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Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting Data Augmentation Method

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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

Some convergence properties for weighted sums of martingale difference random vectors

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Acta Mathematica Sinica,English Series 2022年

作者： Yi WU Xue Jun WANG School of BIG Data and Artificial Intelligence Chizhou University School of BIG Data and Statistics Anhui University

Let{X, F;1≤i≤n, n≥1}be an array of Rmartingale difference random vectors and{A, 1≤i≤n, n≥1}be an array of m×d matrices of real numbers. In this paper, the Marcinkiewicz-Zygmund type weak law of large numbers for maximal weighted sums of martingale difference random vectors is obtained with not necessarily finite p-th （1

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FIR-YOLACT:Fusion of ICIoU and Res2Net for YOLACT on Real-Time Vehicle Instance Segmentation

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Computers, Materials & Continua 2023年第12期77卷 3551-3572页

作者： Wen Dong Ziyan Liu Mo Yang Ying Wu College of BIG Data and Information Engineering Guizhou UniversityGuiyang550025China The State Key Laboratory of Public BIG Data Guizhou UniversityGuiyang550025China

Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems.The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information,which is more accurate and reliable than object detection.However,the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed.Therefore,this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT,which fuses the ICIoU(Improved Complete Intersection over Union)and Res2Net for the YOLACT algorithm.Specifically,the ICIoU function can effectively solve the degradation problem of the original CIoU loss function,and improve the training convergence speed and detection accuracy.The Res2Net module fused with the ECA(Efficient Channel Attention)Net is added to the model’s backbone network,which improves the multi-scale detection capability and mask prediction accuracy.Furthermore,the Cluster NMS(Non-Maximum Suppression)algorithm is introduced in the model’s bounding box regression to enhance the performance of detecting similarly occluded objects.The experimental results demonstrate the superiority of FIR-YOLACT to the based methods and the effectiveness of all components.The processing speed reaches 28 FPS,which meets the demands of real-time vehicle instance segmentation.

关键词： Instance segmentation real-time vehicle detection YOLACT Res2Net ICIoU

An information-volume-based distance measure for decision-making

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Chinese Journal of Aeronautics 2023年第5期36卷 392-405页

作者： Zhanhao ZHANG Fuyuan XIAO School of BIG Data and Software Engineering Chongqing UniversityChongqing 401331China

D-S evidence theory,as a general framework for reasoning with uncertainty,allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy measure.However,the mass assignments given by unknown information sources are disordered.How to measure the difference between the mass assignments has aroused people’s interest.In this paper,inspired by the information volume,a novel distance-based measure is proposed to measure the difference between mass assignments.The method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass assignments.At the same time,it is verified that the measure not only meets the properties of distance,but also proves the superiority of the proposed Information Volume Distance(IVD)through simulation experiments.Meanwhile,in the process of information fusion,the reliability of each source could be quantified through IVD.Therefore,based on IVD,a new multi-source information algorithm is proposed to solve the problem of multi-source information fusion.Moreover,algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.

关键词： Basic belief assignments Decision-making Distance measure Evidence theory Multi-source information fusion

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SOME CONVERGENCE PROBLEMS REGARDING THE FRACTIONAL SCHRODINGER PROPAGATOR ON NONCOMPACT MANIFOLDS

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数学物理学报:B辑英文版 2023年第5期43卷 2309-2319页

作者：潘亚丽 School of Mathematics and BIG Data Chaohu UniversityHefei 238024China

Let L be the Laplace-Beltrami operator.On an n-dimensional(n≥2),complete,noncompact Riemannian manifold M,we prove that if 0α/2 and f∈Hs(M),then the fractional Schr?dinger propagator e(it|L|α/2)(f)(x)→f(x)a.***.as t→0.In addition,for when M is a Lie group,the rate of the convergence is also studied.These results are a non-trivial extension of results on Euclidean spaces and compact manifolds.

关键词： Schrodinger propagator noncompact manifolds spectra

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Blockchain-Based KeyManagement 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

Design of a prediction system based on the dynamical feed-forward neural network

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Science China(Information Sciences) 2023年第1期66卷 43-59页

作者： Xiaoxiang GUO Weimin HAN Jingli REN Henan Academy of BIG Data Zhengzhou University Department of Mathematics University of Iowa

Analysis and prediction of time series play a significant role in scientific fields of meteorology,epidemiology, and economy. Efficient and accurate prediction of signals can give an early detection of abnormal variations, provide guidance on preparing a timely response and avoid presumably adverse impacts.In this paper, a prediction system is designed based on the dynamical feed-forward neural network. The trajectory information in the reconstructed phase space, which is topologically equivalent to the dynamical evolution of the system, is applied to establish the prediction model. Moreover, an integer constrained particle swarm optimization algorithm is employed to select the optimal time delay, which is the parameter of our system. Simulation results for applications on the Lorenz system, stock market index, and influenza data indicate that our proposed method can produce efficient and reliable predictions.

关键词： prediction system phase space reconstruction topological equivalence dynamical feed-forward neural network integer constrained particle swarm optimization algorithm

DSCA-Net: Indoor Head Detection Network Using Dual-Stream Information and Channel Attention

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Chinese Journal of Electronics 2020年第6期29卷 1102-1109页

作者： QIN Pinle SHEN Wenxiang ZENG Jianchao School of BIG Data North University of China

We propose a novel indoor head detection network using dual-stream information and multi-attention that can be used for indoor crowd counting.To solve the problem of object scale diversity in indoor human head detection,especially the problem of smallscale human head,we propose a dual-stream information flow structure to enrich the positioning and category semantic information of small-scale objects.We propose a kind of structure of the channel-attention mechanism which is used to enhance the ability of the network to identify small-scale objects.Our method has achieved a recall rate of 0.91 and an F1 score of 0.92 on SCUT-HEAD,which achieves the state-of-art performance in the field of indoor crowd detection.

关键词： Pedestrian detection Object detection Attention mechanism Deep learning

BIG earth data for achieving the sustainable development goals in the belt and road region

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BIG Earth Data 2022年第1期6卷 1-2页

作者： Fang Chen Zhongchang Sun International Research Center of BIG Data for Sustainable Development Goals

The Belt and Road(B&R)is an important region with historical,economic,cultural,and political significance,including 75%of the global population and numerous social activ-ities.However,many countries along the B&R region are experiencing developmental challenges such as rapid urbanization,land degradation,water shortages,water and food security,frequent disasters,and large-scale ecosystem changes.The UN’s 17 Sustainable Development Goals(SDGs)provide a universal call to action to end poverty,protect the planet,and ensure that all people enjoy peace and prosperity by 2030 and to achieve economic,social,and environmental sustainability at global,regional,and national scales.

关键词： planet sustainable goals