检索结果-内蒙古大学图书馆

IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Nikolaos I. Deligiannis Riccardo Cantoro Matteo Sonza Reorda S. E. D. Habib Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Department of Electronics and Electrical Communications Engineering Cairo University Giza Egypt

Arithmetic circuits form the foundation of modern digital computation, enabling us to conduct precise mathematical operations and drive the digital age. They are integral components in nearly every digital circuit, such as processors' arithmetic and logic units. Especially in safety-critical domains like automotive and aviation, the flawless operation of these circuits is of paramount importance. This paper presents a case study involving two variants of Dadda multipliers and assesses their intrinsic reliability when affected by permanent hardware faults. We conducted extensive fault injection campaigns on the circuit models under various datasets, presenting the aggregated statistical errors in the form of the mean absolute error (MAE) for each case. Specifically, we performed fault injection campaigns in which the operands are sourced from trained quantized weights of a convolutional neural network, as well as randomly generated sets of integers. The results not only reveal differences between the two circuits but also show significant variations when different datasets are used in the fault injection campaigns.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel SCD11 CNN Model Performance Evaluation with Inception V3, VGG16 and ResNet50 Using Surface Crack Dataset

A Novel SCD11 CNN Model Performance Evaluation with Inceptio...

引用

International Conference on Unmanned Vehicle Systems-Oman (UVS)

作者： Adil Hussain Kashif Naseer Qureshi Raja Waseem Anwar Ayesha Aslam School of Electronics and Control Engineering Chang'an University Xi'An China Department of Computer Engineering and Electronics University of Limerick Limerick Ireland Dep of Computer Science German University of Technology in Oman Muscat Oman School of Information Engineering Chang'an University Xi'An China

Image processing techniques can be used to detect potential defects in images of infrastructure elements that have been collected or scanned. Aside from image processing, machine learning technologies are increasingly being applied to increase crack detection performance and resilience. A novel Surface Crack Detection Convolutional Neural Network (CNN) named the SCD11 CNN model is proposed. The main aim of this research paper is to improve efficiency and minimize the loss rate. The proposed CNN model is compared with the CNNs, including Inception V3, VGG16, and ResNet50, using a Surface Cracks Dataset. The accuracy and loss of the proposed model are compared. The results show that the proposed SCD11 CNN model performs better in test accuracy than Inception V3 and VGG16 and has a slightly lower accuracy than ResNet50.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

引用

computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

来源：评论

学校读者我要写书评

暂无评论

An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

来源：评论

学校读者我要写书评

暂无评论

Machine Learning based Early Prediction of Disease with Risk Factors Data of the Patient Using Support Vector Machines

Machine Learning based Early Prediction of Disease with Risk...

引用

International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication, MDCWC 2020

作者： Chelladurai, Usharani Pandian, Seethalakshmi Departments of Computer Science and Engineering and Electronics and Communication Engineering University College of Engineering BIT Campus Anna University Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9789811602887

Early detection of diseases plays an important role in improving the quality of healthcare and can help people to prevent dangerous health conditions. Early detection of chronic disease is a critical task in the area of health data analysis. This paper proposes a novel fuzzy logic-based SVM machine learning technique for patient-centered healthcare analytics for premature prediction of chronic diseases such as hypertension, hypothyroidism, and obesity based on individual patient risk factors data. The proposed method consists of preprocessing, feature selection, feature extraction, fuzzy SVM classification, and post-processing predicts the severity of the disease. The proposed fuzzy-based support vector machines (SVMs) are designed to classify important features by applying machine learning techniques to ensure accuracy in the prediction of chronic disease. The SVM is a margin-based classifier that maps input data onto a high-dimensional space and classifies them with a linear approximation. This technique combines outputs from different classification models and has shown the highest accuracy compared to previous techniques. None of the previous studies have integrated fuzzy with SVM classifiers for chronic disease datasets. The proposed machine learning-based disease prediction model for early diagnosis and timely treatment for non-communicable disease/chronic disease investigates the risk factors data from the patient treatment log. This model provides early risk detection, helps doctors to follow the appropriate precautions, and measures to minimize and prevent a patient from reaching critical phases of the disease. The system would significantly decrease human mortality rates and strengthen health services. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Design and Realization of Unmanned Vehicle Autonomous Navigation System Based on Binocular Camera 3

Design and Realization of Unmanned Vehicle Autonomous Naviga...

引用

3rd International Conference on computer Science, Electronic Information engineering and Intelligent control Technology, CEI 2023

作者： Guo, Tianxing Lin, Lianlei Liu, Huanyu Li, Junbao School of Electronics and Information Engineering Harbin Institute of Technology Department of Automatic Test and Control Harbin China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350372052

Aiming at the navigation problem of unmanned vehicles in extreme environments such as communication interference and limited GPS signals, this study proposes an autonomous navigation method based on binocular cameras. The method enables the unmanned vehicle to complete the task of localization and map building using only binocular images, without the need for other sensors or signal sources. Meanwhile, this study also proposes a local path planning obstacle avoidance method based on depth map, which, when combined with global localization information, can take into account both local obstacle avoidance and global path navigation, and can cope with random environmental changes. The system uses a single sensor for autonomous obstacle avoidance and navigation, which reduces the computational requirements and ensures the low cost of the navigation system. In order to verify the reliability and effectiveness of the system under signal-constrained conditions, the system is evaluated in a simulation environment and a real field scenario, respectively. The experimental results show that the system is able to achieve reliable localization and path planning under signal-constrained conditions. © 2023 IEEE.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

TSC18 Convolutional Neural Network for Traffic Sign Classification

TSC18 Convolutional Neural Network for Traffic Sign Classifi...

引用

Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Adil Hussain Kashif Naseer Qureshi Ayesha Aslam Tariq Tariq Muhammad Rabiu Abdullahi School of Electronics and Control Engineering Chang’an University Xi’An China Department of Electronic and Computer Engineering University of Limerick Limerick Ireland School of Information Engineering Chang’an University Xi’An China

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The classification of traffic signs holds significant importance in the realm of autonomous vehicles. The primary objective of our research is to effectively perform this classification to mitigate the occurrence of accidents and enhance the overall reliability of autonomous vehicles. This research proposed a TSC18 CNN model, using 18 layers only for the Traffic Sign Classification and performing the evaluation using the pre-trained Convolutional Neural Network (CNN) models. The TSC18 CNN model contains four convolutional layers, three max-pooling layers, one flattening layer, four fully connected layers, and batch normalization and dropout layers. The TSC18 CNN model is trained and validated using the Road Cracks dataset, which contains a traffic image collection of 43 classes. The training set includes 34,799 images that have been labeled, while the validation set contains 4,410 labeled images. The performance of the proposed CNN model is compared with the pre-trained CNN models, including EffecientNet, InceptionNet, and VGG-19, using the same dataset. The training and validation accuracy and losses are presented in graphical form for comparison. Also, the wellknown pre-trained CNN models, including EfficientNet, InceptionNet, and VGG- 19, were tested using the same dataset for the performance evaluation with the proposed model. The TSC18 model exhibited a test data accuracy of 99.21%. The performance of the proposed model is compared with the existing models using the GTSRB dataset. The proposed model performs well as compared to the pre-trained CNN models. The TSC18 CNN model can be used for road crack detection using road images.

关键词： Training Performance evaluation Accuracy Roads Data models Convolutional neural networks Reliability

来源：评论

学校读者我要写书评

暂无评论

Classification of Images Based on a System of Hierarchical Features

引用

computers, Materials & Continua 2022年第7期72卷 1785-1797页

作者： Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi Iryna Tvoroshenko Mujahed Al-Dhaifallah Department of Computer Engineering and Networks College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityAl-Kharj11991Saudi Arabia Department of Informatics Kharkiv National University of Radio ElectronicsKharkiv61166Ukraine Control and Instrumentation Engineering Department and Interdisciplinary Research Center(IRC)for Renewable Energy and Power SystemsKing Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia

The results of the development of the new fast-speed method of classification images using a structural approach are *** method is based on the system of hierarchical features,based on the bitwise data distribution for the set of descriptors of image *** article also proposes the use of the spatial data processing apparatus,which simplifies and accelerates the classification *** have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure,for which the sets of descriptors are *** introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of *** noise immunity of the method to additive noise has been experimentally *** to the results of the research,the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit *** costs increase proportionally with decreasing bit *** method can be used for application tasks where object identification time is critical.

关键词： Bitwise distribution computer vision descriptor hierarchical representation image classification keypoint noise immunity processing speed

来源：评论

学校读者我要写书评

暂无评论

A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

引用

Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

来源：评论

学校读者我要写书评

暂无评论

Time-Varying Optimal Sliding-Mode Lag Formation control for High-Order Nonlinear Multiagent Systems Based on Reinforcement Learning

引用

IEEE Transactions on Automation Science and engineering 2025年 22卷 16864-16876页

作者： Qinhong Fu Jin-Liang Wang Tingwen Huang School of Electronics and Information Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tiangong University Tianjin China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

This paper considers a type of high-order nonlinear multiagent systems (MASs), and the time-varying optimal lag formation control problem for such MAS is addressed. To eliminate the influences of the unknown system dynamics and external disturbance on single agent, a neural network (NN) identifier and disturbance observer based compensation controller is developed. Then, an optimal control law to ensure the practical time-varying lag formation for the MAS is introduced by exploiting the sliding-mode control (SMC) method and reinforcement learning (RL) technique. Finally, a numerical example is given to validate the effectiveness of the proposed optimal control law. Note to Practitioners—Recently, leader-following approach has been widely utilized to investigate the time-varying formation (TVF) control problem for various MASs. In fact, time delay is unavoidable during the process of the follower tracking the leader, it is more meaningful and reasonable to address the time-varying lag formation control problem of MASs. Moreover, most of the existing TVF control results ignore the optimal performance. Considering that a balance between system performance and control resources, it is also very significant and challenging to further tackle the optimal TVF control problem of MASs. In this paper, an RL-based time-varying optimal sliding-mode lag formation control strategy for high-order nonlinear MASs is developed, and an MAS consisting of four unmanned surface vehicles (USVs) is utilized to validate the effectiveness of the presented control strategy.

关键词： Formation control Topology Artificial neural networks Symmetric matrices Protocols Automation Vectors Time-varying systems State feedback Reinforcement learning

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：