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Analysis and Optimization of Boost Converter Parameters in Internal Model Control for Voltage Source Converter-based AC Microgrids

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2038-2054页

作者： Chiebuka Eyisi Qifeng Li Department of Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816 USA.

This paper investigates integration of distributed energy resources(DERs)in microgrids(MGs)through two-stage power conversion structures consisting of DC-DC boost converter and DC-AC voltage source converter(VSC)*** contrast to existing investigations that treated DC-link voltage as an ideal constant voltage,this paper considers the non-ideal dynamic coupling between both subsystems for completeness and higher accuracy,which introduces additional DC-side dynamics to the *** analysis shows parameters of the boost converter’s power model that impact stability through the *** selecting these parameters can mitigate this effect on stability and improve dynamic performance across the ***,an optimization framework is developed to facilitate in selecting adequate boost converter parameters in designing a stable voltage source converter-based microgrid(VSC-MG).The developed optimization framework,based on particle swarm optimization,considers dynamic coupling between both subsystems and is also effective in avoiding inadequate boost converter parameters capable of propagating instability through the DC-link to the *** are performed with MATLAB/Simulink to validate theoretical analyses.

关键词： AC microgrid boost converter dead-time droop control internal model control particle swarm optimization small-signal analysis time delay virtual impedance voltage source converter

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Enhancing eyeglasses removal in facial images: a novel approach using translation models for eyeglasses mask completion

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Multimedia Tools and Applications 2024年 1-20页

作者： Esmaily, Zahra Ebrahimpour-Komleh, Hossein Department of Electrical and Computer Engineering University of Kashan Kashan Iran

Accurately removing eyeglasses from facial images is crucial for improving the performance of various face-related tasks such as verification, identification, and reconstruction. This paper presents a novel approach to enhancing eyeglasses removal by integrating a mask completion technique into the existing framework. Our method focuses on improving the accuracy of eyeglasses masks, which is essential for subsequent eyeglasses and shadow removal steps. We introduce a unique dataset specifically designed for eyeglasses mask image completion. This dataset is generated by applying Top-Hat morphological operations to existing eyeglasses mask datasets, creating a collection of images containing eyeglasses masks in two states: damaged (incomplete) and complete (ground truth). A Pix2Pix image-to-image translation model is trained on this newly created dataset for the purpose of restoring incomplete eyeglass mask predictions. This restoration step significantly improves the accuracy of eyeglass frame extraction and leads to more realistic results in subsequent eyeglasses and shadow removal. Our method incorporates a post-processing step to refine the completed mask, preventing the formation of artifacts in the background or outside of the eyeglasses frame box, further enhancing the overall quality of the processed image. Experimental results on CelebA, FFHQ, and MeGlass datasets showcase the effectiveness of our method, outperforming state-of-the-art approaches in quantitative metrics (FID, KID, MOS) and qualitative evaluations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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Maximally Flat Negative Group Delay Prototype Filter Based on Capped Reciprocal Transfer Function of Classical Bessel Filter

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Progress in Electromagnetics Research B 2025年 110卷 91-105页

作者： Kandic, Miodrag Bridges, Greg E. Department of Electrical and Computer Engineering University of Manitoba WinnipegMB Canada

A prototype filter design exhibiting Negative Group Delay (NGD) is presented, based on the ratio of two low-pass classical Bessel filter transfer functions of the same order, but with different 3 dB-bandwidths. The resulting design is a reciprocal-Bessel filter transfer function, capped at a finite out-of-band gain. The proposed capped reciprocal-Bessel design is based on a similar concept applied to previously reported capped reciprocal-Butterworth and reciprocal-Chebyshev NGD designs, which use ratios of corresponding classical low-pass filter transfer functions. It is shown that within the in-band frequency range, the synthesized NGD transfer function exhibits a maximally flat group delay characteristic (Bessel-like property). Due to its near-flat in-band group delay characteristic, the design is suitable for constant phase shifter applications. For high design orders, it is shown that the achieved NGD-bandwidth product has an upper asymptotic limit, given by the square root of the out-of-band gain in decibels. When the prototype baseband transfer function is translated to a non-zero center frequency, it is demonstrated that resonator-based implementations are feasible via Sallen-Key, as well as all-passive ladder topologies. A combined in-band magnitude/phase distortion metric is evaluated for selected design examples and applied Gaussian and sinc input waveforms, and it is shown to be proportional to the design order and out-of-band gain. The proposed design's distortion metric is also shown to be generally lower than the previously reported capped reciprocal-Butterworth and reciprocal-Chebyshev designs. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Group delay

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Electron beam lithography on nonplanar and irregular surfaces

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Microsystems & Nanoengineering 2024年第2期10卷 1-23页

作者： Chenxu Zhu Huseyin Ekinci Aixi Pan Bo Cui Xiaoli Zhu Department of Electrical and Computer Engineering and Waterloo Institute for Nanotechnology(WIN) University of WaterlooWaterlooONCanada

E-beam lithography is a powerful tool for generating nanostructures and fabricating nanodevices with fine features approaching a few nanometers in ***,alternative approaches to conventional spin coating and development processes are required to optimize the lithography procedure on irregular *** this review,we summarize the state of the art in nanofabrication on irregular substrates using e-beam *** overcome these challenges,unconventional methods have been *** instance,polymeric and nonpolymeric materials can be sprayed or evaporated to form uniform layers of electron-sensitive materials on irregular ***,chemical bonds can be applied to help form polymer brushes or self-assembled monolayers on these *** addition,thermal oxides can serve as resists,as the etching rate in solution changes after e-beam ***,e-beam lithography tools can be combined with cryostages,evaporation systems,and metal deposition chambers for sample development and lift-off while maintaining low *** nanopyramids can be fabricated on an AFM tip by utilizing ice as a positive ***,Ti/Au caps can be patterned around a carbon ***,3D nanostructures can be formed on irregular surfaces by exposing layers of anisole on organic ice surfaces with a focused *** advances in e-beam lithography on irregular substrates,including uniform film coating,instrumentation improvement,and new pattern transferring method development,substantially extend its capabilities in the fabrication and application of nanoscale structures.

关键词： lithography beam coating

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Knowledge distillation on spatial-temporal graph convolutional network for traffic prediction

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International Journal of computers and Applications 2025年第1期47卷 45-56页

作者： Izadi, Mohammad Safayani, Mehran Mirzaei, Abdolreza Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as temporal graphs. Despite its capabilities, it often encounters challenges in delivering efficient real-time predictions for real-world traffic data. Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction. In this paper, We introduce a cost function designed to train a network with fewer parameters (the student) using distilled data from a complex network (the teacher) while maintaining its accuracy close to that of the teacher. We use knowledge distillation, incorporating spatial-temporal correlations from the teacher network to enable the student to learn the complex patterns perceived by the teacher. However, a challenge arises in determining the student network architecture rather than considering it inadvertently. To address this challenge, we propose an algorithm that utilizes the cost function to calculate pruning scores, addressing small network architecture search issues, and jointly fine-tunes the network resulting from each pruning stage using KD. Ultimately, we evaluate our proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results indicate that our method can maintain the student's accuracy close to that of the teacher, even with the retention of only (Formula presented.) of network parameters. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cost functions

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Predicting Distant Metastatic Sites in Cancer Using miRNA and mRNA Expression Data

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Current Bioinformatics 2025年第5期20卷 473-485页

作者： Mamatkarimov, Dostonjon Kang, Jiahui Han, Kyungsook Department of Electrical and Computer Engineering Inha University Incheon Korea Republic of

Background: Cancer patients with metastasis face a much lower survival rate and a higher risk of recurrence than those without metastasis. So far, several learning methods have been proposed to predict cancer metastasis, but most of these methods are intended to predict lymph node metastasis rather than distant metastasis. Distant metastasis is more difficult to predict than lymph node metastasis because distant metastasis is detected after a comprehensive examination of the entire body, and there are not enough publicly available tumor samples with distant metastasis that can be used for training learning methods. Predicting distant metastatic sites is even more challenging than predicting whether distant metastasis will occur or not. Methods: The problem of predicting distant metastatic sites is a multi‐class and multi‐label classification problem;there are more than two classes for distant metastatic sites (bone, brain, liver, lung, and other organs), and a single sample can have multiple labels for multiple metastatic sites. We transformed the multi‐label and multi‐class problem into multiple single‐label binary problems. For each metastatic site, we built a random forest model that deals with binary classification and linked the models along a chain. Results: Testing the model on miRNA and mRNA expression datasets of several cancer types showed a high performance in all performance measures. In the comparison of our model with other methods, our method outperformed the others. Conclusion: We developed a new method for predicting multiple metastatic sites using miRNA and mRNA expression data. The technique will be useful in predicting distant metastatic sites before distant metastasis occurs, which in turn will help clinicians determine treatment options for cancer patients. © 2025 Bentham science Publishers.

关键词： Diseases

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Local Search-Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 288-290页

作者： Xin Liao Khoi Hoang Xin Luo the College of Computer and Information Science Southwest University the Department of Computer Science and Engineering Washington University in St.Louis IEEE

Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].

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Motion Signal-based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

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Recent Advances in computer science and Communications 2024年第3期17卷 77-91页

作者： Yadav, Ram Kumar Arockiam, Daniel Semwal, Vijay Bhaskar Department of Computer Science and Engineering Amity University Madhya Pradesh Gwalior India Department of Computer Science and Engineering MANIT Bhopal India

Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. Aim: This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., enhance CNN, LR, RF, DT, KNN, and SVM) approaches. Method: This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. Result: This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. Conclusion: The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach. © 2024 Bentham science Publishers.

关键词： Support vector machines

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Counting vehicles types using deep learning algorithm in video surveillance systems

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Multimedia Tools and Applications 2024年 1-24页

作者： Akoushideh, Alireza Sadat, Seyed Shafiullah Shahbahrami, Asadollah Electrical and Computer Engineering Department National University of Skill Tehran Iran Computer Engineering Department Kabul Polytechnic University Kabul Afghanistan Computer Engineering Department University of Guilan Rasht Iran

Detection, identification, and automatic counting of vehicles using video surveillance cameras plays an essential role in intelligent transportation management. Despite the progress that researchers have made in these cases, its operational implementation still faces challenges such as various environmental conditions, unbalanced data sets, accuracy, and speed. Therefore, research is fundamental in solving these issues. This research investigates the use of deep convolutional neural networks (CNNs) for vehicle detection, classification, and counting in video surveillance applications. We analyze recent research trends and challenges, examining the suitability of popular datasets like MSCOCO, PASCAL, ImageNet, DAWN, DETRAC, and MIO-TCD for validating proposed algorithms. Our study focuses on overcoming challenges like poor lighting conditions and unbalanced traffic scenarios. We propose a novel approach for counting vehicle types based on the YOLO model. The proposed method leverages the YOLO model for object detection and recognition, the ByteTrack algorithm for tracking vehicles across frames, and the CLAHE pre-processing and the Multiple-Zone-Line approach for addressing video surveillance challenges. Experimental results on three benchmark videos demonstrate the superiority of YOLOv8 over YOLOv9 in our benchmarks. Moreover, the application of CLAHE pre-processing algorithms further enhances performance. This research offers a promising solution for accurate and robust vehicle detection, classification, and counting in real-world video surveillance scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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A Two-Level Neural-RL-Based Approach for Hierarchical Multiplayer Systems Under Mismatched Uncertainties

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 759-772页

作者： Zhong, Xiangnan Ni, Zhen Florida Atlantic University Department of Electrical Engineering and Computer Science Boca RatonFL33431 United States

AI and reinforcement learning (RL) have attracted great attention in the study of multiplayer systems over the past decade. Despite the advances, most of the studies are focused on synchronized decision-making to attain Nash equilibrium, where all the players take actions simultaneously. On the other hand, however, in complex applications, certain players may have an advantage in making sequential decisions and this situation introduces a hierarchical structure and influences how other players respond. The control design for such system is challenging since it relies on solving the coupled Hamilton-Jacobi equation. The situation becomes more difficult when the learning process is exposed to complex uncertainties with unreliable data being exchanged. Therefore, in this article, we develop a new learning-based control approach for a class of nonlinear hierarchical multiplayer systems subject to mismatched uncertainties. Specifically, we first formulate this new problem as a multiplayer Stackelberg-Nash game in conjunction with the hierarchical robust-optimal transformation. Theoretical analysis confirms the equivalence of this transformation and ensures that the designed control policies can achieve stable equilibrium. Then, a two-level neural-RL-based approach is developed to automatically and adaptively learn the solutions. The stability of this online learning process is also provided. Finally, two numerical examples are presented to demonstrate the effectiveness of the developed learning-based robust control design. © 2024 IEEE.

关键词： Reinforcement learning

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