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全球变化数据仓储（中英文） 2023年第10期 4-7页

作者： XU Xunpeng ZHANG Yu JI Luyan TANG Hairong Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China the Key Laboratory of Technology in Geo-Spatial information Processing and Application System Chinese Academy of SciencesBeijing 100190China the School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 101408China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China the Key Laboratory of Technology in Geo-Spatial information Processing and Application System Chinese Academy of SciencesBeijing 100190China

Remote sensing data has strong correlation and continuity in space and time,so time series remote sensing images have low-rank *** this dataset,we repaired images using low-rank tensor ***,we preprocessed the MODIS land surface temperature data and employed spatio-temporal interpolation to initially fill in the missing values caused by cloud ***,we treated the land surface temperature time series data as a third-order spatio-temporal tensor and introduced Fourier transform on the time dimension to convert it into a space-frequency *** performing singular value decomposition and Gaussian low-pass filtering on this tensor,followed by inverse Fourier transform,we obtained a space-time ***,we further optimized the missing tensor using the alternating direction method of *** data accuracy using the method was validated through simulation experiments,where artificial masks were added and subsequently *** resulting mean absolute error(MAE)falls within the range of 2.1℃to 4.9℃.This dataset includes the following data for the Tibetan Plateau on a daily basis for the years 2000-2020:(1)the optimized surface temperature data for the cloud-shaded regions of the MOD11A1,MYD11A1 products(MOD11A1_QTP_PART,MYD11A1_QTP_PART);(2)optimized MOD11A1/MYD11A1 data(MOD11A1_QTP_TEMP,MYD11A1_QTP_TEMP);and(3)original MOD11A1 and MYD11A1 products(MOD11A1_QTP_ORIGIN,MOD11A1_QTP_ORIGIN).All data have a spatial resolution of 1 km and are stored in an integer data format,with pixel value representing the thermodynamic temperature of the surface with a scale factor of 0.02 in *** dataset is archived *** format,and consists of 43833 data files with data size of 143 GB(compressed into 21 files with 138 GB).

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Quantitative analysis of risk superposition of shipping traffic accidents based on traffic big data

Advances in Transportation Studies

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Advances in Transportation Studies 2021年第SPECIAL ISSUE 1期2021卷 81-92页

作者： Liu, X.L. Lin, N. Wang, Z.Y. Lin, H. College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

In order to overcome the single factor problem of risk model analysis in traditional quantitative analysis method of shipping traffic accident risk, a quantitative analysis method of shipping traffic accident risk superposition based on traffic big data is proposed. This method constructs a multi factor shipping traffic accident risk measurement model through traffic big data. Through the model, the interaction information between different risk factors is calculated to quantify the risk of shipping traffic accidents. The results show that there are many kinds of risk factors in most shipping accidents, and the risk of shipping accidents increases with the decrease of uncertainty and the increase of interactive information. © 2021, Aracne Editrice. All rights reserved.

关键词： Big data

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An improved multi-attribute decision-making based network selection algorithm for heterogeneous vehicular network

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Frontiers of Computer Science 2022年第3期16卷 211-213页

作者： Lei NIE Bo LIU Peng LI Heng HE Libing WU School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430065China School of Cyber Science and Engineering and School of Computer Science Wuhan UniversityWuhan 430072China Shenzhen Research Institute of Wuhan University Shenzhen 518057China

1 Introduction With sharply rising quantity of urban vehicles over the past few decades,traffic jams and safety have gradually become outstanding *** recent years,Vehicular Ad hoc Network(VANET)is appeared and utilized to solve traffic related *** is a type of self-organized and open-structured network,and provides Vehicle-to-Everything(V2X)*** all kinds of applications in VANET rely on efficient data transmission and interaction[1-3].

关键词： network sharply utilized

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Genetic Algorithm for Block Relocation in Automated Warehouse 5

Genetic Algorithm for Block Relocation in Automated Warehous...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Wang, Gang Lian, Mingchang Ding, Wei Chen, Songhang Chen, Hao Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou362200 China College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350001 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fujian Fuzhou350108 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China

ISBN: (纸本)9781643684840

This paper is based on the research background of block relocation in the automated warehouse and proposes a solution utilizing AGV to relocate goods within the warehouse. Building upon the automated warehouse layout and block relocation process, we formulate a mathematical model to minimize the the number of AGV moving goods and the total travel distance of the AGV. To solve the model, we develop an enhanced genetic algorithm, which zones the block relocation process into multiple stages. Each stage integrates conventional heuristic algorithms to enhance the efficiency of the solution process. The improved genetic algorithm can effectively solve the problem, significantly enhancing the automation level of the warehouse. © 2024 The Authors.

关键词： Genetic algorithms

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Raindrop Removal for In-Vehicle Camera Images with Generative Adversarial Network

Raindrop Removal for In-Vehicle Camera Images with Generativ...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Zhao, Zihao Jiang, Min Guo, Jia Yang, Xiaoyu Hu, Yudie Zhou, Xianlong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Raindrop removal for in-vehicle camera images is useful for surveillance and analysis system such as intelligent driving or case diagnosis. As in rainy days, images taken by in-vehicle cameras such as monitor or drive recorder often suffer from noticeable image degradation, and make it difficult to identify the objects on the road. With the uncertainty of raindrop distribution and the complexity of raindrop status, the attached raindrops of in-vehicle camera images can have different effects. And the large-diameter raindrop will greatly reduce the image quality. However, most of the popular deraining algorithms have been successful in recovering images with small raindrop noise and tiny image distortion, but fails to restore those with raindrops covering large areas. We propose a single image raindrop removal network based on Generative Adversarial Network for in-vehicle camera images. We incorporate two overlapping attention layers into the deraining network, which adopt task-driven visual attention and content perception mechanism. The former obtains features by recursive neural network to guide network more interested in the raindrop regions and the surrounding structures, while the content perception schema extracts the features far from raindrops. Moreover, we collect a new benchmark for In-Vehicle Camera Image Deraining (IVCRID), it chooses in-vehicle camera images from various deraining dataset. The experimental results show that the proposed deraining network outperforms other state-of-the-art raindrop removal methods in image recovering test on the IVCRID dataset and also has the best performance on the experiments of traffic objects detection. © 2022 IEEE.

关键词： Generative adversarial networks

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Mildew Detection for Stored Wheat Using Gas Chromatography–Ion Mobility Spectrometry and Broad Learning Network

SSRN

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SSRN 2023年

作者： Lian, Fei-Yu Fu, Mai-Xia Qin, Yao Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou China School of Information Science and Engineering Henan University of Technology Zhengzhou China

Most of the existing methods for wheat mildew detection are biochemical methods, which have the problems of complicated procedure and slow speed. In this paper, a novel wheat mildew detection and classification model was proposed by combining gas chromatography-ion mobility spectrometry (GC-IMS) with broad learning network (BLN) model. Firstly, GC-IMS fingerprint spectrum of wheat samples with different degrees of mildew were collected by GC-IMS spectrometer, and then a classification models was constructed based on broad learning. To improve the classification accuracy of the model, the incremental learning model and the spatial attention mechanism (SAM) were introduced into the model. The model uses the feature information and structure information of the nodes to calculate the attention weight, and extracts the more important feature information in the fingerprint of the samples. At the same time, regularization mechanism was introduced to enhance the generalization performance of the model. Experimental results show that the training time of the proposed model is greatly reduced compared to existing deep learning models. Under the small sample set condition, the mean average accuracy (mAP) of wheat mildew types reaches 90.32%, and the identification precision of early wheat mildew reach 95.34%, and the overall performance index is better than that of the deep learning model. The experiment also proved that GC-IMS combined with broad learning model is an efficient and accurate method for wheat mildew detection. © 2023, The Authors. All rights reserved.

关键词： Gas chromatography

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Luo, Jialiang Chen, Shichao Lv, Yisheng Sun, Wenqiao Yang, Fan Xu, Ruijie Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University The State Key Laboratory for Management and Control of Complex System Jinan China The Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350312492

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field. © 2023 IEEE.

关键词： Cost effectiveness

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systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

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Intelligent Automation & Soft Computing 2024年第3期39卷 477-510页

作者： Umer Nauman Yuhong Zhang Zhihui Li Tong Zhen Information Science and Engineering College Henan University of TechnologyZhengzhou450001China School of Artificial Intelligence and Big Data Henan University of TechnologyZhengzhou450001China Key Laboratory of Grain Information Processing and Control Ministry of EducationHenan University of TechnologyZhengzhou450001China

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and ***,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data *** the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing *** design employs load-balancing techniques to allocate different user workloads across several virtual *** address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization *** intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource *** provides a thorough approach to evaluating total costs in the cloud computing *** assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource *** investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load *** proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.

关键词： Optimizing cloud computing deployment of virtual machines load-balancing twin-fold moth flame algorithm grid computing computational resource distribution data virtualization

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A Direct Anti-high Repetition Jamming Design Method via The Minimum Interval Period Optimization

A Direct Anti-high Repetition Jamming Design Method via The ...

引用

Chinese control and Decision Conference, CCDC

作者： Jingjing Qiang Hui Liu Xiongjun Wu Yingbo Li Peipei Cao The Eighth Academy of China Aerospace Science and Technology Corporation The 802 Institute of Shanghai Academy of Space Flight Technology Shanghai China Department of Automation Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Semi-active laser detection is used extensively on many kinds of high-precision guidance weapon systems in the varied military fields. Although it is of high detection accuracy, this kind of detection mechanism, on the other hand, suffers from various laser interference modes which are added in view of the semi-active laser detection principle, covering from angle deception interference, synchronous forwarding interference, and high repetition frequency interference, etc. As a consequence, the overall performance is reduced accordingly. Among these types of deception interference, the high repetition frequency interference is the most effective one. In this work, to overcome this specific type of interference, a minimum interval period method (MIPM) is proposed to remove high repetition frequency interference and improve the detection accuracy of the laser semi-active guidance system. Numerical experiments validate that, this method is able to achieve good performance testing on the Semi-active laser detection under high repetition frequency interference via the False Alarm Probability (FAP for short) and Missed Alarm Probability (MAP).

关键词： Laser theory Accuracy Weapons Interference Resists Logic gates Laser modes

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Mixture Gaussian Process Model with Gaussian Mixture Distribution for Big Data

SSRN

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SSRN 2024年

作者： Guan, Yaonan Li, Dewei He, Shaoying Ren, Shuangshuang Liu, Shuren Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Dongchuan Road 800 Shanghai200240 China Northwest Branch China Petroleum Exploration and Development Research Institute Yanerwan Road 535 Gansu Lanzhou730000 China

In the era of chemical big data, the high complexity and strong interdependencies present in the datasets pose considerable challenges when constructing accurate parametric models. The Gaussian process model, owing to its non-parametric nature, demonstrates better adaptability when confronted with complex and interdependent data. However, the standard Gaussian process has two significant limitations. Firstly, the time complexity of inverting its kernel matrix during the inference process is tremendous. Secondly, all data share a common kernel function parameter, which mixes different data types and reduces the model accuracy in mixing-category data identification problems. In light of this, this paper proposes a mixture Gaussian process model that addresses these limitations. This model reduces time complexity and distinguishes data based on different data features. It incorporates a Gaussian mixture distribution for the inducing variables to approximate the original data distribution. Stochastic Variational Inference is utilized to reduce the computational time required for parameter inference. The inducing variables have distinct parameters for the kernel function based on the data category, leading to improved analytical accuracy and reduced time complexity of the Gaussian process model. Numerical experiments are conducted to analyze and compare the performance of the proposed model on different-sized datasets and various data category cases. © 2024, The Authors. All rights reserved.

关键词： Big data

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