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Research Square 2023年

作者： Gatto, Elaine Cecília Ferrandin, Mauri Cerri, Ricardo Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil

Multi-Label Classification is the task of simultaneously predicting a set of labels for an instance. Typically, two approaches are used: global, which trains a single classifier to deal with all classes at once, and local, which divides the problem into many binary problems. In both approaches, learning label correlations is still an open issue. In this paper, we propose a method to cluster the label space and find partitions of disjoint correlated labels called hybrid partition, which can be considered in-between the local and global, combining the benefits of both. Our proposal entails: i) clustering the label space based on label correlations;ii) generating and validating the resulting hybrid partitions;iii) selecting the optimal partitions between them;and iv) evaluating their performances. Since we are also interested in analyzing how hybrid partitions are close to the best possible partitions, we compared our results with the ones obtained using partitions found by an oracle, an exhaustive, and a random search. The oracle and exhaustive strategies generate all possible label partitions for a dataset. While the oracle chooses the best one based on the test set alone, the exhaustive choices are based on validation. Experiments on eight well-known multi-label datasets revealed that both our proposal and the random partitions achieve superior or competitive results compared with the others. This indicates that the conventional global and local approaches may not accurately account for label correlations, requiring significant improvements. In this case, clustering the label space can be a solution. © 2023, CC BY.

关键词： Classification (of information)

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Scene Classification Using Deep Networks Combined with Visual Attention

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Journal of Sensors 2022年第0期2022卷

作者： Shi, Jing Zhu, Hong Li, Yuxing Li, Yanghui Du, Sen School of Automation and Information Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China

In view of the scene's complexity and diversity in scene classification, this paper makes full use of the contextual semantic relationships between the objects to describe the visual attention regions of the scenes and combines with the deep convolution neural networks, so that a scene classification model using visual attention and deep networks is constructed. Firstly, the visual attention regions in the scene image are marked by using the context-based saliency detection algorithm. Then, the original image and the visual attention region detection image are superimposed to obtain a visual attention region enhancement image. Furthermore, the deep convolution features of the original image, the visual attention region detection image, and the visual attention region enhancement image are extracted by using the deep convolution neural networks pretrained on the large-scale scene image dataset Places. Finally, the deep visual attention features are constructed by using the multilayer deep convolution features of the deep convolution networks, and a classification model is constructed. In order to verify the effectiveness of the proposed model, the experiments are carried out on four standard scene datasets LabelMe, UIUC-Sports, Scene-15, and MIT67. The results show that the proposed model improves the performance of the classification well and has good adaptability. © 2022 Jing Shi et al.

关键词： Convolution

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Implicit Scene Context-aware Interactive Trajectory Prediction for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-17页

作者： Lan, Wenxing Li, Dachuan Hao, Qi Zhao, Dezong Tian, Bin Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China James Watt School of Engineering University of Glasgow Glasgow U.K State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions;2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories;3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance. IEEE

关键词： Trajectories

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The influence of color temperature mismatch in star simulator on positioning accuracy and magnitude measurement by star sensor

The influence of color temperature mismatch in star simulato...

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2021 International Conference on Computer Technology, Information engineering and Electron Materials, CTIEEM 2021

作者： Wang, Zhiyong Liu, Shishen Sun, Hao Zhang, Jingjing Li, Ruyou Liu, Chen School of Automation China University of Geosciences Hubei Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration China Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China

During the calibrating of star sensor, the calibration accuracy is greatly affected by the mismatch between the color temperature of the light and the to-be-measured star, which further affects the attitude measurement accuracy. This paper studied the near-infrared spectra of stars with different color temperatures, and analyzed the errors on star positioning and magnitude measurement of star sensor due to the color temperature mismatch. The results showed that in the central field of view, the spot centroid deviation caused by spectral mismatch is smaller than that in the edge field of *** the defocus of the imaging surface also affects the spot centroid deviation. Besides, when calibrating with 6000K color temperature light, the maximum measurement error can reach -1.9126 magnitude. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Stars

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An Algorithm for Solving the Pose of Bio 3D Printing based on Neural Network

An Algorithm for Solving the Pose of Bio 3D Printing based o...

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Chinese control and Decision Conference, CCDC

作者： Jian Liu Zili Song Huixuan Zhu Hang Yuan School of Information and Control Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665478977

Obtaining the pose state information of the printing nozzle in real time and performing error compensation is an important means to improve the accuracy of biological 3D printing. Traditional pose solving methods such as Newton iteration method and Analysis method have problems such as difficulty in selecting the initial value and slow calculation speed. To solve the above problems, a biological 3D printing pose solution algorithm based on the Elman neural network optimized by genetic algorithm is proposed. Firstly, the kinematics model of the biological 3D printing platform is established, then the GA-Elman neural network is established and the inverse solution of the platform is used as a training sample, and the pose information is solved by the method of network learning, and finally the accuracy of the algorithm is verified by simulation. The simulation results show that the algorithm has high calculation accuracy and fast solution speed, and can quickly and accurately solve the pose of biological 3D printing.

关键词： Training Solid modeling Three-dimensional displays Simulation Neural networks Kinematics Three-dimensional printing

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Reachable Set Estimation of a class of Human-in-the-Loop control Systems

Reachable Set Estimation of a class of Human-in-the-Loop Con...

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第40届中国控制会议

作者： Xiu-Mei Zhang Huai-Ning Wu Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University

This paper studies the reachable set estimation problem of a class of human-in-the-loop（Hi TL） control systems described by a controlled hidden Markov jump model. Our aim is to determine an ellipsoid-like set which bounds all the machine state trajectories of the Hi TL system under zero initial condition. By using a stochastic Lyapunov functional, a reachable set estimation condition is established, which is provided in terms of a set of matrix inequalities. Finally, an example is given to show the effectiveness of the proposed result.

关键词： Human-in-the-loop control system Markov jump system reachable set estimation stochastic Lyapunov functional

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Inspection trolley based on the distance detection obstacle

Inspection trolley based on the distance detection obstacle

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Chinese control and Decision Conference, CCDC

作者： Guicheng Wang Ao Sheng Xiaobin Li Min Zhang Changliang Guan Fengfeng Yin School of Electrical and Electronics Engineering Shanghai Institute of Technology Shanghai China Library Shanghai Institute of Technology Shanghai China Zijin Intelligent Control (Xiamen) Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

In Industry 4.0 background, unmanned full-process automated production has gradually become a hot topic, and the realization of intelligent and information factory requires the joint cooperation of multiple industries, in which the inspection device that replaces traditional manual work is an inescapable topic. At present, domestic and foreign research on intelligent inspection trolley is focused on obstacle avoidance function based on various sensors. In this context, it is introduced an algorithm based on monocular camera as a sensor, using Python language to access OpenCV library, and utilizing Gaussian filter function and Canny operator-based image contour extraction to complete the distance measurement. The algorithm can achieve the distance measurement and obstacle avoidance function at a lower cost. The test results show that the system can automatically identify the distance of obstacles in the inspection process, and according to the visual information captured by the camera in real time, it can independently generate steering action and collision reminder, and can complete the preliminary requirement of intelligent inspection.

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Atten-DilatedNet: fault diagnosis method of bearing for mine drilling rig

Atten-DilatedNet: fault diagnosis method of bearing for mine...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Guoqing Sun Xiaoyu Zou Jie Pan Zhongbin Wang Hongquan Ji Dezhong Xin School of Mechanical and Electrical Engineering China University of Mining and Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Chongqing Research Institute of China Coal Science and Industry Group Chongqing China

Currently, bearing fault signal diagnosis of mine drilling rig suffers from strong time dependency and serious signal noises, which leads to low diagnostic accuracy. Aiming at solving the problem of inadequate extraction of key features hidden in strong noise, a temporal attention embedding dilated convolutional neural network (Atten-DilatedNet) is designed for fault diagnosis of mine drilling rig bearing. The method takes the original vibration signals of different faulty bearings of drilling rigs as the input of the model. It adopts a one-dimensional dilated convolutional neural network to automatically extract features from the vibration signals, with larger receptive field and longer time range than traditional convolutional operator. A temporal attention mechanism is further embedded to the network, which adaptively assigns different weights to above feature sequences and model their time dependency. Thus, Atten-DilatedNet can enhance attention to critical-to-fault feature, and restrain interference of noise. Case studies on public data sets and experimental drilling rig demonstrate the validaty and superiority of the proposed Atten-DilatedNet based fault diagnosis method. It shows higher accuracy, better generalization performance, and stronger robustness than some traditional machine learning methods.

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Stp-Based Verification and Synthesis of State Opacity for Logical Finite State Machines

SSRN

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SSRN 2022年

作者： Han, Weiwei Li, Yi Zhang, Zhipeng Xia, Chengyi School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China School of Control Science and Engineering Tiangong University Tianjin300387 China

This paper explores the state opacity-based privacy verification and synthesis problems for finite state machines from an algebraic perspective. Firstly, the dynamics of finite state machine can be established as an algebraic expression by using the semi-tensor product (STP) of matrix. Starting from this novel representation, we propose an algebraic criterion to verify whether the privacy state is opaque to intruders. Subsequently, we investigate the opacity-enhancement synthesis problem from two aspects. On one hand, we design a feedback supervisory controller through disabling the controllable events so that the closed-loop system is opaque as far as the privacy messages are concerned;on the other hand, we investigate how to edit an observable function just by assigning some specific events in the observation channel instead of changing the transition behavior of the system. Meanwhile, we obtain that these synthesis problems can be addressed by calculating some algebraic equation system, and we further develop two algorithms to obtain the controller or function. Finally, one interesting example is illustrated to demonstrate the validity of the algebraic method. Taking together, the current results are conductive to systemically understanding the privacy enhancement issue. © 2022, The Authors. All rights reserved.

关键词： Opacity

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Stability Analysis of a Linear Parameter Varying Adaptive Output Feedback control System

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IFAC-PapersOnLine 2021年第8期54卷 64-68页

作者： Markus Tranninger Michael Ruderman Institute of Automation and Control Graz University of Technology Graz Austria Faculty of Engineering and Science University of Agder Grimstad Norway

Output feedback control systems often require an adaptive filter for properly shaping the loop transfer function, as certain system plant parameters may be uncertain or varying. This renders the overall closed loop to a linear parameter varying (LPV) system, for which the stability analysis is challenging due to non-trivial dynamics of the adaptation law. This paper develops a stability analysis technique of a feedback controlled oscillatory system. A polytopic overapproximation of the parameter set together with the feasibility of certain LMIs guarantees asymptotic stability of the closed loop. The varying filter parameter is only required to be lower and upper bounded, where the admissible bounds can be obtained from the proposed strategy. Hence, any filter adaptation law is allowed for this parameter range. A meaningful numerical example based on a fifth-order plant and a second-order adaptive filter demonstrates the usefulness and appealing simplicity of the proposed approach.

关键词： quadratic stability linear matrix inequality polytopic over-approximation adaptive control

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