检索结果-内蒙古大学图书馆

European Conference on Power Electronics and Applications

作者： D. Cajander I. Viarouge P. Viarouge D. Aguglia ENERGY Inst. Haute Ecole d'Ingénieurie et d’Architecture Fribourg HES-SO University of Applied Sciences and Arts Western Switzerland Boulevard de Pérolles 80 Fribourg Switzerland Electrical and Computer Eng. Dept. LEEPCI Lab. Laval University GIK 7P4 Quebec (QC) Canada ELECTROTECHNOLOGIES Selem Inc 2610 Rue Gérard-Lajoie GIP 3G1 Quebec (QC) Canada Accelerator Systems Dept. Electric Power Converter Group CERN - European Organization for Nuclear Research Geneva 23 Switzerland

ISBN: (数字)9789075815399

ISBN: (纸本)9781665487009

An optimal inductor design methodology using dimensioning models derived from Finite Element Analysis (FEA) supervised Artificial Neural Networks (ANN) is presented. The efficiency of such trained ANN dimensioning models in terms of compromise between precision and computing time is demonstrated for the cylindrical inductor topology with air and magnetic material core including saturation.

关键词： Training Analytical models Systematics Network topology Computational modeling Atmospheric modeling Artificial neural networks

来源：评论

学校读者我要写书评

暂无评论

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

arXiv

引用

arXiv 2023年

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Transportation Systems Engineering Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023, CC BY-NC-SA.

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

Towards advanced interfaces for citizen curation

Towards advanced interfaces for citizen curation

引用

2020 AVI2CH Workshop on Advanced Visual Interfaces and Interactions in Cultural Heritage, AVI2CH 2020

作者： Bruni, Luis Emilio Diaz, Lily Gangemi, Aldo Daga, Enrico Kuflik, Tsvi Mulholland, Paul Pescarin, Sofia Damiano, Rossana Lieto, Antonio Peroni, Silivio Wecker, Alan Department of Architecture Design and Media Technology Alborg University Denmark Department of Media Media lab Aalto University Aalto Finland DHARC - Department of Classical Philology and Italian Studies University of Bologna ISTC-CNR Rome Italy Knowledge Media Institute Open University Milton Keynes United Kingdom Information Systems University of Haifa Haifa Israel CNR ISPC Rome Italy Computer Science and CIRMA University of Turin Turin Italy DHARC - Department of Classical Philology and Italian Studies University of Bologna Rome Italy

The SPICE project builds on the growing trend for museums, rather than providing an authoritative view, to present multiple voices related to their collection and exhibitions. In SPICE, an approach we term citizen curation is proposed as a way of supporting visitors to share their own interpretations of museum objects and reflect on the variety of interpretations contributed by others. In order to capture a wide range of voices, interfaces will be designed specifically to engage minority groups that tend to be under-represented in cultural activities. To achieve this goal, the interface will need to be intuitive, aesthetic and accessible for different audiences. The paper presents the challenges we face and initial proposals for engaging visitors in citizen curation. © Copyright 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Museums

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification 23rd

Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classi...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Dong, Qinglin Qiang, Ning Lv, Jinglei Li, Xiang Liu, Tianming Li, Quanzheng Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA United States School of Physics and Information Technology Shaanxi Normal University Xian China School of Biomedical Engineering and Sydney Imaging Brain and Mind Centre The University of Sydney Camperdown Australia Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States MGH & BWH Center for Clinical Data Science BostonMA United States

ISBN: (纸本)9783030597276

It has been of great interest in the neuroimaging community to model spatiotemporal brain function and disorders based on resting state functional magnetic resonance imaging (rfMRI). A variety of spatiotemporal methods have been proposed for rfMRI so far, including deep learning models such as convolution networks (CNN) and recurrent networks (RNN). However, the dominant models fail to capture the long-distance dependency (LDD) due to their sequential nature, which becomes critical at longer sequence lengths due to memory limit. Inspired by human brains extraordinary ability of long-term memory and attention, the attention mechanism is designed for machine translation to draw global dependencies and achieved state-of-the-art. In this paper, we propose a spatiotemporal attention autoencoder (STAAE) to discover global features that address LDDs in rfMRI. STAAE encodes the information throughout the rfMRI sequence and reveals resting state networks (RSNs) that characterize spatial and temporal properties of the data. Considering that the rfMRI is measured without external tasks, an unsupervised classification framework is developed based on the connectome generated with STAAE. This framework has been evaluated on 281 children with ADHD and 266 normal control children from 4 sites of ADHD200 datasets. The proposed STAAE reveals the global functional interaction in the brain and achieves a state-of-the-art classification accuracy from 59.5% to 77.2% on multiple sites. It is evident that the proposed attention-based model provides a novel approach towards better understanding of human brain. © 2020, Springer Nature Switzerland AG.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE) 23rd

Discovering Functional Brain Networks with 3D Residual Autoe...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

ISBN: (纸本)9783030597276

Functional MRI has attracted increasing attention in cognitive neuroscience and clinical mental health research. Towards understanding how brain give rises to mental phenomena, deep learning has been applied to functional MRI (fMRI) dataset to discover the physiological basis of cognitive process. Considering the unsupervised nature of fMRI due to the complex intrinsic brain activities, an encoder-decoder structure is promising to model hidden structure of latent signal sources. Inspired by the success of deep residual learning, we propose a 68-layer 3D residual autoencoder (3D ResAE) to model deep representations of fMRI in this paper. The proposed model is evaluated on the fMRI data under 3 cognitive tasks in Human Connectome Project (HCP). The experimental results have shown that the temporal representations learned by the encoder matches the task design and the spatial representations can be interpreted to be meaningful functional brain networks (FBNs), which not only include tasks based FBNs, but also intrinsic FBNs. The proposed model also outperforms a 3-layer autoencoder, showing the key factor for the performance improvement is depth. Our work demonstrates the feasibility and success of adopting 2D advanced deep residual networks in computer vision into 3D fMRI volume modeling. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Performance-Boosting Sparsification of the IFDS Algorithm with Applications to Taint Analysis

Performance-Boosting Sparsification of the IFDS Algorithm wi...

引用

IEEE International Conference on Automated Software Engineering (ASE)

作者： Dongjie He Haofeng Li Lei Wang Haining Meng Hengjie Zheng Jie Liu Shuangwei Hu Lian Li Jingling Xue UNSW Sydney Australia SKL of Computer Architecture ICT CAS China University of Chinese Academy of Sciences China Vivo AI Lab China

The IFDS algorithm can be compute-and memory-intensive for some large programs, often running for a long time (more than expected) or terminating prematurely after some time and/or memory budgets have been exhausted. In the latter case, the corresponding IFDS data-flow analyses may suffer from false negatives and/or false positives. To improve this, we introduce a sparse alternative to the traditional IFDS algorithm. Instead of propagating the data-flow facts across all the program points along the program's (interprocedural) control flow graph, we propagate every data-flow fact directly to its next possible use points along its own sparse control flow graph constructed on the fly, thus reducing significantly both the time and memory requirements incurred by the traditional IFDS algorithm. In our evaluation, we compare FLOWDROID, a taint analysis performed by using the traditional IFDS algorithm, with our sparse incarnation, SPARSEDROID, on a set of 40 Android apps selected. For the time budget (5 hours) and memory budget (220GB) allocated per app, SPARSEDROID can run every app to completion but FLOWDROID terminates prematurely for 9 apps, resulting in an average speedup of 22.0x. This implies that when used as a market-level vetting tool, SPARSEDROID can finish analyzing these 40 apps in 2.13 hours (by issuing 228 leak warnings) while FLOWDROID manages to analyze only 30 apps in the same time period (by issuing only 147 leak warnings).

关键词： Memory management Tools Image edge detection Instruction sets Random access memory computer bugs Static analysis

来源：评论

学校读者我要写书评

暂无评论

Disability in space - What ESA should have aimed for

arXiv

引用

arXiv 2021年

作者： Heinicke, Christiane Kaczmarzyk, Marcin Tannert, Benjamin Wasniowski, Aleksander Perycz, Malgorzata Schöning, Johannes Am Fallturm 2 Bremen28359 Germany Rzeszow University of Technology The Faculty of Civil and Environmental Engineering and Architecture Department of Building Engineering Poznańska Street 2 Rzeszow35-084 Poland Hochschule Bremen - City University of Applied Science Applied Media Informatics Flughafenallee 10 Bremen28199 Germany Space Is More ul. Mikulskiego 17 Wroclaw52-420 Poland Institute of Computer Science Polish Academy of Sciences Department of Artificial Intelligence Lab of Computational Biology Jana Kazimierza 5 st. Warsaw01-248 Poland University of Bremen Human-Computer Interaction Bibliothekstraße 5 Bremen28359 Germany

With announcing their "Parastronaut Feasibility Project", the European Space Agency ESA promises to make every reasonable effort to send astronauts with disability to space. This could spark hope in the 15% of all people who live with some form of disability. However, the fine-print of the announcement sets overly tight boundaries to "acceptable" disabilities, allowing only malformations that are expected to have a positive impact on the parastronaut's performance in space. By doing so, ESA is following decade-old traditions in space flight, with some inclusion for the sake of inclusion and public opinion. Instead of accommodating people with disabilities, ESA should send a strong signal for inclusion and truly challenge people with disabilities so they can and will tease out the best of their qualities. © 2021, CC BY.

关键词： Manned space flight

来源：评论

学校读者我要写书评

暂无评论

A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

引用

IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

来源：评论

学校读者我要写书评

暂无评论

Decoding surface code with a distributed neural network based decoder

arXiv

引用

arXiv 2019年

作者： Varsamopoulos, Savvas Bertels, Koen Almudever, Carmen G. Quantum Computer Architecture Lab Delft University of Technology QuTech Delft University of Technology P.O. Box 5046 Delft2600 GA Netherlands

There has been a rise in decoding quantum error correction codes with neural network based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that, successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the Renormalization Group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the Blossom algorithm and a neural network based decoder. We show that similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network based decoders. Copyright © 2019, 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

Edge AIBench: Towards Comprehensive End-to-End Edge Computing Benchmarking 1st

Edge AIBench: Towards Comprehensive End-to-End Edge Computin...

引用

1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Hao, Tianshu Huang, Yunyou Wen, Xu Gao, Wanling Zhang, Fan Zheng, Chen Wang, Lei Ye, Hainan Hwang, Kai Ren, Zujie Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Dover DE United States Beijing Academy of Frontier Sciences and Technology Beijing China Chinese University of Hongkong at Shenzhen Shenzhen China Zhejiang Lab Zhejiang China

ISBN: (纸本)9783030328122

In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for edge computing benchmarking, we must take an end-to-end view, considering all three layers: client-side devices, edge computing layer, and cloud servers. Unfortunately, the previous work ignores this most important point. This paper presents the BenchCouncil’s coordinated effort on edge AI benchmarks, named Edge AIBench. In total, Edge AIBench models four typical application scenarios: ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle with the focus on data distribution and workload collaboration on three layers. Edge AIBench is publicly available from http://***/EdgeAIBench/***. We also build an edge computing testbed with a federated learning framework to resolve performance, privacy, and security issues. © 2019, Springer Nature Switzerland AG.

关键词： Edge computing

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：