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Digital Twin Empowered PV Power Prediction

Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1472-1483页

作者： Xiaoyu Zhang Yushuai Li Tianyi Li Yonghao Gui Qiuye Sun David Wenzhong Gao the School of Information Science and Engi neering Northeastern UniversityShenyang 110004China the Department of Informatics Universi ty of OsloOslo 0316mNorway the Department of Computer Science Aalborg UniversityAal borg 9220Denmark the Electrification and Energy Infrastructures Division Oak Ridge National LaboratoryOak RidgeTN 37830USA the Department of Electrical and Computer Engineering University of DenverDenverCO 80208USA

The accurate prediction of photovoltaic(PV)power generation is significant to ensure the economic and safe operation of power *** this end,the paper establishes a new digital twin(DT)empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power *** this framework,considering potential data contamination in the collected PV data,a generative adversarial network is employed to restore the historical dataset,which offers a prerequisite to ensure accurate mapping from the physical space to the digital ***,a new DT-empowered PV power prediction method is ***,we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model(i.e.,a parallel network of convolution and bidirectional long short-term memory model)for capturing the hidden spatiotemporal *** proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model,resulting in enhanced prediction ***,a real dataset is conducted to assess the effectiveness of the proposed method.

关键词： Photovoltaic power prediction digital twin hybrid prediction data recovery

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Experimental Protocol for Biological Experiments on Petri Dishes 14

Experimental Protocol for Biological Experiments on Petri Di...

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14th International Conference on Measurement, MEASUREMENT 2023

作者： Bajtos, Marek Janousek, Ladislav Radil, Roman Carnecka, Lucia Paulecova, Kristina University of Zilina Faculty of Electrical Engineering and Information Technology Department of Electromagnetic and Biomedical Engineering Zilina Slovakia University of Colorado Boulder Department of Electrical Computer and Energy Engineering BoulderCO United States

ISBN: (纸本)9788097262976

In this paper, the experimental protocol for the further exposition of low-frequency magnetic fields on biological cells is established. Experiments for one-week mass controlling of Petri dishes with YPD agar and Saccharomyces cerevisiae inoculum are performed. Firstly, four groups of samples are prepared to find out the proper volume of inoculum with the lowest standard deviation between the samples. Secondly, the dedicated volume of inoculum is used for another set of experiments for comparison with YPD agar plates without the yeast cells. The statistical evaluation of two groups of data is determined. © 2023 Institute of Measurement Science, Slovak Academy o.

关键词： Yeast

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Surrogate Simulation of Subject-Specific Lateral Pinch via Deep Learning 46

Surrogate Simulation of Subject-Specific Lateral Pinch via D...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Lindbeck, Erica M. Diaz, Maximillian T. Nichols, Jennifer A. Harley, Joel B. University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering GainesvilleFL United States

ISBN: (纸本)9798350371499

Musculoskeletal modeling and simulation is often a lengthy and computationally expensive process, particularly when developing and using personalized models. We present a deep learning-based adaptive surrogate model for lateral pinch, which accepts both musculoskeletal parameters and muscle activations as input for personalization and simulation. This model matches traditional OpenSim forward dynamics with an average root-mean-squared error (RMSE) of 2.27 N, within standard errors of experimental measurements, while demonstrating sensitivity to both categories of input and performing thousand of simulations in seconds (10-1000x faster than traditional multi-body simulations). In addition to direct use as a surrogate, the differentiable nature of the model may support future use in optimization problems, while its flexibility may support adaptation to modeling of experimental data. © 2024 IEEE.

关键词： Adversarial machine learning

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3D phase imaging from intensity measurements with non-paraxial multiple scattering model

3D phase imaging from intensity measurements with non-paraxi...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D, COSI, DH, FLatOptics, IS, pcAOP 2023 - Part of Imaging and Applied Optics Congress 2023

作者： Zhu, Jiabei Tian, Lei Department of Electrical and Computer Engineering Boston University MA02215 United States Department of Biomedical Engineering Boston University MA02215 United States

We propose a novel algorithm based on the split-step non-paraxial model for different intensity diffraction tomography setups to recover the 3D refractive index distribution of multiple-scattering biological samples. ... 详细信息

ISBN: (纸本)9781957171289

关键词： Multiple scattering

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Optimizing DC Motor Control via Leader Harris Hawks Algorithm with ITSE-ZLG Objective Function 10

Optimizing DC Motor Control via Leader Harris Hawks Algorith...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Ayinla, Shehu Lukman Amosa, Temitope Ibrahim Rahman, Md. Siddikur Owonikoko, Waheed Rahman, Md Ashiqur Barua, Sourav Rahman, Tuhibur Liou, Molla Shahadat Hossain Department of Electrical and and Electronics Engineering Seri Iskandar Perak Malaysia University of Ilorin Department of Computer Engineering Ilorin Nigeria University of Vermont Department of Electrical and Biomedical Engineering Burlington United States Green University of Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350318739

Proportional Integral and Derivative (PID) controllers are widely employed to normalize the response of numerous DC motor-powered systems, such as electric locomotives, traction systems, etc. Specifically, this category of controller is ubiquitous due to their simplicity, ease of control, and low-cost. However, their design parameters need to be carefully tuned to enhance control performance. A novel LHHO-PID controller with a compound objective function that combine Integral of Time multiplied Squared Error (ITSE) with Zwee-Lee Gaing's (ZLG) time-domain performance criterion is presented in this current study to enhance the control performance of a DC motor system. The system's performance was assessed by analyzing its response across various dimensions, including frequency responses, convergence profile and time. The results demonstrated that the developed PID-based controller exhibited a commendable performance in regulating the DC motor speed as compared to other existing PID-based controllers in the domain. The time response analysis showed an improvement of 7.2% compared to the best performing controller in rise time, a settling time of 0.1204 seconds, zero overshoot and negligible steady state error. These findings indicate that the proposed LHHO-PID controller has the capacity to regulate the speed of a DC motor efficiently in a variety of industrial applications. © 2023 IEEE.

关键词： DC motors

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Bipartite Graph based Multi-view Clustering (Extended Abstract) 39

Bipartite Graph based Multi-view Clustering (Extended Abstra...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Li, Lusi He, Haibo Old Dominion University Department of Computer Science Norfolk United States University of Rhode Island Department of Electrical Computer and Biomedical Engineering Kingston United States

ISBN: (纸本)9798350322279

In existing graph-based multi-view clustering algorithms, consensus cluster structures are explored by constructing similarity graphs of multiple views and then fusing them into a unified superior graph. However, they overlook consensus information when learning each graph independently, resulting in the undesirable unified graph with biases. To this end, we proposed a framework named bipartite graph based multi-view clustering (BIGMC) in [1] to tackle this challenge. To summarize, the key idea of BIGMC is to employ a small number of uniform anchors to represent the consensus information across views. In this way, BIGMC creates a bipartite graph between data points and anchors for each view, which are then fused to generate a unified bipartite graph. The unified graph would in turn improve each view bipartite graph and the anchor set. Finally, the clusters are formed directly using the unified graph. In this extended abstract, we also summarize the effectiveness of BIGMC as shown in experimental results originally presented in [1]. © 2023 IEEE.

关键词： Clustering algorithms

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An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

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computers, Materials & Continua 2022年第12期73卷 6269-6287页

作者： Martin Augustynek Jan Kubicek Jaroslav Thomas Marek Penhaker Dominik Vilimek Michal Strycek Ondrej Sojka Antonino Proto Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of Ostrava17.Listopadu 15Ostrava–Poruba70800Czech Republic

The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is *** paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker’s *** this work,we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming *** electrical model of the heart allows signals generation(right atrium,right ventricle)and the monitoring of the stimulation *** LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in *** results demonstrate the capability of proposed system to evaluate the paced and sensed *** proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing *** addition,the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.

关键词： Artificial heart cardiac conduction system electrical cardiac stimulation pacemaker

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A Hybrid Adversarial-TransUnet Architecture for Improving LV Segmentation Using Cardiac Cine Images 1st

A Hybrid Adversarial-TransUnet Architecture for Improving L...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Abdeltawab, Hisham Jani, Md. Rafsan Hasan, Md Rakibul Yasser, Ibrahim Khalifa, Fahmi Department of Biomedical Engineering and Systems Cairo University Cairo12613 Egypt Department of Electrical and Computer Engineering Morgan State University BaltimoreMD21251 United States Department of Electronics and Communications Engineering Mansoura University Mansoura35516 Egypt

ISBN: (纸本)9783031823763

Cardiac image segmentation is crucial for the assessment of heart anatomy and function. Deep learning (DL) has been widely employed for cardiac image segmentation and the accuracy of the segmentation has improved due to the advances in DL research and the existence of large cardiac datasets. In this study, we introduce a DL framework based on adversarial training for the delineation of the left ventricle (LV) from cardiac magnetic resonance imagining (CMRI). Our framework consists of two networks: the segmenter which is a neural network that predicts segmentation maps for the myocardium, the discriminator which is a convolutional neural network that discriminates between the ground truth and the predicted segmentation maps. We investigate two different architectures for the segmenter (Unet and TransUnet). The adversarial training can identify and correct the differences between the predicted and the ground truth maps and results in better segmentation accuracy than stand-alone Unet or TransUnet. The proposed framework has been evaluated on cine CMRI, collected from 100 patients, and evaluated using different metrics (Dice coefficient, Jaccard Index, and Hausdorff distance). The results documented an improvement of the segmentation accuracy (0.91, 0.82, 4.69 mm) using the proposed framework with TransUnet compared to the segmentation obtained by stand-alone Unet (0.75, 0.68, 7.54 mm). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Utilizing Different Predictive Methods to Optimize Hyperledger Fabric Instance 2

Utilizing Different Predictive Methods to Optimize Hyperledg...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Aqid, Bukhori Muhammad Chacko, Jeeta Ann Jacobsen, Hans Arno Department of Computer Science University of Indonesia Jakarta Indonesia Department of Informatics Technical University of Munich Munich Germany Electrical and Computer Engineering University of Toronto Toronto Canada

ISBN: (纸本)9798350343069

This paper explores the use of reinforcement learning and various machine learning techniques to optimize the configurations of Hyperledger Fabric v2 Channels and Orderers. Our goal is to increase the average throughput and success rate. To achieve this, we train multiple optimization agents using different algorithms, such as Q-learning and Deep Q Network. These agents interact with a Hyperledger Fabric v2 instance in a single host environment, using FabCar as the smart contract and Hyperledger Caliper as the benchmark platform. Based on the data collected from our reinforcement learning agents, we observed that the agents tend to choose larger batch sizes to optimize their results. Our training data also suggests that it is possible to achieve an average throughput of more than 98% for send rates up to 200 TPS. Our prediction results demonstrate that, even with the limitation of training time, reinforcement learning agents can increase the average throughput by around 59% and the average success rate by around 18%, while machine learning models can increase the average throughput by around 45% and the average success rate by around 8%, compared to the baseline results provided by the default configuration. © 2023 IEEE.

关键词： Reinforcement learning

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Iterative Water-Filling Power and Subcarrier Allocation for Multicarrier NOMA Downlink 48

Iterative Water-Filling Power and Subcarrier Allocation for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Choy Chai, Chin Zhang, Xiao-Ping Department of Electrical Computer and Biomedical Engineering TorontoONM5B 2K3 Canada

ISBN: (纸本)9781728163277

Novel closed form formulae of iterative optimal power control and allocation, and criterion for optimal subcarrier allocation are derived for downlink of multicarrier non-orthogonal multiple access (MC-NOMA) systems. For the first time, we present closed form water-filling formulae that quantify exactly the following effects of multiple access interference for optimal power allocation of MC-NOMA downlink: (i) Each user should take into account the sum of interference from other stronger users plus the receiver Gaussian noise as an equivalent interference in each subcarrier;(ii) The sum of each user's interference to other weaker users should be accounted as factors which determine the distinct water levels in each subcarrier of each of the other weaker users. We also propose novel iterative water-filling algorithm and projected conjugate gradient algorithm that facilitate iterative computation of optimal power allocation for MC-NOMA downlink. © 2023 IEEE.

关键词： Water levels

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