检索结果-内蒙古大学图书馆

Discrete Current Controller for SPMSMs at Low Sampling-to-Fundamental Frequency Ratios Using Critical Damping Feedback

引用

IEEE Transactions on Power Electronics 2025年第8期40卷 11787-11798页

作者： Huang, Zhen Zhu, Rui Zhang, Yun Zanchetta, Pericle Nanchang University School of Information Engineering Nanchang330000 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China University of Pavia Electrical Computer and Biomedical Engineering Department Pavia27100 Italy

The discrete current controllers can generally ensure high-performance machine control. However, for high-speed machines associated with low sampling-to-fundamental frequency ratios (SFR), the unavoidable disturbances will seriously affect the control performance. To address this issue, an advanced discrete current controller is proposed in this article. The critical damping feedback is used to achieve the maximum damping ratio of the disturbance loop at any SFR. Compared with the existing discrete controllers, the proposed one can significantly reduce the impact of multiple parameter mismatches on the performance of the controller while improving the current tracking response and enhancing the antidisturbance capability. Finally, the experimental results on a 10 krpm high-speed permanent magnet synchronous machine with the lowest SFR of 8 have proven the feasibility and advantages of the addressed current controller. © 1986-2012 IEEE.

关键词： Damping

来源：评论

学校读者我要写书评

暂无评论

A Biasing Scheme for the Gain Compression Point Optimization of HBT Cascode D-Band LNAs

A Biasing Scheme for the Gain Compression Point Optimization...

引用

2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium, BCICTS 2024

作者： Serra, Lorenzo De Filippi, Guglielmo Piotto, Lorenzo Mazzanti, Andrea University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798331541248

Cascode amplifiers with a matching network between the common-emitter (CE) and the common-base (CB) HBTs are popular in the sub-THz band for their enhanced gain. The stage is analyzed in this paper with focus on the large-signal behavior. It is first observed that the CE-CB interstage network can push the CB out of the linear class-A regime, limiting the input power at 1 dB gain compression. It is then shown that the issue can be avoided with a suitable biasing scheme, leading to enhanced dynamic range with superior power efficiency. The concept is applied to the design of a D-band LNA in BiCMOS technology. With a quiescent power consumption of 20 mW from 2 V supply, the LNA shows 17.5 dB gain and 5.5 dB NF at 170 GHz. The output-referred 1dB compression point is 4.2dBm with 9% power efficiency at 175 GHz. Compared to previously reported LNAs in the same band, measured results demonstrate a remarkable dynamic range improvement with the the highest power efficiency. © 2024 IEEE.

关键词： BiCMOS technology

来源：评论

学校读者我要写书评

暂无评论

Breakdown of effective medium theory:A study of photonic spin Hall effect in deep-subwavelength all-dielectric multilayers

引用

Science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 173-174页

作者： Yongmin Liu Department of Mechanical and Industrial Engineering Northeastern UniversityBoston 02115USA Department of Electrical and Computer Engineering Northeastern UniversityBoston 02115USA

Effective medium theory(EMT)has been widely applied in material science,electromagnetics and photonics to determine the effective material properties for inhomogeneous composites comprising subwavelength *** versatility of this foundational approach has been established through its application in classical Maxwell-Garnet models,which encompass relatively simple structures。

关键词： composites theory dielectric

来源：评论

学校读者我要写书评

暂无评论

Skin Lesion Classification via ensemble method on deep learning

引用

Multimedia Tools and Applications 2025年第18期84卷 19379-19397页

作者： Amiri, Sekineh Asadi Nasrolahzadeh, Mahda Mohammadpoory, Zeynab Kordkheili, Amir Hossein Zare Department of Computer Engineering University of Mazandaran Babolsar Iran Department of Electrical Engineering University of Torbat Heydarieh Torbat Heydarieh Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran

Deep neural networks have shown promising results in the classification of skin lesion images, particularly when they focus on the most significant regions of an image. However, the identification of melanoma continues to pose a significant challenge, primarily because of the substantial variability both within and between classes in images of moles. A new framework is proposed in this paper to develop an innovative method for effective melanoma diagnosis, presenting a new approach for Skin Lesion Classification (SLC) that combines a modified Inception ResNet v2 and Efficient Net-B4 in an ensemble. In the modified Inception ResNet v2, we incorporate a Soft-Attention (SA) mechanism. The goal is to enhance the network’s performance by using the SA mechanism to highlight informative features and suppress those that induce noise. Given the diversity of skin lesions in terms of type, texture, color, shape, and distribution, our proposed method extracts crucial features from the images by employing both the SA and merging the features of two layers of Inception ResNet ***, we have modified EfficientNet-B4 by adding two dense layers to improve its classification capabilities. After classifying the images with these two models, we employ soft voting for the final classification. We evaluated the performance of our proposed method on the ISIC-2017 and ISIC-2018 datasets, considering metrics such as Accuracy, Precision, Recall, and F-measures. Experimental results and a comparison with existing techniques demonstrate that our method achieves higher accuracy than current state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Cumulative Capacity Credit Estimation for Renewable Energy Projects

引用

Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1643-1651页

作者： Arif S.Malik Majid A.Al Umairi the Electrical and Computer Engineering Department Sultan Qaboos UniversityAl-Khod 123Oman Oman Electricity Transmission Company MuscatOman Electrical and Computer Engineering Department Sultan Qaboos UniversityAl-Khod 123Oman

This paper presents a novel method for accurately estimating the cumulative capacity credit(CCC)of renewable energy(RE)*** data from the main interconnected system(MIS)of Oman for 2028,where a substantial increase in RE generation is anticipated,the method is introduced alongside the traditional effective load carrying capability(ELCC)*** ensure its robustness,we compare CCC results with ELCC calculations using two distinct standards of reliability criteria:loss of load hours(LOLH)at 24 hour/year and 2.4 hour/*** method consistently gives accurate results,emphasizing its exceptional accuracy,efficiency,and simplicity.A notable feature of the method is its independence from loss of load probability(LOLP)calculations and the iterative procedures associated with analytic-based reliability ***,it relies solely on readily available data such as annual hourly load profiles and hourly generation data from integrated RE *** innovation is of particular significance to prospective independent power producers(IPPs)in the RE sector,offering them a valuable tool for estimating capacity credits without the need for sensitive generating unit forced outage rate data,often restricted by privacy concerns.

关键词： Effective load carrying capability renewable energy resource capacity credit generation system reliability loss of load probability

来源：评论

学校读者我要写书评

暂无评论

Counting vehicles types using deep learning algorithm in video surveillance systems

引用

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

来源：评论

学校读者我要写书评

暂无评论

Broadband angular spectrum analog processors based on all-dielectric metasurfaces

引用

Science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 188-190页

作者： Lin Deng Yongmin Liu Department of Electrical and Computer Engineering Northeastern UniversityBoston 02115USA Department of Mechanical and Industrial Engineering Northeastern UniversityBoston 02115USA

Digital signal processors are extensively used to execute mathematical operations and advanced computational tasks on digital ***,they suffer from several inherent limitations,including low speed,high energy consumption,and large memory requirements,because of the hardware bottleneck and the imperative conversion between digital and analogue signals.

关键词： dielectric spectrum hardware

来源：评论

学校读者我要写书评

暂无评论

Analysis of the effect of different scattering mechanisms on the excess noise behavior of Sb-based avalanche photodiodes

引用

Optics Express 2025年第4期33卷 7337-7344页

作者： Karimi, Hannaneh Herrera, Daniel J. Dadey, Adam A. Wei, Dongxia Mcarthur, Andrew Bank, Seth R. Campbell, Joe. C. Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering University of Texas AustinTX78758 United States

In optical applications where avalanche photodiodes (APDs) provide the benefit of high sensitivity, Sb-based materials systems such as AlInAsSb and AlGaAsSb have shown extremely low excess noise factors. The Monte Carlo (MC) model described in this work reveals the effect of high alloy scattering on the excess noise factor in these material systems. A comparison between the excess noise factor of different combinations of scattering rates is investigated. Furthermore, the ionization path length probability distribution is presented for AlInAsSb APDs with different scattering mechanisms. Good agreement is achieved between simulation results and experimental measurements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

来源：评论

学校读者我要写书评

暂无评论

LSTM-SAC reinforcement learning based resilient energy trading for networked microgrid system

AIMS Electronics and Electrical Engineering

引用

AIMS Electronics and electrical engineering 2025年第2期9卷 165-191页

作者： Sharma, Desh Deepak Bansal, Ramesh C. Department of Electrical Engineering MJP Rohilkhnad University Bareilly India Electrical Engineering Department University of Sharjah Sharjah United Arab Emirates Department of Electrical Electronics & Computer Engineering University of Pretoria Pretoria South Africa

On the whole, the present microgrid constitutes numerous actors in highly decentralized environments and liberalized electricity markets. The networked microgrid system must be capable of detecting electricity price changes and unknown variations in the presence of rare and extreme events. The networked microgrid system comprised of interconnected microgrids must be adaptive and resilient to undesirable environmental conditions such as the occurrence of different kinds of faults and interruptions in the main grid supply. The uncertainties and stochasticity in the load and distributed generation are considered. In this study, we propose resilient energy trading incorporating DC-OPF, which takes generator failures and line outages (topology change) into account. This paper proposes a design of Long Short-Term Memory (LSTM) - soft actor-critic (SAC) reinforcement learning for the development of a platform to obtain resilient peer-to-peer energy trading in networked microgrid systems during extreme events. A Markov Decision Process (MDP) is used to develop the reinforcement learning-based resilient energy trade process that includes the state transition probability and a grid resilience factor for networked microgrid systems. LSTM-SAC continuously refines policies in real-time, thus ensuring optimal trading strategies in rapidly changing energy markets. The LSTM networks have been used to estimate the optimal Q-values in soft actor-critic reinforcement learning. This learning mechanism takes care of the out-of-range estimates of Q-values while reducing the gradient problems. The optimal actions are decided with maximized rewards for peer-to-peer resilient energy trading. The networked microgrid system is trained with the proposed learning mechanism for resilient energy trading. The proposed LSTM-SAC reinforcement learning is tested on a networked microgrid system comprised of IEEE 14 bus systems. ©2025 the Author(s), licensee AIMS Press.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Meta-Learning Online Control for Linear Dynamical Systems

引用

IEEE Transactions on Automatic Control 2025年第6期70卷 4163-4169页

作者： Muthirayan, Deepan Kalathil, Dileep Khargonekar, Pramod P. University of California Irvine Department of Electrical Engineering and Computer Sciences IrvineCA United States Texas A&M University Department of Electrical and Computer Engineering United States

In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of N (similar) control tasks. Each task involves controlling a linear dynamical system for a finite horizon of T time steps. The cost function and system noise at each time step are adversarial and unknown to the controller before taking the control action. Meta-learning is a broad approach where the goal is to prescribe an online policy for any new unseen task exploiting the information from other tasks and the similarity between the tasks. We propose a meta-learning online control algorithm for the control setting and characterize its performance by meta-regret, the average cumulative regret across the tasks. We show that when the number of tasks are sufficiently large, our proposed approach achieves a meta-regret that is smaller by a factor D/D∗ compared to an independent-learning online control algorithm which does not perform learning across the tasks, where D is a problem constant and D∗ is a scalar that decreases with increase in the similarity between tasks. Thus, when the sequence of tasks are similar the regret of the proposed meta-learning online control is significantly lower than that of the naive approaches without meta-learning. We also present experiment results to demonstrate the superior performance achieved by our meta-learning algorithm. © 1963-2012 IEEE.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：