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arXiv 2024年

作者： Gao, Xin Ma, Zhaoyang Li, Xueyuan Meng, Xiaoqiang Li, Zirui School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Chair of Traffic Process Automation "Friedrich-List" Faculty of Transport and Traffic Sciences TU Dresden Germany

In the realm of heterogeneous mixed autonomy, vehicles experience dynamic spatial correlations and nonlinear temporal interactions in a complex, non-Euclidean space. These complexities pose significant challenges to traditional decision-making frameworks. Addressing this, we propose a hierarchical reinforcement learning framework integrated with multilevel graph representations, which effectively comprehends and models the spatiotemporal interactions among vehicles navigating through uncertain traffic conditions with varying decision-making systems. Rooted in multilevel graph representation theory, our approach encapsulates spatiotemporal relationships inherent in non-Euclidean spaces. A weighted graph represents spatiotemporal features between nodes, addressing the degree imbalance inherent in dynamic graphs. We integrate asynchronous parallel hierarchical reinforcement learning with a multilevel graph representation and a multi-head attention mechanism, which enables connected autonomous vehicles (CAVs) to exhibit capabilities akin to human cognition, facilitating consistent decision-making across various critical dimensions. The proposed decision-making strategy is validated in challenging environments characterized by high density, randomness, and dynamism on highway roads. We assess the performance of our framework through ablation studies, comparative analyses, and spatiotemporal trajectory evaluations. This study presents a quantitative analysis of decision-making mechanisms mirroring human cognitive functions in the realm of heterogeneous mixed autonomy, promoting the development of multi-dimensional decision-making strategies and a sophisticated distribution of attentional resources. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Dielectrophoretic based Microfluidic for nanoparticles 'viruses' separation 3

Dielectrophoretic based Microfluidic for nanoparticles 'viru...

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3rd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2021

作者： Abdelhamid, Hamdy Khalaf, Ibrahim Hussien, Ahmed Abdelbaset, Reda Ajman University Electrical and Computer Engineering Engineering and Information Technology department Ajman United Arab Emirates Cairo University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Helwan University Faculty of Engineering Biomedical Engineering Department Cairo Egypt Cairo Egypt

ISBN: (纸本)9781665421577

The objective of this paper is to provide a microfluidic platform that may be useful to manipulate and characterize different submicron particles such as latex spheres as well as viruses (E.g. SARS-COV-2) through the concept of dielectrophoresis. The significance of recent research towards microfluidics-based diagnostic chips is apparent in health care, in our homes, and also very prominently in the fight against the COVID 19 pandemic: Coronavirus disease 2019 (COVID-19) is a newly emerging human infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), early diagnosis and management are crucial for containing the outbreak. Here, we report a microfluidic device for separating mixture of different viruses. Using appropriate microfluidic channels, flow velocity, microelectrode arrays and voltage settings, particles can be trapped or moved between regions of high or low electric fields. The magnitude and direction of the dielectrophoretic force on the particle depends on the effective dielectric properties of fluid and particles, so that a heterogeneous mixture of particles can be separated to produce a more homogeneous population. In this paper the controlled separation of nanoparticles is demonstrated. With upper and lower electrode arrays, it is shown that different types of submicron latex beads can be spatially separated. The separation occurs because of differences in magnitude and direction of the dielectrophoretic force and drag force on different populations of particles. © 2021 IEEE.

关键词： Coronavirus

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Auto-encoder and Graph Neural Networks-Based Hybrid Model for Link Prediction on Complex Network

Auto-encoder and Graph Neural Networks-Based Hybrid Model fo...

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Intelligent Computing and Networking - Proceedings of IC-ICN 2023

作者： Vinta, Surendra Reddy Singh, Gurpreet Kaur, Manpreet Kaur, Amrinder Norbaevna, Sapaeva Feruza Kumar, Ashok School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Faculty of Computational Science GNA UNIVERSITY Phagwara India Lovely Professional University Phagwara India Pyramid Institute of Business and Technology Phagwara India Urgench Branch of Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Urgench Uzbekistan Department of Computer Science Banasthali Vidyapith Rajasthan Jaipur304022 India

ISBN: (纸本)9789819931767

Among the many network data mining tasks, link prediction is crucial. Due to the in-depth development of graph neural network research, related models can learn essential features of the network more effectively and have achieved good prediction results in tasks such as link prediction. However, unlike the CNN model in deep learning, the existing graph neural network model only aggregates the first-order neighbor information of nodes and does not fully consider the topological structure characteristics between neighbor nodes. On this basis, a motif-based graph neural network link prediction model is proposed. The model adopts the self-encoder structure. In the encoding process, the adjacency matrix of the node is constructed through the motif, and then the motif neighborhood of the node is obtained. Next, the neighbor information is aggregated according to the neighborhood of each type of motif, and the node is obtained through nonlinear transformation. Finally, concatenate the representations of nodes under each type of motif. However, since different motif structures have other importance in the network, the attention network gives the attention weights expressing different motifs. The vector representation of nodes is provided by connecting the attention network. During decoding, the network is reconstructed by computing the similarity between nodes. Investigational results on several citation collaborator networks show that the proposed method surpasses most baseline algorithms on two metrics, effectively improving the accuracy of network link prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Selected topics on Wiener index

arXiv

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arXiv 2023年

作者： Knor, Martin Škrekovski, Riste Tepeh, Aleksandra Slovak University of Technology in Bratislava Faculty of Civil Engineering Department of Mathematics Bratislava Slovakia FMF University of Ljubljana Faculty of Information Studies Novo mesto & Institute of Mathematics Physics and Mechanics Ljubljana & University of Primorska FAMNIT Koper Slovenia Faculty of Information Studies Novo mesto & Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia

The Wiener index is defined as the sum of distances between all unordered pairs of vertices in a graph. It is one of the most recognized and well-researched topological indices, which is on the other hand still a very active area of research. This work presents a natural continuation of the paper Mathematical aspects of Wiener index (Ars Math. Contemp., 2016) in which several interesting open questions on the topic were outlined. Here we collect answers gathered so far, give further insights on the topic of extremal values of Wiener index in different settings, and present further intriguing problems and conjectures. © 2023, CC BY.

关键词： Graph theory

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Linear Random Early Detection for Congestion Control at the Router Buffer

Informatica (Slovenia)

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Informatica (Slovenia) 2022年第5期46卷 105-114页

作者： Abu-Shareha, Ahmad Adel Al-Kasasbeh, Basil Shambour, Qusai Y. Abualhaj, Mosleh M. Alsharaiah, Mohmmad Abdalla Al-Khatib, Sumaya N. Department of Computer Science Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Riyadh Saudi Arabia Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Networks and Information Security Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan

Active Queue Management (AQM) methods control the router's buffer to maintain high network performance and control congestion at the router buffer. Random Early Detection (RED) method is the most well-known and the most utilized AQM. RED suffers from a high dropping rate, which motivates the later AQM methods to use more complex processes, which reach the limits of using fuzzy systems as a processing technique. Yet, high computational cost affects the router's performance specifically and the network as a whole, with so-called processing delay. In this paper, a linear version of RED (LRED) is presented to reduce the computational cost of the original RED and maintain the network performance in terms of throughput, delay, dropping, and loss. LRED is built based on two distinctive features, simplifying the congestion indicator calculation and reducing the operations in calculating the dropping probability. The experimental results showed that the proposed method reduces the delay and the processing time while maintaining the throughput and loss of the RED method. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Network performance

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A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation

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Energy 2023年 281卷

作者： Elsisi, Mahmoud Amer, Mohammed Dababat, Alya’ Su, Chun-Lien Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung807618 Taiwan Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo11629 Egypt Department of Mechanical Engineering Palestine Technical University – Kadoorie Tulkarm Palestine Department of Computer Systems Engineering Faculty of Engineering and Information Technology Arab American University Palestine

The energy consumption of major equipment in residential and industrial facilities can be minimized through a variety of cost-effective energy-saving measures. Most saving strategies are economically viable where several algorithms can be employed to reduce energy consumption to reduce costs to a considerable extent. Machine learning (ML) is one of these techniques. A review of recent research efforts concerning the application of ML strategies to energy conservation and management problems is presented in this study. In addition, ML approaches and strategies for energy-saving problems, management, technologies, and control methods have been discussed. A comprehensive review of all available publications is also used to make observations about past considerations. As a result, it has been concluded that ML is capable of solving a wide range of decision and management problems within a short period of time with minimal energy consumption. In addition, ML perspectives have been viewed from the perspective of emerging communication technologies, instruments, and cyber-physical systems (CPSs), along with the advancement of ultra-durable and energy-efficient Internet-of-Things (IoT) based communication sensors technology. Moreover, a comprehensive review of recent developments in ML algorithms is also included, including safe reinforcement learning (RL), Deep RL, path integral control for RL, and others not previously. Lastly, critical ML considerations such as emergency and remedial measures, integrity protection, fusion with existing robust controls, and combining preventive and emergent measures have been discussed. The implementation of recently applied ML, RL, and IoT strategies for energy management, conservation, and resilient operation is clarified in this paper. The proposed review highlights the advantages and drawbacks of the recent energy conservation strategies. Finally, the perspective solutions have been clarified to cope with the world direction for zero

关键词： Decision making

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International Conference of computer and Informatics engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. technology also plays an essential role in supporting runners and running races. technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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Spline Global MPPT with PID Controller Based on Levy Invasive Weed Technique for Renewable Energy Resources of Smart Homes

Spline Global MPPT with PID Controller Based on Levy Invasiv...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Aziza I. Hussein Mady Mohammad Passent Elkafrawy Rabab Hamed M. Aly College of Engineering Energy and Technology Research Center Effat University Jeddah Saudi Arabia College of Architecture Effat University Jeddah Saudi Arabia The Higher Institute for Management and Information Technology Minya Egypt Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

Renewable energy systems, particularly photovoltaic (PV) systems, have been played important role in the reducing carbon emissions. A primary concern in the field of photovoltaics (PV) based on the design of the maximum power point tracking (MPPT) is the capacity to accurately monitor power across many parameters in addition to ascertain the power production of solar cells or wind turbines and adjust the load to maximise power efficiency under varying weather conditions. On another hands, the hybrid smart system uses a wind catcher to reduce the amount of energy consumed by buildings from the grid, which is a historically significant architectural component for cross ventilation and passive cooling. This paper presents an updating of model that proposes improvements to the regulation of Spline MPPT with tuning PID controller by using Levy Invasive Weed optimization (LIWO)technique. A rapid, accurate, and straightforward approach for determining the (MPPT) of PV systems under consistent irradiation and partial shade, as well as wind turbines, is presented using the Spline- MPPT technique. The model of this paper employs LIWO in conjunction with a PID controller to improve the selection of PID gains. Moreover, is applied to generate the optimal values of duty cycle of the model. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system. The model has attained an accuracy of 93%. The outcomes of this model contribute greatly to our understanding of the suitability and efficacy of both AI and traditional MPPT controllers. This modeling will be beneficial to the renewable energy industry.

关键词： Maximum power point trackers Photovoltaic systems Renewable energy sources Accuracy Software packages Mathematical models Wind turbines Splines (mathematics) Optimization Tuning

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Beyond the hype: A TAM-based analysis of blockchain adoption drivers in construction industry

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Heliyon 2024年第19期10卷 e38522页

作者： Obidallah, Waeal J. Rashideh, Waleed Kamaruddeen, Ahmed Mohammed Alzahrani, Taher Alduraywish, Yousef Alsahli, Abdulaziz Alshuqayran, Nuha College of Computer and Information Sciences Imam Muhammad Ibn Saud Islamic University (IMSIU) Riyadh 11673 Saudi Arabia Faculty of Engineering and Technology Department of Civil Engineering University of Botswana Gaborone Botswana

In complementing our existing knowledge of blockchain technology adoption in the construction industry, this research investigated how the technology Acceptance Model (TAM) applies to adopting blockchain technology among Saudi Arabian Construction companies. This study utilized cross-sectional data collection, causal research design, a quantitative research approach, and a simple random sampling technique to collect data from 248 Saudi Arabian construction companies. Partial least squares structural equation modelling (PLS-SEM) was utilized to analyze the data. The results showed that attitude toward adopting Blockchain (ATT) and perceived ease of use (PEU) are good predictors of behavioural intention to adopt Blockchain. The findings showed that when construction companies perceive the ease of use of Blockchain, they intend to adopt the technology. Attitude toward adopting Blockchain, directly and indirectly, influences behavioural intention to adopt the technology. However, top management support only directly leads to intention once the companies perceive the usefulness of blockchain technology. This paper complements the existing literature on adopting Blockchain in the Saudi Arabian construction industry. The study provides insight into the influence of top management support, attitude toward adoption, and perceived ease of use in the blockchain adoption process. © 2024 The Authors

关键词： Blockchain Construction industry PLS-SEM Saudi Arabia Smart contracts technology acceptance model

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Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation with Heterogeneous Changes

arXiv

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arXiv 2024年

作者： Yazdani, Danial Branke, Juergen Khorshidi, Mohammad Sadegh Omidvar, Mohammad Nabi Li, Xiaodong Gandomi, Amir H. Yao, Xin Faculty of Engineering & Information Technology University of Technology Sydney Australia Warwick Business school University of Warwick United Kingdom School of Computing Leeds University Business School University of Leeds United Kingdom RMIT University Australia Faculty of Engineering & Information Technology University of Technology Sydney Australia University Research and Innovation Center [EKIK Obuda University Hungary Department of Computing and Decision Sciences Lingnan University China and CERCIA School of Computer Science University of Birmingham United Kingdom

Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiveness in static clustering tasks, their application for tracking optimal clustering solutions or robust clustering over time in dynamic environments remains largely under-explored. This is partly due to a lack of dynamic datasets with diverse, controllable, and realistic dynamic characteristics, hindering systematic performance evaluations of clustering algorithms in various dynamic scenarios. This deficiency leads to a gap in our understanding and capability to effectively design algorithms for clustering in dynamic environments. To bridge this gap, this paper introduces the Dynamic Dataset Generator (DDG). DDG features multiple dynamic Gaussian components integrated with a range of heterogeneous, local, and global changes. These changes vary in spatial and temporal severity, patterns, and domain of influence, providing a comprehensive tool for simulating a wide range of dynamic scenarios. © 2024, CC BY-NC-SA.

关键词： Clustering algorithms

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