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SSRN 2023年

作者： Karhapää, Pertti Behutiye, Woubshet Seppänen, Pertti Rodríguez, Pilar Oivo, Markku Franch, Xavier Martinez-Fernandez, Silverio López, Lidia Choraś, Michal Bagnato, Alessandra Aaramaa, Sanja Partanen, Jari University of Oulu Oulu90014 Finland Department of Service and Information System Engineering UPC-BarcelonaTech Barcelona08034 Spain Kaiserslautern67663 Germany ITTI Sp. z o.o Poznan61-612 Poland University of Science and Technology UTP Univerisy of Science and Technology Bydgoszcz5-796 Poland Softeam Paris75016 France Nokia Oy Oulu90620 Finland Bittium Wireless Ltd. Oulu90590 Finland University of Puget Sound United States Dep. Of Languages Computer Systems and Software Engineering Faculty of Computer Science Universidad Politécnica de Madrid Spain Dep. ESSI GESSI Research Group UPC-BarcelonaTech Barcelona08034 Spain Centro Nacional de Supercomputacíon Barcelona08034 Spain

Context Agile software development (ASD) aims to deliver high-quality software. However, software quality often gets less attention due to focus on *** an evidence-based quality-aware ASD process that organizations can use to create a more quality-aware development process and improve software *** methodUsing design science research, we collaborated with four organizations applying ASD in different contexts. Involving a total of 35 practitioners, we designed company-specific evidence-based quality-aware ASD processes, which were validated through simulated scenarios, implemented, and evaluated by the practitioners. ResultsA general ASD process model, derived from the company-specific models, was created, which shows the specific activities, frequencies, roles, and development phases where evidence can be used to improve software quality. The new process model contributed towards improved bottleneck identification, release readiness, and quality issues prioritization. We also learned that companies must be mindful of the rules that determine the use of evidence. Conclusion Integrating the use of quality related evidence into the ASD process can make it quality-aware, supporting software quality management, and enabling timely reaction through fast feedback loops. The evidence-based quality-aware ASD process is applicable in any Scrum-like ASD process. Applicability in other forms of ASD, however, requires further research. © 2023, The Authors. All rights reserved.

关键词： Quality control

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Deep Learning technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： M. Bheemalingaiah R. Srinivasan V. Vijey Nathan Raju Kolluri Amit Gupta Department of CSE J.B Institute of Engineering and Technology Hyderabad Telangana Department of CSE School of Computer Science and Engineering Galgotia University U.P India Department of CSE SRM TRP Engineering College Tiruchirappalli Tamilnadu India Department of ECE Narasaraopet Engineering College Narasaraopet Andhra Pradesh India Dep. of AI & ML J.B Institute of Engineering and Technology Hyderabad Telangana

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology.

关键词： Training Uncertainty Filters Image color analysis Supervised learning Digital cameras High dynamic range Convolutional neural networks Image reconstruction Painting

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Big Data from Space for Precision Agriculture Applications 11

Big Data from Space for Precision Agriculture Applications

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11th International Symposium on Digital Earth, ISDE 2019

作者： Bovolo, F. Bruzzone, L. Fernández-Prieto, D. Paris, C. Solano-Correa, Y.T. Volden, E. Zanetti, M. Fondazione Bruno Kessler Center for Information and Communication Technology Trento Italy Dep. of Information Engineering and Computer Science University of Trento Trento Italy ESRIN Frascati Italy

This paper presents an approach for precision agriculture large scale applications based on the analysis of big data consisting in Satellite Image Time Series (SITS) acquired by ESA Sentinel-2 (S2) satellite constellation. The approach has been developed in the framework of the ESA SEOM - Scientific Exploitation of Operational Missions - S2-4Sci Land and Water project [1]. To focus only on agricultural areas, images are first filtered based on a land cover (LC) map that is generated by updating available old maps by means of recent images. Then S2 SITS are used to analyse agricultural areas. Two macro challenges are therefore considered: (i) automatic update of LC maps and generation of agricultural areas mask;and (ii) unsupervised multi-temporal (MT) fine characterization of land plots. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Precision agriculture

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Gait-Based User Identification for Smart Home: When Machine Learning Meets FBG Accelerometers

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IEEE Transactions on Consumer Electronics 2024年

作者： Alhussein, Ghada Chi, Hao Ran Alberto, Nelia Antunes, Paulo Hadjileontiadis, Leontios Radwan, Ayman Domingues, M. Fatima Instituto de Telecomunicações-University of Aveiro Aveiro Portugal Instituto de Telecomunicações I3N and Dep. of Physics-University of Aveiro Aveiro Portugal Khalifa University of Science and Technology Department of Biomedical Engineering and Biotechnology Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece Instituto de Telecomunicações Instituto Superior Tècnico -University of Lisbon Lisbon Portugal

Advancements in fiber optic sensor technology and telecommunications have paved the way for innovative health monitoring systems. However, previous works in this area have often been limited by the lack of comprehensive datasets, hindering the development of accurate and robust solutions. This paper addresses this gap by presenting a novel dataset and approach to gait-based user identification using a set of four optical Fiber Bragg Grating (FBG) sensor-based accelerometers, integrated into smart home environments. By computing features such as entropy, mean, standard deviation, kurtosis, and skewness from raw signals, and employing three unsupervised machine learning models-K-means, DB-scan, and Gaussian Mixture Model (GMM)-we achieve high accuracy in distinguishing individuals. Our dataset results for each sensor are as follows: for Sensor 1, K-means with a Silhouette Score of 0.3038 and Davies-Bouldin Index of 1.1238;for Sensor 2, K-means with a Silhouette Score of 0.3543 and Davies-Bouldin Index of 0.9659;for Sensor 3, DB-scan with a Silhouette Score of 0.2650 and Davies-Bouldin Index of 0.6855;and for Sensor 4, K-means with a Silhouette Score of 0.3949 and Davies-Bouldin Index of 1.0438. This approach not only enhances user identification but also facilitates personalized healthcare applications and unobtrusive monitoring. © 1975-2011 IEEE.

关键词： Smart homes

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Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence: Preface

37th Conference on Uncertainty in Artificial Intelligence, U...

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37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 2021年 1-11页

作者： de Campos, Cassio Maathuis, Marloes H. Quaeghebeur, Erik Uncertainty in AI Group Dep. of Math. and Computer Science Eindhoven University of Technology Netherlands Seminar for Statistics Department of Mathematics ETH Zurich Switzerland

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Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence Preface

Proceedings of Machine Learning Research

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Proceedings of Machine Learning Research 2021年 161卷 1-11页

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Implementation of Go language to calculate ground state energy of atoms based on Density Functional Theory (DFT)

Implementation of Go language to calculate ground state ener...

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International Symposium on Nanoscience and Nanotechnology in Life sciences 2017, ISNNLS 2017

作者： Gita Arisha, Lafitiara Alfianto, Enggar Rusydi, Febdian Theoretical Physics Research Group Dep. of Physics Fac. of Science and Technology Airlangga University Jl. Mulyorejo Surabaya60115 Indonesia Dep. of Computer System Institut Teknologi Adhi Tama Surabaya Jl Arif Rachman Hakim 100 Surabaya60111 Indonesia

This study is using Go programming language that support parallel programming for numerical calculation. The program was created is designed for calculate ground-state energy of electron, which is based on Density Functional Theory (DFT). The basic mathematics of this program is using many basic concept of numerical mathematics (matrix calculation, Poisson solver, and standard routine of numerical mathematics). © 2020 Published under licence by IOP Publishing Ltd.

关键词： Density functional theory

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Clustering of longitudinal data: A tutorial on a variety of approaches: A preprint

arXiv

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arXiv 2021年

作者： Den Teuling, Nick G.P. Pauws, Steffen C. van den Heuvel, Edwin R. Dep. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Department Communication and Cognition Tilburg University Tilburg Netherlands Philips Research Eindhoven Netherlands

During the past two decades, methods for identifying groups with different trends in longitudinal data have become of increasing interest across many areas of research. To support researchers, we summarize the guidance from the literature regarding longitudinal clustering. Moreover, we present a selection of methods for longitudinal clustering, including group-based trajectory modeling (GBTM), growth mixture modeling (GMM), and longitudinal k-means (KML). The methods are introduced at a basic level, and strengths, limitations, and model extensions are listed. Following the recent developments in data collection, attention is given to the applicability of these methods to intensive longitudinal data (ILD). We demonstrate the application of the methods on a synthetic dataset using packages available in *** Codes 62H30 © 2021, CC BY-NC-SA.

关键词： Mixtures

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Minimization of wind power curtailment using dynamic line rating 10

Minimization of wind power curtailment using dynamic line ra...

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10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020

作者： Chakrapani Manakari, Vageesh Thengius, Sandra Tammanur Ravi, Akshaya Morozovska, Kateryna Hilber, Patrik KTH Royal Institute of Technology Div. of Electric Power and Energy Systems School of Electrical Engineering and Computer Science Stockholm Sweden Heimdall Power AS Oslo Norway KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Dep. of Electromagnetic Engineering Stockholm Sweden

ISBN: (数字)9781728171005

ISBN: (纸本)9781728171005

Large scale penetration of wind power to the grid and unevenly spread power demand can result in the transmission system not being able to dispatch all the produced wind power, causing in wind power *** line rating (DLR) is a technology which uses thermal properties of overhead conductors and weather data to determine the real-time ampacity limits of transmission lines. In general, dynamic rating allows extending capacity limits of power lines and helps to remove congestion in the grid.(/p)(p This study looks into the possibility of using dynamic line rating for removing congestion in the power system to allow dispatching more wind power and minimize the need for curtailment. The results of case-studies have shown that DLR allows to significantly reduce the curtailment of generation, especially during winter, when wind production is highest and day time, when the power demand is the highest. © 2020 IEEE.

关键词： Wind power

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 23

Convolutional Neural Network Based Deep Neural Network Model...

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Ahmed Mahmood Khudhur Mohammed Hasan Alwan Qayssar Al Omairi Omar Abdulkareem Mahmood Yousif I Hammadi Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Information Technology Department Kirkuk University Iraq Department of Communications Engineering University of Diyala Iraq Department of Computer Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Сardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant dep.etion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly dep.ete resources.

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