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Aggregative Dynamics and Revenue Structuring in Energy Internet User-Prosumer Ecosystems

Aggregative Dynamics and Revenue Structuring in Energy Inter...

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Singh, Anil Pratap Veena, Ch. Anandhi, R.J. Singla, Atul Parmar, Ashish Alabdely, Haider Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University Najaf Iraq

ISBN: (纸本)9798350373783

This study explores income structure and group dynamics in complicated energy internet consumer-prosumer ecosystems.a DRORA (Dynamic Revenue Optimization and Aggregation) is proposed after studying energy consumers and prosumers throughout time. PDPA, EDOA, and RFO are in the system. These boost energy market efficiency, transparency, and fairness. As a way to promote fair pay and customer cost, the PDPA lets prosumers have a say in how prices are set. The EDOA makes sure that resources are used efficiently by distributing power in the best way possible to cut down on costs and improve system performance. With the RFOA's accurate income predictions, stakeholders can make smart decisions that will help them make the most money. Simulations and real-world studies have shown that the suggested model is better than traditional methods at getting stable and fair income sharing in the Energy Internet environment, which is always changing. © 2024 IEEE.

关键词： Signal modulation

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Spatial Modulation Techniques for Improved ISAC Throughputs

Spatial Modulation Techniques for Improved ISAC Throughputs

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Suman, Sanjay Kumar Rajalakshmi, B. Khan, Irfan Alekhya, V. Lakhanpal, Sorabh Ali, Abduljabbar A. St. Martin's Engineering College Telangana Secunderabad India New Horizon College of Engineering Department of Computer Science Bangalore India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India Lovely Professional University Phagwara India National University of Science and Technology College of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This study introduces 'Adaptive Spatial Modulation for Enhanced ISAC Throughputs (ASM-ISAC)' to speed up Intelligent Space-Air-Ground Communication (ISAC). An adaptive spatial modulation technique, CSD for cooperative spatial diversity, and EESM-ISAC for energy-efficient spatial modulation are used. To boost communication throughput and reduce bit error rates, ASM-ISAC adjusts modulation and spatial patterns in real time. Cooperation between close ISAC nodes improves performance in CSD. EESM-ISAC develops energy-efficient communication with restricted resources. This research evaluates ASM-ISAC against earlier approaches like 'Conventional Modulation Scheme for ISAC (CMS-ISAC)'. The test evaluates frequency efficiency, error performance, latency, complexity, energy efficiency, and security. ASM-ISAC outperforms CMS-ISAC in several aspects, suggesting it might increase ISAC throughput. © 2024 IEEE.

关键词： Adaptive modulation

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Advanced Graph Convolutional Networks for Semantic Relationship Mining in Large-Scale Ontologies 2

Advanced Graph Convolutional Networks for Semantic Relations...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Anuradha, R. Swathi, B. Kumar, Rakesh Nagpal, Amandeep Kalra, Ravi Abdulaali, Hayder Saadoon Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Applications Gla University Department of Computer Engineering Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park Ii Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

This research introduces novel methodologies in graph convolutional networks (GCNs) to enhance semantic relationship mining within large-scale ontologies. Traditional approaches to understanding and extracting meaningful patterns from ontological structures often struggle with the complexity and scale of data. The proposed framework leverages advanced GCNs, incorporating a series of innovative mechanisms designed to optimize performance, accuracy, and scalability. Firstly, an enhanced graph convolutional layer is introduced, specifically tailored to capture the nuanced hierarchical and relational data typical of extensive ontological datasets. This layer employs a multidimensional feature extraction technique, significantly improving the depth and quality of semantic relationship identification. Secondly, the paper presents a unique embedding algorithm that efficiently maps semantic and relational data into a lower-dimensional space, facilitating faster computation while retaining critical information. To address the challenges of scalability, a distributed processing architecture is also proposed, enabling the handling of ontologies at an unprecedented scale. Empirical evaluations demonstrate the superiority of the approach in terms of precision, recall, and computational efficiency compared to existing methods. This research contributes to the field by providing a robust tool for experts and practitioners involved in knowledge discovery, data mining, and semantic web technologies, paving the way for more intelligent and efficient ontology management. © 2024 IEEE.

关键词： Scalability

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SLA-SCA: Secure and Lightweight Authentication and Key Agreement Mechanism for Smart City Applications

SLA-SCA: Secure and Lightweight Authentication and Key Agree...

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2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Pant, Bhairvi Obaidat, Mohammad S. Singh, Shreya Pant, Mudita Wazid, Mohammad Das, Ashok Kumar Hsiao, Kuei-Fang Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Ctr. for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500032 India University of Texas-Permian Basin Computer Science Department OdessaTX 79762 United States

ISBN: (纸本)9798350302561

The notion of a smart city is a modern concept characterized by the widespread integration of the Internet of Things (IoT) across various facets of urban life, including healthcare, traffic management, agriculture, and more. This establishes real-time communication and data exchange between all users, servers and devices through the Internet. As we begin to discuss communication between parties, one must also recognize the imperative need for this exchange to be secure and private, as such exchanges are extremely vulnerable to attacks such as man-in-the-middle and replay attacks and even situations like compromising of anonymity during exchanges. This paper focuses on finding an innovative solution to the same challenge. It proposes a mechanism to establish secure communication between the smart IoT devices and the connected cloud servers through a secure mutual authentication and key establishment process with a short name 'SLA-SCA'. The security analysis of the proposed SLA-SCA is done, which proves its resilience against the various potential attacks of such a communication. The proposed SLA-SCA outperformed the other existing schemes regarding important security and functionality features. © 2023 IEEE.

关键词： Internet of things

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IEEE T-BIOM Editorial Board Changes

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity science 2024年第1期7卷 1-2页

作者： Naser Damer Weihong Deng Jianjiang Feng Vishal M. Patel Ajita Rattani Mark Nixon Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Computer Science and Engineering College of Engineering University of North Texas Denton TX USA

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Forecasting and analysis of PM2.5 concentration in Hainan Province based on LSTM Neural Networks 2

Forecasting and analysis of PM2.5 concentration in Hainan Pr...

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2nd International Conference on computer, Remote Sensing and Aerospace, CRSA 2022

作者： Hu, Shuning Liu, Bing Wang, Jizhen Zhang, Youchao College of Geodesy and Geomatics Shandong University of Science and Technology Shandong266590 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong266590 China Shandong GEO-Surveying&Mapping Institute Shandong2500II China

The change of PM2.5 concentration not only reflects the change of air quality level, but also affects human life and health. However, because the PM2.5 change is caused by wide-ranging factors and the occurrence mechanism is complex, it is difficult to build a stable forecasting model with general physical methods. This study builds three different LSTM models to forecast the PM2.5 concentration of Hainan Province from 2019 to 2021. Model 1 is a fully connected neural network, which is also the base model. Model 2 is a single unit LSTM model. Model 3 is a multi-LSTM layer model. The conclusion indicate that the multi-layer LSTM model has the best forecasting precison for PM2.5 concentration, and the single unit LSTM model quite the opposite. The length of the time series has certain influence on the precision of PM2.5 forecasted by LSTM models, suggesting that a suitable time series length should be selected when building LSTM models. © Published under licence by IOP Publishing Ltd.

关键词： Time series

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Automatic Cutting and Flattening of Carotid Artery Geometries

Automatic Cutting and Flattening of Carotid Artery Geometrie...

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2021 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2021

作者： Eulzer, P. Richter, K. Meuschke, M. Hundertmark, A. Lawonn, K. University of Jena Faculty of Mathematics and Computer Science Germany University of Koblenz-Landau Institute for Mathematics Germany University of Magdeburg Department of Simulation and Graphics Germany

ISBN: (纸本)9783038681403

We propose a novel method to cut and flatten vascular geometry that results in an intuitive mapping between the 3D and 2D domains. Our approach is fully automatic, and the sole input is the vessel geometry. We aim to simplify parameter analysis on vessel walls for research on vascular disease and computational hemodynamics. We present a use case for the flattening to aid efforts in investigating the pathophysiology of carotid stenoses (vessel constrictions that are a root cause of stroke). To achieve an intuitive mapping, we introduce the notion of natural vessel cuts. They remain on one side of vessel branches, meaning they adhere to the longitudinal direction and thus result in a comprehensible unfolding of the geometry. Vessel branches and endpoints are automatically detected, and a 2D layout configuration is found that retains the original branch layout. We employ a quasi-isometric surface parameterization to map the geometry to the 2D domain as a single patch. The flattened depiction resolves the need for tedious 3D interaction as the whole surface is visible at once. We found this overview particularly beneficial for exploring temporally resolved parameters. © 2021 The Author(s) Eurographics Proceedings © 2021 The Eurographics Association.

关键词： Geometry

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A Survey of DDoS Attacks Detection Schemes in SDN Environment

A Survey of DDoS Attacks Detection Schemes in SDN Environmen...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Rawat, Surabhi Gusain Obaidat, Mohammad S. Pundir, Sumit Wazid, Mohammad Kumar Das, Ashok Singh, Devesh Pratap Hsiao, Kuei-Fang Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan University of Science and Technology Beijing Beijing100083 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India Computer Science Department University of Texas-Permian Basin 4901 E. University Blvd. OdessaTX79762 United States

ISBN: (纸本)9798350336092

Denial-of-service (DoS) attacks are a major concern in the field of Internet technology, and they pose a difficult security challenge. Among the different types of DoS attacks, distributed denial-of-service (DDoS) attacks are especially concerning because they can quickly drain the resources of a target, disrupting their computational and communication capabilities without warning. In response to the severity of this problem, various defense strategies have been developed to defend against DDoS attacks. In this paper, a survey on the various DDoS attacks detection schemes is presented. An overview of the DDoS attack methodology is given. The information on different types of DDoS detection methods in SDN environment is also provided. Then a comparative study of different DDoS attack detection schemes is given, which contains the comparisons of different schemes in terms of their advantages, limitations, accuracy values, and time complexity. © 2023 IEEE.

关键词： Denial-of-service attack

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On Evaluating Pixel-Level Face Image Quality Assessment

On Evaluating Pixel-Level Face Image Quality Assessment

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European Signal Processing Conference (EUSIPCO)

作者： Marco Huber Philipp Terhöst Florian Kirchbuchner Naser Damer Arjan Kuijper Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Norwegian University of Science and Technology Gjϕvik Norway

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

A decisive factor for face recognition performance is face image quality (FIQ). It describes the utility of face images for automatic recognition. While this FIQ has conventionally been considered as a scalar for the whole image, emerging works suggest assessing pixel-level FIQs to provide higher explainability. However, the value of pixel-level qualities as a measure of utility (value for recognition) is not yet investigated. In this work, we address this by presenting two evaluation schemes, deletion evaluation curve (DEC) and insertion evaluation curve (IEC). The DEC investigates the change in recognition performance as pixels are deleted based on their quality. Complementary, the IEC reports the change in recognition performance as pixels are inserted based on their quality into a blurred image. Since pixel-level face image quality assessment (PLFIQA) methods assign high values to pixels that contain discriminant information, the recognition performance should decrease or increase when they are removed or added, respectively. We have successfully demonstrated the proposed evaluation scheme on two face recognition solutions by comparing a recently proposed PLFIQA method to a random baseline. With the growing interest in explainable face recognition, the proposed metrics will enable adequate comparison of future advances in pixel-level quality assessment.

关键词： Image quality Measurement Image recognition Face recognition Biological system modeling IEC Europe

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Ranking of attributes—comparative study based on data from stylometric domain

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Procedia computer science 2022年 207卷 2737-2746页

作者： Beata Zielosko Urszula Stańczyk Krzysztof Żabiński Institute of Computer Science University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors.

关键词： Feature selection Ranking of attributes Relief Standard deviation Decision rules Stylometry Supervised discretisation Authorship attribution

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