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Data-driven output regulation control for constrained linear systems

Science China(information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

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Intelligent Automation & Soft Computing 2023年第4期36卷 911-927页

作者： Kisaezehra Muhammad Umer Farooq Muhammad Aslam Bhutto Abdul Karim Kazi Department of Computer Science and Information Technology NED University of Engineering&TechnologyKarachi75270Pakistan Department of Civil Engineering NED University of Engineering&TechnologyKarachi75270Pakistan

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime *** in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every *** of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are *** most cases,this leads to serious brain injuries in people present at the site in general and the workers in *** is one of the leading causes of human fatalities at construction *** the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-deﬁned personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not ***,such monitoring through technological aids or automated tools is very much *** present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in *** can indicate whether a worker is wearing a hat or *** proposed system usesﬁve different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 *** results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

关键词： Object detection computer-vision personal protective equipment(PPE) deep learning industry revolution(IR)4.0 safety helmet detection

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A Blockchain based E-Commerce Platform with Supply Chain Management and Authenticated Resale 4

A Blockchain based E-Commerce Platform with Supply Chain Man...

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Shaikh, Akhlaq Ahmed Awan, Samia Masood Zahid, Mahnoor Fahad, Muhammad NED University of Engineering and Technology Department of Computer Science and Information Technology Karachi Pakistan

ISBN: (纸本)9798331504342

The E-commerce industry has seen significant growth over the past few years and many people prefer to purchase their goods online, but with this advancement and growth many fraudulent activities and scams have also gained a hike. Due to this the customers have shown significant concerns and worries while using any such platforms such as deception by vendors by selling fake products or their products getting counterfeit during the delivery process because the supply chain of their ordered product is often veiled in secrecy. This paper presents Commercio, a comprehensive solution that enables individuals with an understanding of how their products are transferred, managed, stored, and delivered to their doorsteps. Our application aims to tackle these challenges by creating an innovative e-commerce platform. This platform will serve as a dynamic marketplace and will connect sellers with customers who prioritize authenticity and dependability. Additionally, it will redefine supply chain management by offering unparalleled transparency and security. Moreover, incorporating blockchain-based payment systems will also improve transaction integrity hence delivering a trustworthy buying experience. © 2024 IEEE.

关键词： Marketplaces

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A charge system search based watermarking technique for gray-scale images using DWT–SVD

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Multimedia Tools and Applications 2024年第18期83卷 53743-53757页

作者： Gupta, Ritu Singh, Abhilasha Department of Information Technology Bhagwan Parshuram Institute of Technology GGSIPU Delhi India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Ghaziabad India

This paper put forward an embedded scheme to execute image watermarking in light of the discrete wavelet transform (DWT), singular value decomposition (SVD) and Charge System Search (CSS) method. In the proposed scheme, Charge System Search technique is applied to advance the multiple scaling factors (MSFs). To approve the proposed scheme, six different images are used to inspect its performance and its outcomes are likewise contrasted with the past work announced in writing. From the results, it very well may be seen that proposed watermarking technique is a vigorous and proficient methodology in the field of grayscale image watermarking technology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Discrete wavelet transforms

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Smart Industrial Machinery Tracking Using Extreme Gradient Boosting and Thermal Mapping 19

Smart Industrial Machinery Tracking Using Extreme Gradient B...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Barzegarzadeh, Marjan Naji, Hamidreza Afzali, Mahbobeh Graduate University of Advanced Technology Department of Computer Engineering and Information Technology Kerman Iran

ISBN: (纸本)9798331507565

This paper introduces a dual-phase methodology aimed at improving the tracking and management of industrial machinery through the application of cutting-edge technologies. The initial phase focuses on the seamless integration of tracking systems with the Extreme gradient boosting algorithm, which facilitates accurate location identification and supports real-time decision-making processes. This integration not only enhances the efficiency of industrial operations but also minimizes the likelihood of human error. The subsequent phase employs a thermal mapping technique to illustrate the movement patterns of machinery. By scrutinizing the thermal maps derived from vehicle location data, it becomes possible to optimize routing, bolster safety measures, and refine maintenance strategies, ultimately leading to heightened operational efficiency and cost reductions. © 2024 IEEE.

关键词： Cost reduction

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Conditional Generative Adversarial Network Approach for Autism Prediction

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computer Systems Science & engineering 2023年第1期44卷 741-755页

作者： K.Chola Raja S.Kannimuthu Department of Computer Science and Engineering Sri Eshwar College of EngineeringCoimbatore641202TamilnaduIndia Department of Information Technology Karpagam College of EngineeringCoimbatore641032TamilnaduIndia

Autism Spectrum Disorder(ASD)requires a precise diagnosis in order to be managed and ***-invasive neuroimaging methods are disease markers that can be used to help diagnose *** majority of available techniques in the literature use functional magnetic resonance imaging(fMRI)to detect ASD with a small dataset,resulting in high accuracy but low *** supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text,images,and videos,but their performance and robustness are restricted by the size of the accompanying training *** learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering *** takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural *** modelling,also known as Generative Adversarial Networks(GANs),is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original *** are an exciting and rapidly changingfield that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains,most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the *** this paper,we present a novel conditional generative adversarial network,or cGAN for short,which is a form of GAN that uses a generator model to conditionally generate *** terms of prediction and accuracy,they outperform the standard *** pro-posed model is 74%more accurate than the traditional methods and takes only around 10 min for training even with a huge dat

关键词： Autism classification attributes imaging adversarial fMRI functional graph neural networks

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Dysphonic Voice Pattern Based Parkinson Disease Detection Using Machine Learning Models 2

Dysphonic Voice Pattern Based Parkinson Disease Detection Us...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rhythm Khandelwal, Rajat Badlani, Prerna Saxena, Bhawna Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350371055

Parkinson's Disease stands as a widespread cerebral degenerative disorder impacting millions of people across the world. Timely and precise diagnosis of PD is of paramount importance, enabling early interventions and enhancing overall patient care. The presented work focuses on non-invasive detection of PD through voice analysis, targeting early intervention. Dysphonic voice patterns, indicative of PD progression, are analyzed using advanced signal processing and machine learning. Methodology involves collecting voice recordings from PD-affected individuals and healthy controls, with rigorous analysis of acoustic features. This approach minimizes patient discomfort and reduces healthcare costs, potentially revolutionizing PD care and enhancing patient quality of life. This study explored the efficacy of 4 machine learning models - Decision Trees, k-Nearest Neighbors, Support Vector Machine, and Naive Bayes- in Parkinson's Disease (PD) detection. k-Nearest Neighbors classifier demonstrates much better performance compared to the other three, achieving an accuracy of 96.61%. © 2024 IEEE.

关键词： Dysphonic Voice Pattern KNN Machine Learning Parkinson's Disease

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Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

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computers, Materials & Continua 2023年第3期74卷 5507-5525页

作者： Subhajit Chatterjee Yung-Cheol Byun Department of Computer Engineering Jeju National UniversityJeju-si63243Korea Department of Computer Engineering Major of Electronic EngineeringJeju National UniversityInstitute of Information Science&TechnologyJeju63243Korea

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient *** kickboards are gradually growing in popularity in tourist and education-centric *** the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is *** to its freefloating nature,the shared electric kickboard is a common and practical means of *** plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is *** demand accurately with small data is *** data is necessary for training machine learning algorithms for effective *** generation is a method for expanding the amount of data that will be further accessible for *** this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original *** proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction *** modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster ***,we applied a regression-based blending ensemble technique that can help us to improve performance of demand *** used various evaluation criteria and visual representations to compare our proposed model’s *** data generated by our suggested GAN model is also *** TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.

关键词： Machine learning generative adversarial networks electric vehicle time-series TGAN WGAN-GP blend model demand prediction regression

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Enabling technology Integrated Learning for Autistic Children Using Augmented Reality Based Cognitive Rehabilitation

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SN computer Science 2024年第1期5卷 151页

作者： Abdullah, A. Sheik Karthikeyan, J. Gomathi, V. Parkavi, R. Rajarajeswari, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering National Engineering College Kovilpatti India Department of Information Technology Thiagarajar College of Engineering Madurai India

Augmented reality (AR)-based rehabilitation is an excellent teaching tool (ICT tool) for simulating daily activities for autistic children. Children with autism may become more mentally capable with this type of AR-based rehabilitation. It is intended to observe the children's performance in terms of concentration, attention, and identification. The observation has been done through placards as a target image to display the 3D objects on a mobile phone or tablet. In this project, observations are made for 21 autism children in the age group of 7–14, out of whom 17 are boys and 5 are girls. Those 21 children are given practice identifying 15 different objects in an augmented reality environment. Their performance was initially evaluated using conventional instructional techniques. The majority of the kids were having more difficulty identifying things during that observation. Then, with an Augmented Reality environment, the identical observation has been made once more. Using a mobile device or tablet, the 3D objects from the provided placard photos are produced in an augmented reality environment with animation and voice in the languages of English and Tamil. Children with autism are able to recognize and also grasp the behaviors of those objects while viewing them in 3D. Their efforts are measured using a two-point scale (0, 1, 2). The pre-assessment and post-assessment reports for the above observations are tabulated. All the observations are made in the presence of the special education teacher (therapist). However, the children observed in this project fall into three different categories: mild, moderate, and severe. In the Mild category, statistical significance is evident with p values of 0.002 in pre-assessment and 0.014 in post-assessment. Likewise, in the Moderate category, where p values are 0.023 in pre-assessment and 0.033 in post-assessment, significance is observed, as all p values fall below the chosen significance level of 0.05. This leads to rejecti

关键词： Augmented reality (AR) Autism ICT tools Observation Rehabilitation Teaching methodology

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