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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Oleksiienko, Illia Nousi, Paraskevi Passalis, Nikolaos Tefas, Anastasios Iosifidis, Alexandros Digit Aarhus University Department of Electrical and Computer Engineering Denmark Aristotle University of Thessaloniki Department of Informatics Greece

ISBN: (纸本)9781665430654

Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor. In this paper, we propose a Variational Neural Network-based version of a Voxel Pseudo Image Tracking (VPIT) method for 3D Single Object Tracking. The Variational Feature Generation Network of the proposed Variational VPIT computes features for target and search regions and the corresponding uncertainties, which are later combined using an uncertainty-aware cross-correlation module in one of two ways: by computing similarity between the corresponding uncertainties and adding it to the regular cross-correlation values, or by penalizing the uncertain feature channels to increase influence of the certain features. In experiments, we show that both methods improve tracking performance, while penalization of uncertain features provides the best uncertainty quality. © 2023 IEEE.

关键词： 3D Single Object Tracking Bayesian Neural Networks Machine Learning for Embedded Devices Point Cloud Uncertainty Estimation Variational Neural Networks

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Model-to-Circuit Cross-Approximation for Printed Machine Learning Classifiers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 3532-3544页

作者： Armeniakos, Giorgos Zervakis, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jorg Karlsruhe Institute of Technology Karlsruhe76131 Germany National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece University of Patras Computer Engineering and Informatics Department Patras26504 Greece Karlsruhe Institute of Technology Department of Computer Science Karlsruhe76131 Germany

Printed electronics (PEs) promises on-demand fabrication, low nonrecurring engineering costs, and subcent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the realization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer approximation framework tailored to bespoke architectures that enable complex ML models, such as multilayer perceptrons (MLPs) and support vector machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage overscaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power and area optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% area and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way toward smart complex printed applications. © 1982-2012 IEEE.

关键词： Timing circuits

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Adaptive 360° Video Streaming Over Wireless Communication Channels 9

Adaptive 360° Video Streaming Over Wireless Communication C...

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9th International Conference on Frontiers of Signal Processing, ICFSP 2024

作者： Valiandi, Ioanna Pattichis, Marios S. Kyriacou, Efthyvoulos Panayides, Andreas S. VIDEOMICS Group CYENS Centre of Excellence Nicosia Cyprus University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States Cyprus University of Technology Department of Electrical Eng. Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350353235

360° video streaming is one of the prevalent communication technologies for enhancing user experience and has thus seen widespread adoption in virtual and mixed reality applications. However, delivering content at scale while securing the quality of wirelessly communicated 360° videos in real-time poses significant challenges. 360° videos come in ultra-high definition, necessitate unprecedented bitrate demands and involve high encoding complexity. The time-varying nature of underlying wireless channels further introduces a destabilizing factor, calling for video systems to seamlessly adjust to varying bandwidth throughput to maintain adequate quality of service and experience. To address this issue, in this study, we have developed a multi-objective optimization framework for real-time video encoding adaptation. The objective is to optimize both video quality and encoding efficiency while minimizing the required bitrate, subject to real-time application constraints. To achieve this, we relied on generating (offline) precise forward prediction models of video quality, bitrate demands, and encoding time, that can be used to select the optimum encoding configuration in real-time. To validate our methods, we implemented an adaptive video encoding controller, and ran emulations employing actual network traces from 5G mobile video streaming scenarios, using the popular open-source x264 and x265 codecs for video encoding. A dataset of 4K omnidirectional videos at 30 frames per second was used. © 2024 IEEE.

关键词： Video streaming

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Overview of Load Disaggregation Models, Methods and Hardware 59

Overview of Load Disaggregation Models, Methods and Hardware

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59th International Universities Power engineering Conference, UPEC 2024

作者： Lancranjan, Mircea N. Muresan, Alexandru Jurj, Dacian I. Berciu, Alexandru Polycarpou, Alexis Czumbil, Levente Micu, Dan D. Technical University of Cluj-Napoca Department Electrical Engineering Cluj-Napoca Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca Romania Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (纸本)9798350379730

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated. © 2024 IEEE.

关键词： AI Data Cleaning Energy Efficiency Energy Signature Load Disaggregation Load Monitoring NILM

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Enhancing Energy Efficiency and Sustainability with Hybrid AC/DC Microgrids for Energy Transition 3

Enhancing Energy Efficiency and Sustainability with Hybrid A...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. University of Cyprus PV-Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350375923

The transition to a lower-carbon energy system across Europe brings numerous opportunities and challenges, necessitating advancements in sustainable and eco-friendly energy solutions. This study examines the design and operation of distribution networks and microgrids, particularly emphasizing the integration of hybrid AC/DC distribution systems. The primary goals of this integration are to enhance overall system performance and reduce operating expenses. Utilizing DIgSILENT Power Factory software, the research simulates a microgrid at the FOSS Research Center for Sustainable Energy at the University of Cyprus, incorporating real load and PV generation data. In order to reduce the amount of energy imported from the main grid, this approach evaluates various combinations of AC and DC loads, power production units, and energy storage solutions. The results show that the most optimal structure, with 65% DC loads and 35% AC loads, maximizes efficiency as well as minimizes running expenses. The analysis highlights a notable decrease in imported energy, which dropped from 60.3 kWh/day at full AC loads to 51.6 kWh/day at the optimal DC ratio. Finally, the design and implementation of effective, sustainable energy systems that are in line with changing European energy policy and technology breakthroughs will be significantly impacted by these outcomes. © 2024 IEEE.

关键词： Energy transition

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Smart Road Damage Survey System Using Machine Vision and IoT Technology 3

Smart Road Damage Survey System Using Machine Vision and IoT...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Mutijarsa, Kusprasapta Alunjati, Figo Agil Hidayat, Fadhil Kurnia, Ranna Arsyad, Zulkifli School of Electrical Engineering and Informatics Bandung Indonesia National Institute of Technology Department of Civil Engineering Bandung Indonesia Bandung State Polytechnic Department of Computer and Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350394559

Road is an essential transportation infrastructure to move people and goods to support economic prosperity. The efficiency, safety, security, and comfort of people have significant consequences by damage and defects on the road surface. A good-condition road ensures the land traffic runs well. This research is a collaboration between the Bandung Institute of Technology and the department of Public Works and Spatial Planning of West Java Province to develop a smart system for road damage surveys. The system was built by leveraging machine vision and IoT technologies to improve the performance of the road survey activities. The previous road damage detection, classification method, and pavement condition index (IKP) computation were performed semi-automatically. The proposed survey system developed in this work uses the Canny edge Detection algorithm and MobileNet V2 architecture to detect road defects automatically. The pavement condition index computation also improved by using the interpolation reduction factor. The system is designed into a four-layer architecture that consists of several functions and modules, i.e., the sensor system that includes a vision sensor, GPS and odometry, a hardware embedded system using a microcontroller, and machine learning to process the road condition image automatically, and a dashboard application to reveal the road condition on map. The performance resulted in accuracy, precision, recall, and F-score of the road damage detection are 54.5%, 24.2%, 78.9%, and 37%, respectively. © 2023 IEEE.

关键词： computer vision

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Distribution Grid Compatible Renewable Energy Through Optimal Exploitation of Bottom Up Flexibility 14

Distribution Grid Compatible Renewable Energy Through Optima...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Seklos, Konstantinos Efthymiopoulos, Nikolaos Michalas, Angelos Dimitriou, Theodoros Vergados, Dimitrios J. Vergados, Dimitrios D. University of Western Macedonia Department of Informatics Kastoria Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Piraeus Department of Informatics Piraeus Greece

ISBN: (纸本)9798350318067

The critical challenge that modern smart grids face in term of their management is the maximum possible exploitation of the energy that Renewable Energy Sources (RES) produce. The non-controllable nature of the rate that RES produce energy depends on the dynamic and non-accurately predictable weather conditions. In this context, flexibility noted as the modification of the energy demand, harmonizes (balances dynamically) energy production and energy consumption. Its advanced modeling and the optimization of its use highly affects energy cost in modern smart grids. In this paper we present a novel Distribution-Level Flexibility Market (DLFM) architecture which is noted Reactive DLFM (R-DLFM). R-DLFM operates after the clearing of the well-known day ahead energy market and/or balance market and ensures: i) the minimization of the energy cost and ii) the compliance of the dispatch with the constraints of the distribution system. The evaluation of R-DFLM depicts that its flexibility exploitation has high impact in the energy cost and compliance with distribution network constraints. © 2023 IEEE.

关键词： Investments

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Decentralized control in active distribution grids via supervised and reinforcement learning

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Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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Increasing Hosting Capacity of Uncontrollable Distributed Energy Resources in Isolated Power Systems

Increasing Hosting Capacity of Uncontrollable Distributed En...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Therapontos, Phivos Stavrou, Andreas Aristidou, Petros Electricity Authority of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9798350396782

Due to the push for more sustainable generation, small-scale distributed energy resources (DERs) penetration has increased significantly. Most countries have no remote monitoring and control requirements for DERs below a certain size. Therefore, they can be labeled as uncontrollable DERs (UDERs). In isolated systems, the UDERs hosting capacity is limited due to frequency stability concerns, as it is impossible to modify or curtail their generation in a centralized manner. In this paper, we propose the utilization of the active power over-frequency reduction curve for increasing the hosting capacity of UDERs in isolated power systems. Cyprus’s power system has been used to evaluate the proposed solution’s performance. The optimal settings of the curve have been identified, and it was found that UDERs hosting capacity can be increased significantly with the proposed methodology. © 2023 IEEE.

关键词： frequency stability grid code requirements hosting capacity islanded systems uncontrollable distributed energy resources

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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