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Fully Distributed Nash Equilibrium Seeking for High-Order Players With Actuator Limitations

IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1434-1444页

作者： Maojiao Ye Qing-Long Han Lei Ding Shengyuan Xu the School of Automation Nanjing University of Science and TechnologyNanjing 210094China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper explores the problem of distributed Nash equilibrium seeking in games, where players have limited knowledge on other players' actions. In particular, the involved players are considered to be high-order integrators with their control inputs constrained within a pre-specified region. A linear transformation for players' dynamics is firstly utilized to facilitate the design of bounded control inputs incorporating multiple saturation functions. By introducing consensus protocols with adaptive and time-varying gains, the unknown actions for players are distributively estimated. Then, a fully distributed Nash equilibrium seeking strategy is exploited, showcasing its remarkable properties: (1) ensuring the boundedness of control inputs;(2) avoiding any global information/parameters;and (3) allowing the graph to be directed. Based on Lyapunov stability analysis, it is theoretically proved that the proposed distributed control strategy can lead all the players' actions to the Nash equilibrium. Finally, an illustrative example is given to validate effectiveness of the proposed method.

关键词： Actuator limitation directed networks games Nash equilibrium

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Stationary wavelet transform and SpinalNet trained light spectrum Tasmanian devil optimization enabled DR detection using fundus images

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Multimedia Tools and Applications 2025年第4期84卷 1597-1626页

作者： Tamboli, Mubin Shoukat Dhumane, Amol Prasad, Rajesh Prasad, Jayashree Rajesh Ranjan, Nihar M. Computer Engineering Pimpri Chinchwad College of Engineering Pradhikaran Maharashtra Pune411044 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune India Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Diabetes is a common disease that causes complications in the eyes known as Diabetic Retinopathy (DR). It aids in discovering the DR and tends to be the main cause behind people’s blindness amidst the previous decades. For prevent the complications of DR and to control the level of blood Glucose, the timely and crucial diagnosis is essential. A model is provided for detecting the DR. Here, Light Spectrum Tasmanian Devil Optimization (LSTDO) is utilized with SpinalNet for DR detection. The method undergoes an adaptive median filter and Region of interest mining process to generate a pre-processed image. The Sparse Fuzzy C-Means (FCM) is adapted for segmenting the optic disks followed by feature extraction. Then, the Hough line transformation with Tyler Coye algorithm (HLTCA) is used for blood vessel segmentation. Concurrently, the Stationary Wavelet Transform (SWT) is adapted to input image to overwhelm the deficiency of translation-invariance issue and then the feature mining is done. The feature vector is generated by unifying the outputs of the three-feature extraction phases and is employed to detect DR. The accuracy, sensitivity, and specificity obtained by the LSTDO-based SpinalNet are 91.6%, 89.9% and 92.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Optimally arranged TiO_(2)@MoS_(2) heterostructures with effectively induced built-in electric field for high-performance lithium-sulfur batteries

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Journal of Energy Chemistry 2023年第8期83卷 496-508,I0012页

作者： Jeongyoub Lee Changhoon Choi Jung Been Park Seungho Yu Jinho Ha Hyungsoo Lee Gyumin Jang Young Sun Park Juwon Yun Hayoung Im Subin Moon Soobin Lee Jung-Il Choi Dong-Wan Kim Jooho Moon Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Republic of Korea School of Civil Environmental and Architectural EngineeringKorea UniversitySeoul 02841Republic of Korea Energy Storage Research Center Korea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea School of Mathematics and Computing(Computational Science and Engineering) Yonsei UniversitySeoul 03722Republic of Korea

To overcome the serious technological issues affecting lithium-sulfur(Li-S) batteries,such as sluggish sulfur redox kinetics and the detrimental shuttle effect,heterostructure engineering has been investigated as a strategy to effectively capture soluble lithium polysulfide intermediates and promote their conversion reaction by integrating highly polar metal oxides with catalytically active metals ***,to fully exploit the outstanding properties of heterostructure-based composites,their detailed structure and interfacial contacts should be designed ***,optimally arranged TiO_(2)and MoS_(2)-based heterostructures(TiO_(2)@MoS_(2)) are fabricated on carbon cloth as a multifunctional interlayer to efficiently trap polysulfide intermediates and accelerate their redox *** to the synergistic effects between TiO_(2)and MoS_(2)and the uniform heterointerface distribution that induces the ideally oriented built-in electric field,Li-S batteries with TiO_(2)@MoS_(2)interlayers exhibit high rate capability(601 mA h g^(-1)at 5 C),good cycling stability(capacity-fade rate of 0.067% per cycle over 500 cycles at2 C),and satisfactory areal capacity(5.2 mA h cm^(-2)) under an increased sulfur loading of 5.2 mg cm^(-2).Moreover,by comparing with a MoS_(2)@TiO_(2)interlayer composed of reversely arranged heterostructures,the effect of the built-in electric field’s direction on the electrocatalytic reactions of polysulfide intermediates is thoroughly investigated for the first *** superior electrocatalytic activities of the rationally arranged TiO_(2)@MoS_(2)interlayer demonstrate the importance of optimizing the built-in electric field of heterostructures for producing high-performance Li-S batteries.

关键词： Lithium-sulfur batteries Shuttle effect TiO_(2)-MoS_(2)heterostructure engineering Built-in electric field Multifunctional interlayers

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Analysis and Prediction of Lettuce Crop Yield in Aeroponic Vertical Farming using Logistic Regression Method 2

Analysis and Prediction of Lettuce Crop Yield in Aeroponic V...

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2nd International Conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Gowtham, R. Jebakumar, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665491990

Human population growth strains food supplies, resulting in more than one billion of the world's 6.5 billion people facing hunger, which is the leading cause of death. In consequence, increasing crop production through traditional farming methods will not address the global food scarcity problem. As a precision agriculture approach, aeroponics, a soilless agriculture technique, can assist in cultivating in-demand food crops, which will help serve the people. Because aeroponics is an indoor farming technology, factors such as pH, EC, light, temperature, PPM, and turbidity will affect crop growth. Hence, monitoring an aeroponic crop growth system efficiently is crucial for achieving a high crop yield and reducing the need for food crops. Even though, many crop monitoring systems use IoT and machine learning algorithms, their prediction accuracy is not up to the level and if they are deployed in the real world, they may not meet customer expectations. In order to efficiently monitor crops, more advanced models are needed. However, understanding the large volume of data can be very tedious. Data visualization techniques can help make the distribution of the dataset easier to understand. Machine learning methods and packages can be used for environmental monitoring and visualization of the data where, the collection of crop growth dataset is carried out using various IoT sensors deployed in the growth environment. This study provides a comprehensive explanation of the data visualization techniques used to study the lettuce crop dataset and predict lettuce yield using the logistic regression machine learning method which is been implemented using Python coding language. © 2023 IEEE.

关键词： Lettuce

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Autism Spectrum Disorder Detection Via Eye Gaze Analysis 2

Autism Spectrum Disorder Detection Via Eye Gaze Analysis

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Das, Sujay Chotia, Akshat Maheswari, K. M. Uma Sadiq, A.K. SRM Institute Of Science & Technology Department Of Computer Science & Engineering Chennai India University of Technology and Applied Sciences College of Computing and Information Science Suhar campus Oman

ISBN: (纸本)9798350365269

Autism is a complex neuro - developmental condition characterized by challenges in social interaction and communication. Early diagnosis and intervention are crucial for effective support. This paper presents a novel approach for autism detection leveraging Convolutional Neural Networks (CNNs) to analyze eye gaze patterns. The proposed method aims to provide an objective and efficient tool for the early screening of Autism. We begin by collecting eye gaze data from a diverse sample of individuals, including both Autism -diagnosed and non Autism individuals. The dataset is preprocessed to extract relevant features from the eye gaze sequences, capturing subtle but significant differences in gaze patterns between the two groups. A CNN architecture is designed and trained on this preprocessed dataset. The network is optimized to automatically learn discriminative features that distinguish between Autism and non Autism gaze patterns. Transfer learning techniques mobilenetv2 are employed to further enhance model performance. The trained CNN demonstrates promising results in accurately classifying individuals into Autism and neurotypical categories. Comprehensive evaluation metrics, including accuracy and loss, attest to the model's effectiveness. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Duplicating the Human Mind Suspicions in AI in Various Divisions

Analysis of Duplicating the Human Mind Suspicions in AI in V...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Santhoshkumar, S.P. Karthika, R. Vijayakumar, N. Gajalakshmi, S. Jayanthi, K. Hariharasudhan, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India School of Computing Department of Information Technology Rathinam Technical Campus Coimbatore India Pollachi Institute of Engineering and Technology Pollachi Coimbatore India

ISBN: (纸本)9798331509828

Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields. © 2025 IEEE.

关键词： Expert systems

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Optimizing Household Energy Consumption: A Web-Based Tool for Real-Time Carbon Footprint Calculation and Sustainable Benchmarking 1

Optimizing Household Energy Consumption: A Web-Based Tool fo...

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1st International Conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Raja, Harsh Kishore, Jayaditya Kalaivani, J. Angayarkanni, V. Rama, P. Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Computing Technologies Kattankulathur Chengalpattu District Tamil Nadu 603203 India

ISBN: (纸本)9798331518578

This is an innovative project from an energy-saving perspective. This project ensures that users become in control of their energy consumption and able to take an actively reducing their carbon footprints. It provides users with an interface to input details about household appliances and usage patterns, through which a correct calculation of the carbon footprint could be done. Compared against standard norms, it would help the users identify certain areas where improvement is possible and adopt more sustainable use of energy. Apart from personalized energy assessments, it has now become the central source that connects users to local schemes and initiatives on sustainable energy, including information on resources and programs into waste energy and carbon emission reduction. This integration, therefore, promotes more engagement and involvement within the community as users are nudged to join in the efforts that are being made for the goal of energy conservation. The platform educates its users about energy consumption and aids them in making green choices in data;however, with a holistic approach in carbon footprint management and education pertaining to sustainable long-term behavior, the process takes place through integrated accurate energy tracking, fine recommendations, and community-driven action. © 2025 IEEE.

关键词： Carbon sequestration

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Ocular Artefact Removal from Electroencephalogram Signals: A Review 4

Ocular Artefact Removal from Electroencephalogram Signals: A...

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4th IEEE International Conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Babeetha, S. Sridhar, S.S. College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamilnadu India

ISBN: (纸本)9798350306118

Electroencephalogram (EEG) is widely utilized since it provides information on brain activity without causing any harm to the subject. EEG's great sensitivity makes it vulnerable to artefacts from both the environment and the brain. Blinks and eye movement, head movement, pulse, and so on may all generate artefacts in EEG recordings, which is the primary source of poor signal quality. Artefacts complicate the analysis of EEG data and may lead to erroneous conclusions about brain activity because they overlap with the signal collected from the brain. The creation of algorithms to detect and eliminate artefacts from EEG signals is crucial for obtaining such precise and trustworthy signal information. In this study, we take a look at the state of the art in removing artefacts caused by blinks and eye movements from EEG data. However, conventional artefact removal methods cannot eradicate all noise and will result in data loss due to the indiscriminate elimination of contaminated parts. The research demonstrates that combining conventional with other approaches is widely utilized because it may enhance artefact removal. The most up-To-date method for eliminating artefacts is an automated one that uses machine learning and deep neural networks. © 2023 IEEE.

关键词： Electroencephalography

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Enabling an On-demand Access to Community Sentiments using LSTM RNNs Web Service Architecture 3

Enabling an On-demand Access to Community Sentiments using L...

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3rd International Conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Prabakaran, N. Anbarasi, A. Deepa, N. Pandiaraja, P. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Department of computing Technologies School of Computing SRM Institute of Science and Technology Chennai Kattankulathur603203 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattankulathur603203 India Department of Computer Science and Engineering M.Kumarasamy college of Engineering Karur639113 India

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users' digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results. © 2023 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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