检索结果-内蒙古大学图书馆

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

Journal of Control and Decision 2024年第1期11卷 117-131页

作者： H.Abbasi Nozari S.J.Sadati Rostami Paolo Castaldi Silvio Simani Faculty of Electrical and Computer Engineering Babol Noshirvani University of TechnologyBabolIran Department of Electrical Electronic and Information EngineeringUniversity of BolognaCesenaItaly Department of Engineering University of FerraraFerraraItaly

In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric *** proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation ***,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external *** residual evaluation module is developed based on the suggested series and parallel ***,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and *** obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.

关键词： Robust fault detection and isolation reaction wheels blended learning series and parallel fault detection and isolation forms satellite attitude control system

来源：评论

学校读者我要写书评

暂无评论

Mitigating Risks in Vehicle Fires: An Integrated Approach for Early Detection and Real-Time Alert System 4th

Mitigating Risks in Vehicle Fires: An Integrated Approach fo...

引用

4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Suryawanshi, Ranjeetsingh Bhagat, Harshita Karande, Aryan More, Parth More, Vaishali Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819751990

Every year, there are about 170,000–220,000 car accidents each year that involve fire. The cause of the fire ranges from collision, engine malfunction, electric short circuits, and many more. Irrespective of the cause, the damage incurred is close to $2 billion in direct property damage and in severe accidents, even the passengers’ lives. The rapid detection and timely notification of fire incidents are crucial for minimizing property damage and ensuring public safety. The current vehicle fire safety system relies on manual notification, leading to delayed responses and potential risks, especially in low-density areas and nighttime scenarios. Additionally, the lack of proactive fire detection technology hinders early identification, increasing the likelihood of extensive damage and harm to passengers. This paper proposes an integrated alert system automatically notifies fire stations, hospitals, and police upon fire detection, streamlining coordination and reducing response times. Rigorous evaluations are undertaken to assess the system's accuracy, sensitivity, and reliability under various conditions, solidifying its efficacy in addressing vehicle fire incidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fires

来源：评论

学校读者我要写书评

暂无评论

Energy Aware Clustering with Medical Data Classification Model in IoT Environment

引用

computer Systems Science & engineering 2023年第1期44卷 797-811页

作者： R.Bharathi T.Abirami Department of Computer Science and Engineering Cheran College of EngineeringK.ParamathiKarur639111India Department of Information Technology Kongu Engineering CollegePerunduraiErode638060India

With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced *** to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly *** the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic *** this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT *** goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT *** proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct ***,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter *** design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the *** show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.

关键词： Internet of things healthcare medical data classification energy efficiency clustering autoencoder

来源：评论

学校读者我要写书评

暂无评论

Multi Attention Convolutional Sparse Coding U-Net for Enhanced Land-Use and Land-Cover Segmentation Using Hyperspectral Images

引用

Sensing and Imaging 2025年第1期26卷 1-32页

作者： Yele, Vijaykumar P. Badhe, Namdeo Baban Alegavi, Sujata Sedamkar, R.R. Department of Electronics & Telecommunication Engineering Thakur College of Engineering and Technology Maharashtra Mumbai400101 India Department of Information Technology Thakur College of Engineering and Technology Maharashtra Mumbai400101 India Department of Internet of Things Thakur College of Engineering and Technology Maharashtra Mumbai400101 India Department of Computer Engineering Thakur College of Engineering and Technology Maharashtra Mumbai400101 India

The process of segmentation of hyperspectral images involves dividing the image into various categories by interpreting the spectral data which in turn enhances the accuracy of environmental, urban, and agricultural monitoring. Most existing land-cover land-use segmentation models have operational inefficiencies due to limitations such as high memory consumption, complicated computing processes, and weak scalability with the architectural complexities hindering the performance across different datasets and tasks. In response to this, a Multi Attention Convolutional sparse coding U-network Feed-forward Network (MACUFN) is developed as a multi-layered and advanced solution for segmentation. The Gaussian Adaptive Bilateral Median Filtering (GABMF) is very effective at noise reduction while still maintaining sharp edges. Moreover, the Adaptive Particle Swarm Dandelion Optimization (APSDO) technique underpins the proposed segmentation model’s parameters processing, which allows for proper management of hyperspectral images in different datasets enhancing precision and consistency beyond the existing approaches. The proposed system demonstrates remarkable performance, achieving accuracy across all the datasets as 99.97%, 99.98%, 99.94%, and 99.35% on Indian Pines, Pavia University, Salinas, and EuroSat respectively. The proposed model enhances the accuracy as well as the efficiency of land-cover segmentation and overcomes the drawbacks faced by current land-cover segmentation models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

引用

Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Optimizing Technical Analysis and Stock Price Analysis Using: BLRM-QNDNL 21

Optimizing Technical Analysis and Stock Price Analysis Using...

引用

21st OITS International Conference on information technology, OCIT 2023

作者： Kalva, Sudhakar Naganjaneyulu, Satuluri Sudhakar, K. Computer Science and Engineering Department of Information Technology Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350358230

The Stock value forecast is a significant issue to determine the future direction of the financial Markets. Many research works are carried out and design many techniques to predict stock price of Individual stocks. But, the forecast precision/ of the regular methods was lower when taking the enormous size of the dataset. To address this downside, a Novel Deep Learning based Broken-Stick Linear Regressive MapReduce Based on Quasi-Newton Deep Neural Learning (BLRM-QNDNL) method is proposed right now. BLRM-QNDNL technique targeting optimizing the parameters engaged with stock profit prediction. Quasi-Newton based Deep Neural Network Learning (Q-NDNL) is applied in the BLRM-QNDNL procedure for precisely foreseeing the stock prices dependent on the news with higher accuracy. Q-NDNL used several hidden layer in order to thoroughly examine the effects of emotions conveyed in news items on stocks with the least amount of time complexity. The BLRM-QNDNL method expands the stock price forecast performance with lower time complexity when compared with cutting edge works. The BLRM-QNDNL technique has been evaluated exploratory on metrics such as prediction accuracy, prediction time, and false positive rate with regard to different numbers of data. When compared to state-of-the-art studies, the experimental results show that the BLRM-QNDNL technique can reduce the time complexity of stock price prediction while simultaneously increasing its accuracy. The suggested BLRM-QNDNL technique reduces the false positive stock price prediction rate by 41% and 57%, respectively and decreases market price prediction time complexity by 9 percent and 29 percent relative to traditional DeepClue [1] and MFNN [2], respectively. © 2023 IEEE.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

Quality Assessment of Selective Laser Melting Components Using Quantitative Ultrasound Measurements and Image Texture Analysis

引用

IEEE Access 2024年 12卷 71797-71807页

作者： Lin, Chun-Hui Lin, Cheng-Jian Wang, Shyh-Hau National Cheng Kung University Department of Computer Science and Information Engineering Tainan 701 Taiwan National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung 411 Taiwan

Successful production of metallic selective laser melting components requires a quality assurance process that can effectively and nondestructively assess internal defects. Ultrasound testing has emerged as a valuable modality for defect identification as well as the characterization of microstructural and material properties. In this study, a comprehensive quality assessment approach was developed, leveraging quantitative ultrasound parameters, images, and image texture analysis. The evaluation focused on Inconel 718 selective laser melting components manufactured using diverse combinations of process parameters. The investigation involved examining the correlation between ultrasound parameters, image texture features, and the overall quality of the fabricated components. To validate the findings obtained through ultrasound testing, micro-computed tomography was employed. The experimental results provided evidence that the ultrasound parameters and image texture features successfully detected nonhomogeneous microstructures present within the selective laser melting components. Notably, the speed of sound and attenuation exhibited a positive correlation of 0.64 and 0.74, respectively, with porosity, indicating their potential as indicators of porosity. Furthermore, image texture features such as entropy, contrast, and homogeneity demonstrated high correlations (R2> 0.7 ) with the quality of the components. Consequently, these identified features hold promising potential for aiding in the selection of optimal process parameters and the prediction of porosity in future research. © 2013 IEEE.

关键词： image texture analysis Quality assessment selective laser melting ultrasound testing

来源：评论

学校读者我要写书评

暂无评论

Design of improved deer hunting optimization enabled multihop routing protocol for wireless sensor networks

引用

International Journal of Cognitive Computing in engineering 2023年第1期4卷 363-372页

作者： Balasubramanian, D. Lubin Govindasamy, V. Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology Puducherry India Department of Information Technology Puducherry Technological University Puducherry India

A wireless sensor network (WSN) encompasses a huge set of sensor nodes employed to collect data and transmit it to a base station (BS). Due to its compact, inexpensive, and scalable nature of sensors, WSN finds its applicability in diverse real-time applications. The battery-operated sensor nodes necessitate the design of a multi-hop routing protocol for the effective utilization of available energy in the network. Routing can be considered an optimization problem and can be solved by the design of bioinspired algorithms. This study introduces an improved deer hunting optimization-enabled multihop routing (IDHO-MHR) protocol for WSN. The major intention of the IDHO-MHR approach is to optimally find the routes to the destination in WSN. The IDHO algorithm is initially derived by the incorporation of the Nelder Mead (NM) concept into the traditional DHO algorithm. In addition, the IDHO-MHR technique primarily derives a fitness function with the inclusion of two major variables, namely residual energy (RE) and distance. The nodes with higher RE and minimum distance have the probability of becoming optimal routes from the networks. The performance validation of the IDHO-MHR approach is performed, and the outcomes are inspected in various aspects. The experimental outcomes reported the supremacy of the IDHO-MHR protocol over the other recent approaches. © 2023 The Authors

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

An approach to on-stream DDoS blitz detection using machine learning algorithms

引用

Materials Today: Proceedings 2023年 80卷 3492-3499页

作者： Manjula, H.T. Neha Mangla Atria Institute Of Technology Bengaluru India Department Of Computer Science & Engineering CMRIT Bengaluru India Department of Information Science & Engineering Atria Institute of Technology Bengaluru India

Distributed Denial of service (DDoS) attacks is an enormous threat to today's cyber world, cyber networks are compromised by the attackers to distribute attacks in a large volume by denying the service to legitimate users. The toughest and challenging task in today's network and network security engineers is to identify compromised traffic (attacked) and legitimate (normal) traffic. The main goal of the paper is to detect DDos attacks using classification algorithms. To achieve the goal the proposed system uses attacking tool to initiate attacks using Loic attacking tool with the data set extracted from open source tool Wireshark and transferring the dataset to apache Spark for detection analysis. The system also uses Apache spark machine learning algorithms (MLib), classification algorithms to classify the dataset. We use Naive Bayes, KNN and Random forest classification algorithms to classify normal traffic and attacked traffic. Our system is capable of detecting attacks with respect to any traffic protocols ICMP, TCP, or UDP. The accuracy of detection is compared on three classification algorithms and noted that random forest gives the accuracy of 96.75%. © 2021

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Aspect-Based Sentiment Analysis Model Based on RoBERTa For Text Sentiment Analysis

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第14期49卷 193-202页

作者： Chauhan, Amit Sharma, Aman Mohana, Rajni Jaypee University of Information Technology HP Solan173234 India Department of Computer Science Amity School of Engineering and Technology Amity University Punjab Punjab Mohali140306 India

Using an aspect-based sentiment analysis task, sentiment polarity towards specific aspect phrases within the same sentence or document is to be identified. The process of mechanically determining the underlying attitude or opinion indicated in the text is known as sentiment analysis. One of the most important aspects of natural language processing is sentiment analysis. The RoBERTa transformer model was pretrained in a self-supervised manner using a substantial corpus of English data. This means it was pretrained solely with raw texts and an algorithmic process to generate inputs and labels from those texts. No human labelling was involved, allowing it to utilise a vast amount of publicly available data. The authors of this work provide a thorough investigation of aspect-based sentiment analysis with RoBERTa. The RoBERTa model and its salient characteristics are outlined in this work, followed by an analysis of the model's optimisation by the authors for aspect-based sentiment analysis. The authors compare the RoBERTa model with other state-of-the-art models and evaluate its performance on multiple benchmark datasets. Our experimental results show that the RoBERTa model is effective for this important natural language processing task, outperforming competing models on sentiment analysis tasks. Based on the SemEval-2014 variant benchmarking datasets, the restaurant and laptop domains have the highest accuracy, scoring 92.35 % and 82.33 %, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Natural language processing systems

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：