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4th International Conference on Industrial automation, Robotics and Control engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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A Thermal-Aware Scheduling Algorithm for Reducing Thermal Risks in DAG-Based Applications in Cyber-Physical Systems 2nd

A Thermal-Aware Scheduling Algorithm for Reducing Thermal R...

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Ali, Irfan Shehzad, Muhammad Naeem Bashir, Qaisar Elahi, Haroon Awais, Muhammad Naeem Geman, Oana Liu, Pin Department of Computer System Engineering Institute of Business Administration Sukkar Sukkur65200 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan Intel Corporation AustinTX78746 United States Department of Computing Science Umeå University Umeå9036 Sweden Department of Computers Electronics and Automation Stefan Cel Mare University of Suceava Suceava720229 Romania School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9789819902712

Directed Acyclic Graph (DAG)-based scheduling applications are critical to resource allocation in the Cloud, Edge, and Fog layers of cyber-physical systems (CPS). However, thermal anomalies in DVFS-enabled homogeneous multiprocessor systems (HMSS) may be exploited by malicious applications posing risks to the availability of the underlying CPS. This can negatively affect the trustworthiness of CPS. This paper proposes an algorithm to address the thermal risks in DVFS-enabled HMSS for periodic DAG-based applications. It also improves the current list scheduling-based Depth-First and Breadth-First techniques without violating the timing constraints of the system. We test the algorithm using standard benchmarks and synthetic applications in a simulation setup. The results show a reduction in the temperature peaks by up to 30%, average temperature by up to 22%, temperature variations up to 3 times, and temperature spatial gradients by up to 4 times as compared to the conventional Depth-First Scheduling algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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Reseed Skipping of Linear Congruential Generator for Multi-level Image Steganography Security of Financial Data

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SN Computer Science 2023年第2期4卷 124页

作者： Danso, Juliana Mantebea Missah, Yaw Marfo Gyamfi, Enoch Opanin Dankwa, Stephen Kwabena, Sarpong Department of Computer Science Kwame Nkrumah University of Science and Technology (KNUST) Kumasi Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Upper East Region Navrongo Ghana School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This paper proposed a multi-level image steganography model that integrates with image and plaintext encryption schemes. The model starts by partitioning the cover-image pixel positions into two regions of respective purposes, one is reserved to hold the data, and through the implementation of the dynamic difference expansion principle, the other sloughs some parts to the reserved region in responsive to the size of the data to embed. Continuously reseeding and initializing the parameter values of a Linear Congruential Generator (LCG), generate a set of random pixel positions as members of the reserved region. Before data embedding, the region reserved to hold the data as well as the data bits to embed, are then encrypted using AES with respective keys. Encryption keys are randomly generated using a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG), the Fortuna algorithm. Multiplex security nature of using a randomization generator to reserve image region for encryption and encrypting plaintext data before performing steganography still ensures stability in computational complexities better than a logarithmic O(n log n). A visual inspection by evaluators who made 3000 choices to correctly differentiate stego images from its cover-images shows that only 47 choices were correct, 1884 choices were wrong, while 1069 choices were uncertain. An experiment shows an average Embedding Rates (ERs) of 0.00109 bpp and an average PSNR of 72. This concludes, as our integrated steganography and image encryption which implements the dynamic difference expansion principle, outputs encrypted stego mages that can deceive the naked human eyes into identifying them as rather cover-images, its embedding rate is faster (i.e. ER < 0.1), suitably good, and ensures better imperceptibility of cover-image alterations. When comparing these experimental results with existing methods, our proposed model shows significant competitiveness. We even show real-life scenarios on how this

关键词： AES encryption scheme Dynamic difference expansion principle Linear congruential generator Pseudo-random number generator Reserving room before encryption Steganography

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An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

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Automotive Innovation 2022年第2期5卷 134-145页

作者： Huanyang Huang Jinhao Meng Yuhong Wang Lei Cai Jichang Peng Ji Wu Qian Xiao Tianqi Liu Remus Teodorescu College of Electrical Engineering Sichuan UniversityChengduChina Faculty of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security TechnologyXi’an University of TechnologyXi’anChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjingChina Department of Automation University of Science and Technology of ChinaHefeiChina Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjinChina Department of Energy Technology Aalborg University9220 AalborgDenmark

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management *** paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized *** Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the *** for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high *** the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing *** of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

关键词： Li-ion battery State of health Gaussian process regression Kernel function Feature optimization

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Burak Dindar Can Berk Saner Dogukan Yigit Polat Hüseyin Kemâl Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore School of Computing National University of Singapore Singapore

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time.

关键词： Data privacy Computational modeling Europe Machine learning Computer architecture Generators Data models Standards Interoperability Load flow

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Adaptive Path Planning for Reaching an Uncertain Set of Targets in a Fruit Tree

Adaptive Path Planning for Reaching an Uncertain Set of Targ...

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International Workshop on Robot Motion and Control (RoMoCo)

作者： Werner Kroneman João Valente A. Frank Van Der Stappen Department of Engineering University College Roosevelt Middelburg The Netherlands Centre for Automation and Robotics (CAR) Spanish National Research Council (CSIC) Madrid Spain Department of Information and Computing Sciences Utrecht University The Netherlands

ISBN: (数字)9798350393965

ISBN: (纸本)9798350393972

When inspecting a set of target points (such as fruit) in a fruit tree, it is not realistic to assume that a perfectly-accurate set of such targets is available beforehand: compared to earlier inspections, new fruit may have appeared, and some fruit may be missing. In previous work, we introduced a shell-based method for rapid global path planning to position the end of a robotic arm close to numerous target points in an environment for inspection. In this work, we extend our method to handle partial knowledge of the set of targets, enabling the robot to adapt its planned path to newly-discovered or missing fruit on-the-fty. In the most extreme case, the robot starts with no knowledge of the workspace at all, except for what is immediately visible on startup. We show that, when not all targets are given beforehand, there is only a negligible increase in the length of the path traversed by the robot, the robot still effectively discovers a-priori unknown targets, at an overall about 35% increase in the CPU running time of the algorithm compared to the full-knowledge scenario.

关键词： Robot motion Conferences Inspection Manipulators Path planning Agriculture

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Designing Discrete Predictor-Based Controllers for Networked Control Systems with Time-varying Delays:Application to A Visual Servo Inverted Pendulum System

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1763-1777页

作者： Yang Deng Vincent Léchappé Changda Zhang Emmanuel Moulay Dajun Du Franck Plestan Qing-Long Han the Department of Automation Tsinghua UniversityBeijing 100084China the Université de Lyon INSA de LyonECLUniversitéClaude Bernard Lyon 1CNRSAmpèreF-69621VilleurbanneFranc the Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and AutomationShanghai UniversityShanghai 20072China the XLIM(UMR CNRS 7252) Université de Poitiers11 bd Marie et Pierre Curie86073 Poitiers Cedex 9France IEEE the Nantes Université école Centrale NantesCNRSLS2NUMR 6004F-44000 NantesFrance the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVictoria 3122Australia

A discrete predictor-based control method is developed for a class of linear time-invariant networked control systems with a sensor-to-controller time-varying delay and a controller-to-actuator uncertain constant delay,which can be potentially applied to vision-based control *** control scheme is composed of a state prediction and a discrete predictor-based *** state prediction is used to compensate for the effect of the sensor-to-controller delay,and the system can be stabilized by the discrete predictor-based ***,it is shown that the control scheme is also robust with respect to slight message ***,the main theoretical results are illustrated by simulation results and experimental results based on a networked visual servo inverted pendulum system.

关键词： Discrete predictor-based control inverted pendulum system networked control system time-varying delay vision-based control

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Simplified Control Strategies for Power Converters in a Grid-Connected Hybrid DC-AC Microgrid

Simplified Control Strategies for Power Converters in a Grid...

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Future Energy Solutions (FES), International Conference on

作者： Tiago Davi Curi Busarello Marcelo Godoy Simões Hannu Laaksonen Kimmo Kauhaniemi Department of Control Automation and Computing Engineering Federal University of Santa Catarina Blumenau Brazil School of Technology and Innovations University of Vaasa Vaasa Finland

Many typical converter control strategies used in microgrids are complex or require modifications in the power structure. The complexities are due coupling effects among controllers and time-delay caused in exchanging information in the system. This paper proposes simplified control strategies for power converters in a grid-connected hybrid DC-AC microgrid, aiming to ensure precise voltage and current regulation. The used controllers are proportional-resonant, integral-double-lead, proportional-integral and single integral controller. In this paper, the methodology for tuning each controller is carefully described in detail and recommendation for choosing their bandwidth is given. Then, the designed controllers are discretized through the Backward-Euler approximation. The power converters of the microgrid are an inverter, a Buck, a Boost and a bidirectional DC-DC. A case study is conducted through simulations with Matlab/Simulink. Results show that the controlled variables follow their reference signals with negligible steady-state error, proving the efficacy of the simplified control strategies and allowing an stable operation of the microgrid.

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An integrated monitoring system based on deep learning tools for industrial process ⁎

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IFAC-PapersOnLine 2024年第4期58卷 168-173页

作者： Adrián Rodríguez-Ramos Cristina Verde Orestes Llanes-Santiago Department of Automation and Computing Universidad Tecnológica de la Habana José Antonio Echeverría Cujae La Habana Cuba Engineering Institute Universidad Nacional Autónoma de México (UNAM) Distrito Federal Mexico

The Industry 4.0 initiative aims to obtain high levels of productivity and efficiency more competitive final products, and compliance with demanding regulations related to industrial safety and cybersecurity. To achieve these goals, industrial systems must be equipped with condition monitoring systems for the early detection and localization of cyberattacks and faults. This paper proposes a robust condition-monitoring strategy to detect and locate faults and cyberattacks in an integrate way using Deep Learning algorithms. The proposal was validated using a Tennessee Eastman (TE) process with excellent results. The proposed strategy was compared with other condition monitoring schemes showing a better performance.

关键词： Industry 4.0 Cybersecurity Fault diagnosis Deep learning

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