检索结果-内蒙古大学图书馆

Robust 3D point clouds classification based on declarative defenders

Neural Computing and Applications 2025年第3期37卷 1209-1221页

作者： Li, Kaidong Zhang, Tianxiao Zhong, Cuncong Zhang, Ziming Wang, Guanghui Department of Electrical Engineering & Computer Science University of Kansas 1520 West 15th St. LawrenceKS66045 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA01609 United States Department of Computer Science Toronto Metropolitan University 350 Victoria Street TorontoONM5B 2K3 Canada

3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are structured and dense. Bridging the domain gap between these two data types is a non-trivial challenge to enable model interchangeability. Recent research using lattice point classifier (LPC) highlights the feasibility of cross-domain applicability. However, the lattice projection operation in LPC generates 2D images with disconnected projected pixels. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. Through extensive experiments, we thoroughly examine and analyze their performance and defense mechanisms. Leveraging current large foundation models, we scrutinize the feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. The generative model-based mapping algorithms yield regular 2D images, further minimizing the domain gap from regular 2D classification tasks. The source code is available at https://***/KaidongLi/***. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Compton Camera for 3D SPECT Imaging 15

Compton Camera for 3D SPECT Imaging

引用

15th SPIE Conference on Developments in X-Ray Tomography

作者： Liang, Yuxuan Li, Mengzhou Han, Shuo Yu, Hengyong Wang, Ge Department of Biomedical Engineering Rensselaer Polytechnic Institute 12180 United States Department of Electrical and Computer Engineering University of Massachusetts Lowell01854 United States

ISBN: (纸本)9781510679641

Single photon emission computed tomography (SPECT) is commonly used with radioiodine scintigraphy to evaluate patients with multiple diseases such as thyroid cancer. The clinical gamma camera for SPECT contains a mechanical collimator that greatly compromises dose efficiency and limits diagnostic performance. The Compton camera is emerging as a promising alternative for mapping the distribution of radio-pharmaceuticals in the thyroid, since the Compton camera does not require mechanical collimation and in principle does not reject gamma ray photons. In this study, a high-efficiency tomographic imaging system is designed with a Compton camera for thyroid cancer imaging. A Timepix3-based Compton camera is selected for collecting gamma photons emitted from an I-131 phantom, and the backprojection filtration algorithm is applied for image reconstruction. The results demonstrate the feasibility of the Compton camera for high efficiency SPECT imaging and also the limitations that need further efforts to address. © 2024 SPIE.

关键词： Single photon emission computed tomography

来源：评论

学校读者我要写书评

暂无评论

Challenges and Opportunities in Healthcare and Industrial IoT: A Comparative Analysis 1

Challenges and Opportunities in Healthcare and Industrial Io...

引用

1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Berjis, Zaid Ameen, Siddeeq Y. Al-Jammas, Mohammed H. Electrical and Computer Engineering Department College of Engineering University of Duhok Duhok Iraq Energy Engineering Department College of Technical Engineering Duhok Polytechnic University Duhok Iraq Computer and Information Engineering Department College of Electronics Ninevah University Mosul Iraq

ISBN: (纸本)9798331533557

The Internet of Things (IoT) has changed many industries by enabling smart devices to transmit data, operate autonomously, and interact in real-time. Among its most prominent applications are in healthcare and industrial sectors, where IoT is classified into two classes: massive Machine-Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). While mMTC devices prioritize low energy consumption and can handle large-scale deployments with minimal data rates, URLLC devices are focused on mission-critical applications requiring ultra-low latency and high reliability. This paper examines the challenges and opportunities of IoT implementation in healthcare and industrial sectors, focusing on interoperability, data security, and scalability issues. The objective is to provide actionable solutions for the effective integration of IoT technologies. The investigation shows that in healthcare, mMTC is utilized for non-critical monitoring, while URLLC supports critical tasks like remote surgery. In industrial settings, mMTC improves processes like supply chain management, while URLLC enables automation and real-time control of machinery. Despite these opportunities, challenges such as data privacy, security, interoperability, and reliability remain. Addressing these issues is crucial for unlocking the full potential of IoT in healthcare and industrial sectors. © 2024 IEEE.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Imitation Learning Based Energy Management of a Microgrid With Hybrid Energy Storage and Real-Time Pricing

引用

IEEE Internet of Things Journal 2025年第12期12卷 19801-19817页

作者： Gao, Shuhua Xu, Yizhuo Zhang, Zhaoqian Wang, Zhengfang Zhou, Xiaoyu Wang, Jing Shandong University School of Control Science and Engineering Jinan250100 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore Second Construction Limited Company of China Construction Eighth Engineering Division Jinan Shandong China

Microgrids equipped with hybrid energy storage systems (ESSs) are increasingly critical for balancing the intermittency of renewable energy sources (RESs) and the fluctuations in demand. This paper introduces a novel Multi-Agent Imitation Learning (MAIL) framework for real-time energy management in microgrids, particularly under real-time pricing conditions. The approach leverages a problem decomposition strategy, which separates the energy management task into two phases: first, optimal actions for each ESS are estimated using individual agents, each employing a deep neural network (DNN) trained to emulate an ideal mixed-integer linear programming (MILP) solver;then, a one-step online optimization reacts to these decisions to optimize the remaining system components holistically. Rigorous proofs establish the equivalence of the decomposed subproblems to the original optimization problem, ensuring integrity and effectiveness. Comparative simulation studies with real-world data reveal that our MAIL framework offers significant cost advantages and superior training efficiency relative to both traditional single and emerging multi-agent reinforcement learning (RL) methods. Notably, the operational costs achieved through our approach are markedly lower than those of its counterparts, closely approximating the theoretical minimum. This underscores the method's proficiency and its potential as an effective solution for microgrid energy management. Moreover, the MAIL strategy's multi-agent design allows for straightforward scalability to more extensive systems involving multiple ESSs and shows promising potential for cooperative management across interconnected microgrids. © 2014 IEEE.

关键词： Costs

来源：评论

学校读者我要写书评

暂无评论

A Multi-strategy Improved Osprey Optimization Algorithm for UAV path Planning 7

A Multi-strategy Improved Osprey Optimization Algorithm for ...

引用

7th International Conference on Robotics, Control and Automation engineering, RCAE 2024

作者： Yang, Long Wang, Weimin Xu, Binzi School of Computer Jiangsu University of Science and Technology Zhenjiang212000 China School of Electrical Engineering Anhui Polytechnic University Wuhu24100 China

ISBN: (纸本)9798350355642

Addressing the challenges of reduced population diversity and susceptibility to local optima in the later iterations of the Osprey algorithm, this paper presents a Multi-Strategy Improved Osprey Algorithm (IOOA). Firstly, Piecewise mapping is employed to initialize the population, providing a robust foundation for global search. Secondly, during the exploration stage, we design an adaptive parameter Levy exploration method based on the fishhawk leader. Additionally, we integrate a random disturbance strategy inspired by trigonometric functions to enhance population diversity and improve global exploration capacity. During the exploring phase, we propose nonlinear control parameters to effectively balance exploration and exploitation, thus decreasing the risk of the algorithm stagnating to local optimum. Additionally, we introduce an update formula based on the positions of elite leaders to expedite convergence. Finally, we conduct simulation experiments and statistical tests on 13 benchmark test functions, comparing the performance of IOOA with seven popular algorithms. Simultaneously, we apply IOOA to the UAV path planning problem to validate its optimization performance in practical scenarios. The results indicate that IOOA exhibits faster convergence speed and higher effectiveness compared to other optimizers. © 2024 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient UAV-Assisted Federated Learning in Wireless Networks 15

Energy-Efficient UAV-Assisted Federated Learning in Wireless...

引用

15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Fu, Zhenyu Liu, Juan Mao, Yuyi Xie, Lingfu Ningbo University School of Electrical Engineering and Computer Science Zhejiang315211 China Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9798350324662

With the proliferation of smart mobile devices and next-generation wireless communication technologies, federated learning (FL) has garnered significant attention as an emerging paradigm for privacy-preserving distributed model training. However, the traditional FL frameworks assume a static model aggregator such as the base station, which face multiple challenges including high energy consumption, frequent device dropout, and compromised model convergence. To address these issues, this study explores a novel FL framework called unmanned aerial vehicle (UAV)-assisted FL. The primary objective is to leverage UAVs as movable model aggregators, which collaborate with devices to minimize the energy consumption and ensure satisfactory convergence accuracy of FL. By adopting the distributed approximate newton (DANE) algorithm as the local optimizer, we first analyze the convergence of UAV-assisted FL and derive a device scheduling constraint to foster convergence. Subsequently, an optimization problem that aims at minimizing the total device energy consumption is formulated, which jointly optimizes the UAV trajectory, user selection, time slot length, and the uplink transmission power, CPU frequency, and local convergence accuracy of devices, while maintaining a desired global accuracy. This non-convex optimization problem is then decomposed into three subproblems and solved via the alternating direction method of multipliers (ADMM). Simulation results demonstrate that our proposed UAV-Assisted FL framework significantly reduces the total device energy consumption compared to baseline approaches and achieves a better balance with the model accuracy. © 2023 IEEE.

关键词： convergence analysis device scheduling energy efficiency Federated leaning resource allocation trajectory optimization unmanned aerial vehicle (UAV)

来源：评论

学校读者我要写书评

暂无评论

Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0: Wild-And-Crazy-Idea Paper

Neuro-Symbolic Empowered Denoising Diffusion Probabilistic M...

引用

2023 Forum on Specification and Design Languages, FDL 2023

作者： Capogrosso, Luigi Mascolini, Alessio Girella, Federico Skenderi, Geri Gaiardelli, Sebastiano Dall'Ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Di Cataldo, Santa Vinco, Sara Macii, Enrico Fummi, Franco Cristani, Marco Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina at Chapel Hill United States

ISBN: (纸本)9798350307375

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-Time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before. copy;2023 IEEE. © 2023 IEEE computer Society. All rights reserved.

关键词： Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

H2/H∞ Robust Observed-State Feedback Control Based on Slack LMI-LQR for LCL-Filtered Inverters

引用

IEEE Transactions on Industrial Electronics 2023年第5期70卷 4785-4798页

作者： Tran, Thuy Vi Kim, Kyeong-Hwa Lai, Jih-Sheng Seoul National University of Science and Technology Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul01811 Korea Republic of Virginia Polytechnic Institute and State University Future Energy Electronics Center Department of Electrical and Computer Engineering BlacksburgVA24061 United States

A robust controller for an LCL-filtered grid-connected inverter (GCI) is presented in this article in the context of the system model uncertainties and nonideal grid environment. Under the occurrence of exogenous inputs from parameter drifts affecting the controller and observer, the H2/H∞ approach of the linear quadratic regulator control is developed to obtain the state-feedback gain and observer gain with the system stability guarantee by means of Lyapunov stability theory. Furthermore, the use of slack-linear matrix inequalities to handle parameter-dependent Lyapunov functions in synthesizing the robust controller can achieve a less conservative condition than the conventional quadratic stability. System robustness and stability are assessed through the closed-loop pole map and the discrete-time frequency analysis approaches. Hardware experiments are conducted for the LCL-filtered GCI to validate the feasibility and effectiveness of the proposed control method. Fair comparisons with the conventional schemes are also performed to highlight the superior performance of the proposed scheme. © 1982-2012 IEEE.

关键词： Stability criteria

来源：评论

学校读者我要写书评

暂无评论

A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges

引用

IEEE Communications Surveys and Tutorials 2023年第3期25卷 1841-1876页

作者： Jouhari, Mohammed Saeed, Nasir Alouini, Mohamed-Slim Amhoud, El Mehdi Mohammed Vi Polytechnic University School of Computer Science Ben Guerir43150 Morocco United Arab Emirates University Department of Electrical and Communication Engineering Al Ain United Arab Emirates King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal23955-6900 Saudi Arabia

Long-range (LoRa) technology is most widely used for enabling low-power wide area networks (WANs) on unlicensed frequency bands. Despite its modest data rates, it provides extensive coverage for low-power devices, making it an ideal communication system for many Internet of Things (IoT) applications. In general, LoRa is considered as the physical layer, whereas LoRaWAN is the medium access control (MAC) layer of the LoRa stack that adopts a star topology to enable communication between multiple end devices (EDs) and the network gateway. The chirp spread spectrum modulation deals with LoRa signal interference and ensures long-range communication. At the same time, the adaptive data rate mechanism allows EDs to dynamically alter some LoRa features, such as the spreading factor (SF), code rate, and carrier frequency to address the time variance of communication conditions in dense networks. Despite the high LoRa connectivity demand, LoRa signals interference and concurrent transmission collisions are major limitations. Therefore, to enhance LoRaWAN capacity, the LoRa Alliance released many LoRaWAN versions, and the research community has provided numerous solutions to develop scalable LoRaWAN technology. Hence, we thoroughly examine LoRaWAN scalability challenges and state-of-the-art solutions in both the physical and MAC layers. These solutions primarily rely on SF, logical, and frequency channel assignment, whereas others propose new network topologies or implement signal processing schemes to cancel the interference and allow LoRaWAN to connect more EDs efficiently. A summary of the existing solutions in the literature is provided at the end of the paper, describing the advantages and disadvantages of each solution and suggesting possible enhancements as future research directions. © 1998-2012 IEEE.

关键词： Wide area networks

来源：评论

学校读者我要写书评

暂无评论

Bearing-only Localization Based on an Array Network with Distributed ADMM 2

Bearing-only Localization Based on an Array Network with Dis...

引用

2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Sun, Guanxi Liu, Wei Queen Mary University of London School of Electronic Engineering and Computer Science United Kingdom The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9798331515669

The two-dimensional target localization problem for a network of distributed sensor sub-arrays (with a sub-array at each receiver) is investigated in the compressive sensing framework based on group sparsity, and the well-known alternating direction method of multipliers (ADMM) method is applied to derive a distributed implementation algorithm. It does not require transmission of the impinging signals from each sub-array to a central processor, while the whole optimisation process is realised in a distributed way. Simulation results show that the proposed algorithm under certain conditions performs better than the existing group sparsity based localization method in terms of localization accuracy, speed and computational complexity. © 2024 IEEE.

关键词： Compressed sensing

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：