检索结果-内蒙古大学图书馆

Aggregated DER_A Model Parameterization Via Online Moving Horizon Estimation

IEEE Transactions on Smart Grid 2025年第4期16卷 3030-3044页

作者： Vasquez-Plaza, Jesus D. Subedi, Sunil Bhujel, Niranjan Hansen, Timothy M. Tonkoski, Reinaldo Andrade, Fabio Department of Electrical and Computer Engineering University of Puerto Rico Mayaguez Mayagüez PR USA Electrification and Infrastructures Division Oak Ridge National Laboratory Knoxville TN USA Electrical Engineering and Computer Science Department The University of Maine Orono ME USA Electrical Engineering and Computer Science Department South Dakota State University Brookings SD USA Chair of Electric Power Transmission and Distribution School of Engineering and Design Technical University of Munich Munich Germany

This paper introduces a methodology for parameterizing the DER A model using a novel smooth mathematical representation, simplifying the process and preserving accuracy in modeling inverter-based generator (IBG). The methodology employs an online parameterization process that can operate in real-time. The model parameterization process is structured into five sequential steps, each targeting a specific aspect of the DER A model through moving horizon estimation. This approach adapts to systems with varying voltage and frequency support requirements by selectively applying each step. Simulation results on systems with both known and unknown parameters validate the methodologys effectiveness. The online moving horizon estimation technique accurately captures the dynamics of the overall system and ensures that the parameterized DER A model closely mirrors the real systems voltage, current, and power dynamics. The findings highlight the potential of this methodology to substantially improve and simplify the dynamic modeling of power systems, paving the way for more reliable and robust IBG and grid integration. © 2010-2012 IEEE.

关键词： Parameterization

来源：评论

学校读者我要写书评

暂无评论

Joint Quantum Reinforcement Learning and Neural Myerson Auction for High-Quality Digital-Twin Services in Multi-Tier Networks

引用

IEEE Internet of Things Journal 2025年第13期12卷 23722-23735页

作者： Park, Soohyun Kim, Gyu Seon Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of

In order to build realistic digital-twin systems, this paper proposes a novel two-stage algorithm for high-quality digital-twin services in cloud-assisted multi-tier networks. In our proposed algorithm, the first stage is quantum multi-agent reinforcement learning (QMARL)-based scheduling for differentiated quality control of individual segments of digital-twin virtual objects in our cloud. As the number of segments selected by each edge increases, the edge's action dimension expands exponentially, posing significant challenges to learning with conventional MARL. To solve this problem, the quantum-inspired MARL-based scheduler is considered in order to reduce the scheduling action dimensions into a logarithmic-scale. For the scheduling formulation, age-of-information (AoI) is also considered for low-latency high-quality digital-twin services. Additionally, the second stage is for the fast and seamless distribution of differentiated quality-controlled segments of virtual objects. For this objective, each user requests its desired segments and one of nearby edges is selected. Among various approaches, this second stage considers second price auction for truthful and distributed computation. Furthermore, low-complexity computation can be realized by avoiding integer programming based computation which is NP-hard. The proposed two-stage algorithm achieves performance levels that are 8.33 and 1.18 times higher in terms of reward value in high dimensions and revenue, respectively, compared to other benchmarks. © 2014 IEEE.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Reliable Task Offloading for 6G-Based IoT Applications

引用

computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

来源：评论

学校读者我要写书评

暂无评论

Cross-Polarised Chipless RFID Sensor: A Novel Approach for Measuring Dielectric Constants in Solid Minerals and Rocks

引用

IEEE Sensors Journal 2025年第12期25卷 22804-22814页

作者： Lasantha, Likitha Ray, Biplob Masoumi, Hossein Karmakar, Nemai C. Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia Central Queensland University School of Engineering and Technology MelbourneVIC3000 Australia Monash University Department of Civil Engineering ClaytonVIC3800 Australia

This paper introduces an innovative approach to measuring the dielectric constants of solid geological samples using a crosspolarised chipless radio frequency identification (RFID) sensor. Copolarised RFID sensors often face interference from the material under test (MUT), resulting in significant deviations in resonance frequency. To address this issue, we developed a cross-polarised chipless RFID sensor that transmits and receives signals in different polarisation directions, enhancing measurement accuracy. Our sensor features resonatorintegrated ground plane cylindrical capacitors designed to detect variations in the dielectric properties of minerals and rocks. Both simulation and experimental validation indicate that the sensor can identify dielectric constant variations ranging from 1 to 10, with a frequency shift sensitivity of 63.5 MHz per dielectric unit. The sensor was tested on various mineral and rock samples, demonstrating alignment with established dielectric values. Operating effectively across a wide frequency range of 3.1 to 10.6 GHz, the system is well-suited for ultra-wideband (UWB) applications. This proposed method is cost-effective, compact, and compatible with remote sensing platforms, presenting a promising alternative for mineralogical and geophysical testing applications. © 2001-2012 IEEE.

关键词： Geological surveys

来源：评论

学校读者我要写书评

暂无评论

A Wirelessly Powered, Battery-Less, and Miniaturized Microchip for Implantable Biopotential-Monitoring Applications

引用

IEEE Sensors Journal 2025年第11期25卷 19545-19554页

作者： Jang, Jaeeun Habibagahi, Iman Mathews, Roshan P. Gwak, Wonseop Rahmani, Hamed Babakhani, Aydin Kwangwoon University School of Semiconductor Systems Engineering Seoul01897 Korea Republic of University of California at Los Angeles Department of Electrical Engineering Los AngelesCA90095 United States New York University Department of Electrical and Computer Engineering BrooklynNY11201 United States

This article presents a low-power, battery-less biopotential-recording integrated circuit (IC) designed for implantable leadless cardiac monitoring applications, addressing full autonomy based on wireless energy harvesting and closed-loop control. A key technical feature is the closed-loop capability with integration of a dual-band radio frequency (RF) energy harvester and communication system utilizing industrial, scientific, and medical (ISM) bands at 40.68 MHz for downlink and 915 MHz for uplink, ensuring efficient wireless power transfer and robust bidirectional data communication. The RF harvester achieves a peak conversion efficiency exceeding 40%, a critical design challenge in maintaining consistent performance under variable power conditions. Also, the proposed IC overcomes the need for a local oscillator (LO) by utilizing an externally provided clock, reducing system complexity and power consumption. Operating with a clock frequency of 100 kHz, the IC consumes only 8.8 µW, a major achievement in energy efficiency for wireless, battery-less systems. Fabricated in a 180-nm complementary metaloxidesemiconductor (CMOS) process, the IC successfully demonstrates cardiac signal measurement and heart rate (HR) extraction under a fully battery-less environment with air medium, providing a promising solution for next-generation leadless cardiac monitoring systems. © 2001-2012 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

引用

Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

来源：评论

学校读者我要写书评

暂无评论

Social NSTransformers: Low-Quality Pedestrian Trajectory Prediction

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5575-5588页

作者： Jiang, Zihan Ma, Yiqun Shi, Bingyu Lu, Xin Xing, Jian Goncalves, Nuno Jin, Bo Suzhou215000 China Northeast Forestry University College of Computer and Control Engineering Harbin150000 China University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal

This article introduces a novel model for low-quality pedestrian trajectory prediction, the social nonstationary transformers (NSTransformers), that merges the strengths of NSTransformers and spatiotemporal graph transformer (STAR). The model can capture social interaction cues among pedestrians and integrate features across spatial and temporal dimensions to enhance the precision and resilience of trajectory predictions. We also propose an enhanced loss function that combines diversity loss with logarithmic root mean squared error (log-RMSE) to guarantee the reasonableness and diversity of the generated trajectories. This design adapts well to complex pedestrian interaction scenarios, thereby improving the reliability and accuracy of trajectory prediction. Furthermore, we integrate a generative adversarial network (GAN) to model the randomness inherent in pedestrian trajectories. Compared to the conventional standard Gaussian distribution, our GAN approach better simulates the intricate distribution found in pedestrian trajectories, enhancing the trajectory prediction's diversity and robustness. Experimental results reveal that our model outperforms several state-of-the-art methods. This research opens the avenue for future exploration in low-quality pedestrian trajectory prediction. © 2024 IEEE.

关键词： Stars

来源：评论

学校读者我要写书评

暂无评论

STABILITY AND INTERACTION ANALYSIS OF GRID-FORMING TYPE IV WIND TURBINES 23

STABILITY AND INTERACTION ANALYSIS OF GRID-FORMING TYPE IV W...

引用

23rd Wind and Solar Integration Workshop, WIW 2024

作者： Udawatte, Harith E. Ravanji, Mohammad H. Bahrani, Behrooz Department of Electrical & Computer Systems Engineering Monash University Melbourne Australia Department of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781837242689

The global shift to renewable energy, especially wind power, is critical for achieving carbon-neutral power systems but poses challenges to grid stability due to reduced system inertia from inverter-based resources. Grid-forming (GFM) control modes are emerging as key solutions to these stability issues. Given the vast potential of wind energy, ensuring that wind turbine generators (WTGs) comply with GFM control standards is essential. This paper explores GFM capabilities in Type IV WTGs, focusing on the interactions between grid-side and machine-side control dynamics. A simplified small-signal model is developed to efficiently analyse the direct coupling between active power and DC-link control, highlighting how GFM parameters like droop and inertia, along with the DC controller bandwidth, affect system stability. Time-domain simulations validate the proposed tuning guidelines and reveal distinct WTG response characteristics at different wind speeds during grid contingencies. These insights lay the groundwork for robust GFM control designs, essential for a stable integration of wind turbines into high-renewable power systems. © Energynautics GmbH.

关键词： Windmill

来源：评论

学校读者我要写书评

暂无评论

Development of a Bias Compensating Q-Learning Controller for a Multi-Zone HVAC Facility

引用

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1704-1715页

作者： Syed Ali Asad Rizvi Amanda J.Pertzborn Zongli Lin IEEE the Department of Electrical and Computer Engineering Tennessee Technological UniversityCookevilleTN 38505 USA the Mechanical Systems and Controls Group in the Building Energy and Environment Division at the National Institute of Standards and Technology(NIST) GaithersburgMD 20899 USA the Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743 USA

We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zone heating,ventilation,and air-conditioning(HVAC)lab facility subject to unmeasurable disturbances and unknown *** is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias(even with integral action support),and in the extreme case,the divergence of the learning *** demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation(LQR)of a multi-zone HVAC environment and showing that,even with integral support,the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains,occupancy variations,light sources,and outside weather *** address this difficulty,we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances(and possibly other sources)in conjunction with the optimal control *** results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances,demonstrating the effectiveness of the algorithm in addressing the above challenges.

关键词： HVAC control optimal tracking Q-learning reinforcement learning(RL)

来源：评论

学校读者我要写书评

暂无评论

An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques

引用

IEEE Access 2025年 13卷 96317-96336页

作者： Haque, Fariha Asif Hasan, Mohammad Siddique, Md. Abu Ismail Roy, Tonmoy Kanti Shaha, Tonmoy Islam, Yamina Paul, Avijit Chowdhury, Muhammad E. H. Rajshahi University of Engineering and Technology Department of Electronics and Telecommunication Engineering Rajshahi6204 Bangladesh Utah State University Department of Data Analytics and Information Systems LoganUT84322 United States Chittagong University of Engineering and Technology Department of Electrical and Electronic Engineering Chattogram4349 Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chattogram4318 Bangladesh Qatar University Department of Electrical Engineering Doha Qatar

In the field of medical imaging, deep learning (DL) techniques have made significant contributions to the detection and classification of various cancers. Identifying the precise regions in medical images containing cancerous cells plays a crucial role in the diagnostic process. Early and accurate cancer detection is essential for effective treatment and improved patient outcomes. However, manual diagnosis is labor-intensive, requiring the specialized expertise of radiologists, and the increasing number of cancer cases presents challenges in processing large volumes of image data efficiently. To address these challenges, an end-to-end concatenated Convolutional Neural Network (CNN) attention model has been proposed for automatic lung cancer classification. This approach integrates two distinct CNNs, followed by a multi-layer perceptron (MLP) and a multi-head attention (MHA) mechanism, to enhance performance. The model leverages explainable AI techniques, such as gradient-weighted class activation mapping (grad-CAM) and Shapley additive explanations (SHAP), to highlight critical regions within the images that influence the decision-making process. This model achieves impressive performance, with an accuracy of 99.54%, precision of 99.31%, recall of 99.95%, F1-score of 99.66%, and an AUC of 99.97%. These results demonstrate that this approach not only surpasses existing methods but also provides a highly accurate and interpretable solution. By reducing the need for extensive manual intervention, this model enables faster and more reliable lung cancer diagnosis, paving the way for timely and effective treatments. © 2013 IEEE.

关键词： Convolutional neural networks

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：