检索结果-内蒙古大学图书馆

Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

Computers, Materials & Continua 2024年第11期81卷 2357-2394页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Razaz Waheeb Attar Farman Ali Ahmed Alhomoud Babar Shah School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia College of Technological Innovation Zayed UniversityDubai19282United Arab Emirates

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control *** these networks,nodes function as senders,receivers,and *** such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely *** the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power *** optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in *** paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in *** energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient *** link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of *** simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing *** evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

关键词： FANET UAV internet of UAVs relay mechanism energy consumption routing IEE-DLSA

来源：评论

学校读者我要写书评

暂无评论

Towards efficient implementation of MLP-ANN classifier on the FPGA-based embedded system

引用

IFAC-PapersOnLine 2022年第4期55卷 207-212页

作者： Rijad Sarić Nejra Beganović Dejan Jokić Edhem Čustović School of Computing Engineering and Mathematical Sciences La Trobe University 3086 Victoria Melbourne Australia Chair of Dynamics and Control University of Duisburg-Essen 47057 Duisburg Germany Department of Electrical and Electronics Engineering International Burch University 71000 Sarajevo Bosnia and Herzegovina

Epilepsy is a widely known neurological disease, causing atypical brain activity such as seizures. Apparently, it is critical to analyse possible triggers as well as establish appropriate medical treatment based on the seizure type. In general, diagnosis of epilepsy is usually conducted by applying various state-of-the-art algorithms to extract useful information from electroencephalography (EEG) signals. For effective real-time epilepsy diagnosis, the biomedical device integrated with the best performing machine learning algorithm is needed. Multilayer Perceptron (feed-forward) Artificial Neural Network (MLP-ANN) has proven to be a much better choice over traditional supervised machine learning algorithms in achieving high accuracy diagnosis. Challenges exist in handling extremely long data processing time in the case of many features in EEG signals plus complex MLP-ANN structure and computations. This research proposes the 5-12-3 MLP-ANN configuration to classify different types of epileptic seizures using Field Programmable Gate Array (FPGA) as a real-time embedded system.

关键词： MLP-ANN FPGA Epileptic seizure Real-time diagnosis

来源：评论

学校读者我要写书评

暂无评论

A Sub-mother UAV System Capable of Autonomous Aerial Docking

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年

作者： Li, Gang Lu, Yi He, Bin Pang, Tao Cheng, Bin Zhang, Pengpeng College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200120 China Shanghai Sunshine Rehabilitation Center Shanghai201613 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai200120 China East China Institute of Computing Technology Shanghai201822 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China College of Electronic and Information Engineering Tongji University Shanghai China

Completing the docking between two multi-rotor unmanned aerial vehicles (UAVs) in the air has great significance for expanding the application scenarios of multiple UAVs and improving their endurance capability. In this regard, this paper first designs a safe and autonomous docking scheme and constructs a sub-mother UAV *** address the issue of unstable flight in downwash airflow, this paper designs a high-performance control method suitable for the sub-mother UAV system based on Incremental Nonlinear Dynamic Inversion (INDI) and achieves robust attitude control with strong disturbance rejection capability. A UWB-based Ortho-UWB 3D positioning method is proposed in this paper with orthogonal installation, which, after being integrated with visual methods, enables high-precision 3D relative positioning during the docking process of the sub-mother UAV ***, the paper designs a complete docking process for the sub-mother UAVs and incorporates safety constraints into the docking process to ensure the safety of the docking process for both UAVs. Experiments of successfully docking the sub UAV to the mother UAV from bottom to top prove the feasibility and effectiveness of this scheme. © 2024 IEEE.

关键词： Disturbance rejection

来源：评论

学校读者我要写书评

暂无评论

Attention-Driven Graph Convolutional Networks for Deadline-Constrained Virtual Machine Task Allocation in Edge computing

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ahmad Ullah, Inam Singh, Sushil Kumar Jiang, Weiwei Alturise, Fahad Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen 518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Beijing University of Posts and Telecommunications Beijing China Qassim University College of Computer Department of Cybersecurity Buraydah Saudi Arabia

Effective load balancing is critical in contemporary mobile computing environments to ensure optimal resource utilization and adherence to strict deadlines. This study presents a novel framework for deadline-constrained load balancing, termed DCLD-net, which leverages the integration of Graph Convolutional Networks (GCN) and an attention-based Bidirectional Gated Recurrent Unit (BiGRU). The proposed framework utilizes the spatial modeling capabilities of GCNs to capture intricate network topologies, while the temporal dynamics and attention mechanisms of BiGRU enable the identification of critical patterns in traffic flow data. By incorporating key factors such as sticky sessions, load distribution, content-based factors such as videos or file downloads, instance health checks, and other relevant parameters, the DCLD-net model achieves high accuracy in predicting task schedules. These schedules allocate tasks to virtual machines (VMs), enabling dynamic traffic routing through a load balancer that efficiently assigns tasks. Extensive experimental evaluations demonstrate that the proposed attention-based BiGRU-GCN model significantly outperforms traditional methods in both deadline compliance and load balancing efficiency, offering a robust solution to the challenges of modern cloud computing. Experimental results demonstrate that DCLD-net outperforms traditional methods by 27% in deadline compliance and 29.3% in load balancing efficiency. © 1975-2011 IEEE.

关键词： attention consumer electronics Edge computing load balancing machine learning resource utilization virtual machine

来源：评论

学校读者我要写书评

暂无评论

A new model to correlate solubility of drug compounds in supercritical carbon dioxide

引用

Chemical Thermodynamics and Thermal Analysis 2025年 18卷

作者： Raghavan, Amrithaa Alwi, Ratna Surya M, Geetha Devi Garlapati, Chandrasekhar Centre for Pollution Control and Environmental Engineering Pondicherry University Puducherry 605014 India Research Center for Computing National Research and Innovation Agency (BRIN) Jl. Raya Jakarta-Bogor KM 46 Cibinong Indonesia Department of Mechanical & Industrial Engineering College of Engineering National University of Science & Technology Oman Department of Chemical Engineering Puducherry Technological University Puducherry 605014 India

A new solubility model based on enhancement factor concept is proposed to correlate solubility of drug compounds in supercritical carbon dioxide (SCCO2). The correlating ability of the new model is compared with exist... 详细信息

A new solubility model based on enhancement factor concept is proposed to correlate solubility of drug compounds in supercritical carbon dioxide (SCCO₂). The correlating ability of the new model is compared with existing solubility model. The proposed model and existing models were tested with twenty nine drug-supercritical carbon dioxide systems reported in the literature. The proposed model correlating performance is indicated in terms of absolute relative deviation percentage. Additionally, Akaike's Information Criterion (AIC) was used to assess the model's performance compared with existing solubility model. The results show the compared to existing solubility model, the proposed model accurately correlates the *** global absolute average relative deviation percentage for the existing model and proposed new model are 22.17 % and 16.11 % respectively. © 2025

关键词： Akaike's information criterion Enhancement factor Solubility Supercritical carbon dioxide

来源：评论

学校读者我要写书评

暂无评论

Stress Severity Detection in College Students Using Emotional Pulse Signals and Deep Learning

引用

IEEE Transactions on Affective computing 2025年

作者： Li, Mi Li, Junzhe Chen, Yanbo Hu, Bin School of Information Science and Technology Beijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education China Engineering Research Center of Digital Community Ministry of Education Beijing China Beijing University of Technology School of Information Science and Technology Beijing China Beijing Institute of Technology Institute of Engineering Medicine Beijing China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou China

College students face increasing stress from difficulties with studies, employment, and social interactions, which, if left unaddressed, may lead to depression and physical illnesses. Currently, the detection of stress severity relies on self-assessment scales, while machine learning or deep learning-based approaches primarily focus on classification. This study proposes an approach using pulse signals containing emotional cues and deep learning to automatically detect the severity of stress in college students. Firstly, pulse signals of 177 college students were collected using photoplethysmography (PPG) during they watched five virtual reality (VR) emotional scenes, including calm, sadness, happiness, fear, and tension. Pulse rate variability (PRV) and discrete PPG (dPPG) were extracted from these signals as input for detecting stress severity. Then, the proposed stress detection framework, 1DCNN-BiLSTM + Cross-Attention + XGBoost, was employed to detect stress severity, incorporating an emotional Cross-Attention mechanism. The impact of induced emotions on stress severity detection performance was examined. The results indicated that stress severity detection in emotional scenes outperformed in calm. Furthermore, the detection performance that integrates multiple emotions surpassed single emotions. The fusion of PRV and dPPG signals yielded the best detection performance. This study provides an end-to-end automated approach for detecting stress severity in college students. © 2010-2012 IEEE.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Few-shot Object Detection Algorithm Based on YOLOv8-Mamba-Like Linear Attention

Few-shot Object Detection Algorithm Based on YOLOv8-Mamba-Li...

引用

International Conference on Communication Software and Networks, ICCSN

作者： Hongliang Wang Kaizhu Wang Binglong Ji Changyi Deng Liaoning Province Human- Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Industrial Control Systems Cyber Emergency Response Team Institute of Software China Beijing China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

In the production and assembly process, the accuracy of detection is usually low due to limited resources and small quantities. In this paper, we propose a few-shot learning method for object detection based on the combination of YOLOv8 and Mamba-Like Linear Attention (MLLA) module. This method utilizes a high-pixel camera to capture the shape and size of the workpiece. By consistency-based mutual augmentation structure for few-shot samples data augmentation. By introducing the efficient and lightweight MLLA module, the model’s speed and accuracy in object detection and segmentation tasks on mobile devices are significantly improved. Specifically, MLLA mainly integrates the two key elements of “forget gate” and “block design” in Mamba, and MLLA replaces forget gate by using position coding (RoPE) to provide necessary location information while maintaining parallel computation and fast inference speed. This makes MLLA improves the accuracy of detection and segmentation recognition. Experimental results show that compared to the original YOLOv8 model, the model with the MLLA module exhibits improvements in segmentation precision and recognition accuracy. This method demonstrates high accuracy and robustness in workpiece recognition, making it suitable for real-time industrial applications.

关键词： Accuracy Shape Computational modeling Object detection Production Logic gates Feature extraction Robustness Software Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud computing: An Algorithm-Level Review

arXiv

引用

arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

来源：评论

学校读者我要写书评

暂无评论

Geometry Problem Solving Based on Counter-Factual Evolutionary Reasoning 19

Geometry Problem Solving Based on Counter-Factual Evolutiona...

引用

19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Song, Bing Xiong, Gang Shen, Zhen Zhu, Fenghua Lv, Yisheng Ye, Peijun Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences The Beijing Engineering Research Center of Intelligent Systems and Technology China Chinese Academy of Sciences The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266000 China

ISBN: (纸本)9798350320695

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models. © 2023 IEEE.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

High-Performance Convolutional Neural Network Emulation Via Fpga-Integrated Memristive Circuitry

SSRN

引用

SSRN 2024年

作者： Yang, Yucheng Tan, Aolong Duan, Shukai Wang, Lidan State Key Laboratory of Intelligent Vehicle Safety Technology China College of Artificial Intelligence Southwest University China National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips China

In this study, to tackle the challenges of high cost, insufficient reliability, and short lifespan associated with physical memristors, a FPGA-based memristor simulation circuit is proposed. This circuit is deployed on the Xilinx ZYNQ-7000 FPGA XC7Z020, with a resource utilization rate of less than 1%, and has been successfully integrated and validated. Moreover, the paper leverages a memristor model to build a convolutional neural network for MNIST image recognition, which achieves an accuracy of 90.22% with a parameter count of 512. © 2024, The Authors. All rights reserved.

关键词： Memristors

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：