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arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Model Predictive Control Method for Ten-Switch Three-Phase Three-Level Inverter

Model Predictive Control Method for Ten-Switch Three-Phase T...

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Annual Conference of Industrial Electronics Society

作者： Naki Guler Ugur Fesli Hasan Komurcugil Sertac Bayhan Electrical and Electronic Engineering Technology Faculty Gazi University Ankara Turkey Renewable Energy and Smart Grids Application and Research Center Gazi University Ankara Turkey Electronic and Automation Department Technical Sciences Vocational School Gazi University Ankara Turkey Department of Computer Engineering Eastern Mediterranean University Famagusta Turkey Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper proposes a multi-objective model predictive control (MPC) for a grid-connected ten-switch three-phase three-level inverter. The main objective of the proposed MPC method is to control the grid current. In addition, the voltage balancing of the DC capacitors in the ten-switch three-level inverter is essential to generate voltage levels properly. Therefore, two objectives need to be achieved simultaneously. The multi-objective cost function feature of the MPC method is used to achieve these objectives. The simulation studies are performed using MATLAB/Simulink environments to investigate the effectiveness of the proposed MPC method. The results show that the proposed control method exhibits accurate performance under steady-state conditions and fast dynamic responses under transient conditions. Moreover, the grid current spectrums show that the total harmonic distortion is 2.21% in normal operation, and it is under the desired limits, even with a 50% parameter mismatch in filter inductance.

关键词： Industrial electronics Total harmonic distortion Capacitors Cost function Inverters Steady-state Voltage control Transient analysis MATLAB Predictive control

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Cryptocurrency and Data Privacy in Human Resource Management

Cryptocurrency and Data Privacy in Human Resource Management

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Indhumathi Chandrasekeran A Dharmaraj Ashima Juyal M. Shravan Rajesh Deb Barman Melanie Lourens Department of Commerce With Computer Application PSG College of Arts & Science Coimbatore Tamil Nadu India Department of Management Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Uttaranchal Institute of Technology Uttaranchal University School of Business SR University Warangal Telangana India Department of Commerce Bodoland University Kokrajhar BTR Assam India Faculty of Management Sciences Durban University of Technology South Africa

Cryptocurrency and data privacy are two important considerations in human resource management. Cryptocurrency, such as Bitcoin, can be used to pay employees, provide incentives, and manage payroll, but it also poses risks such as price volatility and regulatory issues. Data privacy, on the other hand, is the protection of personal information from unauthorized access and use. The use of blockchain technology, which is the underlying technology behind many cryptocurrencies, can enhance data privacy in human resource management by providing a secure and tamper-proof record of all transactions. However, it’s important to comply with all applicable laws and regulations related to the use of cryptocurrency and data privacy in human resource management.

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MCLA Task Offloading Framework for 5G-NR-V2X Based Heterogeneous VECNs

TechRxiv

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TechRxiv 2023年

作者： Mirza, Muhammad Ayzed Yu, Junsheng Raza, Salman Ahmed, Manzoor Asif, Muhammad Irshad, Azeem Kumar, Neeraj BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Physics and Electronic Information Anhui Normal University Wuhu241003 China School of Intelligence and Digital Engineering Luoyang Vocational College of Science and Technology Luoyang471000 China Department of Computer Science National Textile University Faisalabad Pakistan School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Faculty of Computer Science Asghar Mall College Govt. of the Punjab Rawalpindi Pakistan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Engineering Graphics Era University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India Department of Electrical and Computer Engineering Lebanese American University Beirut Lebanon Faculty of Computing and IT King Abdulaziz University Jeddah Saudi Arabia

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs. © 2023, CC BY.

关键词： Delay-sensitive applications

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Construction Equipment Performance with Cloud Data Analysis for Predictive Maintenance

Construction Equipment Performance with Cloud Data Analysis ...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Senthil Kumar Seeni P. Maheswari Jothibabu Konidhala M. Raja Suguna M.V. Ishwarya S. Srinivasan Department of Mobile Application Development Cognizant Technology Solutions Buffalo Grove Illinois USA Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering University of Technology and Applied Sciences- Al Musannah Musanna South Al Batina Oman Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry.

关键词： Electric breakdown Predictive models Real-time systems Sensor systems Maintenance Sensors Trajectory Internet of Things Machinery Predictive maintenance

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TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

arXiv

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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images

TechRxiv

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TechRxiv 2022年

作者： Li, Jiabao Li, Yuqi Wang, Chong Ye, Xulun Heidrich, Wolfgang The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315600 China Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application China The Visual Computing Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Hyperspectral images (HSIs) provide rich spectral information that has been widely used in numerous computer vision tasks. However, their low spatial resolution often prevents their use in applications such as image segmentation and recognition. Fusing low-resolution HSIs with high-resolution RGB images to reconstruct high-resolution HSIs has attracted great research attention recently. In this paper, we propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-of-the-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model. The experimental results demonstrate the effectiveness of our method both in simulations and in our real experiments. The proposed method outperforms other state-of-the-art nonblind and blind fusion methods on two popular HSI datasets. Our related code and data is available at https://***/CPREgroup/Real-Spec-RGB-Fusion. © , CC BY-NC-SA.

关键词： Image fusion

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A Comprehensive Survey of Few-shot Learning: Evolution, applications, Challenges, and Opportunities

arXiv

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arXiv 2022年

作者： Song, Yisheng Wang, Ting Mondal, Subrota K. Sahoo, Jyoti Prakash The Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education The Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China The Faculty of Information Technology Macau University of Science and Technology Macao China The Department of Computer Science & Information Technology Institute of Technical Education and Research Siksha 'o' Anusandhan University India

Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge. In this context, we extensively investigated 200+ latest papers on FSL published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL along with impartial comparisons of strengths and weaknesses of the existing works. For the sake of avoiding conceptual confusion, we first elaborate and compare a set of similar concepts including few-shot learning, transfer learning, and meta-learning. Furthermore, we propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge in accordance with the challenges of FSL. To enrich this survey, in each subsection we provide in-depth analysis and insightful discussion about recent advances on these topics. Moreover, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into the technology evolution trends together with potential future research opportunities in the hope of providing guidance to follow-up research. © 2022, CC BY.

关键词： Surveys

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Supervised neighborhood based ensemble attribute reduction 19

Supervised neighborhood based ensemble attribute reduction

引用

19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Song, Jingjing Jiang, Zehua Dou, Huili Tsang, Eric C. C. Jiangsu University of Science and Technology School of Computer Jiangsu Province Zhenjiang212003 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou363000 China Macau University of Science and Technology Faculty of Information Technology China

ISBN: (纸本)9780738124261

In neighborhood based attribute reduction, neighborhood relation is a typical tool for distinguishing samples. Notably, the neighborhood relation may be powerless in providing satisfactory distinguishing ability. In view of this, the supervised neighborhood based attribute reduction has been explored. However, the supervised neighborhood based reduct may be lack of universality. To file such gap, an ensemble strategy for computing supervised neighborhood based reduct is proposed in our paper. Such ensemble strategy is realized through considering the requirement of each decision class. The experimental results on 8 UCI data sets show that the supervised neighborhood based ensemble strategy can generate reduct not only with higher generalization performance but also with higher stability. © 2020 IEEE.

关键词： Machine learning Tools Cybernetics

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Enhancing drug-target interaction predictions in context of neurodegenerative diseases using bidirectional long short-term memory in male Swiss albino mice pharmaco-EEG analysis

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Heliyon 2024年第21期10卷 e39279页

作者： Qureshi, Shahnawaz Iqbal, Syed Muhammad Zeeshan Ameer, Asif Karrila, Seppo Ghadi, Yazeed Yasin Shah, Syed Aziz Intelligent Biomedical Application Lab Sino-Pak center for Artificial Intelligence School of Computing Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Haripur 22620 Pakistan Research and Development BrightWare LLC Riyadh 13323 Saudi Arabia Department of Computer Science National University of Computing and Emerging Sciences Faisalabad 38000 Pakistan Faculty of Science and Industrial Technology Prince of Songkla University Surat Thani Campus Muang Surat Thani 84000 Thailand Department of Computer Science Al Ain University Abu Dhab Al Ain United Arab Emirates Healthcare Sensing Technology Faculty Research Centre for Intelligent Healthcare Coventry University Coventry United Kingdom

Background and Objective: Emerging diseases like Parkinson or Alzheimer's, which are not curable, endanger human mental health and are challenging to research. Drug target interactions (DTI) are pivotal in the screening of candidate drugs and focus on a small pool of drug targets. Electroencephalogram shows the responses to psychotropic medicines in the brain bioelectric activity. Synaptic activity can be analyzed by using Local Field Potential recordings obtained from micro-electrodes implanted in the brain. The aim is to evaluate the effects of drug on brain bioelectric activity and increase the drug classification accuracy. The ultimate goal is to advance our understanding of how drugs affect synaptic activity and open the door to more focused treatment for neurodegenerative diseases. Methods: In this study, Pharmaco-EEG recordings are processed using Advanced neural network models, particularly Convolutional Neural Networks, to assess the effects of medications. The five different medicines used in this study are Ephedrine, Fluoxetine, Kratom, Morphine, and Saline. The signals observed are local field potential signals. To overcome some limits of DTI prediction, we propose Bidirectional Long Short-Term Memory (LSTM) for the categorization of intracranial EEG (i-EEG) data, departing from standard approaches. Similar EEG patterns are presumably caused by drugs that work by homologous pharmacological pathways, producing similar psychotropic effects. To improve accuracy and reduce training loss, our study introduces a bidirectional LSTM model for classification along with Bayesian optimization Results: High recall, precision, and F1-Scores, particularly a 95% F1-Score for morphine, ephedrine, fluoxetine, and saline, suggest good performance in predicting these drug classes. Kratom produces a somewhat lower recall of 94%, but a high F1-Score of 97% and perfect precision of 1.00. The weighted average F1-Score, macro average, and overall accuracy are all consistently h

关键词： Convolutional neural network Drug target interaction Intracranial EEG Local field potential Long short-term memory

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