检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Guo, Di Tu, Zhangren Guo, Yi Zhou, Yirong Wang, Jian Wang, Zi Qiu, Tianyu Xiao, Min Chen, Yinran Feng, Liubin Huang, Yuqing Lin, Donghai Hong, Qing Goldbourt, Amir Lin, Meijin Qu, Xiaobo The School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Xiamen University Xiamen361102 China College of Chemistry and Chemical Engineering Key Laboratory for Chemical Biology of Fujian Province High-field NMR Center Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China

—Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. By modelling the acquired signal as the superimposed exponentials, we proposed a virtual peak (VIP) approach to self-adapt the prior spectral information, such as the resonance frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. Results on simulated and experimental data demonstrate that, compared with the low-rank Hankel matrix method, the new approach reconstructs high-fidelity NMR spectra from highly undersampled data and achieves more accurate quantification. The maximum quantitative errors of distances between nuclear pairs and concentrations of metabolites in mixtures have been reduced by 61.1% and 57.7%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

来源：评论

学校读者我要写书评

暂无评论

RadioGAT: A Joint Model-based and data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

arXiv

引用

arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

Design for manufacture in the aerospace extended enterprise

World Class Design to Manufacture

引用

World Class Design to Manufacture 1995年第2期2卷 28-33页

作者： Kamal Sehdev Ip‐Shing Fan Stephen Cooper Gareth Williams Research Officer at the CIM Institute. As well as his involvement in the EPSRC‐funded Design for Manufacture in the Extended Enterprise Project he lectures to the Master′s course on the application of AI and expert systems in business on data communications and on computer‐aided design and manufacture. He can be contacted at the CIM Institute Cranfield University Cranfield Bedford MK43 0AL UK. Tel: 01234 754073 Fax: 01234 750852. Lecturer and the Research Manager at the CIM Institute. He is the Principal Investigator of this Project. He also works in various European Commission funded research projects in design for manufacture and concurrent engineering. Research Assistant at the CIM Institute at Cranfield University and is working towards a PhD. He is the full‐time Research Associate on the Design for Manufacture in the Extended Enterprise Project. He is involved with many aspects of the project but is particularly involved in the area of design for manufacture/assembly tools and techniques. Manufacturing engineering research group at the BAe Cranfield Manufacturing Centre. He is a Research Associate on the Design for Manufacture in the Extended Enterprise project contributing aerospace manufacturing and business process expertise.

Design for manufacture (DFM) is accepted as an important tool toimprove manufacturing competitiveness. Reports on the results of thefirst phase of a study conducted by Cranfield University to establishthe user require... 详细信息

关键词： Aerospace industry Co‐makership Europe Supply‐chain management

来源：评论

学校读者我要写书评

暂无评论

Enabling Temporal-Spectral Decoding in Pre-movement Detection

arXiv

引用

arXiv 2022年

作者： Jia, Hao Caiafa, Cesar F. Duan, Feng Zhang, Yu Sun, Zhe Solé-Casals, Jordi The Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic Spain The Instituto Argentino de Radioastronomía CONICET CCT La Plata/CIC-PBA/UNLP V. Elisa Argentina The College of Artificial Intelligence Nankai University Tianjin China The College of Artificial Intelligence Nankai University Tianjin China The Department of Bioengineering The Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States The Computational Engineering Applications Unit Head Office for Information Systems and Cybersecurity RIKEN Saitama Japan The Department of Psychiatry University of Cambridge United Kingdom

Non-invasive brain-computer interfaces help the subjects to control external devices by brain intentions. The multi-class classification of upper limb movements can provide external devices with more control commands. The onsets of the upper limb movements are located by the external limb trajectory to eliminate the delay and bias among the trials. However, the trajectories are not recorded due to the limitation of experiments. The delay cannot be avoided in the analysis of signals. The delay negatively influences the classification performance, which limits the further application of upper limb movements in the brain-computer interface. This work focuses on multi-channel brain signals analysis in the temporal-frequency approach. It proposes the two-stage-training temporal-spectral neural network (TTSNet) to decode patterns from brain signals. The TTSNet first divides the signals into various filter banks. In each filter bank, task-related component analysis is used to reduce the dimension and reject the noise of the brain. A convolutional neural network (CNN) is then used to optimize the temporal characteristic of signals and extract class-related features. Finally, these class-related features from all filter banks are fused by concatenation and classified by the fully connected layer of the CNN. The proposed method is evaluated in two public datasets. The results show that TTSNet has an improved accuracy of 0.7456±0.1205 compared to the EEGNet of 0.6506±0.1275 (p Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

引用

arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

引用

arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Nessi: An EEG-controlled web browser for severely paralyzed patients

引用

Computational Intelligence and Neuroscience 2007年第1期2007卷 71863页

作者： Bensch, Michael Karim, Ahmed A. Mellinger, Jürgen Hinterberger, Thilo Tangermann, Michael Bogdan, Martin Rosenstiel, Wolfgang Birbaumer, Niels Department of Computer Engineering University of Tübingen 72076 Tübingen Germany Institute of Medical Psychology and Behavioral Neurobiology University of Tübingen 72074 Tübingen Gartenstrasse 29 Germany Graduate School of Neural and Behavioral Sciences International Max Planck Research School University of Tübingen 72076 Tübingen Germany Fraunhofer FIRST Intelligent Data Analysis Group 12489 Berlin Germany Department of Computer Engineering University of Leipzig 04103 Leipzig Germany Human Cortical Physiology Unit National Institute of Neurological Disorders and Stroke (NINDS) National Institutes of Health (NIH) Bethesda MD 20892 United States

We have previously demonstrated that an EEG-controlled web browser based on self-regulation of slow cortical potentials (SCPs) enables severely paralyzed patients to browse the internet independently of any voluntary muscle control. However, this system had several shortcomings, among them that patients could only browse within a limited number of web pages and had to select links from an alphabetical list, causing problems if the link names were identical or if they were unknown to the user (as in graphical links). Here we describe a new EEG-controlled web browser, called Nessi, which overcomes these shortcomings. In Nessi, the open source browser, Mozilla, was extended by graphical in-place markers, whereby different brain responses correspond to different frame colors placed around selectable items, enabling the user to select any link on a web page. Besides links, other interactive elements are accessible to the user, such as e-mail and virtual keyboards, opening up a wide range of hypertext-based applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

On computing reduced-order bilinear models from time-domain data

引用

PAMM 2021年第1期21卷

作者： Ion Victor Gosea Dimitrios S. Karachalios Athanasios C. Antoulas Max Planck Institute for Dynamics of Complex Technical Systems Data-Driven System Reduction and Identification (DRI) group Sandtorstraße 1 Magdeburg 39106 Rice University Houston Electrical and Computer Engineering Department 6100 Main St. Houston TX 77005 Baylor College of Medicine 1 Baylor Plaza Houston TX 77030

We propose a procedure for identification of reduced-order bilinear models from time-domain data, i.e., sampled values of the control input and of the observed output. To accomplish this, we require two sets of data corresponding to the response of the system to two differently-scaled step functions, as control inputs. Then, the Markov parameters for two time-varying linear models are extracted, and from this information, we are able to infer a reduced-order bilinear model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

engineering information system for a demonstration plant facility. A newly developed system has, within the first year, led to operating savings well in excess of the cost of the complete system

引用

Process Safety Progress 1983年第3期2卷 160-164页

作者： L. W. Bonnell E. C. Heydorn J. R. Couch Air Products and Chemicals Inc. Allentown Pa. 18105 Leo W. Bonnell is a Senior Process Engineer with Air Products and Chemicals Inc. (Box 538 Allentown PA 18105–215-481-5530). He received S.B. and S.M. degrees in chemical engineering from MIT. He has been active in process development and demonstration of new technologies related to synthetic fuels since joining the company's Process Systems Group in 1978. Edward C. Heydorn is a Process Engineer with Air Products and Chemicals Inc. Allentown PA. The holder of a B.S.Ch.E. degree from Drexel Univ. he works in the Process Systems Group in the areas of new technology development and design of data acquisition systems. James R. Couch is a Sr. Principal Engineering Analyst with Air Products and Chemicals in the MIS/ESS Computer Process Controls Group. After receiving a B.S. in Engineering Chemistry from Lehigh Univ. he worked as a Research Chemist and Computer Specialist for Standard Oil of Ohio before joining APCI in 1977.

来源：评论

学校读者我要写书评

暂无评论

A bilinear identification-modeling framework from time domain data

引用

PAMM 2019年第1期19卷

作者： Dimitrios S. Karachalios Ion Victor Gosea Athanasios C. Antoulas Max Planck Institute for Dynamics of Complex Technical Systems Data-Driven System Reduction and Identification (DRI) group Sandtorstraße 1 Magdeburg 39106 Rice University Houston Electrical and Computer Engineering Department 6100 Main St. Houston TX 77005 Baylor College of Medicine 1 Baylor Plaza Houston TX 77030

An ever-increasing need for improving the accuracy includes more involved and detailed features, thus inevitably leading to larger-scale dynamical systems [1]. To overcome this problem, efficient finite methods heavily rely on model reduction. One of the main approaches to model reduction of both linear and nonlinear systems is by means of interpolation. The Loewner framework is a direct data-driven method able to identify and reduce models derived directly from measurements. For measured data in the frequency domain, the Loewner framework is well established in linear case [2] while it has already extended to nonlinear [6]. On the other hand in the case of time domain data, the Loewner framework was already applied for approximating linear models [3]. In this study, an algorithm which uses time domain data for nonlinear (bilinear) system reduction and identification is presented.

关键词：

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：