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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

IEEE Transactions on Automatic Control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Energy Management Strategy with a Hybrid Renewable Energy System for a Standalone House 16

Energy Management Strategy with a Hybrid Renewable Energy Sy...

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16th International Conference on applied and Theoretical Electricity, ICATE 2024

作者： Mustata, Laurentiu-Alex Sorea, Gheorghe-Dan Helerea, Elena Transilvania University Electrical Engineering and Computer Science Faculty Electrical Engineering and Applied Physics Department Brasov Romania

ISBN: (纸本)9798350388107

In the case of standalone houses, ensuring a continuous and regulated power supply from renewable sources is crucial. To address their unpredictable nature, an environmentally conscious hybrid renewable energy system combining photovoltaic panels, a battery, and a Diesel generator is considered. A software application simulates the power flow in a standalone house with common AC loads, analysing summer and winter scenarios over 24 hours. The strategy employed provides an optimal energy management by using the Diesel generator only during peak demand when PV is available and to cover loads and maintain battery life when PV is not in operation. The simulation results provide valuable insights for designing such hybrid energy systems and for enhancing system responsiveness to dynamic energy demands. © 2024 IEEE.

关键词： Hybrid power

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IOT-Enabled Smart Crop Recommendation System Using Machine Learning 5

IOT-Enabled Smart Crop Recommendation System Using Machine L...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Saranya, S.S. Dharani, A.S. Kavitha, M.N. Selya Dharsnee, M. Pragatheeswari, E. Selya Varsnee, M. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Technology - Ug Perundurai India Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Agriculture, a foundation of India's economy, faces challenges in maximizing yield and resource efficiency. This research presents a machine learning-based system to recommend optimal crops and fertilizers based on soil quality and geographic location. By providing data-driven insights, the proposed system aims to enhance crop productivity, promote sustainable farming practices, and empower farmers to make informed decisions. © 2024 IEEE.

关键词： Smart agriculture

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Analysis of Bio Inspired Based Hybrid Learning Model for Software Defect Prediction

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SN computer science 2024年第7期5卷 825页

作者： Shankar, Sahana P. Chaudhari, Shilpa Shashikant Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology (Affiliated to Visvesvaraya Technological University Belgaum) Bengaluru India Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

The software’s quality can be ensured through software testing, which is one of the critical methods. However, it was found that testing consumes more than half of the project's total expenses. Effective and efficient software testing utilizes minimal software resources to find as many flaws in the software system as possible. This paper presents research on software defect prediction using hybrid machine learning algorithm. The algorithm incorporates the benefits of supervised as well as unsupervised learning methodology that improves the accuracy of defect prediction. The study uses publicly available datasets from the open source PROMISE repository provided by NASA MDP to perform machine learning analysis and maximize accuracy. In particular, the study suggested a hybrid learning model and conducted a comparative analysis among Naïve Bayes, Multilayer Perceptron, Random Forest, CNN, Adaboost, and KNN on four different datasets namely CM1, KC1, KC2 and PC1. With respect to F1-score, accuracy, precision, and recall, the results show that the hybrid method performed better than the other methods. The study achieved an accuracy of 93.67% (BAT + CNN) on PC1 dataset, 92% on (Bat + CNN) for CM1 and KC1 respectively. This indicates that the hybrid learning technique of (BAT + CNN) is more potent than other methodologies in prediction of defects. The research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. One useful approach for predicting software defects is the hybrid machine learning method, which can lead to improved software quality and lower maintenance costs. This aids the software testing phase by predicting the faulty modules with better accuracy thereby reducing the testing effort. The main objectives of the research include applying different machine learning algorithms and methods to the given datasets and determine the most effective ones. © The Author(s), under exclusive licence to

关键词： Bat CNN PROMISE Sparrow Search Squirrel Search Testing

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Design and Development of Digital Asset Market Platform 9

Design and Development of Digital Asset Market Platform

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Ashritha, H.A. Hari Krishna, S.M. Siddiqa, Ayesha Lakshmi, Lakshmi Khan, Kayani Nawaz Ulla M S Ramaiah University of Applied Sciences Department of Computer Science And Engineering Bangalore India

ISBN: (纸本)9798350394474

In an era characterized by the increasing digitization of assets, the demand for secure, efficient, and user-friendly platforms to facilitate the exchange of digital assets has surged. This project focuses on addressing this need through the creation of a robust marketplace that accommodates a diverse range of digital assets, from cryptocurrencies to digital art and intellectual *** present an innovative avenue for creators, musicians, and artists to showcase their work and monetize their talent. Our objective is to provide users with a decentralized platform, enabling them to promote and acquire these NFTs directly from creators, free from concerns about interference by central authorities in their transactions. © 2024 IEEE.

关键词： Blockchain

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Efficiently Optimizing Crane Scheduling: A Rule-Based Approach for Dynamic and Uncertain Environments 26

Efficiently Optimizing Crane Scheduling: A Rule-Based Approa...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Pham, Philipp Bao-Phuc Lee, Zu Hyun Logofatu, Doina Frankfurt University of Applied Sciences Department of Computer Science and Engineering Frankfurt am Main Germany

ISBN: (纸本)9798331532833

In logistics and industrial operations, optimizing crane scheduling is crucial for efficiency and resource allocation. Given the dynamic nature of crane scheduling environments, influenced by factors such as fluctuating order volumes and varying crane availability, robust scheduling solutions are imperative. This paper introduces a rule-based approach designed to optimize crane scheduling within a simulated dynamic and uncertain environment, focusing on real-time responsiveness, efficient task allocation, and continuous operational efficiency. Our approach was developed while participating in the 'Crane Scheduling' track of the 'Dynamic Stacking Optimization in Uncertain Environments' (DynStack) competition, held with the Genetic and Evolutionary Computation Conference (GECCO) 2024. Our experimental results underscore the efficacy of our approach in achieving zero delivery errors, maximizing the handling of blocks, and minimizing parking durations, hereby promoting cost-effectiveness and environmental sustainability in crane operations. As of July 17, 2024, our approach has secured first place in the 'Crane Scheduling' competition. © 2024 IEEE.

关键词： Resource allocation

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AVirtual Cloud Storage Architecture for Enhanced Data Security

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computer Systems science & engineering 2023年第2期44卷 1735-1747页

作者： M.Antony Joans Kumar C.Christopher Columbus E.Ben George T.Ajith Bosco Raj Department of Information Technology St.Xavier’s Catholic College of EngineeringNagercoil629003Tamil NaduIndia Department of Computer Science Engineering PSN College of Engineering and TechnologyTirunelveli627152India Department of Computer Science Engineering University of Technology and Applied SciencesMuscat112Oman Department of Electronics and Communications Engineering PSN College of Engineering and TechnologyTirunelveli627152India

The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and *** proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and conﬁdentiality,as well as *** provide conﬁdentiality and availability,theﬁle to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct *** the encoded chunks ensured theﬁle integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk ***ﬁle could be retrieved by performing the opera-tions in *** the regenerating code,the model could regenerate the missing and corrupted chunks from the *** proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage *** results analysis show that the proposed model has been tested with storage space and response time for storage and *** VCSA model consumes 1.5x(150%)storage *** was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisﬁes the CIA *** response time VCSA model was tested with different sizedﬁles starting from 2 to 16 *** response time for storing and retrieving a 2 MBﬁle is 4.96 and 3.77 s respectively,and for a 16 MBﬁle,the response times are 11.06 s for storage and 5.6 s for retrieval.

关键词： Erasure code regenerating code cryptography virtual cloud storage architecture circular shift chunk allocation algorithm

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Level Generation Using ChatGPT: A Case Study on the science Bird Game 26

Level Generation Using ChatGPT: A Case Study on the Science ...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Garbaruk, Julia Ray, Jiban Kumar Logofatu, Doina Frankfurt University of Applied Sciences Department of Computer Science and Engineering Frankfurt am Main Germany

ISBN: (纸本)9798331532833

The increasing complexity of video games and development costs have necessitated innovative approaches to content creation. Procedural Content Generation (PCG) and AI tools like ChatGPT offer promising solutions. This paper explores the application of ChatGPT in Procedural Level Generation (PLG) for the game 'science Birds'. Through a combination of theoretical exploration and practical experiments, we demonstrate how ChatGPT can be leveraged to automate level design, enhancing both efficiency and creativity in game development. © 2024 IEEE.

关键词： Game design

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Semantic Data Management for Managing Heterogeneous Data Sources in Chemistry 4.0

Semantic Data Management for Managing Heterogeneous Data Sou...

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Doctoral Consortium at International Semantic Web Conference 2024, ISWC-DC 2024

作者： Hoseini, Sayed Department of Electr. Engineering and Computer Science Niederrhein University of Applied Sciences Krefeld Germany

Managing large volumes of data poses significant challenges due to the variety of formats, distribution across departments, and different governance structures within organizations. In research and industry environments, this complexity is compounded by the need for streamlined data handling processes to support automated workflows and machine learning (ML) applications. Integrating implicit contextual knowledge alongside data artifacts is critical, especially for non-expert users accessing the data. Data lakes provide a scalable solution by aggregating raw data from disparate sources with minimal upfront integration costs. However, without proper integration, data analysis and interpretation is hindered, rendering the data lake effectively inoperable. This PhD research addresses these challenges by applying semantic data management (SDM) techniques inside a semantic data lake. While initial milestones have been achieved through a systematic literature review and a concrete implementation, further efforts lie ahead. First, the emergence of large language models offers numerous opportunities for automating previously manual processes. Leveraging these models can significantly improve the efficiency of common SDM tasks. Second, extending the application of SDM techniques to data analytics can facilitate the integration of diverse data sources into ML pipelines. Ultimately, we aim to bridge the gap between Big Data and Semantic Web technologies, anticipating the development of advanced semantic data lake solutions in the foreseeable future. © 2024 Copyright for this paper by its authors.

关键词： Data assimilation

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