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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Jorge Bracho Chrysanthos Charalambous Venizelos Efthimiou Dimitrios Lagos Tomasz Samotyjak Magnus Askeland George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Energy Systems Analytics Group Catalonia Institute for Energy Research (IREC) Barcelona Spain Instytut Energetyki Gdansk Poland Energy Systems SINTEF Energy Research Trondheim Norway

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

It is imperative for energy systems to reduce their environmental footprint in a cost-efficient manner, for which renwable energy sources (RES) and complementary technologies become desirable options to achieve said task. This is particularly the case for systems with different energy sources, as they’d be able to either address one specific type of energy demand and/or allow for the coupling of different energy carriers that would allow for technologies that normally don’t interact to start complementing each other. Therefore, this paper proposes various scenarios for the UCY campus in which hydrogen technologies would allow for the electrification of thermal demand while harnessing the surplus production coming from a solar park, which is modelled through the Integrate optimization tool and validated through DidSILENT PowerFactory. The results showcased a rise of up to 58% in self-sufficiency while the annual energy costs and CO2 emissions were reduced by 45% and 48% respectively.

关键词： Renewable energy sources Energy consumption Costs Hydrogen Fuel cells Production Optimization Carbon emissions Investment Resistance heating

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Recent Advancements in Vehicle to Vehicle Charging

Recent Advancements in Vehicle to Vehicle Charging

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International Conference on Power Electronics and Drive systems (PEDS)

作者： Anindita Golder Kushan Tharuka Lulbadda Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada

More and more electric vehicles and charging stations are penetrating into the power system today which introduce new challenges. As a result, the concept of vehicle to vehicle charging (V2V) is gaining interest. Therefore, it is important to understand the concept of V2V charging. In this paper, some of the recent developments in the area of vehicle to vehicle (V2V) charging are explored.

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Design Considerations for Electric Traction Power systems

Design Considerations for Electric Traction Power Systems

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Jigyesh Sharma Kushan Tharuka Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada

There is growing interest in e-mobility which is not only limited to electric vehicles but also mass transit systems, heavy duty vehicles, off-road vehicles and so on. With the intense focus on sustainability and achieving net zero emissions, most transit systems will be electrified. The impact of mass transit electrification on the existing power system needs a thorough and detailed analysis. Also, the upgrade from fuel powered transit systems to electric transit systems needs to be done with minimal changes to the existing power network and maximum utilization of available resources in the transit system. Therefore, the study of electric transit systems, its impact on the power system and challenges that can arise from electrifying railways are presented in this paper.

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Hybrid Data-Driven Physics-Based Model Framework Implementation: Towards a Secure Cyber-Physical Operation of the Smart Grid

Hybrid Data-Driven Physics-Based Model Framework Implementat...

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International Conference on Environment and Electrical engineering (EEEIC)

作者： Valeria Vega-Martinez Austin Cooper Brandon Vera Nader Aljohani Arturo Bretas Electrical &#x0026 Computer Engineering University of Florida Gainesville Florida United States Distributed Systems Group Pacific Northwest National Laboratory Richland Washington United States

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

关键词： Adaptation models Software packages Simulation Data models Real-time systems Smart grids Security

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Optical Wireless Communications for Swarm Connectivity

Optical Wireless Communications for Swarm Connectivity

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IEEE Southeastcon

作者： Yazan A Alqudah Donald Sofge Tarek Yousef Alexander Maxseiner Electrical and Computer Engineering University of West Florida Pensacola FL Distributed Autonomous Systems Group U.S. Naval Research Laboratory Washington DC

Optical Wireless (OW) provides an alternative to RF for wireless connectivity. In applications where Radio Frequency (RF) might interfere with system operations, OW can be used to provide connectivity. This work studies the applicability of OW to connect two agents in a swarm. The work discusses transmitter and receiver designs and shares the results of simulation and lab experiments for different circuits configurations. The work shows the results of a reliable 100Kbps link between agents operating at a distance of approximately equal to 3 meters.

关键词： Wireless communication Radio frequency Stimulated emission Optical feedback Light emitting diodes Optical receivers Optical fiber communication

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GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations

arXiv

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arXiv 2023年

作者： Ranasinghe, Nisal Senanayake, Damith Seneviratne, Sachith Premaratne, Malin Halgamuge, Saman AI Optimization and Pattern Recognition Research Group Dept. of Mechanical Eng. University of Melbourne Australia Melbourne School of Design University of Melbourne Australia Department of Electrical and Computer Systems Engineering Monash University Australia

Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. However, the ability to discover such interpretable functions is desirable. In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial. This is facilitated by a new type of interpretable neural network block, named the "power-term approximator block", consisting of logarithmic and exponential activation functions. GINN-LP is end-to-end differentiable, making it possible to use backpropagation for training. We propose a neural network growth strategy that will enable finding the suitable number of terms in the Laurent polynomial that represents the data, along with sparsity regularization to promote the discovery of concise equations. To the best of our knowledge, this is the first model that can discover arbitrary multivariate Laurent polynomial terms without any prior information on the order. Our approach is first evaluated on a subset of data used in SRBench, a benchmark for symbolic regression. We first show that GINN-LP outperforms the state-of-the-art symbolic regression methods on datasets generated using 48 real-world equations in the form of multivariate Laurent polynomials. Next, we propose an ensemble method that combines our method with a high-performing symbolic regression method, enabling us to discover non-Laurent polynomial equations. We achieve state-of-the-art results in equation discovery, showing an absolute improvement of 7.1% over the best contender, by applying this ensemble method to 113 datasets within SRBench with known ground-truth equations. © 2023, CC BY-NC-ND.

关键词： Polynomials

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Optimized Human Drowsiness Detection using Spatial and Temporal features of Eye and Mouth

Optimized Human Drowsiness Detection using Spatial and Tempo...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Anushka Gupta Vinod Maan Kuldip Singh Sangwan Dhiraj Department of Electronics and Instrumentation Birla Institute of Technology and Science Pilani Rajasthan Department of Computer Science & Engineering Mody Unversity Rajasthan Department of Mechanical Engineering Birla Institute of Technology and Science Pilani Rajasthan Integrated Circuits & Systems Group CSIR-CEERI Pilani Rajasthan

Drowsy drivers can cause casualties, grave injuries, and fimancial waste; thus, it is essential to have a mechanism that detects drowsiness among drivers. This paper presents a compressed non-intrusive drowsiness monitoring system in three phases. In phase 1, we target the detection of human drowsiness using a set of state-of-the-art machine learning architectures and a custom architecture by using the state of eye and mouth regions from a combination of the MRL Eye and YawDD datasets. Out of VGG-16 (97% test accuracy), Xception (98%), MobileNet (97%), InceptionV3 (98%), and custom architecture (98%), the custom architecture is selected to get carried forward to the subsequent phases because it gives comparable results to deeper models with 100 times fewer parameters. The satisfactory performance of this model was also verified on a selective subset of the NTHU-DDD video dataset. In phase 2, the custom architecture is employed to extract appearance-related features from a set of frames used by an LSTM network to learn motion-related characteristics. The CNN+LSTM model gives 98% test accuracy on the mouth test dataset. In phase 3, to target an embedded board implementation, we used the knowledge distillation technique where the thinned-out version of custom architecture (student network) to get trained under the guidance of Xception (teacher network). Under this experimental phase, the accuracy of the student network got increased from 92% to 96.4% on eye detection and from 97% to 98% on mouth detection.

关键词： Knowledge engineering Convolution Mouth Deep architecture Machine learning Feature extraction Monitoring

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Federated Domain Generalization: A Survey

arXiv

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arXiv 2023年

作者： Li, Ying Wang, Xingwei Zeng, Rongfei Donta, Praveen Kumar Murturi, Ilir Huang, Min Dustdar, Schahram College of Computer Science and Engineering Northeastern University Shenyang110819 China Distributed Systems Group TU Wien Vienna1040 Austria College of Software Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China

Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it’s to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization in recent years. Federated Domain Generalization synergizes Federated Learning and Domain Generalization techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This paper provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional machine learning to domain adaptation and domain generalization, leading to federated domain generalization as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment, data manipulation, learning strategies, and aggregation optimization, detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Relabel the noise: Joint extraction of entities and relations via cooperative multiagents 58

Relabel the noise: Joint extraction of entities and relation...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Chen, Daoyuan Li, Yaliang Lei, Kai Shen, Ying Alibaba Group School of Electronics and Computer Engineering Peking University School of Intelligent Systems Engineering Sun Yat-Sen University

ISBN: (纸本)9781952148255

Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts. In this paper, we consider the problem of shifted label distribution, which is caused by the inconsistency between the noisy-labeled training set subject to external knowledge graph and the human-annotated test set, and exacerbated by the pipelined entity-then-relation extraction manner with noise propagation. We propose a joint extraction approach to address this problem by re-labeling noisy instances with a group of cooperative multiagents. To handle noisy instances in a fine-grained manner, each agent in the cooperative group evaluates the instance by calculating a continuous confidence score from its own perspective;To leverage the correlations between these two extraction tasks, a confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels. Further, the confidences are used to adjust the training losses of extractors. Experimental results on two real-world datasets verify the benefits of re-labeling noisy instance, and show that the proposed model significantly outperforms the state-of-the-art entity and relation extraction methods. © 2020 Association for Computational Linguistics

关键词： Extraction

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Kid Activity Recognition: A Comprehensive study of kid activity recognition with monitoring activity level using YOLOv8s Algorithms

Kid Activity Recognition: A Comprehensive study of kid activ...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Tiya Bisla Ritik Shukla Mansi Dhawan Md. Rahatul Islam Takao Koyasu Keisuke Teramoto Yui Kataoka Keiichi Horio Dept. of Computer Science & Engineering Dronacharya Group of Institution Greater Noida India Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Kyushu Women’s Junior College Fukuoka Japan Aichi University of Education Aichi Japan Fukuoka Kodomo Junior College Fukuoka Japan

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Recognizing human activities and behavior is a cutting-edge field of research because of the complexity and limited availability of data. Our research involves the use of the trending YOLOv8s deep learning algorithm for the classification of active states (sitting, standing, running, laying, and jumping). The research focuses on the detection of a child’s activities in the kindergarten playroom. To train the model, we created the KAR (Kids Action Recognition) dataset using our own sources. We labeled over 6,489 images in the dataset using ***. Each image is annotated with activity labels and the location of the student (in a room or on a mat). This assisted in presenting insights on the student’s vitality level and physical involvement over a period of approximately an hour. We analyzed the whole activity and compared it with the times. Standing in the room and mat, standing in the room, and jumping in the room and mat are most of the activities of the kid. The model achieved an impressive overall accuracy of 88.7 %. We conducted all the training in a Google collaborative environment with A-100 GPU specifications. The results demonstrate that the model can identify and classify each activity with high precision.

关键词： Training Pediatrics Accuracy Hospitals Graphics processing units Real-time systems Energy states

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