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IEEE Workshop on Control and Modeling for Power Electronics

作者： L. Ortombina E. Liegmann P. Karamanakos F. Tinazzi M. Zigliotto R. Kennel University of Padova Dept. of Management and Engineering Technical University Munich Chair of Electrical Drive Systems and Power Electronics Tampere University of Technology Faculty of Computing and Electrical Engineering

ISBN: (纸本)9781538655429

A finite control set model predictive control strategy for the control of the stator currents of a synchronous reluctance motor driven by a three-level neutral point clamped inverter is presented in this paper. The presented algorithm minimizes the stator current distortions while operating the drive system at switching frequencies of a few hundred Hertz. Moreover, the power electronic converter is protected by overcurrents and/or overvoltages owing to a hard constraint imposed on the stator currents. To efficiently solve the underlying integer nonlinear optimization problem a sphere decoding algorithm serves as optimizer. To this end, a numerical calculation of the unconstrained solution of the optimization problem is proposed, along with modifications in the algorithm proposed in [1] so as to meet the above-mentioned control objectives. Simulation results show the effectiveness of the proposed control algorithm.

关键词： stator current synchronous reluctance motor drive train Model predictive control Predictive control reluctance motor drives Computer algorithms Automobile driving PROBLEM SOLUTION numerical calculation Inventors DRIVES

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Convolutional neural networks for authorship attribution of short texts 15

Convolutional neural networks for authorship attribution of ...

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15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017

作者： Shrestha, Prasha Sierra, Sebastian González, Fabio A. Rosso, Paolo Montes-Y-Gómez, Manuel Solorio, Thamar Dept. of Computer Science University of Houston HoustonTX77004 United States Computing Systems and Industrial Engineering Dept. Universidad Nacional de Colombia Bogotá Colombia Universitat Politècnica de València Valencia Spain Instituto Nacional de Astrofisica Optica y Electronica Puebla Mexico

ISBN: (纸本)9781510838604

We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams. We also present a strategy that improves model interpretability by estimating the importance of input text fragments in the predicted classification. The experimental evaluation shows that text CNNs perform competitively and are able to outperform previous methods. © 2017 Association for Computational Linguistics.

关键词： Convolution

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Toward the starting line: A systems engineering approach to strong AI

arXiv

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arXiv 2017年

作者： Alpcan, Tansu Leckie, Christopher Erfani, Sarah M. Dept. of Electrical and Electronic Engineering University of Melbourne VIC Australia School of Computing and Information Systems University of Melbourne VIC Australia

Artificial General Intelligence (AGI) or Strong AI aims to create machines with human-like or human-level intelligence, which is still a very ambitious goal when compared to the existing computing and AI systems. After many hype cycles and lessons from AI history, it is clear that a big conceptual leap is needed for crossing the starting line to kick-start mainstream AGI research. This position paper aims to make a small conceptual contribution toward reaching that starting line. After a broad analysis of the AGI problem from different perspectives, a system-theoretic and engineering-based research approach is introduced, which builds upon the existing mainstream AI and systems foundations. Several promising cross-fertilization opportunities between systems disciplines and AI research are identified. Specific potential research directions are discussed. Copyright © 2017, The Authors. All rights reserved.

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Smells Are Sensitive to Developers! On the Efficiency of (Un)Guided Customized Detection

Smells Are Sensitive to Developers! On the Efficiency of (Un...

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IEEE Workshop on Program Comprehension

作者： Mario Hozano Alessandro Garcia Nuno Antunes Baldoino Fonseca Evandro Costa Department of Computing Systems UFAL Paraíba Brazil Informatics Dept. PUC-Rio Brazil Department of Informatics Engineering University of Coimbra Portugal Computing Institute UFAL Brazil

ISBN: (纸本)9781538605363

Code smells indicate poor implementation choices that may hinder program comprehension and maintenance. Their informal definition allows developers to follow different heuristics to detect smells in their projects. Machine learning has been used to customize smell detection according to the developer's perception. However, such customization is not guided (i.e. constrained) to consider alternative heuristics used by developers when detecting smells. As a result, their customization might not be efficient, requiring a considerable effort to reach high effectiveness. In fact, there is no empirical knowledge yet about the efficiency of such unguided approaches for supporting developer-sensitive smell detection. This paper presents Histrategy, a guided customization technique to improve the efficiency on smell detection. Histrategy considers a limited set of detection strategies, produced from different detection heuristics, as input of a customization process. The output of the customization process consists of a detection strategy tailored to each developer. The technique was evaluated in an experimental study with 48 developers and four types of code smells. The results showed that Histrategy is able to outperform six widely adopted machine learning algorithms - used in unguided approaches - both in effectiveness and efficiency. It was also confirmed that most developers benefit from using alternative heuristics to: (i) build their tailored detection strategies, and (ii) achieve efficient smell detection.

关键词： Machine learning algorithms Training Measurement Electronic mail Software Informatics Context

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Constructions of optimal orthogonal arrays with repeated rows

arXiv

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arXiv 2018年

作者： Colbourn, Charles J. Stinson, Douglas R. Veitch, Shannon School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287 United States David R. Cheriton School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada Dept. of Combinatorics and Optimization University of Waterloo WaterlooONN2L 3G1 Canada

We construct orthogonal arrays OAλ(k, n) (of strength two) having a row that is repeated m times, where m is as large as possible. In particular, we consider OAs where the ratio m/λ is as large as possible;these OAs are termed optimal. We provide constructions of optimal OAs for any k ≥ n + 1, albeit with large λ. We also study basic OAs;these are optimal OAs in which gcd(m, λ) = 1. We construct a basic OA with n = 2 and k = 4t + 1, provided that a Hadamard matrix of order 8t + 4 exists. This completely solves the problem of constructing basic OAs wth n = 2, modulo the Hadamard matrix *** Codes 05B15 Copyright © 2018, The Authors. All rights reserved.

关键词： Hadamard matrices

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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Gated multimodal units for information fusion 5

Gated multimodal units for information fusion

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5th International Conference on Learning Representations, ICLR 2017

作者： Arevalo, John Solorio, Thamar Montes-Y-Gómez, Manuel González, Fabio A. Dept. of Computing Systems and Industrial Engineering Universidad Nacional de Colombia Cra 30 No 45 03-Ciudad Universitaria Colombia Dept. of Computer Science University of Houston HoustonTX77204-3010 United States Instituto Nacional de Astrofísica Óptica y Electrónica Computer Science Department Luis Enrique Erro No. 1 Sta. Ma. Tonantzintla C.P. Puebla72840 Mexico

This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. It was evaluated on a multilabel scenario for genre classification of movies using the plot and the poster. The GMU improved the macro f-score performance of single-modality approaches and outperformed other fusion strategies, including mixture of experts models. Along with this work, the MM-IMDb dataset is released which, to the best of our knowledge, is the largest publicly available multimodal dataset for genre prediction on movies. © 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. All Rights Reserved.

关键词： Network architecture

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Intra-chip traffic generation under autoregressive models based on time series obtained by TLM simulation 29

Intra-chip traffic generation under autoregressive models ba...

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29th IEEE International System on Chip Conference, SOCC 2016

作者： Bueno Filho, Jose Eduardo Chiarelli Gonzalez Reano, Jorge Luis Chau, Wang Jiang Dept. of Electronic Systems School of Engineering University of Sao Paulo Brazil Institute of Computing University of Campinas Brazil

ISBN: (纸本)9781509013661

In the design flow of multi-processing system-on-chips (MPSoCs), the evaluation of communications structures, particularly, networks on chip (NoCs), plays a very important role, since it may show relevant characteristics on performance, energy consumption or cost. Simulation under a number of stimulus given by a traffic generator is a relevant solution for MPSoCs performance analysis. Traditional synthetic trace generators based on Poisson and classic Markovian models are not able to maintain the characteristics of an original application trace, such as burstiness or self-similarity. After Long Range Dependence characteristics had been found in intra-chip traffic, several approaches on the modeling of this kind of traffic were proposed, but restricted to the use of data obtained at RTL. In this work we present a methodology based on a fast hardware simulation at TLM to generate synthetic intra-chip traffic. The methodology encompasses the capture of the real data traffic, evaluation of the time series to determine the presence of Short or Long Range Dependence, time series fitting to the autoregressive moving-average (ARMA) or autoregressive fractionally integrated moving-average (ARFIMA) models, and the implementation of such models as a traffic generator. © 2016 IEEE.

关键词： Network-on-chip

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Data security issues in cloud-based Software-as-a-Service ERP

Data security issues in cloud-based Software-as-a-Service ER...

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Iberian Conference on Information systems and Technologies (CISTI)

作者： Pablo Saa Oswaldo Moscoso-Zea Andrés Cueva Costales Sergio Luján-Mora Faculty of Engineering Sciences Universidad Tecnológica Equinoccial Quito Ecuador DGPCTI Public Company Yachay Quito Ecuador Dept. of Software and Computing Systems University of Alicante Alicante Spain

ISBN: (纸本)9781509050475

This paper discusses the data security issues and concerns that are prevalent when organizations are moving their Enterprise Resource Planning (ERP) systems to the cloud. Cloud computing has reinvented how organizations conduct business and has enabled them to innovate and compete in a dynamic environment through new and innovative business models. The growing popularity and success of the cloud has led to the emergence of cloud-based Software-as-a-Service (SaaS) ERP systems, a new alternative approach to traditional on-premise ERP systems. Cloud-based ERP has a myriad of benefits for organizations. However, infrastructure engineers need to address data security issues before moving their enterprise applications to the cloud. Cloud-based ERP raises specific concerns about the confidentiality and integrity of the data stored in the cloud. Such concerns that affect the adoption of cloud-based ERP are based on the size of the organization. Small to medium enterprises (SMEs) gain the maximum benefits from cloud-based ERP as many of the concerns around data security are not relevant to them. On the contrary, larger organizations are more cautious in moving their mission critical enterprise applications to the cloud. A hybrid solution where organizations can choose to keep their sensitive applications on-premise while leveraging the benefits of the cloud is proposed in this paper as an effective solution that is gaining momentum and popularity for large organizations.

关键词： Cloud computing Organizations Software as a service Data security Computational modeling

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A parallel thermal reservoir simulator on distributed-memory supercomputers

A parallel thermal reservoir simulator on distributed-memory...

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SPE Asia Pacific Oil and Gas Conference and Exhibition 2016

作者： Zhong, He Liu, Hui Cui, Tao Wang, Kun Yang, Bo Yang, Min Chen, Zhangxin Dept. of Chemical and Petroleum Engineering University of Calgary 2500 University Drive NW CalgaryAB Canada State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences China

ISBN: (纸本)9781510835849

This paper describes the algorithms and implementation of a parallel thermal reservoir simulator designed for heavy oil simulation using distributed-memory supercomputers that can solve prohibitive problems efficiently. The thermal simulator inherits the features of a sequential thermal simulator. The performance of supercomputers is proportional to the number of CPUs. As a result, thermal problems can be solved thousands of times faster using supercomputers. This parallel simulator is based on our in-house platform, which provides grid management, data management, linear solvers and a visualization module. The exchange of information between CPUs is achieved by using the message passing interface standard, MPI. Results indicate that this simulator exhibits excellent scalability on the IBM Blue Gene/Q system and is thousands of times faster than serial simulators on a workstation. The simulator is also capable of simulating models with billions of grid blocks and fine resolution results can be obtained. © 2016 Society of Petroleum Engineers. All rights reserved.

关键词： Simulators

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