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作者： Wang, Haoping Wang, Xiaokun Xu, Yanrui Zhang, Yalan Yao, Chao Guo, Yu Ban, Xiaojuan School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Faculty of Science and Engineering University of Groningen Groningen Netherlands School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Advanced Innovation Center for Materials Genome Engineering School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang China

This paper introduces a particle-based framework for simulating the behavior of elastoplastic materials and the formation of fractures, grounded in Peridynamic theory. Traditional approaches, such as the Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH), to modeling elastic materials have primarily relied on discretization techniques and continuous constitutive model. However, accurately capturing fracture and crack development in elastoplastic materials poses significant challenges for these conventional models. Our approach integrates a Peridynamic-based elastic model with a density constraint, enhancing stability and realism. We adopt the Von Mises yield criterion and a bond stretch criterion to simulate plastic deformation and fracture formation, respectively. The proposed method stabilizes the elastic model through a density-based position constraint, while plasticity is modeled using the Von Mises yield criterion within the bond of particle paris. Fracturing and the generation of fine fragments are facilitated by the fracture criterion and the application of complementarity operations to the inter-particle connections. Our experimental results demonstrate the efficacy of our framework in realistically depicting a wide range of material behaviors, including elasticity, plasticity, and fracturing, across various scenarios. © 2024 John Wiley & Sons Ltd.

关键词： Elastoplasticity

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Network Time Synchronization as a Quantum Physical Layer Service 28

Network Time Synchronization as a Quantum Physical Layer Ser...

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28th European Wireless Conference, EW 2023

作者： Nunavath, Nikhitha Garbugli, Andrea Nande, Swaraj Shekhar Foschini, Luca Bassoli, Riccardo Fitzek, Frank H.P. The Deutsche Telekom Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Cluster of Excellence Dresden Germany research group Technische Universität Dresden Dresden Germany The Department of Computer Science and Engineering University of Bologna Italy

ISBN: (纸本)9783800762262

In the context of 6G architecture development, the concept of a softwarized (orchestration) continuum is a key pillar. Nevertheless, achieving complete softwarization of network functionalities, tasks, and operations presents inherent challenges, leading to critical trade-offs and limitations. This article explores a novel approach to address these issues by integrating quantum technologies and the Physical Layer Service Integration (PLSI) paradigm. Specifically, we propose the formulation and analysis of network synchronization as a quantum PLSI problem. Our study evaluates synchronization time offset in both conventional Precision Time Protocol (PTP) and quantum-based approaches within the network. We investigate the impact of various network conditions on the precision of PTP synchronization, ranging from nanoseconds under ideal circumstances to microseconds when utilizing virtual network devices. Further, we perform a simulation to generate frequency-entangled photon pairs to access nonlocal temporal correlations and calculate the time offsets. Our findings reveal that entanglement-based PLSI for network synchronization achieves precision at the picosecond level. These results emphasises the high precision achievable by interpreting the network synchronisation problem in the perspective of PLSI and not as a service of the softwarized continuum. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Quantum communication

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Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model

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Biomedical Signal Processing and Control 2024年第PartB期87卷

作者： Putro, Nur Achmad Sulistyo Avian, Cries Prakosa, Setya Widyawan Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia

Human-machine interfaces frequently use electromyography (EMG) signals. Based on previous work, feature extraction has a great deal of influence on the performance of EMG pattern recognition. Furthermore, the Deep Learning method is supposed to increase performance and not depend on feature engineering. However, directly processing raw signals will require a higher computation rate. This study proposed a new method that combines feature extraction and Deep Learning to address those issues while improving performance, reducing architecture size, and producing a more representative output. The proposed architecture employs the Transformer model as the backbone to get the correlation between elements and focus on the important information for estimating the flexion-extension of finger joint angles. This study uses experiment three of the NinaPro (Non-Invasive Adaptive Hand Prosthetics) DB5 dataset. Each experiment produces 16 Surface EMG data streams from two Myo Armbands devices representing 22 finger joint angles as output. This study compares the windowing process, feature extraction, execution time, and results with previous studies. The results show that the proposed model outperforms the previous study, from 0.957 in the previous study to become 0.970 in this study for the R-Square score. This result is obtained using 100 data points for the windowing process and Median Frequency for the best feature extraction method. © 2023

关键词： Feature extraction

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Accurate Prediction of Workloads and Resources with Multi-Head Attention and Hybrid LSTM for Cloud Data Centers

Accurate Prediction of Workloads and Resources with Multi-He...

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作者： Bi, Jing Ma, Haisen Yuan, Haitao Zhang, Jia Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high prediction performance for resource usage and workload sequences. Besides, there is much noise in original time series of resources and workloads. If these time series are not de-noised by smoothing algorithms, the prediction results can fail to meet the providers' requirements. To do so, this work proposes a hybrid prediction model named VAMBiG that integrates Variational mode decomposition, an Adaptive Savitzky-Golay (SG) filter, a Multi-head attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and non-linear original time series into low-frequency intrinsic mode functions. Then, it adopts an adaptive SG filter as a data pre-processing tool to eliminate noise and extreme points in such functions. Afterwards, it adopts bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Finally, it adopts a multi-head attention mechanism to explore importance of different data dimensions. VAMBiG aims to predict resource usage and workloads in highly variable traces in clouds. Extensive experimental results demonstrate that it achieves higher-Accuracy prediction than several advanced prediction approaches with datasets from Google and Alibaba cluster traces. © 2016 IEEE.

关键词： Time series analysis

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Slotted V Shaped Compact Wideband Antenna with Partial Ground Plane for Microwave Based Head Imaging Applications

Slotted V Shaped Compact Wideband Antenna with Partial Groun...

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International Conference on Electrical Information and Communication technology (EICT)

作者： Sonia Akter Moue Md. Samsuzzaman Md Zulfiker Mahmud Sefat Siddiquea Md Siam Talukder Sarna Majumder Faculty of Computer Science And Engineering Patuakhali Science And Technology University Bangladesh Dept. of Computer Science and Engineering Jagannath University Faculty of Computer Science and Engineering Patuakhali Science and Technology University Dumki Patuakhali Bangladesh

This study presents a detailed description of a Slotted V Shaped Compact Wideband Antenna designed for the purpose of microwave-based head imaging. The antenna under consideration comprises a radiating patch with a Vshaped slot and a partial ground with slots. Slots have been found to significantly improve the efficiency, dispersion of current, gain, and various other aspects of antennas. The antenna under consideration has a maximum gain of 5.85, operates within the frequency range of 4 to 8.06 GHz, and is deemed suitable for head imaging applications. The dimensions of the antenna under consideration are 0.33λ × 0.667λ × 0.21λ, where λ represents the wavelength, at a lower frequency of 4 GHz. Additionally, the antenna is equipped with a 50 ohm microstrip transmission line. The antenna is fabricated on a cost-effective FR4 substrate with a thickness of 1.5 mm. The antenna under consideration achieves a fractional bandwidth of 67.33% within the frequency range of 4 to 8 GHz. In order to optimize antenna performance, a comprehensive parametric analysis is carried out utilizing the widely recognized software tools HFSS and CST. The experimental testing and analysis of the prototype antenna yielded findings that exhibited a satisfactory level of concurrence with the corresponding calculations.

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Mechanical System Vulnerabilities: Advanced Fusion Machine Learning Model for Detecting Ethereum Attack Patterns in IIoT-5.0 Environments 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Sulakhe, Vishal N. Goyal, S.B. Gadekar, Amit Wasnik, Manisha Agarkar, Pankaj Rajawat, Anand Singh Mechanical Engineering Department Sandip University Nashik422213 India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Sandip Institute of Technology and Research Centre Nashik India Ajeenkya D Y Patil School of Engineering Charoli Lohegaon Maharashtra Pune412105 India School of Computer Science and Engineering Sandip University Nashik422213 India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

It is of the utmost significance to keep the reliability and confidentiality of these systems in check in light of the coming advent of Industry 5.0 as well as the growing penetration of the Industrial Internet of Things (IIoT) into complicated mechanical systems. The broad adoption of blockchain technology, and in particular Ethereum, brings with it a new set of opportunities as well as hazards that have an impact on the complicated dynamic that exists between machines, data, and decentralized applications. This research investigates an advanced fusion machine learning model with the intention of locating dangerous patterns and likely attack vectors that target Ethereum transactions and contracts within IIoT-5.0 systems. The results of this investigation are intended to be used in future research. The detection of these patterns and vectors is the primary focus of the model. In an era in which there is a transition taking place in the industrial sector, this research presents a novel technique for improving cybersecurity. Examining the specific flaws that may exist in mechanical systems after they have been integrated with blockchain solutions is the strategy that will be utilized. Our model shows tremendous promise in recognizing abnormalities and potential dangers by utilizing a combination of supervised learning techniques and unsupervised learning approaches. These techniques allow for the model to develop without being directly monitored by a human. This paves the way for preventative safety precautions to be included in the design of the future generation of industrial systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ethereum

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Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto-optimal profits for the utility and prosumers

Energy Conversion and Economics

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Energy Conversion and Economics 2022年第3期3卷 156-169页

作者： Mohan, Vivek Antonis, Anjula Mary Jisma, M. Krishnakumar, Nila Bu, Siqi Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Electrical Sustainable Energy Faculty of Electrical Engineering Applied Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Electrical Engineering The Hong Kong Polytechnic University Kowloon SAR Hong Kong

The increasing penetration of renewable energy sources (RES) and electric vehicles (EVs) demands the building of a microgrid energy portfolio that is cost-effective and robust against generation uncertainties (energy risk). Energy risk may trigger financial risk in the local energy market, depending on bid values, cost of generation and price of upstream grid power. In this study, a microgrid energy portfolio is built based on adjustments to both the financial and energy risks. These risks are managed in two ways: (1) by pre-tuning and prioritizing the bid prices for wind and solar energy sources based on their relative levels of energy risk as quantified through a conditional value-at-risk (CVaR) approach;and (2) by co-optimizing the conflicting profits of the utility and prosumers using non-dominated sorting particle swarm optimization (NSPSO) to obtain a risk-adjusted Pareto-optimal energy mix. Thus, the utility predicts the net power balancing cost from the scheduling time horizon, thereby moderating the adverse effect that the uncertainties in renewable energy could have on the collective welfare. The proposed method is tested on a grid-connected CIGRE low-voltage (LV) benchmark microgrid with solar and wind sources, microturbines, and EVs. The results demonstrate that the obtained portfolio is realistic, welfare-optimized and cost-efficient. © 2022 The Authors. Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology and the State Grid Economic & Technological Research Institute Co., Ltd.

关键词： Profitability

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Review and Comparative Analysis of Deep Learning Techniques for Smart Grid Load Forecasting

Review and Comparative Analysis of Deep Learning Techniques ...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Hossein Shahinzadeh Hamidreza Sadrarhami Mohammad Mohsen Hayati Hassan Majidi-Gharehnaz Mehdi Abapour Gevork B. Gharehpetian Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

In the last decade, the water and electricity industry has experienced significant investments in smart grid technologies. Within a smart grid framework, information and energy engage in bidirectional transmission, opening up diverse applications for artificial intelligence, including artificial neural networks, machine learning, and deep learning. This comprehensive review investigates the dynamic landscape of deep learning methodologies applied to load forecasting within smart grids, spanning short-term (STLF), medium-term (MILF), and long-term (LTLF) Forecasting horizons. We scrutinize a range of techniques, encompassing Auto-Encoder Method, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Graph Neural Networks (GNNs), Attention Mechanisms, and Hybrid Models. This article introduces and reviews common deep-learning algorithms used in load forecasting for smart grids and power systems. It also offers a comparative assessment based on the reduction percentage in four indicators: accuracy, speed, mean absolute error (MAPE), and root mean square error (RMSE). The research aims to provide valuable insights into the strengths and weaknesses of each deep learning method, guiding researchers and practitioners in making informed decisions when selecting the most suitable approach for diverse load forecasting scenarios in smart grid environments.

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DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain 38

DAT: Improving Adversarial Robustness via Generative Amplitu...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Fengpeng Li, Kemou Wu, Haiwei Tian, Jinyu Zhou, Jiantao State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer Science City University of Hong Kong Hong Kong Faculty of Innovation Engineering Macau University of Science and Technology China

To protect deep neural networks (DNNs) from adversarial attacks, adversarial training (AT) is developed by incorporating adversarial examples (AEs) into model training. Recent studies show that adversarial attacks disproportionately impact the patterns within the phase of the sample's frequency spectrum-typically containing crucial semantic information-more than those in the amplitude, resulting in the model's erroneous categorization of AEs. We find that, by mixing the amplitude of training samples' frequency spectrum with those of distractor images for AT, the model can be guided to focus on phase patterns unaffected by adversarial perturbations. As a result, the model's robustness can be improved. Unfortunately, it is still challenging to select appropriate distractor images, which should mix the amplitude without affecting the phase patterns. To this end, in this paper, we propose an optimized Adversarial Amplitude Generator (AAG) to achieve a better tradeoff between improving the model's robustness and retaining phase patterns. Based on this generator, together with an efficient AE production procedure, we design a new Dual Adversarial Training (DAT) strategy. Experiments on various datasets show that our proposed DAT leads to significantly improved robustness against diverse adversarial attacks. The source code is available at https://***/Feng-peng-Li/DAT. © 2024 Neural information processing systems foundation. All rights reserved.

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PDDM: IP image design via diffusion model 4

PDDM: IP image design via diffusion model

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4th IEEE International Conference on Data Science and computer Application, ICDSCA 2024

作者： Yao, Mingbo Wang, Haiyi Yang, Shuxiang School of Mathematics and Statistics Shandong University of Technology Zibo China College of Computer and Control Engineering Northeast Forestry University Harbin China Faculty of Computational Mathematics and Cybernetics Shenzhen MSU- BIT University Shenzhen China

ISBN: (纸本)9798350368239

Traditional methods and models, while capable of generating high-quality images, often require substantial human resources and time, limiting their feasibility for large-scale applications. To address these challenges, this paper proposes the Pose-Directed Diffusion Model. Through optimized training of this module, the model in this paper significantly reduces training time. Additionally, the model in this paper integrates the Pro-ControlNet module to enhance fine control during the image generation process. Importantly, this paper constructs a new CCC Image Collection dataset, featuring precise classifications, detailed annotations, and high-resolution images. The experimental results on the CCC Image Collection dataset demonstrate that our model performs excellently in terms of evaluation metrics such as MSE, PSNR, and SSIM. It shows significant improvement in training effectiveness on the original dataset, with outstanding performance both in terms of absolute error measurement and visual perception by the human eye. ©2024 IEEE.

关键词： Image annotation

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