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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031220241

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars-backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Keeping Eyes on the Road: Understanding Driver Attention and Its Role in Safe Driving 9

Keeping Eyes on the Road: Understanding Driver Attention and...

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9th Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2023

作者： Fiani, Francesca Ponzi, Valerio Russo, Samuele 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 Psychology Sapienza University of Rome Roma00185 Italy

Monitoring the driver’s attention is an important task to maintain the driver’s safety. The estimation of the driver’s gaze direction can help us to evaluate if the drivers are not focusing their attention on the street. For an evaluation of this type, comparing the inside view and outside scenery of the vehicle is essential, therefore we decided to create a specific dataset for this task. In this work, we realize a machine-learning-oriented approach to driver’s attention evaluation using a coupled visual perception system. By analyzing the road and the driver’s gaze simultaneously it is possible to understand if the driver is looking at the traffic signs detected. We evaluate if a determined Region Of Interest (ROI) contains a road sign or not through YOLOv8. © 2023 Copyright for this paper by its authors.

关键词： Machine learning

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Modified SDRE Method for Finite-time Nonlinear Optimal control Problem

Modified SDRE Method for Finite-time Nonlinear Optimal Contr...

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2022 International Russian Automation Conference, RusAutoCon 2022

作者： Kabanov, Aleksey A. Department of Informatics and Control in Technical Systems Sevastopol State University Russia Federal Research Center 'Computer Science and Control' of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

the optimal control problem on a finite-time interval for a nonlinear system with state-dependent coefficients is considered in this paper. The most common method for such systems is the state-dependent Riccati equation (SDRE) method. Ehe implementation of SDRE in a finite-time interval requires the solution of the state-dependent Riccati differential equation (SDDRE). Since the initial conditions for the SDDRE are set at a final time moment, the numerical solution requires integration from right to left. The lack of information about the system state at future time points does not allow this integration to be performed. This complexity can be overcome through an approximate solution based on the state-dependent Lyapunov differential equation. However, this approximate solution may be less accurate, for example, compared to Riccati Sequence Approximate Method (ASRE). A modification of the SDRE method based on preliminary calculations of the system matrices by solving an auxiliary terminal control problem on the inverse time interval is presented. In such a situation, the backward integration of the auxiliary problem on the specified time interval will take place in direct time for the original problem. Two examples of the application of this method in nonlinear problems are shown. © 2022 IEEE.

关键词： Nonlinear systems

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On Some Issues of Mathematical Modeling of Various Approaches to Learning 15

On Some Issues of Mathematical Modeling of Various Approache...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Gonchar, Dmitry Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Modeling of Design Systems Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The enlarged model of comparison of various concepts of education (culturally appropriate and nature-like) in compulsory secondary school proposed earlier by the author is supplemented by taking into account feedback on the health of students, which makes it possible to move from the appropriate modeling of the school 'in the moment' to modeling it in time dynamics. © 2022 IEEE.

关键词： Education computing

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Learning neural contracting dynamics: extended linearization and global guarantees 24

Learning neural contracting dynamics: extended linearization...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Sean Jaffe Alexander Davydov Deniz Lapsekili Ambuj K. Singh Francesco Bullo Center for Control Dynamical Systems and Computation University of California Santa Barbara and Department of Computer Science University of California Santa Barbara Center for Control Dynamical Systems and Computation University of California Santa Barbara Department of Computer Science University of California Santa Barbara

ISBN: (纸本)9798331314385

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dynamical system with global contractivity guarantees in arbitrary metrics. The key feature of ELCD is a parametrization of the extended linearization of the nonlinear vector field. In its most basic form, ELCD is guaranteed to be (i) globally exponentially stable, (ii) equilibrium contracting, and (iii) globally contracting with respect to some metric. To allow for contraction with respect to more general metrics in the data space, we train diffeomorphisms between the data space and a latent space and enforce contractivity in the latent space, which ensures global contractivity in the data space. We demonstrate the performance of ELCD on the high dimensional LASA, multi-link pendulum, and Rosenbrock datasets.

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Cluster Synchronization of Kuramoto Oscillators via Pacemakers and Mean-phase Feedback control 63

Cluster Synchronization of Kuramoto Oscillators via Pacemake...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Kokubo, Ryota Matsubara, Mitsuaki Kato, Rui Ishii, Hideaki Tokyo Institute of Technology Department of Computer Science Yokohama226-8502 Japan Tokyo Institute of Technology Department of Systems and Control Engineering Yokohama226-8502 Japan Villeurbanne69622 France The University of Tokyo Department of Information Physics and Computing Tokyo113-8656 Japan

ISBN: (纸本)9798350316339

Brain networks typically exhibit characteristic synchronization patterns where several synchronized clusters coexist. On the other hand, neurological disorders are considered to be related to pathological synchronization such as excessive synchronization of large populations of neurons. Motivated by these phenomena, this paper presents two approaches to control the cluster synchronization of Kuramoto oscillators. One is based on the use of pacemakers to the clusters, and the other is based on feeding back the mean phases to the clusters. We first show conditions on the pacemaker weights and the feedback gains for the network to achieve cluster synchronization. Then, we propose a method to find optimal feedback gains through convex optimization. A numerical example demonstrates the effectiveness of the proposed methods. © 2024 IEEE.

关键词： Pacemakers

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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Research on the behaviour of Bluetooth Low Energy protocol in the heart rate monitoring application

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Procedia computer science 2023年 225卷 63-69页

作者： Zuzanna Balas Krzysztof Tokarz Bartłomiej Zieliński Tomasz Guźniczak Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

In the paper, the authors check the behaviour of Bluetooth Low Energy protocol in a popular smart wristband and a microcontroller in the heart rate monitoring application. The measurements were collected using a development board with the ESP32 System on Chip. The authors tested measurement period stability and measurement reliability in various conditions.

关键词： Smart wristband Internet of Things Health monitoring Bluetooth Low Energy Wireless data transmission

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On fractional discrete financial system:Bifurcation,chaos,and control

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Chinese Physics B 2024年第10期33卷 129-140页

作者： Louiza Diabi Adel Ouannas Amel Hioual Shaher Momani Abderrahmane Abbes Laboratory of Dynamical Systems and Control University of Larbi Ben M’hidiOum El Bouaghi 04000Algeria Department of Mathematics and Computer Science University of Larbi Ben M’hidiOum El Bouaghi 04000Algeria Nonlinear Dynamics Research Center Ajman UniversityAjman 346United Arab Emirates Department of Mathematics The University of JordanAmman 11942Jordan Laboratory of Mathematics Dynamics and ModelizationUniversity Badji MokhtarAnnaba 23000Algeria

The dynamic analysis of financial systems is a developing field that combines mathematics and economics to understand and explain fluctuations in financial *** paper introduces a new three-dimensional(3D)fractional financial map and we dissect its nonlinear dynamics system under commensurate and incommensurate *** such,we evaluate when the equilibrium points are stable or unstable at various fractional *** use many numerical methods,phase plots in 2D and 3D projections,bifurcation diagrams and the maximum Lyapunov *** techniques reveal that financial maps exhibit chaotic attractor *** study is grounded on the Caputo-like discrete operator,which is specifically influenced by the variance of the commensurate and incommensurate ***,we confirm the presence and measure the complexity of chaos in financial maps by the 0-1 test and the approximate entropy ***,we offer nonlinear-type controllers to stabilize the fractional financial *** numerical results of this study are obtained using MATLAB.

关键词： financial model stability chaos commensurate and incommensurate orders complexity

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