optical phase-change material,Sb_(2)S_(3).Using a modified holographic optimization strategy,we achieved highfidelity asymmetric holographic imaging of a nanostructured metasurface.By controlling the incident direction and wavelength of visible light,as well as the level of crystallization of Sb_(2)S_(3),this reconfigurable metasurface enables the precise manipulation of tunable color holographic image displays.In particular,in the semi-crystalline state of Sb_(2)S_(3),the encoded information can be securely encrypted using a two-channel color-holographic image,whereas only a preset camouflaged image is displayed in the crystalline or amorphous state of Sb_(2)S_(3).The proposed multiencrypted Janus metasurface provides a potential approach for dynamic holographic displays with ultrahigh capacity,holographic encryption,and information storage.

关键词： holographic asymmetric visible

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