检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Javaid, Shumaila Saeed, Nasir He, Bin Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210 China Al Ain15551 United Arab Emirates

Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. We comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, we focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, we investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, we highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs. © 2024, CC BY-SA.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Feature Extraction of Motor Imagination EEG Signals for a Collaborative Exoskeleton Robot Based on PSD Analysis 42

Feature Extraction of Motor Imagination EEG Signals for a Co...

引用

42nd Chinese control Conference, CCC 2023

作者： Wu, Yunfeng Li, Junze Ren, Zichen Lu, Bo Zhang, Changzhu Qi, Peng Tongji University Department of Control Science and Engineering No. 1239 Siping Road Shanghai200092 China Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States School of Mechanicaland Electric Engineering Soochow University Robotics and Microsystems Center Suzhou215021 China

ISBN: (纸本)9789887581543

The integration of brain-machine interface and exoskeleton robot has been widespread application in gait correction, walking assistance, and numerous other scenarios. To effectively extract the electroencephalogram (EEG) signal features of motor imagery while wearing an exoskeleton, this study proposes a frequency band pre-determination method based on power spectral density (PSD) that enables common spatial patterns (CSP) to extract features from the frequency band with the highest energy in the EEG. The signal power spectral density of all channels is obtained at or near the average frequency of the maximum short interval frequency component energy. A second round of filtering is performed on the data, and the signal components are used as input for the subsequent feature extraction step. The CSP method extracts features from the spatial domain signal and generates feature maps. Finally, Support Vector Machines (SVM) are utilized to classify the EEG signals. Based on the pre-set gait of the exoskeleton robot and the motor imagery paradigm, feature extraction and classification of motor imagery EEG data during exoskeleton use were conducted, with an average accuracy rate of 77%. The experimental results demonstrate the effectiveness of this method in extracting the motor imagery EEG features during exoskeleton use. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A CNN Approach To Predicting Non-Invasive Blood Pressure of Surgical Patients 5

A CNN Approach To Predicting Non-Invasive Blood Pressure of ...

引用

5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Ko, Pin-You Lai, Lien-Fu Ku, Tien-Hsiung Lin, Yue-Der National Changhua University of Education Department of Computer Science and Information Engineering Changhua Taiwan Changhua Christian Hospital Ai Development Center Department of Anesthesiology Changhua Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

The occurrence of intraoperative hypertension/ hypotension may cause danger to a patient. Therefore, the monitoring of blood pressure change during surgical operation is momentous for anesthetized patient undergoing surgical procedures. The invasive method of measuring arterial blood pressure (ABP) provides accurate and much information, but it requires evaluation by an anesthesiologist and with its long-term use, it also brings a higher risk of infection, and even some patients have pain and discomfort at the catheter placement site after surgery, so it is not suitable for all types of patients. On the other hand, non-invasive method of measuring cuff blood pressure provides little information because of non-continuous measurement. With the emerging of deep learning, there has been more and more discussion on whether non-invasive Photoplethysmography (PPG) can replace invasive ABP as an alternative to continuous BP measurement and even BP prediction. This paper aims to implement a deep learning model to predict SBP/DBP signal by PPG, PPG extended data and Electrocardiography (ECG) data. We trained the CNN-based architecture on preprocessed data including ECG, PPG, 1st and 2nd derivative PPG signal extracted from MIMIC-III(Medical Information Mart for Intensive Care III) waveform database matched subset to predict continuous ABP signals using real world data. The value of SBP and DBP are directly predicted to effectively estimate the accuracy of the predictions. The prediction results fulfill the grade A in British Hypertension Society (BHS) standard and most part of Association for the Advancement of Medical Instrumentation (AAMI)'s standard. The proposed model has been applied for CHANGHUA CHRISTIAN HOSPITAL's IRB NO:191240. The results depict that our proposed model could effectively predict SBP/DBP signal and be deployed to the operating room realistically. © 2022 IEEE.

关键词： Blood pressure

来源：评论

学校读者我要写书评

暂无评论

Editorial:Special Topic on Federated Learning for IoT and Edge Computing

引用

ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

来源：评论

学校读者我要写书评

暂无评论

Integrating Deep Neural Network and Optimization Algorithm for Superior User Comfort and Energy Efficiency in Smart Home

Integrating Deep Neural Network and Optimization Algorithm f...

引用

International Conference on control, Decision and Information Technologies (CoDIT)

作者： Junaid Khan Umar Zaman Eunkyu Lee Awatef Salem Balobaid Kyungsup Kim Department of Environemntal IT Engineering Chungnam National University Daejeon Republic of Korea Department of Computer Engineering Chungnam National University Daejeon Republic of Korea Autonomous Ship Research Center Samsung Heavy Industries Daejeon Republic of Korea Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Efficient energy utilization while ensuring occupants’ comfort remains a primary objective in smart home environments. This study presents a novel smart home management methodology that integrates Deep Neural Networks (DNNs) with an optimization algorithm to enhance energy efficiency and user comfort. Unlike traditional approaches, which often focus on individual aspects of energy management or user comfort, our method comprehensively addresses both aspects by considering key environmental factors such as air quality, temperature, and illumination. By employing specialized DNNs, our approach dynamically adjusts home conditions to align with user preferences, thereby enhancing overall comfort and satisfaction. The primary innovation of our methodology lies in the Comfort Index Calculation System, which utilizes advanced DNNs to precisely quantify user comfort based on environmental conditions. This system enables our optimization algorithm to iteratively adjust environmental settings, striking a balance between user comfort and energy consumption. By continuously learning from user feedback and environmental data, our integrated approach ensures that smart homes adapt to occupants’ evolving preferences and external conditions, thereby offering a more personalized and responsive living environment. Our results demonstrate the capability of this integrated approach in improving smart home environments, offering significant implications for environmental living and smart building automation. By bridging the gap between energy efficiency and user comfort, our study contributes to advancing the field of smart home management, ultimately realizing the full potential of energy-efficient and comfortable living environments.

关键词： Energy consumption Temperature Heuristic algorithms Lighting Smart homes Artificial neural networks Air quality Energy efficiency Environmental factors Optimization

来源：评论

学校读者我要写书评

暂无评论

Point-Plane Better than Point-Point – for Embedding-based USAD 8

Point-Plane Better than Point-Point – for Embedding-based U...

引用

8th International Conference on Video and Image Processing, ICVIP 2024

作者： Zhao, Zhirong Zhang, Yuanhao Yu, Zujun Zhu, Liqiang Science and-Technology Department Guoneng Shuohuang Railway Development Co Ltd. Cangzhou Cangzhou062350 China Frontiers Science Center for Smart High-Speed Rail System Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

As deep neural networks (DNNs) have advanced, high-quality pre-trained models have provided excellent feature extraction methods for embedding-based unsupervised anomaly detection (EUSAD). However, when calculating anomaly scores, common EUSAD models often rely on point-to-point distance computations between test features and reference features. This approach not only fails to fully utilize the carefully integrated reference feature library but may also increase pixel-level false positives due to the incorrect selection of reference points. To address this issue, this paper adopts the least squares method, evaluating anomalies by calculating the distance between test feature points and the reference feature plane. Experimental results demonstrate that this method not only performs well in global image detection but also significantly reduces pixel-level false positives, showing strong performance across many anomaly detection datasets. © 2025 SPIE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

引用

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

来源：评论

学校读者我要写书评

暂无评论

Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

引用

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

来源：评论

学校读者我要写书评

暂无评论

Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

引用

arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Underwater Image Enhancement by Convolutional Spiking Neural Networks

arXiv

引用

arXiv 2025年

作者： Sudevan, Vidya Zayer, Fakhreddine Kausar, Rizwana Javed, Sajid Karki, Hamad De Masi, Giulia Dias, Jorge Center for Autonomous Robotic Systems Khalifa University United Arab Emirates Department of Computer and Information Engineering Khalifa University United Arab Emirates Department of Computer Science Khalifa University United Arab Emirates Department of Mechanical and Nuclear Engineering Khalifa University United Arab Emirates Department of Science and Engineering Sorbonne University Abu Dhabi United Arab Emirates

Underwater image enhancement (UIE) is fundamental for marine applications, including autonomous vision-based navigation. Deep learning methods using convolutional neural networks (CNN) and vision transformers advanced UIE performance. Recently, spiking neural networks (SNN) have gained attention for their lightweight design, energy efficiency, and scalability. This paper introduces UIE-SNN, the first SNN-based UIE algorithm to improve visibility of underwater images. UIE-SNN is a 19- layered convolutional spiking encoder-decoder framework with skip connections, directly trained using surrogate gradient-based backpropagation through time (BPTT) strategy. We explore and validate the influence of training datasets on energy reduction, a unique advantage of UIE-SNN architecture, in contrast to the conventional learning-based architectures, where energy consumption is model-dependent. UIE-SNN optimizes the loss function in latent space representation to reconstruct clear underwater images. Our algorithm performs on par with its non-spiking counterpart methods in terms of PSNR and structural similarity index (SSIM) at reduced timesteps (T = 5) and energy consumption of 85%. The algorithm is trained on two publicly available benchmark datasets, UIEB and EUVP, and tested on unseen images from UIEB, EUVP, LSUI, U45, and our custom UIE dataset. The UIE-SNN algorithm achieves PSNR of 17.7801 dB and SSIM of 0.7454 on UIEB, and PSNR of 23.1725 dB and SSIM of 0.7890 on EUVP. UIE-SNN achieves this algorithmic performance with fewer operators (147.49 GSOPs) and energy (0.1327 J) compared to its non-spiking counterpart (GFLOPs = 218.88 and Energy=1.0068 J). Compared with existing SOTA UIE methods, UIE-SNN achieves an average of 6.5× improvement in energy efficiency. The source code is available at UIE-SNN. Copyright © 2025, The Authors. All rights reserved.

关键词： Marine navigation

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：