检索结果-内蒙古大学图书馆

Correction to: A method of VR‑EEG scene cognitive rehabilitation training

Health information science and systems 2021年第1期9卷 7页

作者： Wenjun Tan Yang Xu Pan Liu Chunyan Liu Yujin Li Yanrui Du Chao Chen Yuping Wang Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110189 China. Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou 510006 China. Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China. Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin 300384 China. Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne VIC 8001 Australia.

[This corrects the article DOI: 10.1007/s13755-020-00132-6.].

关键词：

来源：评论

学校读者我要写书评

暂无评论

Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment

arXiv

引用

arXiv 2025年

作者： Bonaldi, A. Hartley, P. Braun, R. Purser, S. Acharya, A. Ahn, K. Aparicio Resco, M. Bait, O. Bianco, M. Chakraborty, A. Chapman, E. Chatterjee, S. Chege, K. Chen, X. Chen, Z. Conaboy, L. Cruz, M. Darriba, L. De Santis, M. Denzel, P. Diao, K. Feron, J. Finlay, C. Gehlot, B. Ghosh, S. Giri, S.K. Grumitt, R. Hong, S.E. Ito, T. Jiang, M. Jordan, C. Kim, S. Kim, M. Kim, J. Krishna, S.P. Kulkarni, A. López-Caniego, M. Labadie-García, I. Lee, H. Lee, D. Lee, N. Line, J. Liu, Y. Mao, Y. Mazumder, A. Mertens, F.G. Munshi, S. Nasirudin, A. Ni, S. Nistane, V. Norregaard, C. Null, D. Offringa, A. Oh, M. Oh, S.-H. Parkinson, D. Pritchard, J. Ruiz-Granda, M. Salvador López, V. Shan, H. Sharma, R. Trott, C. Yoshiura, S. Zhang, L. Zhang, X. Zheng, Q. Zhu, Z. Zuo, S. Akahori, T. Alberto, P. Allys, E. An, T. Anstey, D. Baek, J. Basavraj Brackenhoff, S. Browne, P. Ceccotti, E. Chen, H. Chen, T. Choudhuri, S. Choudhury, M. Coles, J. Cook, J. Cornu, D. Cunnington, S. Das, S. De Lera Acedo, E. Delouis, J.-M. Deng, F. Ding, J. Elahi, K.M.A. Fernandez, P. Fernández, C. Alcázar, A. Fernández Galluzzi, V. Gao, L.-Y. Garain, U. Garrido, J. Gendron-Marsolais, M.-L. Gessey-Jones, T. Ghorbel, H. Gong, Y. Guo, S. Hasegawa, K. Hayashi, T. Herranz, D. Holanda, V. Holloway, A.J. Hothi, I. Höfer, C. Jelić, V. Jiang, Y. Jiang, X. Kang, H. Kim, J.-Y. Koopmans, L.V. Lacroix, R. Lee, E. Leeney, S. Levrier, F. Li, Y. Liu, Y. Ma, Q. Meriot, R. Mesinger, A. Mevius, M. Minoda, T. Miville-Deschenes, M.-A. Moldon, J. Mondal, R. Murmu, C. Murray, S. Nirmala, S.R. Niu, Q. Nunhokee, C. O’Hara, O. Pal, S.K. Pal, S. Park, J. Parra, M. Patra, N.N. Pindor, B. Remazeilles, M. Rey, P. Rubino-Martin, J.A. Saha, S. Selvaraj, A. Semelin, B. Shah, R. Shao, Y. Shaw, A.K. Shi, F. Shimabukuro, H. Singh, G. Sohn, B.W. Stagni, M. Starck, J.-L. Sui, C. Swinbank, J.D. Sánchez, J. SKA Observatory Jodrell Bank Lower Whitington MacclesfieldSK11 9FT United Kingdom Max-Planck Institute for Astrophysics Karl-Schwarzschild-Straße 1 Garching85748 Germany Department of Earth Sciences Chosun University Gwangju61452 Korea Republic of Universidad Europea de Madrid Madrid28670 Spain Observatoire de Sauverny Versoix1290 Switzerland Department of Physics Trottier Space Institute McGill University 3600 rue University MontréalQCH3A 2T8 Canada School of Physics and Astronomy The University of Nottingham University Park NottinghamNG7 2RD United Kingdom Department of Physics and Astronomy University of the Western Cape Cape Town Bellville7535 South Africa Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Astronomy The University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Avda. de los Castros s/n SantanderE-39005 Spain Glorieta de la Astronomía s/n Granada18008 Spain Saint-Imier Switzerland Centre for Artificial Intelligence ZHAW Zurich University of Applied Sciences Technikumstrasse 71 Winterthur8400 Switzerland Department of Astronomy Tsinghua University Beijing100084 China Département de Physique Théorique Center for Astroparticle Physics Université de Geneve 24 quai Ernest Ansermet Geneve 41211 Switzerland Nordita KTH Royal Institute of Technology Stockholm University Hannes Alfv´ens väg 12 StockholmSE-106 91 Sweden Korea Astronomy and Space Science Institute Daejeon34055 Korea Republic of Astronomy Campus University of Science and Technology Daejeon34055 Korea Republic of Kumamoto University International Research Organization for Advanced Science and Technology 2-39-1 Kurokami Chuo-ku Kumamoto860-8555 Japan National Key Laboratory of Radar Signal Processing

We present and analyse the results of the Science data challenge 3a (SDC3a, https://***/challenges/ foregrounds), an EoR foreground-removal community-wide exercise organised by the Square Kilometre Array Observatory (SKAO). The challenge ran for 8 months, from March to October 2023. Participants were provided with realistic simulations of SKA-Low data between 106 MHz and 196 MHz, including foreground contamination from extragalactic as well as Galactic emission, instrumental and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned from all corruptions, and the corresponding confidence levels. Here we describe the approaches taken by the 17 teams that completed the challenge, and we assess their performance using different metrics. The challenge results provide a positive outlook on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations. The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some cases there are some significant outliers. The smallest residual overall is 4.2+−204.2 × 10−4 K2h−3cMpc3 across all considered scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error being typically underestimated, sometimes very significantly. The most accurate error bars account for 60 ± 20% of the true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and improve error estimation. © 2025, CC BY.

关键词： Cosmology

来源：评论

学校读者我要写书评

暂无评论

Implementing the Matrix Multiplication with DFC on Kunlun Small Scale Computer

Implementing the Matrix Multiplication with DFC on Kunlun Sm...

引用

IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Zheng Du Jing Zhang Shihao Sha Qiuming Luo Department of Computer Science and Technology Tsinghua University NHPCC/Guangdong Key Laboratory of popular UPC & Guangdong Province Engineering Center of China-made High Performance Data Computing System in College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512.

关键词： Distributed computing Handheld computers

来源：评论

学校读者我要写书评

暂无评论

Fake Colorized Image Detection with Channel-wise Convolution based Deep-learning Framework

Fake Colorized Image Detection with Channel-wise Convolution...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Long Zhuo Shunquan Tan Jishen Zeng Bin Lit Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781728102436;9789881476852

Colorization is one remarkable emerging image manipulating technique, which maybe potentially used for illegal purpose. In this paper, we introduce WISERNet (Wider Separate-then-reunion Network), a recently proposed deep-learning based data-driven color image steganalyzer in the field of fake colorized image detection. We believe that statistical inconsistencies introduced by different automatic colorization methods can be captured by advanced deep-learning based data-driven color-image steganalyzers such as WISERNet. Experimental evidences reported in this paper supports our claims: the detection performance of our proposed detector obviously outperforms FCID-HIST and FCID-FE, two state-of-the-art hand-crafted features specific to fake colorized image detection. Please note that in our approach we have never explicitly utilized information from the specific channels other than the ordinary red, green, and blue color channel, which is completely different from prior works in this field.

关键词： Detectors Training Image color analysis Error analysis Convolution Forensics

来源：评论

学校读者我要写书评

暂无评论

Generalized Association and Deep Understanding Method for Big Video Data Processing

Generalized Association and Deep Understanding Method for Bi...

引用

IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Lin Xu Wei Wu Wanghan Lv Dong Wang Science and Technology in Information System Engineering Lab Nanjing Research institute of Electronic Engineering Nanjing China School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781728152103

In Big Data Era, it is becoming more and more important to timely and efficient processing of massive videos, and mining of the value information contained in them. This paper studies chaotic compressed sensing theory and uses Long-term and short-term memory networks (LSTM) algorithm to generate a summary of the content of video behavior track. It has already to apply the proposed method in target recognition and tracking, and the recognition rate achieves more than 86%.

关键词： Big Data compressed sensing data compression data mining video coding big data Compressed sensing Data compression Video coding data mining video data Target recognition

来源：评论

学校读者我要写书评

暂无评论

A Novel Epidemic Model Coupling the Infectious Disease with Awareness Diffusion on Multiplex Networks

A Novel Epidemic Model Coupling the Infectious Disease with ...

引用

第30届中国控制与决策会议

作者： Chunyuan Zheng Zhishuang Wang Chengyi Xia Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System (Ministry of Education) Tianjin University of Technology IEEE

In this paper, we propose a two-layered multiplex network model to characterize the coupling between the infectious disease spreading and disease-related information or awareness diffusion, in which one layer is used to denote the contact network for the disease spreading and the other one represents the propagation of information about the infectious diseases. Meanwhile, the topology between two-layered networks is distinct although each node can appear simultaneously on two networks or the mapping mode between network nodes is one-to-one. Through the microscopic Markov chain approach(MMCA), we build the probability transition tree among 6 possible states and the corresponding dynamical equations. After that, we analytically derive the epidemic threshold by the MMCA method, which has been correlated with the network topology, the information diffusion mechanism and the coupling properties between two kinds of spreading dynamics. Finally, we perform extensive numerical simulations to demonstrate the analytical predictions, and in particular we find that self perception can greatly reduce the disease outbreaks. Current results are helpful to deeply understand the spreading behaviors among the real-world population.

关键词： Epidemic Modeling Disease Spreading Awareness Diffusion Multiplex Networks

来源：评论

学校读者我要写书评

暂无评论

A robust facial descriptor for face recognition

A robust facial descriptor for face recognition

引用

International Conference on Applications and Techniques in Cyber Security and Intelligence, ATCSI 2017

作者： Liu, Na Duan, Huixian Song, Lei Yan, Zhiguo Cyber Physical System R&D Center The Third Research Institute of Ministry of Public Security Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai International Technology and Trade United Co. Ltd Shanghai China

ISBN: (纸本)9783319670706

Illumination, occlusion, pose and expression variations are the most common challenging problems for face recognition in many real-world applications. However, existing face recognition methods are proposed to handle part of these variations. In this paper, we propose a robust facial descriptor to address this issue. First, we apply a chain of three processing to tackle the illumination variation. Second, we compute the facial sparse local descriptor to handle the occlusion, pose, and expression variations. Experimental evaluation on the FRGC database shows that our approach is able to achieve very promising recognition rates under uncontrolled environments. © 2018, Springer International Publishing AG.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

A speed estimation method of vehicles based on road monitoring video-images

A speed estimation method of vehicles based on road monitori...

引用

International Conference on Applications and Techniques in Cyber Security and Intelligence, ATCSI 2017

作者： Huixian, Duan Jun, Wang Lei, Song Yixin, Zhao Liu, Na The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Cyber Physical System R&D Center The Third Research Institute of Ministry of Public Security Shanghai China Shanghai International Technology and Trade United Co. Ltd Shanghai China

ISBN: (纸本)9783319670706

In order to reduce traffic accidents and road congestion in many cities, vehicle speed estimation is very critical and important to observe speed limitation law and traffic conditions. In this paper, we present a speed estimation method of vehicles based on road monitoring video-images. Firstly, we set up a word coordinate system on the license plate in one vehicle image. Next, for small vehicles in China, according to the known length and width of the license plate, the spatial transformation matrix between the word coordinate system and the image coordinate system is derived. Then, based on the spatial transform matrix, compute the corner spatial coordinates of the license plate in each frame, and then estimate the vehicle speed. Finally, experimental results on read data have shown that the vehicle speed can be estimated within the acceptable error range (±3 km/h), and then have demonstrated the effectiveness of the proposed vehicle speed estimation method. © 2018, Springer International Publishing AG.

关键词： Speed

来源：评论

学校读者我要写书评

暂无评论

Abnormal event detection based on in vehicle monitoring system

Abnormal event detection based on in vehicle monitoring syst...

引用

International Conference on Applications and Techniques in Cyber Security and Intelligence, ATCSI 2017

作者： Song, Lei Dai, Jie Duan, Huixian Liu, Zheyuan Liu, Na The Third Research Institute of the Ministry of Public Security Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai International Technology & Trade United Co. Ltd Shanghai China

ISBN: (纸本)9783319670706

Nowadays, the cameras of traffic monitoring systems are mounted toward roads or interactions. The views are fixed and limited. To extend the monitoring area, a novel onboard abnormal event detection system is proposed based on the In Vehicle Monitoring system (IVMS). Videos are captured by the camera mounted in the front of a vehicle. Traffic information extracted by the system is combined with the GPS and electronic map to discover abnormal events. The system can be installed on police vehicles, buses, even private cars. Therefore, the anomaly monitoring can be processed anywhere, instead of on the certain interactions or roads. © 2018, Springer International Publishing AG.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Traffic sign classification base on latent dirichlet allocation 1

引用

6th International Conference on Frontier computing, FC 2017

作者： Song, Lei Liu, Zheyuan Zhang, Xiaoteng Duan, Huixian Liu, Na Dai, Jie The Third Research Institute of the Ministry of Public Security Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai International Technology and Trade United Co. Ltd Shanghai China

ISBN: (数字)9789811073984

ISBN: (纸本)9789811073977

Traffic sign classification is a significant issue in the intelligent vehicle domain, which helps vehicles to follow the traffic rules and ensure the safety. Feature selection and description are very important and difficult for classification. In this paper, a novel traffic sign classification method is proposed which is based on the Latent Dirichlet Allocation (LDA) model. Feature topics are modeled based on various traffic signs by the LDA automatically. And traffic signs captured onboard are classified according to the modeled features. The experiment results show the efficiency of our work. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Traffic signs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：