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10th IEEE Smart World Congress, SWC 2024

作者： Cheng, Long Yan, Huiru Zhou, Hanlin Wang, Ying Tang, Haichuan Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Ai Lab of Crrc Academy Beijing China

ISBN: (纸本)9798331520861

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines. © 2024 IEEE.

关键词： Federated learning

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Coherence-based Input Design for Sparse System Identification

arXiv

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arXiv 2024年

作者： Parsa, Javad Rojas, Cristian R. Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

The maximum absolute correlation between regressors, which is called mutual coherence, plays an essential role in sparse estimation. A regressor matrix whose columns are highly correlated may result from optimal input design, since there is no constraint on the mutual coherence, so when this regressor is used to estimate sparse parameter vectors of a system, it may yield a large estimation error. This paper aims to tackle this issue for fixed denominator models, which include Laguerre, Kautz, and generalized orthonormal basis function expansion models, for example. The paper proposes an optimal input design method where the achieved Fisher information matrix is fitted to the desired Fisher matrix, together with a coordinate transformation designed to make the regressors in the transformed coordinates have low mutual coherence. The method can be used together with any sparse estimation method and in a numerical study we show its potential for alleviating the problem of model order selection when used in conjunction with, for example, classical methods such as AIC and BIC. © 2024, CC BY.

关键词： Fisher information matrix

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computer System for Energy-saving control of Electrotechnological Chemical Plants 3

Computer System for Energy-saving Control of Electrotechnolo...

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3rd International Conference on control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Chistjakova, Tamara Ershova, Olga Saint-Petersburg State Institute of Technology Computer Design and Control Department Saint-Petersburg Russia

ISBN: (纸本)9781665439817

The architecture of a computer system for controlling electrotechnological chemical plants in an energy-saving mode is presented. The system is a flexible configurable environment for different types of equipment, using different raw materials, the required quality of the target products and productivity. The core of the system is mathematical models that are reconfigured for various equipment, raw materials, products obtained and productivity. Databases and mathematical software have been developed that allow determining the values of control actions. To develop control interfaces as close as possible to real objects, a SCADA system was used. The development is tested on the example of energy-intensive production of aluminum, calcium carbide and phosphorus, for which energy-saving management is relevant. The system forms control tips for production personnel that ensure safe, energy-saving modes of operation of plants. Testing based on experimental and literary data confirmed the adequacy of the models and the effectiveness of the system due to the reduction of development time and the convenience of software implementation. The efficiency is confirmed by the results of approbation and certificates of implementation at Russian industrial enterprises, in scientific and educational organizations. © 2021 IEEE.

关键词： Energy conservation

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State Derivative Normalization for Continuous-Time Deep Neural Networks

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IFAC-PapersOnLine 2024年第15期58卷 253-258页

作者： Jonas Weigand Gerben I. Beintema Jonas Ulmen Daniel Görges Roland Tóth Maarten Schoukens Martin Ruskowski Chair of Machine Tools and Control Systems RPTU Kaiserslautern and the German Research Center for Artificial Intelligence Kaiserslautern Germany Control Systems (CS) Group at the Department of Electrical Engineering Eindhoven University of Technology Netherlands. R. Tóth is also affiliated to the Systems and Control Laboratory at the Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.

关键词： System Identification Continuous-Time Neural Ordinary Differential Equations Nonlinear State-Space

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On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community

On final opinions of the Friedkin-Johnsen model over random ...

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IEEE Conference on Decision and control

作者： Lingfei Wang Yu Xing Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and with Digital Futures Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random graph model, in which each link is added independently from a Bernoulli distribution. It is shown that the final opinions of the FJ model will concentrate around those of an FJ model over the expected graph as the network size grows, on the condition that the stubborn agents are well connected to other agents. Probability bounds are proposed for the distance between these two final opinion vectors, respectively for the cases where there exist non-stubborn agents or not. Numerical experiments are provided to illustrate the theoretical findings. The simulation shows that, in presence of non-stubborn agents, the link probability between the stubborn and the non-stubborn communities affect the distance between the two final opinion vectors significantly. Additionally, if all agents are stubborn, the opinion distance decreases with the agent stubbornness.

关键词： Vectors Numerical models

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Weighted Least-Squares PARSIM

arXiv

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arXiv 2024年

作者： He, Jiabao Rojas, Cristian R. Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last two decades, where most of them involve a common intermediate step, that is to estimate the range space of the extended observability matrix. In this contribution, an optimized version of the parallel and parsimonious SIM (PARSIM), PARSIMopt, is proposed by using weighted least-squares. It not only inherits all the benefits of PARSIM but also attains the best linear unbiased estimator for the above intermediate step. Furthermore, inspired by SIMs based on the predictor form, consistent estimates of the optimal weighting matrix for weighted least-squares are derived. Essential similarities, differences and simulated comparisons of some key SIMs related to our method are also presented. Copyright © 2024, The Authors. All rights reserved.

关键词： State space methods

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On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community

arXiv

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arXiv 2024年

作者： Wang, Lingfei Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology with Digital Futures Stockholm Sweden

关键词：

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Forecasting-Aided Graphical Learning for Robust State Estimation of Distribution System 1

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8th Purple Mountain Forum on Smart Grid Protection and control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, State Grid Electric Power.

关键词： Electric power distribution

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Enhanced Model-Free Predictor for Latency Compensation in Remote Driving Systems

Enhanced Model-Free Predictor for Latency Compensation in Re...

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IEEE Symposium on Intelligent Vehicle

作者： Lin Zhao Mikael Nybacka Malte Rothhämel Jonas Mårtensson Department of Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Remote driving plays a vital role in coordinating automated vehicles in challenging situations. Data transmission latency, however, can cause several problems in remote driving. Firstly, it can degrade the performance of remote-controlled vehicles, evident in issues like lane-following deviation and vehicle stability. Additionally, the remote control tower’s driving feedback is affected by delayed vehicle signals, leading to delayed driving experience. To address this, a model-free-based predictor is employed to compensate for the delay in remote driving. This approach does not require any dynamic model of the system and only needs tuning of two parameters to reduce communication delay. This study enhances the previous work by mitigating the amplitude of overshoot around peak points. It leverages the principle of the second-order derivative to predict the signal’s peak time and uses it to address the predictor’s overshoot issue. The effectiveness of the proposed method is validated using real car data from multiple participants in two scenarios, including Slalom and lane-following. Simulation results indicate that the proposed method can reduce prediction error by nearly 25% compared to previous works. Moreover, the solutions in this study are capable of managing not only delays in remote driving vehicles but also in traditional mechanical systems, such as CAN bus delays in conventional cars.

关键词： Intelligent vehicles Simulation Predictive models Stability analysis Delays Automobiles Mechanical systems

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Bandwidth Exploration for Spatial-Temporal Kernel Density Visualizations

SSRN

引用

SSRN 2024年

作者： Yin, Hongbo Zhang, Kaiqi Yang, Donghua Gao, Hong Li, Jianzhong School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Shenyang Aircraft Design and Research Institute Liaoning Shenyang110035 China School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

As an important and extensively used data visualization tool for understanding a dataset, STKDV has been extensively used in a wide range of applications, such as crime hotspot detection, traffic accident hotspot detection, and disease outbreak analysis. In order to obtain a high-quality STKDV, choosing suitable bandwidths is the most important and challenging issue. However, the best bandwidth values are usually quite subjective and depend on different applications and datasets. Domain experts usually need to iteratively generate multiple STKDVs by tuning different bandwidth values. And finaly choose the best STKDV with the most suitable bandwidths. However, the expensive computation heavily limits the bandwidth exploration for STKDVs. The computation of just one single STKDV, let alone multiple STKDVs, is quite time-consuming using existing methods. To significantly improve the efficiency of the bandwidth exploration, we propose a parallel voxel-based method named PVSI Algorithm and a data-pointbased method named SPN Algorithm. Extensive comparison experiments on four real datasets have been done, and the results demonstrate superior performance of PVSI Algorithm. PVSI Algorithm can be applied to bring near real-time computing to the bandwidth exploration for STKDVs in the shortest time. © 2024, The Authors. All rights reserved.

关键词： Visualization

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