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arXiv 2023年

作者： Michalakopoulos, Vasilis Pelekis, Sotiris Kormpakis, Giorgos Karakolis, Vagelis Mouzakitis, Spiros Askounis, Dimitris Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece

The analytical prediction of building energy performance in residential buildings based on the heat losses of its individual envelope components is a challenging task. It is worth noting that this field is still in its infancy, with relatively limited research conducted in this specific area to date, especially when it comes for data-driven approaches. In this paper we introduce a novel physics-informed neural network model for addressing this problem. Through the employment of unexposed datasets that encompass general building information, audited characteristics, and heating energy consumption, we feed the deep learning model with general building information, while the model’s output consists of the structural components and several thermal properties that are in fact the basic elements of an energy performance certificate (EPC). On top of this neural network, a function, based on physics equations, calculates the energy consumption of the building based on heat losses and enhances the loss function of the deep learning model. This methodology is tested on a real case study for 256 buildings located in Riga, Latvia. Our investigation comes up with promising results in terms of prediction accuracy, paving the way for automated, and data-driven energy efficiency performance prediction based on basic properties of the building, contrary to exhaustive energy efficiency audits led by humans, which are the current status quo. © 2023, CC BY.

关键词： Energy efficiency

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An efficient memory data organization strategy for application-characteristic graph processing

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Frontiers of computer Science 2022年第1期16卷 191-193页

作者： Peng FANG Fang WANG Zhan SHI Dan FENG Qianxu YI Xianghao XU Yongxuan ZHANG Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage SystemEngineering Research Center of data storage systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memor... 详细信息

关键词： application processing pattern

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Sustainable electric vehicles fault detection based on monitoring by deep Learning Architectures in feature extraction and classification

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 57卷

作者： Wongchai, Anupong Aoudni, Yassine Yesubabu, Mannava Reegu, Faheem Ahmad Gowri, N. Vasantha Vijayakumar, P. [a]The Independent researcher and Social Worker Thailand [b]Department of Computers and Information Technology Faculty of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia [c]Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 3038 Sfax Tunisia [d]Department Of Computer Science & Engineering Vardhaman College Of Engineering Shamshabad Hyderabad 501218 India [e]College of Computer Science And Information Technology Jazan University Jazan 45142 Saudi Arabia [f]Department of EEE Chaitanya Bharathi Institute of Technology (A) Hyderabad India [g]School of Electronics Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu India

Numerous industrial sector paradigms have been altered by the necessity to produce more competitive ma-chinery and the introduction of digital technologies from so-called Industry 4.0. This research proposes novel technique in electric vehicle fault detection based on monitoring data classification and feature extraction using deep learning architectures. The real Proton exchange membrane fuel cell (PEMFC) experiment dataset has been collected as sustainable electric vehicles data using multi-cell parallel electric vehicle and it has been processed for noise removal, dimensionality reduction and extraction using deep stacking auto gradient descent for-classifyingthrough monte Carlo regressive Gaussian naive bayes *** of experiments demonstrate that suggested model achieves over 99% accuracy in identifying flooding fault of fuel cell under load-varying situations. The experimental analysis has been carried out in terms of accuracy, robustness, reliability, preci-sion, recall. The proposed technique attained 99% of accuracy, 89% of robustness, 85% of Reliability, 95% of precision and 81% of recall.

关键词： Electric vehicles Fault detection Monitoring Data classification Feature extraction Deep learning

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Exploring the Integration of Educational Robotics and the Internet of Things in Learning Environments

Exploring the Integration of Educational Robotics and the In...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Elena Kakoulli Salomi Evripidou Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This study explores the integration of Educational Robotics (ER) and the Internet of Things (IoT) in learning environments, highlighting their collective impact on educational practices. It assesses ER and IoT’s application, challenges, and opportunities, offering insights into their role in enhancing pedagogy and learning outcomes. Specifically, our exploration uncovers the transformative potential of ER and IoT in fostering critical thinking, problem-solving skills, and digital literacy among students and reveals the pivotal role these technologies play in preparing learners for the demands of the digital age and Industry 4.0, while also highlighting the need for strategic implementation and teacher support. Our findings elucidate the potential of ER and IoT to innovate teaching strategies and curriculum design, serving as a guide for educational stakeholders, such as curriculum developers, educators, researchers, and policymakers, in leveraging these technologies to revolutionize instructional practices.

关键词： Training Information age Fourth Industrial Revolution Internet of Things Stakeholders Problem-solving Distributed computing

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A Compositional Resilience Index for Computationally Efficient Safety Analysis of Interconnected systems 62

A Compositional Resilience Index for Computationally Efficie...

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62nd IEEE Conference on Decision and Control, CDC 2023

作者： Niu, Luyao Maruf, Abdullah Al Clark, Andrew Mertoguno, J. Sukarno Poovendran, Radha University of Washington Network Security Lab Department of Electrical and Computer Engineering SeattleWA98195-2500 United States California State University Department of Electrical and Computer Engineering Los AngelesCA90032 United States McKelvey School of Engineering Washington University in St. Louis Electrical and Systems Engineering Department St. LouisMO63130 United States School of Cybersecurity and Privacy Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9798350301243

Interconnected systems such as power systems and chemical processes are often required to satisfy safety properties in the presence of faults and attacks. Verifying safety of these systems, however, is computationally challenging due to nonlinear dynamics, high dimensionality, and combinatorial number of possible faults and attacks that can be incurred by the subsystems interconnected within the network. In this paper, we develop a compositional resilience index to verify safety properties of interconnected systems under faults and attacks. The resilience index is a tuple serving the following two purposes. First, it quantifies how a safety property is impacted when a subsystem is compromised by faults and attacks. Second, the resilience index characterizes the needed behavior of a subsystem during normal operations to ensure safety violations will not occur when future adverse events occur. We develop a set of sufficient conditions on the dynamics of each subsystem to satisfy its safety constraint, and leverage these conditions to formulate an optimization program to compute the resilience index. When multiple subsystems are interconnected and their resilience indices are given, we show that the safety constraints of the interconnected system can be efficiently verified by solving a system of linear inequalities. We demonstrate our developed resilience index using a numerical case study on chemical reactors connected in series. © 2023 IEEE.

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An Integrated ML-Ops Framework for Automating AI-based Photovoltaic Forecasting

An Integrated ML-Ops Framework for Automating AI-based Photo...

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IEEE International Conference on Big Data

作者： Symeon Chorozoglou Elissaios Sarmas Vangelis Marinakis Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

Energy digitization holds significant importance for various energy applications, encompassing aspects like production, consumption, and distribution within power grids. The digital transformation of energy plays a pivotal role in enhancing the integration of Artificial Intelligence (AI) into energy management systems, leveraging extensive datasets. The development of AI systems and the utilization of Machine Learning (ML) techniques empower users with precise predictions related to renewable energy production, thereby expediting the shift towards clean energy. Nevertheless, the effective use of data demands a high level of expertise, thereby excluding energy stakeholders from the benefits modern technologies offer. In this paper, we introduce an AI forecasting system designed to bridge the knowledge gap in data processing methods and ML models for energy stakeholders. This system focuses on delivering a user-friendly interface for photovoltaic (PV) production forecasting by automating the entire ML operations pipeline. Consequently, users can obtain data-driven model results without the need to manually code all the requisite steps for model training and fine-tuning. To demonstrate the system’s capabilities, we provide an experimental application using real PV data from a Portuguese aggregator.

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Classifying Bicycle Infrastructure Using On-Bike Street-Level Images

arXiv

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arXiv 2024年

作者： Backman, Kal Beck, Ben Kulić, Dana Department of Electrical and Computer Systems Engineering School of Public Health and Preventive Medicine Monash University Australia

While cycling offers an attractive option for sustainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hierarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis. © 2024, CC BY.

关键词： Bicycles

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Analysis on the Hesitation and its Application to Decision Making

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2024年第2期7卷 15-34页

作者： Yang, Youpeng Lee, Sanghyuk Kim, Kyeong Soo Zhang, Haolan Huang, Xiaowei Pedrycz, Witold School of Advanced Technology Xi’an Jiaotong-Liverpool University Jiangsu Suzhou China Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computer Science New Uzbekistan University Tashkent Uzbekistan SCDM Centre Zhejiang University Ningbo China Department of Electrical & Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

A novel score function based on the Poincaré metric is proposed and applied to a decision-making problem. Decision-making on Fuzzy Sets (FSs) has been considered due to the flexibility of the data, and it is applied to the decision-making. However, decisions with FSs are sometimes nondecisive even for different membership degrees. Hence, Intuitionistic Fuzzy Sets (IFSs) data is applied to design a score function for the decision-making with the Poincaré metric. This function is supported by the profound information of IFSs;IFSs include hesitation degree together with membership and non-membership degree. Hence, IFS membership and non-membership degree are expressed as two-dimensional vectors satisfying the Poincaré metric for simplification. At the same time, the proposed approach addresses the hesitation information in the IFS data. Next, a score function is proposed, constructed and provided. The proposed score function has a strict monotonic property and addresses the preference without resorting to the accuracy function. The strict monotonic property guarantees the preference of all attributes. Additionally, the existing problem of score function design in IFSs is addressed: they return zero scores even with different meanings for the same membership and non-membership degree. The advantages of the proposed score function over existing ones are demonstrated through illustrative examples. From the calculation results, the proposed decision score function discriminates between all candidates. Hence, the proposed research provides a solid foundation for the hesitation analysis on the decision-making problem. © 2024 The Authors.

关键词： Decision making

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An Adaptive RPC Mechanism for Performance and Node Fault Tolerance Optimization in HDFS 24

An Adaptive RPC Mechanism for Performance and Node Fault Tol...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Jingyu Shu, Zhengwen Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Systems Engineering National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350319934

With the rapid development and evolution of information technologies, the big data industry has been experiencing exponential explosive data growth. Since the huge business and research value behind the large-scale data, big data technology has become one of the hotest research fields in academia and industry. To the big data, storing and processing the data is core function of the file systems. Hadoop Distributed File System (HDFS) is the most typical distributed big data architecture, which has the characteristics of high reliability, high fault tolerance, and low hardware cost. HDFS provides the efficient task implementation and fault tolerance among nodes by Remote Procedure Call (RPC) mechanism. However, the traditional RPC mechanism has remarkable defects with the fixed timing method, and cannot quickly establish the relationship between node downtime and network congestion. In this paper, we propose an improved adaptive RPC mechanism for node fault tolerance and performance. The proposed method classifies nodes by the data block access statistic, and dynamically adjusts the RPC interval time. This method can be used to reduce the networking traffic and processing pressure of NameNode nodes, and improve the I/O and task performance. Through the node classification, an inrack method is used to achieve better performance on fault tolerance. Eventually, we design the extensive experiments to evaluate the proposed method, and experimental results show it improves the performance compared with state-of-the-art method. © 2022 IEEE.

关键词： Big data

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A Comprehensive Approach to Real-Time and Batch Processing for Energy-Efficient IoT Homes: Leveraging Lambda Architecture and Data Lakes

A Comprehensive Approach to Real-Time and Batch Processing f...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Filippos Serepas Ioannis Papias Nikolaos Bellos Vangelis Marinakis Holistic IKE Athens Greece Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

The incorporation of IoT technology into energy-efficient home systems has resulted in a surge in data volume, prompting the need for sophisticated storage and processing solutions. This paper proposes a system that integrates the Lambda Architecture with data lakes to address real-time and batch processing needs in the context of energy-efficient homes. By leveraging technologies such as TimescaleDB for short-term storage and Apache Hudi for long-term storage, coupled with Kafka for data streaming, the system ensures efficient data management and analysis. Real-time insights are provided through GraphQL-powered visualizations, while batch processing facilitates advanced analytics and machine learning model training. The proposed system addresses the dual demands of end-users seeking real-time insights and data scientists requiring extensive datasets for analysis.

关键词： Training Analytical models Batch production systems Data visualization Machine learning Big Data applications Real-time systems Energy efficiency Surges

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