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2022 ECEEE Summer Study on Energy Efficiency: Agents of Change

作者： Patrão, Carlos Matos, Luisa Landeck, Jorge Klein, Lurian Mendes, Gonçalo da Silva, Nuno Pinho Fulgêncio, Nuno O'Callaghan, Tim Stevens, Peter Institute of Systems and Robotics and Cleanwatts Dep. of Electrical and Computer Engineering University of Coimbra Portugal Cleanwatts Coimbra Portugal University of Coimbra and Cleanwatts LIBPhys Department of Physics University of Coimbra Portugal Lappeenranta-Lahti University of Technology LUT Finland R&D Nester Centro de Investigação em Energia REN Sacavém Portugal Electric Corby Corby Enterprise Centre England United Kingdom

ISBN: (纸本)9789198827002

The Clean Energy for all Europeans policy package has opened the way for a major transition of the European energy landscape towards customer empowerment and local energy markets development. The European Green Deal, the Fit for 55 % package and the European Directives revision are pushing forward Renewable Energy Communities, which will disrupt the energy sector and overcome limitations of existing rigid energy markets. The rise of distributed energy technologies is fostering the development of customer-centered services such as peer-to-peer energy trading. However, adequate tools and business models to interface and engage with energy markets are still lacking. The H2020 FleXunity project is addressing the emerging market needs by developing a technological platform, that combines advanced artificial intelligence algorithms, blockchain technology and demand response services, to exploit new business models that could value the aggregation of small-scale energy assets. Although the concept of these platforms is relatively well known, their real-world implementation and validation is still at an early stage. This paper presents the FleXunity project's main objectives, details of the Virtual Power Plant managing Platform under development and the characteristics of two distinct real world pilot Energy Communities already dep.oyed in the UK and Iberia. It also presents an analysis of the role that Demand Response activities and indep.ndent aggregation could play in Energy Communities, and specifically explores the role of Demand Response in the balancing markets of Finland, UK, Spain, and Portugal. Focusing on the pilot markets, the project also identifies the most promising balancing services to be provided by Energy Communities, by surveying and framing the existing regulatory qualification requirements. © 2022 European Council for an Energy Efficient Economy. All rights reserved.

关键词： Smart power grids

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Tung-Ling Wu Chih-Ming Kung Guan-Ting Lin Kuen-Yu Tsai Guang-Yun Meng Yu-Che Huang Wan-Shu Cheng Institute of Transportation at Ministry of Transportation and Communications Taiwan R.O.C. Dep. Of Information Technology&Communication Shin Chin Univeristy Kaohsiung Campus Taiwan R.O.C. Dep. of Computer Science and Information Management Providence University Taichung Taiwan R.O.C. Department of Electrical Engineering National Taiwan Univeristy Taipei Taiwan R.O.C. Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics NTU Taiwan R.O.C.

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development,

关键词： Industries Technological innovation Transportation Autonomous aerial vehicles Regulation Communications technology Aircraft Standards Drones Investment

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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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Vegetation and Drought Trends in Sweden's Mälardalen Region-Year-on-Year Comparison by Gaussian Process Regression

Vegetation and Drought Trends in Sweden's Mälardalen Region...

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2020 Swedish Workshop on Data Science, SweDS 2020

作者： Paszkowsky, Nuria Agues Brannvall, Rickard Carlstedt, Johan Milz, Mathias Kovacs, Gyorgy Liwicki, Marcus Unit for Data Center Systems and Applied Data Science Research Institutes of Sweden Sweden Luleå University of Technology Dep. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9781728192048

This article describes analytical work carried out in a pilot project for the Swedish Space Data Lab (SSDL), which focused on monitoring drought in the Mälardalen region in central Sweden. Normalized Difference Vegetation Index (NDVI) and the Moisture Stress Index (MSI)-commonly used to analyse drought- A re estimated from Sentinel 2 satellite data and averaged over a selection of seven grassland areas of interest. To derive a complete time-series over a season that interpolates over days with missing data, we use Gaussian Process Regression, a technique from multivariate Bayesian analysis. The analysis show significant differences at 95% confidence for five out of seven areas when comparing the peak drought period in the dry year 2018 compared to the corresponding period in 2019. A cross-validation analysis indicates that the model parameter estimates are robust for temporal covariance structure (while inconclusive for the spatial dimensions). There were no signs of over-fitting when comparing in-sample and out-of-sample RMSE. © 2020 IEEE.

关键词： Remote sensing

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An Overview on Big Data Mining Using Evolutionary Techniques

An Overview on Big Data Mining Using Evolutionary Techniques

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2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020

作者： Alaeddin, Fadia Khalifeh, Ala' Darabkh, Khalid A. Princess Sumaya University for Technology Luminus Technical University College Computer Science Department Amman Jordan German Jordanian University Dep. of Electrical Eng. Amman Jordan University of Jordan Dept. of Computer Engineering Amman Jordan

ISBN: (纸本)9781728196732

Big Data processing suffers from several limitations due to its magnitude making it time consuming to implement any kind of analysis such as data mining. Evolutionary Algorithms (EAs) are metaheuristic optimization algorithms inspired by the natural behavior of the population evolution such as Genetic Algorithm, Artificial Bee Colony, Artificial Ant Colony and other swarm intelligence algorithms. EAs have recently been used to overcome Big Data limitations, especially memory consumption and the long execution time. This paper provides an overview of the recent research papers that utilize evolutionary algorithms to deal with the optimization problems related to Big Data mining such as clustering, classification and features' selection. © 2020 IEEE.

关键词： K-means clustering

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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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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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Evidence-Based Quality-Aware Agile Software Development Process: Design and Evaluation

SSRN

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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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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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