检索结果-内蒙古大学图书馆

Cybersecurity of SMART Technologies Using Deep Learning Methods

学校读者我要写书评

暂无评论

Cybersecurity of SMART Technologies Using Deep Learning Meth...

International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Oleksandr Milov Irada Rahimova Alla Havrylova Sergii Golovashych Iryna Aksonova Dmytro Pecherytsia Department of Cyber Security National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of “Computer Technologies” Azerbaijan Technical University Baku Azerbaijan Department of Software Engineering and Intelligent Control Technologies National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

The concept of “smart”, the concept of smart technology, and its main elements are considered. An analysis of the factors influencing the formation and development of the concept of smart technology is provided. Aspects of the development of smart technology are highlighted. A description of deep learning methods is given and their classification is proposed. The areas of application of the described methods in various cybersecurity applications are identified, including detection of intrusions and malware, analysis of network traffic and some other tasks related to smart technologies.

关键词： Deep learning Telecommunication traffic Artificial neural networks Malware Classification algorithms computer crime

Tilted Least-Squares Parameter Estimation of Linear Regression Models in the Presence of Outliers

学校读者我要写书评

暂无评论

Tilted Least-Squares Parameter Estimation of Linear Regressi...

IEEE Conference on Decision and control

作者： Biqiang Mu Er-Wei Bai Wei Xing Zheng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa USA School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

The least squares estimator is the most popular identification method. In the absence of prior knowledge on the unknown noise, uniform weights on all samples are often as-sumed. In reality, potentially unknown contamination is always present and the uniform weights are not necessarily the best. Further, explicit information about the nature of contamination is usually absent. To this end, a relaxed-tilted least squares method is proposed here to assign unequal weights so that the effect of undesired noise contamination can be mitigated. The relaxed-tilted least squares method tilts the uniform prior on the samples so as to move the uniform distribution in a direction that enjoys the smallest estimation error in the neighborhood of the uniform distribution. Theoretical results are established including the ability of outlier removal and the guaranteed parameter convergence in the presence of outliers. Numerical algorithms are proposed and simulated, which support the theoretical derivations.

关键词：

Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded Systems with GPUs

学校读者我要写书评

暂无评论

Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded System...

IEEE International Conference on Robotic Computing (IRC)

作者： Marc Eisoldt Alexander Mock Mario Porrmann Thomas Wiemann Computer Engineering Group Osnabrück University Osnabrück Germany Knowledge-based Systems Group Osnabrück University Osnabrück Germany Robotics in Computer Science Fulda University of Applied Sciences Fulda Germany DFKI Niedersachsen Plan-based Robot Control Osnabrück Germany

Monte Carlo Localization is a widely used approach in the field of mobile robotics. While this problem has been well studied in the 2D case, global localization in 3D maps with six degrees of freedom has so far been too computationally demanding. Hence, no mobile robot system has yet been presented in the literature that is able to solve it in realtime. The computationally most intensive step is the evaluation of the sensor model, but it also offers high parallelization potential. This work investigates the massive parallelization of the evaluation of particles in truncated signed distance fields for three-dimensional laser scanners on embedded GPUs. The implementation on the GPU is 30 times as fast and more than 50 times more energy efficient compared to a CPU implementation.

关键词：

Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded Systems with GPUs

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Eisoldt, Marc Mock, Alexander Porrmann, Mario Wiemann, Thomas Computer Engineering Group Osnabrück University Osnabrück Germany Knowledge-based Systems Group Osnabrück University Osnabrück Germany Robotics in Computer Science Fulda University of Applied Sciences Fulda Germany DFKI Niedersachsen Plan-based Robot Control Osnabrück Germany

Monte Carlo Localization is a widely used approach in the field of mobile robotics. While this problem has been well studied in the 2D case, global localization in 3D maps with six degrees of freedom has so far been too computationally demanding. Hence, no mobile robot system has yet been presented in literature that is able to solve it in real-time. The computationally most intensive step is the evaluation of the sensor model, but it also offers high parallelization potential. This work investigates the massive parallelization of the evaluation of particles in truncated signed distance fields for three-dimensional laser scanners on embedded GPUs. The implementation on the GPU is 30 times as fast and more than 50 times more energy efficient compared to a CPU implementation. Copyright © 2023, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

ERROR ANALYSIS OF KERNEL EDMD FOR PREDICTION AND control IN THE KOOPMAN FRAMEWORK

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Philipp, Friedrich Schaller, Manuel Worthmann, Karl Peitz, Sebastian Nüske, Feliks Technische Universität Ilmenau Optimization-Based Control Group Institute for Mathematics Ilmenau Germany Optimization-Based Control Group Institute of Mathematics Technische Universität Ilmenau Ilmenau Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Mathematics and Computer Science Freie Universität Berlin Germany

Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the Koopman operator for deterministic and stochastic (control) systems. This operator is linear and encompasses full information on the (expected stochastic) dynamics. In this paper, we analyze a kernel-based EDMD algorithm, known as kEDMD, where the dictionary consists of the canonical kernel features at the data points. The latter are acquired by i.i.d. samples from a user-defined and application-driven distribution on a compact set. We prove bounds on the prediction error of the kEDMD estimator when sampling from this (not necessarily ergodic) distribution. The error analysis is further extended to control-affine systems, where the considered invariance of the Reproducing Kernel Hilbert Space is significantly less restrictive in comparison to invariance assumptions on an a-priori chosen dictionary. © 2023, CC BY.

关键词： Dynamic mode decomposition

Safe Barrier-Constrained control of Uncertain Systems via Event-triggered Learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lederer, Armin Begzadić, Azra Hirche, Sandra Cortés, Jorge Herbert, Sylvia The Learning and Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland The Department of Mechanical and Aerospace Engineering University of California San Diego United States The Chair of Information-oriented Control TUM School of Computation Information and Technology Technical University of Munich Germany

While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel control barrier function-based framework for safe control through event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model. By updating a Gaussian process model with training points gathered online, the approach guarantees the feasibility of control barrier function conditions with high probability, such that safety can be ensured in a data-efficient manner. Furthermore, we establish the absence of Zeno behavior in the triggering scheme, and extend the algorithm to sampled-data realizations by accounting for inter-sampling effects. The effectiveness of the proposed approach and theory is demonstrated in simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

General Impedance Modeling for Modular Multilevel Converter with Grid-forming and Grid-following control

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sun, Chu Zhang, Fei Xiao, Huafeng Wang, Na Chen, Jikai The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden The School of Electrical Engineering Southeast University Nanjing210096 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Modular multilevel converter (MMC) has complex topology, control architecture and broadband harmonic spectrum. For this, linear-time-periodic (LTP) theory, covering multi-harmonic coupling relations, has been adopted for MMC impedance modeling recently. However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies. To this end, this paper proposes a general impedance modeling procedure applicable to various power converters with grid-forming and grid-following control strategies. The modeling is based on a unified representation of MMC circuit as the input and output relation between the voltage or current on the AC side and the exerted modulation index, while the control part vice versa, thereby interconnected as closed-loop feedback. With each part expressed as transfer functions, the final impedance model keeps the explicit form of harmonic transfer function matrix, making it convenient to directly observe and analyze the influence of each part individually. Thereby the submodule capacitance is found as the main cause of difference between MMC impedance compared to two-level converter, which will get closer as the capacitance increases. Effectiveness and generality of the impedance modeling method is demonstrated through comprehensive comparison with impedance scanning using electromagnetic transient simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic analysis

Asymmetry of the Relative Entropy in the Regularization of Empirical Risk Minimization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Daunas, Francisco Esnaola, Iñaki Perlaza, Samir M. Vincent Poor, H. The Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom INRIA Centre Inria d'Université Côte d'Azur Sophia Antipolis06902 France The Department of Automatic Control and Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The GAATI Mathematics Laboratory University of French Polynesia Fa'a'ā98702 French Polynesia

The effect of relative entropy asymmetry is analyzed in the context of empirical risk minimization (ERM) with relative entropy regularization (ERM-RER). Two regularizations are considered: (a) the relative entropy of the measure to be optimized with respect to a reference measure (Type-I ERM-RER);or (b) the relative entropy of the reference measure with respect to the measure to be optimized (Type-II ERM-RER). The main result is the characterization of the solution to the Type-II ERM-RER problem and its key properties. By comparing the well-understood Type-I ERM-RER with Type-II ERM-RER, the effects of entropy asymmetry are highlighted. The analysis shows that in both cases, regularization by relative entropy forces the solution's support to collapse into the support of the reference measure, introducing a strong inductive bias that can overshadow the evidence provided by the training data. Finally, it is shown that Type-II regularization is equivalent to Type-I regularization with an appropriate transformation of the empirical risk function. © 2024, CC BY.

关键词： Risk assessment

Acoustic Emission Recognition Based on a Two-Streams Convolutional Neural Network

学校读者我要写书评

暂无评论

computers, Materials & Continua 2020年第7期64卷 515-525页

作者： Weibo Yang Weidong Liu Jinming Liu Mingyang Zhang School of Information and Communication Engineering Nanjing Institute of TechnologyNanjing211167China School of Information and Control Engineering China University of Mining and TechnologyXuzhou221000China School of Information Science and Engineering Southeast UniversityNanjing211100China Department of Electrical and Computer Engineering National University of Singapore119077Singapore

The Convolutional Neural Network(CNN)is a widely used deep neural *** with the shallow neural network,the CNN network has better performance and faster computing in some image recognition *** can effectively avoid the problem that network training falls into local *** present,CNN has been applied in many different fields,including fault diagnosis,and it has improved the level and efficiency of fault *** this paper,a two-streams convolutional neural network(TCNN)model is *** on the short-time Fourier transform(STFT)spectral and Mel Frequency Cepstrum Coefficient(MFCC)input characteristics of two-streams acoustic emission(AE)signals,an AE signal processing and classification system is constructed and compared with the traditional recognition methods of AE signals and traditional CNN *** experimental results illustrate the effectiveness of the proposed *** with single-stream convolutional neural network and a simple Long Short-Term Memory(LSTM)network,the performance of TCNN which combines spatial and temporal features is greatly improved,and the accuracy rate can reach 100%on the current database,which is 12%higher than that of single-stream neural network.

关键词： Convolutional neural network acoustic emission fault detection