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IEEE Conference on Decision and control

作者： Xinhui Liu Meiqi Cheng Dawei Shi Ling Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of $\mathscr{E}(\varepsilon)$-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement.

关键词： Protocols Event detection Scheduling algorithms Redundancy Approximation algorithms Real-time systems Sensors State estimation Observability Sensor arrays

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Research on Engraving Recognition Method of Nodular Cast Iron Pipe Bearing End Based on Improved YOLOX

Research on Engraving Recognition Method of Nodular Cast Iro...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Mingxuan Han Jun Wang Kecheng Zhou Jian Wang School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This study proposes a method for recognizing the engravings on the spigot ends of ductile iron pipes based on an improved YOLOX model. Ductile iron pipes, known for their excellent mechanical properties and corrosion resistance, are widely used in critical fields such as municipal water supply, drainage, and gas transportation. To ensure product quality and achieve traceability, identification information is typically engraved on the spigot ends of ductile iron pipes during production. However, traditional manual recognition methods are inefficient and prone to human error. To address this issue, this paper proposes an automated engraving recognition system based on an improved YOLOX model. By integrating techniques such as Depthwise Separable Convolutions, a multi-scale feature extraction network, and an adaptive attention mechanism, the YOLOX model structure is further optimized to enhance recognition accuracy and robustness in complex industrial environments. Experimental results demonstrate that the improved YOLOX model surpasses traditional models in both recognition accuracy and computational efficiency, proving its effectiveness and reliability for industrial production applications.

关键词： Water Adaptation models Attention mechanisms Accuracy Computational modeling Transportation Production Feature extraction Robustness Computational efficiency

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GRAPH CONVOLUTIONAL HOURGLASS NETWORKS FOR SKELETON-BASED ACTION RECOGNITION

GRAPH CONVOLUTIONAL HOURGLASS NETWORKS FOR SKELETON-BASED AC...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhu, Yiran Xu, Xing Ji, Yanli Shen, Fumin Shen, Heng Tao Lu, Huimin Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Mechanical and Control Engineering Kyushu Institute of Technology Japan

ISBN: (纸本)9781665438643

Graph convolution networks (GCNs) have become the mainstream framework for the skeleton-based action recognition task, since the skeleton representation of human action can be naturally modeled by the graph structure. Generally, most of the existing GCN based models extract and aggregate skeleton features by exploiting single-scale joint information, while neglecting the valuable multi-scale information such as part and body features in the skeleton. To address this issue, we propose a novel Graph Convolutional Hourglass Network (GCHN) model, which is scalable by stacking several basic modules of Graph Convolutional Hourglass Block (GCHB). Each GCHB module consists of the sequential operations of graph convolution, graph pooling and graph unpooling, which can promote the interaction of multi-scale information in the skeleton and effectively improve the recognition performance. Extensive experiments on the challenging NTU-RGB+D and Kinetics-Skeleton datasets demonstrate that the proposed GCHN model achieves state-of-the-art performance. © 2021 IEEE

关键词： Convolution

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Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features 1

Audio Deepfake Detection Based on a Combination of F0 Inform...

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1st International Workshop on Deepfake Detection for Audio Multimedia, DDAM 2022

作者： Xue, Jun Fan, Cunhang Lv, Zhao Tao, Jianhua Yi, Jiangyan Zheng, Chengshi Wen, Zhengqi Yuan, Minmin Shao, Shegang School of Computer Science and Technology Anhui University Hefei China NLPR Institute of Automation Chinese Academy of Sciences Beijing China Institute of Acoustics Chinese Academy of Sciences Beijing China Qiyuan Laboratory Beijing China National Environmental Protection Engineering and Technology Center for Road Traffic Noise Control Research Institute of Highway Ministry of Transport Beijing China

ISBN: (纸本)9781450394963

Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining good performance, and showing that different subbands have different contributions to audio deepfake detection. However, this lacks an explanation of the specific information in the subband, and these features also lose information such as phase. Inspired by the mechanism of synthetic speech, the fundamental frequency (F0) information is used to improve the quality of synthetic speech, while the F0 of synthetic speech is still too average, which differs significantly from that of real speech. It is expected that F0 can be used as important information to discriminate between bonafide and fake speech, while this information cannot be used directly due to the irregular distribution of F0. Insteadly, the frequency band containing most of F0 is selected as the input feature. Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately. Finally, the results of F0, real and imaginary spectrogram features are fused. Experimental results on the ASVspoof 2019 LA dataset show that our proposed system is very effective for the audio deepfake detection task, achieving an equivalent error rate (EER) of 0.43%, which surpasses almost all systems. © 2022 Association for Computing Machinery.

关键词： Feature extraction

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Augmented Reality Display of Robot with Graphs of Property Response Using Its USD Model

Augmented Reality Display of Robot with Graphs of Property R...

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International Conference on control, Automation and Systems ( ICCAS)

作者： Kazuki Tsukamoto Masanobu Koga Department of Creative Informatics Kyushu Institute of Technology Department of Intelligent and Control Systems Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology

This study proposes a method that can easily grasp the relationship between the actual machine and the graphs. In recent years, there has been a lot of research on augmented reality displays. The fields of research range from education to welfare. In the development of control systems, when evaluating the performance of a system by simulation or experiment, the results are often checked as graphs. Since the graphs are checked on a PC using CAD or other means, it is difficult to know which part of the actual machine each graph corresponds to. Therefore, we developed a tool that displays graphs in augmented reality around the actual machine through a camera on a mobile terminal. To display graphs in augmented reality, it is important to obtain the coordinates of the actual machine and display them in a location associated with the device. Therefore, a USD model with the same shape and size as the actual machine is used. This is achieved by displaying the USD model in augmented reality so that it is superimposed on the actual machine. The accuracy of the tool was also examined and its usefulness was evaluated.

关键词： Solid modeling Automation Shape Robot kinematics Robot vision systems Education control systems

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Locally Differentially Private Online Federated Learning With Correlated Noise

arXiv

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arXiv 2024年

作者： Zhang, Jiaojiao Zhu, Linglingzhi Fay, Dominik Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States

We introduce a locally differentially private (LDP) algorithm for online federated learning that employs temporally correlated noise to improve utility while preserving privacy. To address challenges posed by the correlated noise and local updates with streaming non-IID data, we develop a perturbed iterate analysis that controls the impact of the noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed for several classes of nonconvex loss functions. Subject to an (ϵ,δ)-LDP budget, we establish a dynamic regret bound that quantifies the impact of key parameters and the intensity of changes in the dynamic environment on the learning performance. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024, CC BY.

关键词： Federated learning

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On the Connection Between Magnetic-Field Odometry Aided Inertial Navigation and Magnetic-Field SLAM

On the Connection Between Magnetic-Field Odometry Aided Iner...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Isaac Skog Manon Kok Gustaf Hendeby Chuan Huang Thomas Edridge School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Delft Center for Systems and Control Delft University of Technology Delft The Netherlands Dept. of Electrical Engineering Linköping University Linköping Sweden

ISBN: (数字)9798331523176

ISBN: (纸本)9798331523183

Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulation in the inertial navigation process limits the feasible exploratory phases of these systems. Advances in magnetometer array processing have demonstrated that odometry information, i.e., displacement and rotation information, can be extracted from local magnetic field variations and used to create magnetic-field odometry-aided inertial navigation systems. The error growth rate of these systems is significantly lower than that of standalone inertial navigation systems. This study seeks an answer to whether a magnetic-field SLAM system fed with measurements from a magnetometer array can indirectly extract odometry information — without requiring algorithmic modifications — and thus sustain longer exploratory phases. The theoretical analysis and simulation results show that such a system can extract odometry information and indirectly create a magnetic field odometry-aided inertial navigation system during the exploration phases. However, practical challenges related to map resolution and computational complexity remain significant.

关键词： Phased arrays Magnetic field measurement Simultaneous localization and mapping Magnetometers Simulation Inertial navigation Odometry Magnetic fields Data mining Sensor arrays

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A Duality-Based Approach to Inverse Kalman Filtering

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IFAC-PapersOnLine 2023年第2期56卷 10258-10263页

作者： Yibei Li Bo Wahlberg Lihua Xie Xiaoming Hu School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Division of Optimization and Systems Theory Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden

In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.

关键词： Inverse filtering Kalman filters duality covariance estimation identifiability

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Translating Assembly Accuracy Requirements to Cut-Off Frequencies for Component Mode Synthesis

arXiv

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arXiv 2023年

作者： Janssen, Lars A.L. Besselink, Bart Fey, Rob H.B. van de Wouw, Nathan Dynamics & Control Department of Mechanical Engineering Eindhoven University of Technology Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands

One of the most popular methods for reducing the complexity of assemblies of finite element models in the field of structural dynamics is component mode synthesis. A main challenge of component mode synthesis is balancing model complexity and model accuracy, because it is difficult to predict how component reduction influences assembly model accuracy. This work introduces an approach that allows for the translation of assembly model accuracy requirements in the frequency domain to the automatic selection of the cut-off frequencies for the model-order reduction (MOR) of components. The approach is based on a mathematical approach for MOR for coupled linear systems in the field of systems and control. We show how this approach is also applicable to structural dynamics models. We demonstrate the use of this approach in the scope of component mode synthesis (CMS) methods with the aim to reduce the complexity of component models while guaranteeing accuracy requirements of the assembly model. The proposed approach is illustrated on a mechanical, three-component structural dynamics system for which reduced-order models are computed that are reduced further compared to reduction using standard methods. This results in lower simulation cost, while maintaining the required accuracy. © 2023, CC BY-NC-ND.

关键词： Structural dynamics

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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

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7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of Engineering & technology 2023.

关键词： Smart city

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