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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

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FA-MSVNet: multi-scale and multi-view feature aggregation methods for stereo 3D reconstruction

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Multimedia Tools and Applications 2024年 1-23页

作者： Li, Yao Zhou, Yong Zhao, Jiaqi Du, Wen-Liang Yao, Rui School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou221116 China Engineering Research Center of Mine Digitization of the Ministry of Education of the People’s Republic of China Jiangsu Xuzhou221116 China Disaster Intelligent Prevention and Control and Emergency Rescue Innovation Research Center Jiangsu Xuzhou221116 China

Stereo 3D reconstruction is pivotal in computer vision, requiring effective fusion of local detail and global semantic features. This paper introduces a novel multi-scale, multi-view cascading network that enhances both accuracy and completeness in 3D reconstruction. In this paper, we addresse the critical challenge of fusing local detail and global semantic features across multiple views. Firstly, the Multi-Scale Feature Aggregation Module (SFAM) is proposed which integrates features at various scales within each view to capture fine-grained details and improve point cloud resolution. Secondly, the Feature Enhancement Module (FEM) is designed to enhance features representation by aggregating spatial context, improving point cloud boundary integrity and reducing occlusion artifacts. Thirdly, the Multi-View Feature Aggregation Module (VFAM) utilizes rectified linear attention to merge global contextual information across views, ensuring more coherent and semantically accurate reconstructions. Extensive experiments on three benchmark datasets resulted in experimental improvements of 3.1%, 8.0%, and 3.1%, respectively, demonstrating that our approach achieves competitive results. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： 3D reconstruction

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Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

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IEEE Internet of Things Journal 2025年第11期12卷 17123-17136页

作者： Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data DIIS Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi’an710071 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In this article, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multiuser broadcast (broadcast communication (BC)) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multiuser semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multiuser interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multiuser interference. More importantly, SFDMA can protect the privacy of users’ semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta–Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. © 2014 IEEE.

关键词： Image reconstruction

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Optimizing Membrane Distillation Configuration for Superior Heat and Mass Transfer

Optimizing Membrane Distillation Configuration for Superior ...

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2024 International Conference on Emerging Trends in Smart Technologies, ICETST 2024

作者： Arslan, Muhammad Haizhen, Xian Nouman Khalid Khan, M. Ali, Kashif Baig, Zaid Bin Tariq North China Electric Power University School Of Energy Power And Mechanical Engineering Beijing China University Of Engineering & Technology School Of Mechanical Engineering Lahore Pakistan North China Electric Power University School Of Renewable And Clean Energy Beijing China North China Electric Power University Department Of Control And Computer Science Beijing China

ISBN: (纸本)9798331515850

Membrane distillation (MD) has emerged as a promising technique for desalination and water purification, offering potential advantages in terms of energy efficiency and operational flexibility. However, challenges related to heat and mass transfer inefficiencies limit its widespread adoption. This study explores the use of innovative materials and optimized configurations to enhance heat and mass transfer in direct contact membrane distillation (DCMD) systems. By integrating advanced hybrid membranes with nano-coating techniques and evaluating their performance through simulation-based methodologies, we demonstrate significant improvements in thermal conductivity, mass transfer efficiency, and fouling resistance. The results indicate that the newly developed hybrid membranes not only enhance heat and mass transfer rates but also maintain high separation efficiency, ultimately leading to improved overall system performance. These findings provide a foundation for the further development of MD technologies and suggest pathways for overcoming existing limitations, paving the way for more sustainable and efficient water treatment solutions. © 2024 IEEE.

关键词： Desalination

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Verification of Real Time Operating System Exception Management Based on SPARCv8

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Journal of computer science & Technology 2021年第6期36卷 1367-1387页

作者： Zhi Ma Lei Qiao Meng-Fei Yang Shao-Feng Li Jin-Kun Zhang Beijing Institute of Control Engineering Beijing 100190China China Academy of Space Technology Beijing 100094China School of Computer Science and Technology Xidian UniversityXi'an 710071China

Exception management,as the lowest level function module of the operating system,is responsible for making abrupt changes in the control flow to react to exception events in the *** correctness of the exception management is crucial to guaranteeing the safety of the whole ***,existing formal verification projects have not fully considered the issues of exceptions at the assembly *** for real-time operating systems,in addition to basic exception handling,there are nested exceptions and task switching by exceptions service *** our previous work,we used high-level abstraction to describe the basic elements of the exception management and verified correctness only at the requirement *** on earlier work,this paper proposes EMS(Exception Management SPARCv8),a practical Hoare-style program framework to verify the exception management based on SPARCv8(Scalable Processor Architecture Version 8)at the design *** framework describes the low-level details of the machine,such as registers and memory *** divides the execution logic of the exception management into six phases for comprehensive formal *** the executing scenario of the real-time operating system SpaceOS on the Beidou-3 satellite as an example,we use the EMS framework to verify the exception *** the formalization and proofs are implemented in the interactive theorem prover Coq.

关键词： operating system exception Scalable Processor Architecture Version 8(SPARCv8) Coq formal verification

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Fault-tolerant Hamiltonian cycle strategy for fast node fault diagnosis based on PMC in data center networks

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Mathematical Biosciences and engineering 2024年第2期21卷 2121-2136页

作者： Zhao, Zhipeng Hu, Zhenyu Zhao, Zhiyu Du, Xiaoyu Chen, Tianfei Sun, Lijun Minister of Education Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China College of Engineering Economics Henan Finance University Henan Zhengzhou450001 China Luoyang Railway Station of China Railway Zhengzhou Bureau Group Co. LTD Henan Luoyang450001 China College of Computer and Information Engineering Henan University Henan Kaifeng475000 China

System-level fault diagnosis model, namely, the PMC model, detects fault nodes only through the mutual testing of nodes in the system without physical equipment. In order to achieve server nodes fault diagnosis in large-scale data center networks (DCNs), the traditional algorithm based on the PMC model cannot meet the characteristics of high diagnosability, high accuracy and high efficiency due to its inability to ensure that the test nodes are fault-free. This paper first proposed a fault-tolerant Hamiltonian cycle fault diagnosis (FHFD) algorithm, which tests nodes in the order of the Hamiltonian cycle to ensure that the test nodes are faultless. In order to improve testing efficiency, a hierarchical diagnosis mechanism was further proposed, which recursively divides high scale structures into a large number of low scale structures based on the recursive structure characteristics of DCNs. Additionally, we proved that 2(n−2)nk−1 and (n−2)tn,k/tn,1 faulty nodes could be detected for BCuben,k and DCelln,k within a limited time for the proposed diagnosis strategy. Simulation experiments have also shown that our proposed strategy has improved the diagnosability and test efficiency dramatically. © 2024 the Author(s), licensee AIMS Press.

关键词： Failure analysis

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Stress Matrix-Based Formation control of Multi-Agent Systems with Discrete-Time Communication 12

Stress Matrix-Based Formation Control of Multi-Agent Systems...

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2024 12th International Conference on Systems and control, ICSC 2024

作者： Onuoha, Okechi Ubochi, Brendan Chijioke Kurawa, Suleiman Tang, Zezhi Wu, Changshun Dong, Yi University of Hertfordshire United Kingdom Federal University of Technology The Department of Electrical and Electronic Engineering Akure Nigeria University of Manchester The Department of Electrical Engineering United Kingdom University of Sheffield The Department of Automatic Control and Systems Engineering United Kingdom University Grenoble Alpes Grenoble38000 France The School of Electronics & Computer Science University of Southampton United Kingdom

ISBN: (纸本)9798331531812

This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws. © 2024 IEEE.

关键词： Continuous time systems

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AI and data-driven infrastructures for workflow automation and integration in advanced research and industrial applications

AI and data-driven infrastructures for workflow automation a...

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Forni, Tommaso Vozza, Mario Le Piane, Fabio Lorenzoni, Andrea Baldoni, Matteo Mercuri, Francesco DAIMON Lab CNR-ISMN via P. Gobetti 101 Bologna40129 Italy Department of Control and Computer Engineering Polytechnic University of Turin Corso Castelfidardo 34/d Turin10138 Italy Department of Computer Science and Engineering University of Bologna Viale del Risorgimento 2 Bologna40136 Italy

The use of AI and data-driven technologies and infrastructures for innovation and development of advanced research and industrial applications requires a strong degree of integration across a broad range of tools, disciplines and competences. In spite of a huge disruptive potential, the role of AI for research and development in the context of industrial applications is often hampered by the lack of consolidated and shared practices for transforming domain-specific processes for generating knowledge into added value. These issues are particularly striking for small-medium enterprises (SMEs), which must adopt clear and effective policies for implementing successful technology transfer paths for innovation. The activities of the DAIMON Lab of the CNR-ISMN focus on the design, development, implementation and application of integrated modelling, data-driven and AI methods and infrastructures for innovation in hi-tech applications. Our approach is based on the development of horizontal platforms, which can be applied to a broad range of vertical use-cases. Namely, we target the realisation of high-throughput workflows, related to specific domains and use cases, which are able to collect and process simulations and/or physical data and information. The implementation of an interoperable integration framework is a prerequisite for further application of AI tools for predictivity and automation. With a strong focus on the development of key enabling technologies (KETs), such as advanced materials, the approach pursued is extended to a broad range of application fields and scenarios of interest in industry, including electronic and ICT, advanced and sustainable manufacturing, energy, mobility. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Automation

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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