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Residential electrical storage: feasibility study for hybrid residential PV/battery systems in Cyprus

Residential electrical storage: feasibility study for hybrid...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MEDPOWER 2024)

作者： Kypros Tillyros Michael Komodromos Marios Lestas Nicholas Christofides Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9781837242689

This paper presents a simulation study on integrating grid-connected photovoltaic (PV) systems with a potential to implement battery storage in residential settings. It analyses the performance and feasibility of such systems in enhancing self-consumption and energy export. The simulation model considers factors like household energy demand profiles, and PV output profiles. Through this analysis, it is anticipated to offer insights for homeowners, policymakers, and industry stakeholders on achieving greater energy independence and resilience. Finally, findings show that the attempt to effectively utilise battery storage in a residence while aiming for the most economically feasible system heavily depends on the load demand. It was observed that no matter the profile of the building or the energy utilisation, there is no significant change in energy costs. However, the results manifested an upcoming trend for utilising energy that will prove beneficial for costumers.

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Maritime radar odometry inspired by visual odometry 26

Maritime radar odometry inspired by visual odometry

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26th International Conference on Information Fusion, FUSION 2023

作者： Flemmen, Henrik D. Mester, Rudolf Stahl, Annette Bryne, Torleiv H. Brekke, Edmund Førland Norwegian University of Science and Technology Department of Engineering Cybernetics Norway Norwegian University of Science and Technology Department of Informatics and Computer Science Norway

ISBN: (纸本)9798890344854

Future autonomous ships will need several redundant positioning systems to navigate reliably. Global Navigation Satellite Systems are highly accurate but they are susceptible to disruptions and intentional jamming. Maritime radars have long range and are robust against bad weather and darkness, but the use for ownship motion estimation has received relatively little attention in the research field. In this work, we present a radar odometry estimation method inspired by advances in visual odometry and simultaneous localization and mapping. The method works on raw radar data in a coastal environment and combines the Kanade-Lucas-Tomashi tracker with a factor graph back-end. We test it on data from a large ship with a maritime radar with a range of 19 km. We find that it is robust with only a small drift and no erroneous jumps in the estimate. © 2023 International Society of Information Fusion.

关键词： Motion estimation

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Predicting miRNA-disease associations based on graph convolutional network with path learning

Predicting miRNA-disease associations based on graph convolu...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chu, Shuang Duan, Guihua Yan, Cheng Hunan University of Chinese Medicine School of Informatics Changsha China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Networks (GCNs) showing promising results in MDAs prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCNs, leading to improved prediction performance. We evaluate PGCNMDA using 5-fold cross-validation (5-CV) on databases HMDD v2.0 and HMDD v3.2. Additionally, we compare it with other methods. The experimental results demonstrate that PGCNMDA outperforms other miRNA-disease association prediction methods. © 2023 IEEE.

关键词： graph convolutional network miRNA-disease associations path learning spatial convolution

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Optimizing Symbol Visibility Through Displacement 19

Optimizing Symbol Visibility Through Displacement

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19th Scandinavian Symposium on Algorithm Theory, SWAT 2024

作者： Gärtner, Bernd Kalani, Vishwas Reddy, Meghana M. Meulemans, Wouter Speckmann, Bettina Stojaković, Miloš Department of Computer Science ETH Zürich Switzerland Department of Computer Science and Engineering I.I.T. Delhi India Department of Mathematics and Computer Science TU Eindhoven Netherlands Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Serbia

ISBN: (纸本)9783959773188

In information visualization, the position of symbols often encodes associated data values. When visualizing data elements with both a numerical and a categorical dimension, positioning in the categorical axis admits some flexibility. This flexibility can be exploited to reduce symbol overlap, and thereby increase legibility. In this paper we initialize the algorithmic study of optimizing symbol legibility via a limited displacement of the symbols. Specifically, we consider unit square symbols that need to be placed at specified y-coordinates. We optimize the drawing order of the symbols as well as their x-displacement, constrained within a rectangular container, to maximize the minimum visible perimeter over all squares. If the container has width and height at most 2, there is a point that stabs all squares. In this case, we prove that a staircase layout is arbitrarily close to optimality and can be computed in O(n log n) time. If the width is at most 2, there is a vertical line that stabs all squares, and in this case, we give a 2-approximation algorithm (assuming fixed container height) that runs in O(n log n) time. As a minimum visible perimeter of 2 is always trivially achievable, we measure this approximation with respect to the visible perimeter exceeding 2. We show that, despite its simplicity, the algorithm gives asymptotically optimal results for certain instances. © Bernd Gärtner, Vishwas Kalani, Meghana M. Reddy, Wouter Meulemans, Bettina Speckmann, and Miloš Stojaković;licensed under Creative Commons License CC-BY 4.0.

关键词： Containers

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Accurate and Energy-Efficient Stochastic Computing with Van der Corput Sequences 18

Accurate and Energy-Efficient Stochastic Computing with Van ...

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18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023

作者： Shoushtari Moghadam, Mehran Aygun, Sercan Riahi Alam, Mohsen Schmidt, Jonas I Najafi, M. Hassan Taherinejad, Nima School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA United States Institute of Computer Engineering Heidelberg University Heidelberg Germany

ISBN: (纸本)9798400703256

In stochastic computing (SC), data is represented using random bit-streams. The efficiency and accuracy of SC systems rely heavily on the stochastic number generator (SNG), which converts data from binary to stochastic bit-streams. While previous research has shown the benefits of using low-discrepancy (LD) sequences like Sobol and Halton in the SNG, the potential of other well-known random sequences remains unexplored. This study investigates new random sequences for potential use in SC. We find that Van Der Corput (VDC) sequences hold promise as a random number generator for accurate and energy-efficient SC, exhibiting intriguing correlation properties. Our evaluation of VDC-based bit-streams includes basic SC operations (multiplication and addition) and image processing tasks like image scaling. Our experimental results demonstrate high accuracy, reduced hardware cost, and lower energy consumption compared to state-of-the-art methods. © 2023 ACM.

关键词： Image processing

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Exploring Network Dynamics: Community Detection and Influencer Analysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kanavos, Andreas Vonitsanos, Gerasimos Karamitsos, Ioannis Al-Hussaeni, Khalil Ionian University Department of Informatics Corfu Greece University of Patras Computer Engineering and Informatics Department Patras Greece Rochester Institution of Technology Dubai United Arab Emirates Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates

ISBN: (纸本)9798350362480

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network's structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health. © 2024 IEEE.

关键词： Tweets

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Sequential Patch Analysis Framework for Lung Disease Classification 11th

Sequential Patch Analysis Framework for Lung Disease Classi...

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11th International Conference on Future Data and Security engineering, FDSE 2024

作者： Le, Kim-Ngoc T. Le, Duc-Tai Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of College of Computing and Informatics Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819604364

Deep neural network (DNN) models have shown promise in lung disease classification using spectrogram transformations of respiratory sounds. However, these models typically process entire images, which can be computationally expensive, and they require large datasets for effective training. With the largest available respiratory dataset, ICBHI, containing only 920 recordings, training a robust DNN becomes challenging. To address this issue, this paper introduces a simplified DNN framework that incorporates novel techniques such as patch splitting and transfer learning. Specifically, the proposed framework splits the spectrogram image into a sequence of fixed-size patches, effectively reducing computational demands. These patches are processed with the position embeddings by DNN encoders to capture acoustic characteristics, leveraging a pre-trained ImageNet model to make efficient use of the limited dataset. Evaluation experiments on the ICBHI dataset show that the proposed method improves the state-of-the-art 8-class classification accuracy by 1.91%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Revisiting the Distortion of Distributed Voting 22

Revisiting the Distortion of Distributed Voting

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Filos-Ratsikas, Aris Voudouris, Alexandros A. School of Informatics University of Edinburgh Edinburgh United Kingdom School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

We consider a setting with agents that have preferences over alternatives and are partitioned into disjoint districts. The goal is to choose one alternative as the winner using a mechanism which first decides a representative alternative for each district based on a local election with the agents therein as participants, and then chooses one of the district representatives as the winner. Previous work showed bounds on the distortion of a specific class of deterministic plurality-based mechanisms depending on the available information about the preferences of the agents in the districts. In this paper, we first consider the whole class of deterministic mechanisms and show asymptotically tight bounds on their distortion. We then initiate the study of the distortion of randomized mechanisms in distributed voting and show bounds based on several informational assumptions, which in many cases turn out to be tight. Finally, we also experimentally compare the distortion of many different mechanisms of interest using synthetic and real-world data. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Multi agent systems

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Enhancing Collaborative Learning through Virtual Escape Rooms: Impact on Participants’ Perceptions

Enhancing Collaborative Learning through Virtual Escape Room...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Akrivi Krouska Christos Troussas Panagiotis Karkazis Phivos Mylonas Cleo Sgouropoulou Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

Digital revolution has transformed education, allowing students to interact, communicate, and collaborate beyond traditional classroom settings. computer-Supported Collaborative Learning (CSCL) provides opportunities for teamwork and knowledge sharing regardless of geographic location. Recently, Virtual Reality (VR) has emerged as a promising tool to address these challenges, offering immersive, three-dimensional environments that enhance collaborative learning experiences. Collaborative Virtual Environments (CVEs) extend VR’s potential by enabling users to interact and work together in shared virtual spaces. Moreover, virtual escape rooms have gained traction as an innovative approach to game-based learning, combining the benefits of collaboration, problem-solving, and engagement to create an immersive and interactive educational experience. In view of the above, this paper explores the innovative use of virtual reality (VR) and educational digital escape room games as tools to promote collaborative learning in synchronous distance education and lifelong learning. By designing a cooperative game-based learning environment, the study seeks to leverage the problem-solving and teamwork skills inherent in such games to enhance the learning experience. The impact of these technologies on participants’ perceptions was assessed comparing the information emerged from two surveys conducted before and after the intervention. The findings show that the incorporation of virtual escape room into collaborative learning environments effects positively participants’ perceptions.

关键词： Surveys Federated learning Education Virtual environments Games Real-time systems Teamwork Problem-solving

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The Effect of Data Augmentation and Optimization Technique on the Performance of EfficientNetV2 for Plant-Parasitic Nematode Identification

The Effect of Data Augmentation and Optimization Technique o...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

作者： Shabrina, Nabila Husna Lika, Ryukin Aranta Indarti, Siwi Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Computer Engineering Tangerang Indonesia Universitas Gadjah Mada Faculty of Agriculture Department of Plant Protection Yogyakarta Indonesia

ISBN: (纸本)9798350313635

Plant-parasitic nematodes are major agricultural pathogens contributing to massive crop losses worldwide. It is crucial to identify plant-parasitic nematodes to decide the best pest control and management strategy. The current identification technique is based on visual observation from nematode microscopic images done by the nematologist. However, this method requires a long process and is prone to error. A deep learning-based method can be implemented to speed up the current identification process. This study explores the effect of combining several data augmentation techniques, namely brightness, contrast, blur, and noise, on the performance of the EfficientNetV2B0 and EfficientNetV2M models for identifying plant-parasitic nematodes. Moreover, this study also compared three optimizers while training the models to find the best optimizer for each model and data augmentation. The results show that the EfficientNetV2B0 model yielded the highest test accuracy of 96.91% when employing no augmentation and trained using SGD and RMSProp optimizer. Furthermore, the EfficientNetV2M model gave the highest test accuracy of 96.91% when the combination of brightness and contrast augmentations was applied and trained using the RMSProp optimizer. © 2023 IEEE.

关键词： Luminance

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