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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Martin, Cameron Ke, Fucai Wang, Hao Monash University Faculty of Information Technology Department of Human Centred Computing Australia Monash University Faculty of Information Technology Department of Data Science and Ai Australia Monash Energy Institute Monash University Australia

ISBN: (纸本)9798350345094

Understanding electric vehicle (EV) charging on the distribution network is key to effective EV charging management and aiding decarbonization across the energy and transport sectors. Advanced metering infrastructure has allowed distribution system operators and utility companies to collect high-resolution load data from their networks. These advancements enable the non-intrusive load monitoring (NILM) technique to detect EV charging using load measurement data. While existing studies primarily focused on NILM for EV charging detection in individual households, there is a research gap on EV charging detection at the feeder level, presenting unique challenges due to the combined load measurement from multiple households. In this paper, we develop a novel and effective approach for EV detection at the feeder level, involving sliding-window feature extraction and classical machine learning techniques, specifically models like XGBoost and Random Forest. Our developed method offers a lightweight and efficient solution, capable of quick training. Moreover, our developed method is versatile, supporting both offline and online EV charging detection. Our experimental results demonstrate high-accuracy EV charging detection at the feeder level, achieving an F-Score of 98.88% in offline detection and 93.01% in online detection. © 2023 IEEE.

关键词： Electric power distribution

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Analysis of different phase change materials (PCMs) and wall material in a nano-circular space thermal energy storage (TES) system: A molecular dynamics approach

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International Journal of Thermal sciences 2025年 215卷

作者： Cao, Cheng Ali, Ali B.M. Hussein, Zahraa Abed Sawaran Singh, Narinderjit Singh Abdullaeva, Barno Zubair, Ahmad Salahshour, Soheil Baghaei, Sh School of Mechanical Engineering Changshu Institute of Technology Jiangsu Changshu215500 China Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Al-Manara College for Medical Sciences Amarah Maysan Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nila71800 Malaysia Department of Mathematics and Information Technologies Vice-Rector for Scientific Affairs Tashkent State Pedagogical University Tashkent Uzbekistan Central Labs King Khalid University AlQura'a Abha P.O. Box 960 Saudi Arabia Applied College Mahala Campus King Khalid University Abha P.O. Box 9004 61413 Saudi Arabia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Thermal energy storage (TES) play a vital role in overcoming the fluctuating nature of solar thermal energy. To study and understand the performance of these systems, using new techniques such as computer simulations can be useful. In this article, a specific circular nanochannel containing a phase change material (PCM) is introduced and its thermal and mass performance are investigated. By using two types of PCM and three wall metals (platinum, copper, and aluminum) the effects of changing several geometric and thermodynamic parameters are evaluated. In general, two different plans are proposed and parameters such as thermal conductivity, heat flux, charging, and discharging time are defined and evaluated. The obtained results show that the use of paraffin reduces the phase change time from 1.36 ns to 1.21 ns. Geometrical investigations also show that increasing the diameter ratio leads to a decrease in heat flux. Increasing the velocity of argon atoms in the inner tube also leads to an increase in the mobility of atoms and as a result improves the heat transfer rate. Using copper, the thermal conductivity is 54.3 % and 13.5 % higher than platinum and aluminum. The maximum heat flux for the two proposed cases is about 1500 and 1285 W/m2, respectively. Increasing the velocity of argon atoms from 0.01 Å/fs to 0.05 Å/fs leads to a decrease in the phase change time from 1.12 ns to 1.15 ns. Regarding the type of PCM, paraffin performs better than the combination of water-hydrocarbon. © 2025 Elsevier Masson SAS

关键词： Copper

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Adaptive distributed honeypot detection network for enhanced cybersecurity against DoS and DDoS attacks

Results in Engineering

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Results in Engineering 2025年 26卷

作者： V. Selva Kumar K.R. Mohan Raj S. Gopalakrishnan G. Vennila D. Dhinakaran P. Kavitha Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi India Department of Information Technology Velammal Engineering College Chennai India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Artificial Intelligence and Machine Learning School of Computing Mohan Babu University Tirupati India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering college Chennai India

The increasing prevalence and sophistication of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks present significant challenges in ensuring the security and stability of modern networked systems. These attacks, characterized by their ability to disrupt services and compromise resources, require innovative and robust detection mechanisms to safeguard highly interactive environments such as honeypot systems. Traditional detection techniques often fall short in addressing the complexities posed by dynamic traffic patterns, diverse attack types, and real-time processing demands. This study introduces the Adaptive Distributed Honeypot Detection Network (ADHDN), a novel framework that leverages deep learning and probabilistic modeling to address the limitations of existing solutions. ADHDN employs a combination of Deep Generative Adversarial Networks (DGANs) and Discrete Hidden Markov Models (DHMMs) to achieve superior detection precision across various DoS attack types, including application-level, protocol-level, and data volume attacks. Implemented in a highly interactive honeypot environment with distributed server and virtual machine configurations, ADHDN demonstrates remarkable adaptability and resilience. Performance evaluation using the IoTID20 dataset reveals that ADHDN consistently outperforms contemporary models, such as RBMD, BNDH, and AHDL. ADHDN achieves a true positive rate of 99.7% for protocol-level attacks, 99.4% for application-level attacks, and 97.5% for data volume attacks under low attack volumes, maintaining robust performance even as attack intensity scales. These results underscore ADHDN’s potential to redefine DoS detection in dynamic and high-interaction environments, offering a scalable and efficient solution to contemporary cybersecurity challenges.

关键词： Denial of service Distributed denial of service Intrusion detection systems Generative adversarial networks Real-time attack detection Deep learning

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A Quantum Secret Sharing Scheme Using Orbital Angular Momentum onto Multiple Spin States Based on Fibonacci Compression Encoding

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Communications in Theoretical Physics 2018年第10期70卷 384-390页

作者： Hong Lai Ming-Xing Luo Yong-Jian Xu Josef Pieprzyk Jun Zhang Lei Pan Mehmet A.Orgun School of Computer and Information Science and Centre for Research and Innovation in Software Engineering（RISE） Southwest UniversityChongqing 400715China Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China Data61 Commonwealth Scientific and Industrial Research OrganisationSydneyAustralia Institute of Computer Science Polish Academy of SciencesWarsaw01-248Poland School of Software and Electrical Engineering Swinburne University of TechnologyHawthornVIC3122Australia School of Information Technology Deakin UniversityGeelongVIC3220Australia Department of Computing Macquarie UniversitySydneyNSW 2109Australia Faculty of Information Technology Macao University of Science and TechnologyAvenida Wai LongTaipa999078Macao

Since the use of a quantum channel is very expensive for transmitting large messages, it is vital to develop an effective quantum compression encoding scheme that is easy to implement. Given that, with the single-photon spin-orbit entanglement, we propose a quantum secret sharing scheme using orbital angular momentum onto multiple spin states based on Fibonacci compression encoding. In our proposed scheme, we can represent the frequency of any secret message which is typically collection of bits encodings of text or integers as a bitstring using the base Fibonacci sequence, which is encoded multiple spin states for secret shares transmitted to participants. We demonstrate that Fibonacci compression encoding carries excellent properties that enable us to achieve more robust quantum secret sharing schemes with fewer number of photons.

关键词： spin-orbit entanglement multiple spin states Fibonacci compression encoding quantum secretsharing

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer 39

RNTrajRec: Road Network Enhanced Trajectory Recovery with Sp...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Chen, Yuqi Zhang, Hanyuan Sun, Weiwei Zheng, Baihua Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Shanghai Institute of Intelligent Electronics & Systems China Singapore Management University School of Computing and Information Systems Singapore

ISBN: (纸本)9798350322279

GPS trajectories are the essential foundations for many trajectory-based applications. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and less spatial consistent. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model to a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach. © 2023 IEEE.

关键词： Recovery

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Rough Set Model and Approximations in Fuzzy Formal Contexts 5

Rough Set Model and Approximations in Fuzzy Formal Contexts

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5th Asia Conference on Machine Learning and computing, ACMLC 2022

作者： Syau, Yu-Ru Lin, En-Bing National Formosa University Department of Information Management Yunlin Huwei63201 Taiwan Wentworth Institute of Technology School of Computing and Data Science BostonMA02132 United States

ISBN: (纸本)9798350333923

Given a set of objects G with a set of attributes M and a binary relation I, a formal context (G, M, I) corresponds to a Boolean-valued information table in rough set theory, and the kernel relation of the function from G to M which maps each object of G to its afterset. This coincides with the indiscernibility relation of the Boolean-valued information table. Rough set model and Variable-Precision model (VP-model) under a formal context can be formulated based on the associated kernel relation on the objects set. α-fuzzified rough set model and VP-model under a fuzzy formal context are formulated analogously. We present an example to demonstrate the proposed α-fuzzified rough set model and VP-model under a fuzzy formal context. © 2022 IEEE.

关键词： Rough set theory

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Causal Augmentation for Causal Sentence Classification 1

Causal Augmentation for Causal Sentence Classification

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1st Workshop on Causal Inference and Natural Language Processing, CI+NLP 2021

作者： Tan, Fiona Anting Hazarika, Devamanyu Ng, See-Kiong Poria, Soujanya Zimmermann, Roger Institute of Data Science National University of Singapore Singapore School of Computing National University of Singapore Singapore Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9781954085893

Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification. In particular, we found that models misclassify on augmented sentences that have been negated or strengthened with respect to its causal meaning. This is worrying since minor linguistic differences in causal sentences can have disparate meanings. Therefore, we propose the generation of counterfactual causal sentences by creating contrast sets (Gardner et al., 2020) to be included during model training. We experimented on two model architectures and predicted on two out-of-domain corpora. While our strengthening schemes proved useful in improving model performance, for negation, regular edits were insufficient. Thus, we also introduce heuristics like shortening or multiplying root words of a sentence. By including a mixture of edits when training, we achieved performance improvements beyond the baseline across both models, and within and out of corpus' domain, suggesting that our proposed augmentation can also help models generalize. © 2021 Association for Computational Linguistics.

关键词： Classification (of information)

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Artificial intelligence for advanced functional materials: exploring current and future directions

JPhys Materials

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JPhys Materials 2025年第2期8卷

作者： Malica, Cristiano Novoselov, Kostya S Barnard, Amanda S Kalinin, Sergei V Spurgeon, Steven R Reuter, Karsten Alducin, Maite Deringer, Volker L Csányi, Gábor Marzari, Nicola Huang, Shirong Cuniberti, Gianaurelio Deng, Qiushi Ordejón, Pablo Cole, Ivan Choudhary, Kamal Hippalgaonkar, Kedar Zhu, Ruiming von Lilienfeld, O Anatole Hibat-Allah, Mohamed Carrasquilla, Juan Cisotto, Giulia Zancanaro, Alberto Wenzel, Wolfgang Ferrari, Andrea C Ustyuzhanin, Andrey Roche, Stephan Bremen Center for Computational Materials Science MAPEX Center for Materials and Processes University of Bremen BremenD-28359 Germany Institute for Functional Intelligent Materials National University of Singapore Singapore117544 Singapore Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore School of Computing Australian National University Acton Australia Department of Materials Science and Engineering University of Tennessee KnoxvilleTN United States Pacific Northwest National Laboratory RichlandWA United States National Renewable Energy Laboratory GoldenCO United States University of Colorado BoulderCO United States Fritz-Haber-Institut der Max-Planck-Gesellschaft Berlin Germany Donostia-San Sebastián Spain Donostia International Physics Center Donostia-San Sebastián Spain Inorganic Chemistry Laboratory Department of Chemistry University of Oxford Oxford United Kingdom Engineering Laboratory University of Cambridge Cambridge United Kingdom Lausanne Switzerland Institute for Materials Science Max Bergmann Center for Biomaterials TUD Dresden University of Technology Dresden Germany School of Engineering RMIT University Melbourne Australia Campus UAB Bellaterra Spain School of Engineering Australia National University Acton Australia Materials Science and Engineering Division National Institute of Standards and Technology GaithersburgMD United States School of Materials Science and Engineering Nanyang Technological University Singapore639798 Singapore Singapore138634 Singapore Departments of Chemistry Materials Science & Engineering Physics and the Acceleration Consortium University of Toronto Canada Vector Institute TorontoON Canada ML Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Applied Mathematics University of Waterloo WaterlooON Canada Institute for Theoretical Physics ETH Zürich Switzerland Depar

This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy. © 2025 The Author(s). Published by IOP Publishing Ltd.

关键词： Yarn

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Reinforcement Learning in Cognitive Robots for Autonomous Path Planning 5

Reinforcement Learning in Cognitive Robots for Autonomous Pa...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Das, Archana Poornima, R. Indu Deepa, G. Gokila Sheela, K. Selva Akshya, J. Choudhry, Mani Deepak New Horizon College of Engineering Department of Information Science and Engineering Bengaluru India KGiSL Institute of Technology Department of Information Technology Coimbatore India Ppg Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350389609

Cognitive robots are intelligent systems that learn from their environment, adapt to dynamic changes, and make decisions without a human's direct intervention. This research studies the integration of Reinforcement Learning in cognitive robots with Q-Learning for autonomous path planning. The study addresses the most critical issues in traditional A∗ and Dynamic Programming algorithms by devising path-planning techniques, mainly the lack of adaptability and computational efficiency in real-time and unpredictable environments. Our approach is based on Q-Learning, which is a model-free RL algorithm allowing the robots to find paths autonomously by optimizing their paths and avoiding obstacles. The Q-Learning algorithm provides the possibility of learning optimal policies for the robot through iterative interaction with its environment balanced between exploration and exploitation of the decision process. A reward system is utilized by the proposed model, encouraging the robot to explore shorter, collision-free paths and adjust based on feedback in real time from its surroundings. The model was found to be significantly better than its counterparts. In this context, as indicated, the Q-Learning model runs at 11.5 seconds, faster than A∗ at 18.3 seconds and Dynamic Programming at 16.7 seconds, yet with an accuracy of 94% and was found to have the highest collision avoidance rate at 98%. Additionally, the adaptability of the model about the environment presents a marked difference in terms of path length optimization, having done so with a mean path length of 12.3, compared to approaches or models. The robustness and scalability of the Q-Learning model make it highly applicable in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Grade It: A Quantitative Essay Grading System 6th

Grade It: A Quantitative Essay Grading System

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6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Vanga, Roopchand Reddy Bharath, M.S. Sindhu, C. Vadivu, G. Hsu, Hsiu Chun Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur603203 India Department of Information Management National Chung Cheng University Minxiong Taiwan

ISBN: (纸本)9789819930098

Automated writing evaluation system that employs automated essay scoring technologies generates a rating to the writings which helps students with self-assessment. It provides an efficient and easy way for grading essays, which usually takes abundance of time to be evaluated by human graders;this can be greatly useful for educational institutions like schools and colleges. It provides ratings for essays based on the grammatical errors and topic relevancy by using specific tools implemented in the auto grading system. The generated result will help students grading essays which helps students in self-assessment, this will be useful for the student to understand the mistakes that he makes which will be pinpointed by the system in matter of time, not only that the system also displays the strengths of the user, but also the system will improve the accuracy compared to already existing automated grading system. The system uses scores from multiple features (handcraft features, coherence score, prompt-relevant score, and semantic score). These key features illustrated above will provide enough information on the essay for the student. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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