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14th International Conference on computers in Urban Planning and Urban Management, CUPUM 2015

作者： Tisma, Alexandra De Weerdt, Mathijs M. Van Riemsdijk, Birna Warnier, Martijn Van Der Velde, René Faculty of Architecture Delft University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Netherlands Faculty of Technology Policy and Management Netherlands

ISBN: (纸本)9780692474341

Technologies collecting location-based data in the real world have advantages over traditional methods for landscape perception research. The possibility to relate geo-referenced responses of inhabitants to the physical and social data in expert GIS databases can lead to new insights into the difference between laymen and expert opinions and may result in adjustments of policy forming. To date, the use of Social Sensing in Landscape perception and valuing is limited. This research presents the set of requirements for a mobile application for landscape and urban planning, discusses some of the main challenges, and concludes that a number of evaluated existing mobile applications just partly meet those requirements.

关键词： Urban planning

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Sight-seeing in the eyes of deep neural networks 14

Sight-seeing in the eyes of deep neural networks

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14th IEEE International Conference on escience, e-science 2018

作者： Khademi, Seyran Shi, Xiangwei Mager, Tino Siebes, Ronald Hein, Carola De Boer, Victor Van Gemert, Jan TU Delft Faculty of Electrical Engineering Mathematics and Computer Science Department of Intelligent Systems Netherlands TU Delft Faculty of Architecture and the Built Environment Department of Architecture Netherlands VU Universiteit Amsterdam Faculty of Sciences Department of Computer Science Netherlands

ISBN: (纸本)9781538691564

We address the interpretability of convolutional neural networks (CNNs) for predicting a geo-location from an image. In a pilot experiment we classify images of Pittsburgh vs Tokyo and visualize the learned CNN filters. We found that varying the CNN architecture leads to variating in the visualized filters. This calls for further investigation of the effective parameters on the interpretability of CNNs. © 2018 IEEE.

关键词： Deep neural networks

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Yet Another Robot Application?

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IEEE ROBOTICS & AUTOMATION MAGAZINE 2016年第2期23卷 12-+页

作者： Salvini, Pericle Korsah, Ayorkor Nourbakhsh, Illah Department of Computer Architecture and Automation Computer Science Faculty Complutense University of Madrid c∖José García Santesmases s∖n Madrid Spain Key Laboratory of Dependable Service Computing in the Cyber Physical Society of the Ministry of Education Chongqing University and the College of Automation Chongqing University Chongqing China School of Automation Chongqing University Chongqing China Department of Electrical Engineering and Computer Science Northwestern University Evanston IL School of Electrical and Electronic Engineering Nanyang Technological University Singapore

The articles in this special section is to find out what lies beyond the fascination toward robots that we have inherited from popular culture, the pleasure of learning with robots derived from the gamification of learning, and the strong appeal for robotics technologies given by its aura of innovation. Are there authentic ways in which robotics can improve education, and what affordances does robotics, in particular, provide for such educational transformation? Indeed, one of the biggest gray areas of educational robotics concerns the lack of empirical evidence on the educational (pedagogical as well as psychological) effectiveness of robots that is related to both the lack of standardized evaluation criteria and the methodological difficulties in conducting sound scientific tests.

关键词： Special issues and sections Robots computer applications Technology forecasting engineering education STEM Education Programming

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Building term hierarchies using graph-based clustering 6

Building term hierarchies using graph-based clustering

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6th International Conference on Natural Language Processing and Information Retrieval, NLPIR 2022

作者： Hloch, Mark Van Meegen, Markus Kubek, Mario Unger, Herwig Faculty of Electrical Engineering and Computer Science Hochschule Niederrhein - University of Applied Sciences Krefeld Germany Department of Computer Science Georgia State University Atlanta United States Fern Universität Hagen Chair of Computer Engineering Hagen Germany

ISBN: (纸本)9781450397629

Classical tasks of a librarian, such as screening and categorizing new documents based on their content, are increasingly replaced by search engines or through the use of cataloging software. A first overview of a corpus topical orientation can be achieved by combining graph-based search engines and clustering methods. Existing classical clustering methods, however, often require an a priori specification of the desired number of clusters to be output and do not consider term relationships in graphs, which is deficient from a practical point of view. Therefore, fully unsupervised graph-based clustering approaches at the term level offer new possibilities that mitigate these shortcomings. Within this work, a set of novel graph-based clustering algorithms have been developed. The hierarchical clustering algorithm (HCA) forms term hierarchies by iteratively isolating nodes of a given co-occurrence graph based on the evaluation of the edge weight between the nodes. Based on the co-occurrence graph inherent relationships of terms, a new graph is built agglomerative forming individual term clusters of related terms. The feasibility of the outlined methods for text analysis is shown. © 2022 ACM.

关键词： Cluster analysis

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Classification of Arrhythmia ECG Signals using Convolutional Neural Network 30

Classification of Arrhythmia ECG Signals using Convolutional...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Habijan, Marija Galic, Irena Pizurica, Aleksandra Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia Ghent University TELIN-GAIM Faculty of Engineering and Architecture Ghent Belgium

ISBN: (纸本)9798350337297

The electrocardiogram (ECG) has been established as a reliable tool for monitoring cardiovascular health. Vast amount of ECG recordings can pose a challenge for its processing and analysis and seeking out experts to analyze such a large amount of ECG data can deplete valuable medical resources. Recently, there has been a growing interest in automatic methods for accurate heartbeat categorization. Our motivation behind this work is to improve efficiency and accuracy for arrhythmia classification based on convolutional neural networks (CNNs). We first pre-process the input signals by normalizing them using a z-score. Following the data segmentation, the under-represented classes are oversampled using random duplication. By randomly duplicating the samples of underrepresented categories and eliminating instances from the overrepresented categories, this approach minimizes the imbalance in the training data. We propose an efficient 12-layer 1D-CNN to classify cardiac arrhythmia into five classes. We demonstrate the oversampling technique's impact on the proposed CNN's performance by comparing its performance before and after its application. © 2023 IEEE.

关键词： Electrocardiograms

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Heart Sound Classification using Deep Learning 8

Heart Sound Classification using Deep Learning

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8th International Conference on Smart and Sustainable Technologies, SpliTech 2023

作者： Habijan, Marija Galić, Irena Pizurica, Aleksandra Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia Ghent University TELIN-GAIM Faculty of Engineering and Architecture Ghent Belgium

ISBN: (纸本)9789532901283

Cardiac auscultation is the process of listening to the sounds of the heart with a stethoscope, which can provide important diagnostic information about a patient's heart function. It is a key component of a physical examination and can help doctors identify potential heart problems such as murmurs, arrhythmias, and valve disorders. Deep learning-based cardiac auscultation is of great interest to medical professionals as it can reduce the burden of manual auscultation by automatically detecting abnormal heartbeats. However, automatic cardiac auscultation is a challenging task due to the need for dependable and precise results, as well as the interference of background noise in heart sounds. In this work, we propose a heart sound classification method that could potentially accelerate automated cardiac auscultation. The method is based on Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN). We use heart sounds from the dataset Classifying Heart Sounds Challenge and extract input features using Mel Frequency Cepstral Coefficients (MFCCs). Further, we examine the use of a simple data augmentation technique and show that adding noise, stretching, and shifting of heart sound signals significantly improves the classification accuracy of the normal, murmur, and artifact heart sounds. © 2023 University of Split, FESB.

关键词： Heart

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Reducing Energy Consumption by Load Aggregation with an Optimized Dynamic Live Migration of Virtual Machines

Reducing Energy Consumption by Load Aggregation with an Opti...

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International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Daniel Versick Djamshid Tavangarian Faculty of Electrical Engineering and Computer Science Chair of Computer Architecture University of Rostock Rostock Germany

Energy consumption of data centers has been in-creasing continuously during the last years due to rising demands of computational power especially in current Grid- and Cloud-Computing systems. One promising approach of reducing this energy consumption is the consolidation of servers by virtualization. Many low loaded computer systems are virtualized and run on few physical servers for reducing the number of energy-consuming computers. At present this consolidation is usually done statically, thus, the administrator of a data center manually migrates many virtual machines with low load onto one physical server which may lead to overloading when the workload is rising unexpectedly. Dynamic server migration that adapts the number of running physical machines to the current workload overcomes these problems. Physical machines can be highly loaded and in case of further rising load virtual machines are migrated to other physical server systems that have been switched on. Such dynamic load aggregation approaches are rarely used and typically only consider few criteria for migration. This paper presents a classification of migration criteria for live migration of virtual machines in load aggregation environments and proposes an algorithm for combining many different kinds of migration criteria to a clustering-based metric. Thus, the novel load aggregation algorithm optimizes energy consumption as well as other migration criteria like runtime performance of applications.

关键词： Virtual machining Servers Software Clustering algorithms Hardware Energy consumption Partitioning algorithms

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Fatigue crack propagation across grain boundary of Al-Cu-Mg bicrystal based on crystal plasticity XFEM and cohesive zone model

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Journal of Materials science & Technology 2022年第31期126卷 275-287页

作者： Qi Zhao Magd Abdel Wahab Yong Ling Zhiyi Liu School of Materials Science and Engineering Hubei University of Automotive TechnologyShiyan 442002China Faculty of Mechanical-Electrical and Computer Engineering School of Engineering and TechnologyVan Lang UniversityHo Chi Minh CityVietnam Soete Laboratory Faculty of Engineering and ArchitectureGhent UniversityTechnologiepark Zwijnaarde 903B-9052 ZwijnaardeBelgium School of Materials Science and Engineering Central South UniversityChangsha 410083China

In this paper,a methodology integrating crystal plasticity(CP),the eXtended finite element method(XFEM)and the cohesive zone model(CZM)is developed for an Al-Cu-Mg alloy to predict fatigue crack propagation(FCP)across grain boundary(GB)of Al-Cu-Mg alloy during stageІІ.One GB model is incor-porated into FCP constitutive law to describe grain interaction at GB.A bicrystal containing GB is built up to simulate FCP behavior through L participated *** features including GB characteristic,cumulative plastic strain(CPS)distribution and crystal slipping evidence can be *** numer-ical results are compared with published experimental data to check the accuracy of *** work demonstrates that the combination of CP containing GB constitutive laws,XFEM and CZM is a promising methodology in predicting twist angle-controlled crack deflection through GBs.

关键词： Grain boundary Twist angle Crystal plasticity Extended finite element method Fatigue crack propagation Cumulative plastic strain

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Design and testing tool for a safe monitoring system for neurodegenerative disorder patients 3

Design and testing tool for a safe monitoring system for neu...

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3rd International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2016

作者： Halabi, Nashaat El Achkar, Roger Daou, Roy Abi Zeid Hayek, Ali Borcsok, Josef Department of Computer and Communications Engineering Faculty of Engineering American University of Science and Technology Beirut Lebanon Department of Biomedical Technologies Faculty of Public Health Lebanese German University Sahel Alma Lebanon Department of Computer Architecture and System Programming Faculty of Electrical Engineering/Computer Science University of Kassel Kassel Germany

ISBN: (纸本)9781467385237

This paper aims to develop a sensor based monitoring and analyzing system for Neuro-Degenerative Disorder patients (NDD);this may consist on SpO2 sensor, Electrophysiological sensors, NIBP, Motion Capture sensors and Eye Monitoring sensor, taking into consideration an acceptable cost for the whole system. Recorded data will be sent to an embedded decision making unit where detection, analysis, classification, prediction and action control will occur. The sensors and the decision making unit will be implemented in a comfortable jacket that doesn't affect the patients' movements and that can be used by several patients with reduced sensor placement alterations. The decision that can be made is creating a stimulus to avoid falling in case of sudden stop while moving, initiating an alarm, sending a notification to a mobile phone application, and/or telemedicine monitoring features. Artificial Neural Networks will be used to classify and predict the abnormal cases where action should be taken, and since the sensors will be continuously recording, it is possible to achieve continuous learning for the ANN as a first phase. Initial models will be defined by testing normal behaviors and some known abnormal behaviors or symptoms related to the electrical activity and other characteristics of the heart, oxygen saturation level, eye activity and body motion. © 2016 IEEE.

关键词： Neural networks

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NPU Thermal Management

NPU Thermal Management

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作者： Amrouch, Hussam Zervakis, Georgios Salamin, Sami Kattan, Hammam Anagnostopoulos, Iraklis Henkel, Jorg in the Computer Science Electrical Engineering Faculty University of Stuttgart Stuttgart Germany Chair for Embedded Systems Karlsruhe Institute of Technology Karlsruhe Germany Department of Electrical and Computer Engineering Southern Illinois University CarbondaleIL United States

Neural processing units (NPUs) are becoming an integral part in all modern computing systems due to their substantial role in accelerating neural networks (NNs). The significant improvements in cost-energy-performance stem from the massive array of multiply accumulate (MAC) units that remarkably boosts the throughput of NN inference. In this work, we are the first to investigate the thermal challenges that NPUs bring, revealing how MAC arrays, which form the heart of any NPU, impose serious thermal bottlenecks to on-chip systems due to their excessive power densities. For the first time, we explore: 1) the effectiveness of precision scaling and frequency scaling (FS) in temperature reductions and 2) how advanced on-chip cooling using superlattice thin-film thermoelectric (TE) open doors for new tradeoffs between temperature, throughput, cooling cost, and inference accuracy in NPU chips. Our work unveils that hybrid thermal management, which composes different means to reduce the NPU temperature, is a key. To achieve that, we propose and implement PFS-TE technique that couples precision and FS together with superlattice TE cooling for effective NPU thermal management. Using commercial signoff tools, we obtain accurate power and timing analysis of MAC arrays after a full-chip design is performed based on 14-nm Intel FinFET technology. Then, multiphysics simulations using finite-element methods are carried out for accurate heat simulations in the presence and absence of on-chip cooling. Afterward, comprehensive design-space exploration is presented to demonstrate the Pareto frontier and the existing tradeoffs between temperature reductions, power overheads due to cooling, throughput, and inference accuracy. Using a wide range of NNs trained for image classification, experimental results demonstrate that our novel NPU thermal management increases the inference efficiency (TOPS/Joule) by $1.33\times $ , $1.87\times $ , and $2\times $ under different temperature constrain

关键词： Temperature control

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