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Multi‐feature fusion and memory‐based mobile robot target tracking system

IET Cyber-Systems and Robotics 2024年第3期6卷 62-73页

作者： Hanqing Sun Shijie Zhang Qingle Quan College of Information Engineering Henan University of Animal Husbandry and EconomyZhengzhouHenanChina College of Information Technology and Computer Science University of the CordillerasBaguioPhilippines College of Electrical Engineering Henan University of TechnologyZhengzhouHenanChina

In crowded settings,mobile robots face challenges like target disappearance and occlusion,impacting tracking *** existing optimisations,tracking in complex environments remains *** address this issue,the authors propose a tailored visual navigation tracking system for crowded *** target disappearance,an autonomous navigation strategy based on target coordinates,utilising a path memory bank for intelligent search and re‐tracking is *** significantly enhances tracking *** handle target occlusion,the system relies on appearance features extracted by a target detection network and a feature memory bank for enhanced *** control technology ensures robust target tracking by fully utilising appearance information and motion characteristics,even in occluded *** testing on the OTB100 dataset validates the system's effectiveness in addressing target tracking challenges in diverse crowded environments,affirming algorithm robustness.

关键词： autonomous navigation Kalman filtering memory bank mobile robots proportional‐integral‐derivative

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Fusion of color and hallucinated depth features for enhanced multimodal deep learning-based damage segmentation

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Earthquake engineering and engineering Vibration 2023年第1期22卷 55-68页

作者： Tarutal Ghosh Mondal Mohammad Reza Jahanshahi Civil Architectural and Environmental EngineeringMissouri University of Science and TechnologyRollaMOUSA Lyles School of Civil Engineering Purdue UniversityWest LafayetteINUSA School of Electrical and Computer Engineering Purdue UniversityWest LafayetteINUSA

Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation ***,alongside the advantages,depth-sensing also presents many practical *** instance,the depth sensors impose an additional payload burden on the robotic inspection platforms limiting the operation time and increasing the inspection ***,some lidar-based depth sensors have poor outdoor performance due to sunlight contamination during the *** this context,this study investigates the feasibility of abolishing depth-sensing at test time without compromising the segmentation *** autonomous damage segmentation framework is developed,based on recent advancements in vision-based multi-modal sensing such as modality hallucination(MH)and monocular depth estimation(MDE),which require depth data only during the model *** the time of deployment,depth data becomes expendable as it can be simulated from the corresponding RGB *** makes it possible to reap the benefits of depth fusion without any depth perception per *** study explored two different depth encoding techniques and three different fusion strategies in addition to a baseline RGB-based *** proposed approach is validated on computer-generated RGB-D data of reinforced concrete buildings subjected to seismic *** was observed that the surrogate techniques can increase the segmentation IoU by up to 20.1%with a negligible increase in the computation ***,this study is believed to make a positive contribution to enhancing the resilience of critical civil infrastructure.

关键词： multimodal data fusion depth sensing vision-based inspection UAV-assisted inspection damage segmentation post-disaster reconnaissance modality hallucination monocular depth estimation

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Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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computer Systems science & engineering 2023年第7期46卷 255-271页

作者： T.Satyanarayana Murthy P.Udayakumar Fayadh Alenezi E.Laxmi Lydia Mohamad Khairi Ishak Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science and Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)*** security issues directly result from inappropriate security management protocols and their implementation by IoT gadget ***-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)*** this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their *** recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying *** current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT *** presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT *** accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square *** detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this ***,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition *** proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct *** comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

关键词： False data injection attack security internet of things deep learning coot optimization algorithm

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Task Offloading and Approximate Computing in Solar Powered IoT Networks

IEEE Networking Letters

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IEEE Networking Letters 2024年第1期6卷 26-30页

作者： Zhan, Junfei Wu, Jiayi He, Tengjiao Chin, Kwan-Wu Jinan University College of Information Science and Technology Guangzhou510632 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

This letter considers approximate computing and task offloading in a solar powered Internet of Things (IoT) network. Specifically, it addresses the novel problem of minimizing the energy consumption of IoT devices by either offloading their tasks or executing these tasks in approximate mode. To this end, this letter outlines a novel mixed integer linear program (MILP) that computes the minimum total energy required to execute tasks. It optimizes four key factors: (i) task offloading decision of devices, (ii) use of approximate computing by devices, (iii) channel allocation, and (iv) virtual machine (VM) assignment. Further, it outlines a novel solution that determines these factors using channel gain and energy arrival estimates obtained from digital twins (DTs). The results show that our DT-based solution uses at most 1.62x more energy than MILP. © 2019 IEEE.

关键词： Energy harvesting

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Design,simulation,and testing of a tunable MEMS multi-threshold inertial switch

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Microsystems & Nanoengineering 2024年第2期10卷 37-46页

作者： Qiu Xu Rodrigo T.Rocha Yousef Algoos Eric Feron Mohammad I.Younis Faculty of Electronic Information Engineering Huaiyin Institute of TechnologyHuai’an223003PR China Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Piezoelectric Microsystem Technologies Silicon Austria LabsVillach9524Austria Physical Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal23955Saudi Arabia Mechanical Engineering Department State University of New YorkBinghamtonNY13902USA

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

关键词： tunable acceleration testing

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Learning label-specific features for decomposition-based multi-class classification

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Frontiers of computer science 2023年第6期17卷 101-110页

作者： Bin-Bin JIA Jun-Ying LIU Jun-Yi HANG Min-Ling ZHANG College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 210096China

Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.

关键词： machine learning multi-class classification error-correcting output codes label-specific features

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Logistic Regression based Sentiment Analysis System: Rectify

Logistic Regression based Sentiment Analysis System: Rectify

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Singh, Harsh Pratap Singh, Nagendra Mishra, Anuprita Sen, Santosh Kumar Swarnkar, Mamta Pandey, Deepak Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Computer Science and Engineering Indore India Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar India IES College of Technology Department of Electrical and Electronics Engineering Bhopal462044 India IES University Department of Electrical and Electronics Engineering Bhopal426044 India Corporate institute of Science and Technology Department of Computer Science and Engineering Bhopal505001 India MANIT Department of Computer Science and Engineering Bhopal India TIT Department of EEE Bhopal India

ISBN: (纸本)9798350374100

The detection of various reactions using computer vision, machine learning, and artificial intelligence is a rapidly growing field of research. In this paper, we present a sentiment analysis model based on the Python, NLTK (natural language toolkit) libraries, and machine learning algorithms that can detect multiple reactions from the public on a single platform. The proposed Sentiment Analysis System deals with the very usual problem faced by different companies, manufacturers, and sellers about knowing the customer review for their product. The model classifies the public reviews and the normal text as positive, negative, or neutral. This proposed system is categorised into two parts, one being a field-specific sentiment analysis and the other being a generalised system that can judge any particular word or sentence to be either positive, negative, or neutral. The proposed model is also capable of representing a huge dataset (input as a csv file) in the form of a graph, which can be easily understood by the desired person, and the graphical representation is possible with the Matplotlib library of Python. The model's performance is evaluated using several metrics, such as precision, recall, and others. The model's accuracy and efficiency make it a promising tool for sentiment analysis, which can be used by anyone in any field. In conclusion, the system can work for any specific field provided the data set, and the generalized way can help out with random sentences, and the model's potential can be boosted with some further research. © 2024 IEEE.

关键词： Python

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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