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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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An Assessment of Multilevel Converters Submodules EMI Emissions Considering Three Configurations Including Parasitic Parameters

An Assessment of Multilevel Converters Submodules EMI Emissi...

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IEEE International Symposium on Electromagnetic Compatibility - EMC Europe

作者： Amr Madi Waseem Elsayed Ahmed Hebala Michal Przybylski Piotr Lezynski Robert Smolenski Institute of Automatic Control Electronics and Electrical Engineering Doctoral School of Exact and Technical Sciences University of Zielona Gora Zielona Gora Poland Electrical Engineering Mathematics & Computer Science (EEMCS) University of Twente Enschede Netherlands Electrical and Control Engineering College of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Institute of Automatic Control Electronics and Electrical Engineering University of Zielona Gora Zielona Gora Poland

With the expanding use of modular multilevel converters in power installations it became more important to lower the total conducted emissions. One of the most important key factors in controlling electromagnetic interference emissions is the type of submodules used and the utilized switches. This paper compares three well-known submodules which are half bridge, full bridge and double-clamped half bridge effect on the electromagnetic interference emissions. For making the model more accurate the parasitic components were included in all the circuits Additionally, the same configurations are carried out and compared under two switches which are insulated gate bipolar transistors and metal oxide semiconductor field effect transistors operating at two different switching frequencies. The results were obtained by a simulation in a Matlab/Simulink environment.

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Dynamic Adjustment of Packet Based on Fair Bandwidth Allocation in IEEE 802.11 Wireless Networks

Dynamic Adjustment of Packet Based on Fair Bandwidth Allocat...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xiong Zou Jinfeng Gao Yu Zhang Huyong Li Fang Liu Haitao Wu Shejie Lu Siping Hu College of Computer and Artificial Intelligence International Joint Laboratory of Behavior Optimization Control for Smart Robots Huanghuai University Zhumadian China Institute of Engineering and Technology Hubei University of Science and Technology Xianning China

In view of the imbalance of upstream and downstream flows in the access of local manufacturing resources, this paper proposes a dynamic adjustment algorithm based on bandwidth fair allocation (DAPFBA, Dynamic Adjustment of Packet based on Fair Bandwidth Allocation), which dynamically adjusts the packet size of nodes according to the number of downflows and realizes the downstream throughput balance, so as to realize the fair distribution of upstream and downstream bandwidth. According to the actual situation of the network, the algorithm can be implemented in the AP without modifying the MAC of other wireless nodes, which saves the workload of the implementation. Especially in the case of more and scattered wireless nodes, it only needs to modify the AP without modifying other nodes, eliminating a lot of work to configure the decentralized nodes.

关键词： Heuristic algorithms Wireless networks Bandwidth Channel allocation Dynamic scheduling Throughput Manufacturing

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Partial-Node-Based State Estimation for Complex Networks With Energy Harvesting Sensors

Partial-Node-Based State Estimation for Complex Networks Wit...

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International Conference on Automation and Computing (ICAC)

作者： Yu-Ang Wang Zidong Wang Fan Wang Lei Zou College of Information Science and Technology Donghua University Shanghai China Department of Computer Science Brunel University London Uxbridge Middlesex United Kingdom Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen Duisburg Germany

In this paper, the recursive partial-node-based (PN-B) state estimation problem is investigated for a class of time-varying complex networks (CNs) with energy harvesting sensors (EHSs). Measurements are transmitted to the remote state estimator by the EHSs only when the current energy level can compensate for the energy consumption. Only a part of the network nodes' measurements, which are available to the users, are utilized to estimate the network states. The objective of this problem is to design a recursive state estimator for CNs with EHSs based on available measurements observed from partial nodes. The upper bound of the estimation error covariance is first established with the help of intensive scholastic techniques and induction approach. Then, the required estimator gain is determined by minimizing such an upper bound. Finally, an illustrative example is presented to verify the effectiveness of the proposed PNB state estimation method.

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Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field

Path Planning Algorithm for Multiple UAVs Based on Artificia...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Chongde Ren Jinchao Chen Chenglie Du Wenquan Yu School of Software Northwestern Polytechnical University Xi'an China Department of Computer Science Northwestern Polytechnical University Xi'an China Department of Land Unmanned System Research and Development North Automatic Control Technology Research Institute Taiyuan China

Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy.

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Multi-operation blank localization with hybrid point cloud and feature-based representation

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Procedia CIRP 2023年 120卷 756-761页

作者： Tamás Cserteg András Kovács József Váncza EPIC Centre of Excellence Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network Budapest H-1111 Hungary Doctoral School of Informatics ELTE Eötvös Loránd University Budapest H-1117 Hungary Department of Manufacturing Science and Technology Budapest University of Technology and Economics Budapest H-1111 Hungary

Sustainability objectives, including the endeavor to reduce waste, energy consumption and machining effort gave rise to the near net shape (NNS) machining concept, which requires the initial rough blank to be as close to the final machined product as possible. Nevertheless, the opportunity of savings in material, energy and effort come with a risk of manufacturing scrap even in case of a very small geometrical error of the blank. This issue is addressed by blank localization, i.e., the act of placing the final machined product in the geometry of the rough blank. Multi-operation blank localization was proposed recently to exploit tolerances in the product design to compensate potential geometrical errors of the blank. It places each feature group, machined together in the same operation, separately in the blank. When tolerances connecting different feature groups allow, these feature groups can be moved slightly according to the measured actual blank geometry. This paper proposes a novel multi-operation blank localization approach that models the rough blank as a free-form geometry, capturing all possible geometrical errors, whereas represents the final product using a feature-based model. The problem of blank localization for minimizing tolerance errors while leaving sufficient allowance is formulated and solved as a convex quadratically constrained quadratic program (QCQP). In a case study from the automotive industry, it is shown that the proposed multi-operation approach outperforms earlier methods that handle the product as a single solid geometry.

关键词： blank localization free-form machining point clouds quadratic programming optimization

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Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach

arXiv

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arXiv 2022年

作者： Jafarpour, Saber Davydov, Alexander Abate, Matthew Bullo, Francesco Coogan, Samuel School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Center for Control Dynamical Systems and Computation University of California Santa Barbara United States

This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks based upon non-Euclidean contraction theory. The basic idea is to cast the robustness analysis of a neural network as a reachability problem and use (i) the `∞-norm input-output Lipschitz constant and (ii) the tight inclusion function of the network to over-approximate its reachable sets. First, for a given implicit neural network, we use `∞-matrix measures to propose sufficient conditions for its well-posedness, design an iterative algorithm to compute its fixed points, and provide upper bounds for its `∞-norm input-output Lipschitz constant. Second, we introduce a related embedded network and show that the embedded network can be used to provide an `∞-norm box over-approximation of the reachable sets of the original network. Moreover, we use the embedded network to design an iterative algorithm for computing the upper bounds of the original system’s tight inclusion function. Third, we use the upper bounds of the Lipschitz constants and the upper bounds of the tight inclusion functions to design two algorithms for the training and robustness verification of implicit neural networks. Finally, we apply our algorithms to train implicit neural networks on the MNIST dataset and compare the robustness of our models with the models trained via existing approaches in the literature. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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Data-driven Optimal Cooperative Output Regulation of Unknown Linear Discrete-time Multi-agent Systems

Data-driven Optimal Cooperative Output Regulation of Unknown...

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Chinese control and Decision Conference, CCDC

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer approach. However, the majority of the existing research assumes that the dynamics is known previously. To remove this condition, the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is unknown. First, a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external inputs. Second, the unknown regulator equations are solved using the iterative recurrent neural network approach. Third, the state-based data-driven distributed control law is synthesized to solve the problem. The optimal gains are derived by solving convex optimization problems using input and state data. Finally, a numerical example is presented to verify the feasibility of the proposed framework.

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Thermal Images Based Mobile Application for Identifying Faults in Photovoltaics Modules

Thermal Images Based Mobile Application for Identifying Faul...

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International Symposium on Fundamentals of Electrical Engineering (ISFEE)

作者： Alexandru-Ionel Constantin Ramona-Nicoleta Banaseanu Paula Anghelita Gabriela Iosif Dorian Marin Department of Renewable Resources and Energy Efficiency National Institute for Research & Development in Electrical Engineering ICPE-CA Faculty of Automatic Control and Computer Science National University of Science and Technology POLITEHNICA Bucharest Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology POLITEHNICA Bucharest Office of Management for Knowledge and Information National Institute for Research & Development in Electrical Engineering ICPE-CA

ISBN: (数字)9798350308563

ISBN: (纸本)9798350308570

The paper describes few faults that can be present in Photovoltaic modules and presents results related to training and the testing of a mobile application designed for identifying faults in photovoltaic modules based on thermal images. The mobile application was trained and then tested with different individual and multiple faults. The mobile application can identify with high accuracy both the individual and multiple faults if the thermal images are preprocessed before to be uploaded for analysis.

关键词： Fault diagnosis Training Temperature distribution Temperature dependence Accuracy Thermal engineering Mobile applications

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