检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Wang, Jing Li, Chao Li, Lu Huang, Zhihua Wang, Chao Zhang, Hong Zhang, Zhengjia Research Institute of Intelligent Computing Zhejiang Laboratory. Hangzhou311121 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences No.9 Dengzhuang South Road Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Information Engineering China University of Geosciences. 388 Lumo Road Wuhan430074 China

The open source strategy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large *** deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau (QTP). However, the convolutional and recurrent neural networks cannot accurately model long-term and complex relations in multi-variate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. We propose an InSAR deformation prediction integrated algorithm based on the transformer-based models to predict time series deformation more accurately in permafrost areas. This method takes advantage of the self-attention mechanism to study complicated dynamic deformation features of permafrost caused by temperature and other variables from InSAR time series ***, Sentinel-1 dataset acquired with 12-day repeat are collected to implement the Small Baseline Subset (SBAS) method and retrieve the seasonal and long-term deformation surrounding Salt Lake. Then, the InSAR time series deformation and meteorological data are combined to establish the deformation prediction model using the transformer neural network. These results demonstrated that the InSAR deformation prediction method achieves better prediction performance (the root mean square errors are 3.207mm and 2.27mm) in predicting permafrost deformation trends with a point scale compared with the prediction results of other models. Finally, the predicted time series deformation maps for the period 2021/04/05 to 2022/03/19 are mapped surrounding Salt Lake. Meanwhile the total area of Salt Lake increased by 57.32km 2 during the period 2015-2019 based on the predicted deformation and the water extraction *** the expansion rate of Salt Lake was slow, and the area decreased by 3.26 km 2 from 2019 to 2022. The time series deformation forecasting method provides early warn

关键词： Deformation

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CASIA-Face-Africa: A large-scale African face image database

arXiv

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arXiv 2021年

作者： Muhammad, Jawad Wang, Yunlong Wang, Caiyong Zhang, Kunbo Sun, Zhenan The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without retraining are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our website* Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Vector Quantization: Timeline-Based Location data Extraction and Route Fitting for Crowdsourcing 1

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5th China High-resolution Earth Observation Conference, CHREOC 2018

作者： Xu, Naiting Wang, Yi Chen, Xing Lian, Haiming Institute of Electronics Chinese Academy of Sciences SuzhouJiangsu215123 China Key Laboratory of Intelligent Aerospace Big Data Application Technology SuzhouJiangsu215123 China

ISBN: (数字)9789811365539

ISBN: (纸本)9789811365522

Because of the superiority of crowdsourcing, many companies are eager to seek the solutions for some issues through crowdsourcing, e.g., assembling crowd wisdom crystallization for a wonderful design, collecting personal-privacy information or geographical location information, etc. Location services are becoming more and more important to the lives and lifestyles of modern people, so the Internet companies and traditional enterprises placed great emphasis on this theme. We can obtain valuable information from the large-scale datasets, but in certain circumstances, a large amount of data will be a burden for us to analyze problems. On account of redundant data that carries little weight with us, in this paper, we mainly consider trajectory data extraction of timeline-based location information and route fitting in the field of military (e.g., GIS) and civilian (e.g., traffic). For the purpose, we refer to the idea of Vector Quantization, denoted as VQ, and give a model of TDERTM (Timeline-based data Extraction and Route Fitting Model) to solve that. In this sense, it is efficient and convenient to determine whether the target (ship, aircraft, etc.) cross the border and draw a navigation trajectory with low redundancy. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Crowdsourcing

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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A CNN-ELM Compound Fault Diagnosis Method Based on Joint Distribution Modification

A CNN-ELM Compound Fault Diagnosis Method Based on Joint Dis...

引用

International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Jiechao Dong Liping Yan Yuanqing Xia Jinhui Zhang Xianghua Wang Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems Shandong China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

In the industrial field, compound faults often occur on rolling bearings and it's difficult to diagnose them correctly. To solve this problem, this article proposes a CNN-ELM compound fault diagnosis method based on joint distribution modification. Firstly, considering the complementarity and coupling of data from multiple sensors, a data input trick of multi-sensor data connected in parallel is designed. Secondly, due to the discrepancy of distribution between the compound fault data features and the single fault data features, the marginal distribution matrix and the posterior distribution matrix are used to modify the CNN-ELM network, so that the network can extract more reliable data features for fault diagnosis. Finally, referring to the categories and criteria of bearing damage proposed by Paderborn University, the label code is defined. The corresponding data set is used to verify the proposed algorithm. Experimental results show that the algorithm can accurately obtain detailed fault information such as fault location, fault type, and fault severity.

关键词： Fault diagnosis Rolling bearings Materials reliability Feature extraction Sensor systems Sensors Compounds

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Synchronization of Multi-Weighted Complex Networks with Mixed Variable Delays and Uncertainties Via Impulsive Pinning Control

SSRN

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SSRN 2023年

作者： Fan, Hongguang Shi, Kaibo Wen, Hui Zhao, Yi College of Computer Chengdu University Chengdu610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu610106 China School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China New Engineering Industry College Putian University Fujian Putian351100 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Province University Fujian Putian351100 China College of Mathematical and Statistical Shenzhen University Shenzhen518060 China

In this paper, we examine the synchronization problem in nonlinearly-coupled multi-weighted complex networks that contain uncertainties and varying delays. The complexities of these networks arise from the presence of both internal time-varying delays and multiple coupling time-varying delays. To address this challenge, we employ impulsive pinning control schemes and incorporate the impulsive pinning control ratio in the construction of the Lyapunov function, which allows us to determine the optimal number of pinned nodes at each pulse instant. Our analysis, based on stability theory, provides synchronization criteria that are strongly dependent on the multi-weights and time delays present in the network. Finally, we present numerical simulations of a delayed complex network to demonstrate the validity and efficacy of our derived theoretical results. © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Group-wise inhibition based feature regularization for robust classification

arXiv

引用

arXiv 2021年

作者： Liu, Haozhe Wu, Haoqian Xie, Weicheng Liu, Feng Shen, Linlin 1Computer Vision Institute College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Copy-Proof Scheme Based on the Spectral and Spatial Barcoding Channel Models

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2020年 15卷 1056-1071页

作者： Chen, Changsheng Li, Mulin Ferreira, Anselmo Huang, Jiwu Cai, Rizhao Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China Tencent Holdings Ltd Shenzhen 518057 Peoples R China Univ Cagliari Dept Math & Informat I-09100 Cagliari Italy Nanyang Technol Univ Rapid Rich Object Search Lab Singapore 639798 Singapore

The traditional two-dimensional (2D) barcode has been employed in anti-counterfeiting systems as a storage media for serial numbers. However, an attack can be initiated by simply copying the 2D barcode and attaching it to a counterfeit product. In this paper, we aim at proposing an authentication scheme with a mobile imaging device for a 2D barcode. This work presents a competitive solution among the 2D barcode authentication schemes that have been verified under mobile imaging conditions. The proposed copy-proof scheme is composed of two sets of features which are extracted by exploiting the characteristics of barcoding channel models. The proposed features identify the intrinsic differences between genuine and counterfeit barcode images in the frequency and spatial domains. An efficient two-stage barcode authentication framework is then proposed by combining the two sets of features in a cascading manner. To evaluate the practicality of the proposed authentication scheme, four databases with different devices (printers, scanners, mobile cameras), barcode sizes, and barcode designs are considered in the experiments. By comparing with the existing texture descriptors and some deep learning-based approaches, it is shown that the proposed scheme has a higher authentication accuracy under various conditions, such as cross-database, cross-size and cross-pattern experiments which study the generalities of a pre-trained model towards challenging conditions commonly found in real-world scenarios. Last but not least, the proposed scheme has been evaluated under some state-of-the-art attack scenarios where the attacker employs several realizations of genuine patterns or the deep learning-based technique to produce a counterfeit copy. The source code and data for producing the results in our experiments are available at https://***/2FOlJH7.

关键词： Copy-proof 2D barcode Fourier domain local binary pattern

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Rockburst prediction using artificial intelligence techniques:A review

岩石力学通报（英文）

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岩石力学通报（英文） 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Correction to: Core sample consensus method for two-view correspondence matching

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Multimedia Tools and Applications 2024年第8期83卷 24631-24631页

作者： Ding, Xintao Li, Boquan Zhou, Wen Zhao, Cheng School of Computer and Information Anhui Normal University Wuhu China Anhui Province Key Laboratory of Network and Information Security Wuhu China Anhui Engineering Research Center of Medical Big Data Intelligent System Wuhu China School of Mathematics and Statistics Anhui Normal University Wuhu China

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