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International Joint Conference on Neural Networks (IJCNN)

作者： Weili Guo Liping Xie Zhenyong Fu Jianhui Guo Guochen Pang Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing P.R. China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing P.R. China School of Automation and Electrical Engineering Linyi University Linyi P.R. China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

For the widely used multilayer perceptrons (MLPs), the existed singularities in the parameter space have seriously affected the learning dynamics of MLPs, which cause several singular learning behaviors. Since the Fisher information matrix (FIM) plays a significant role in analyzing the singular learning dyanmics of MLPs, it is very worthy to obtain the analytical form of FIM to do further investigation. In this paper, by choosing the bipolar error function as the activation function, the analytical form of FIM are obtained, where the validity of the obtained results are verified by taking three experiments.

关键词： Training Analytical models Multilayer perceptrons Indexes Mathematical model Standards

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Copy and paste GAN: Face hallucination from shaded thumbnails

arXiv

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arXiv 2020年

作者： Zhang, Yang Tsang, Ivor W. Luo, Yawei Hu, Changhui Lu, Xiaobo Yu, Xin School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China Centre for Artificial Intelligence University of Technology Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology China School of Automation Nanjing University of Posts and Telecommunications China Australian Centre for Robotic Vision Australian National University Australia

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and nonuniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details;while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternately in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Design of Cascade control Framework for ROV control and Simulation

Design of Cascade Control Framework for ROV Control and Simu...

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IEEE International Conference on Mechatronics and automation

作者： Changlin Qiu Shihan Kong Chao Zhou Junzhi Yu State Key Lab Management and Control for Complex Systems Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Naval Research Academy Beijing China BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a cascade control framework is proposed a class of remote operated vehicles (ROVs) to enhance the control performance and capacity of interference resistance. Specifically, the control framework is composed of a fuzzy-based controller in primary loop and a PID neural network based controller in secondary loop. In the primary loop, fuzzy controller is implemented to avoid precise establishment of ROV model for calculating the expected velocity of the vehicle. Furthermore, in the secondary loop, a PID neural network with adaptation is adopted to accomplish the capacity of resisting disturbance. Eventually, the control framework is verified by theoretical and simulation analyses. The obtained results indicate that the proposed cascade control framework endows the ROV with the ability to guarantee the control performance and attenuate external disturbance effectively.

关键词： Resistance Couplings Adaptation models Mechatronics Software packages Neural networks Interference

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Identification of muscle morphology with noncontact capacitive sensing: Preliminary study

Identification of muscle morphology with noncontact capaciti...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Enhao Zheng Jiacheng Wan Dongfang Xu Qining Wang Hong Qiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Information Engineering China university of geosciences (Beijing) Beijing China The Robotics Research Group College of Engineering Peking University Beijing China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previously proposed a noncontact capacitive sensing approach that could record the limb shape changes. The sensing approach frees the human skin from contacting to the metal electrodes, thus enabling the measurement of muscle signals by dressing the sensing front-ends outside of the clothes. We validated the capacitive sensing in human motion intent recognition tasks with the wearable robots and produced comparable results to existing studies. However, the biological significance of the capacitance signals is still unrevealed, which is an indispensable issue for robot intuitive control. In this study, we address the problems of identifying the relationships between the muscle morphological parameters and the capacitance signals. We constructed a measurement system that recorded the noncontact capacitive sensing signals and the muscle ultrasound (US) images simultaneously. With the designed device, five subjects were employed and the US images from the gastrocnemius muscle (GM) and the tibialis anterior (TA) muscle during level walking were sampled. We fitted the calculated muscle morphological parameters (the pinnation angles and the muscle fascicle length) and the capacitance signals of the same gait phases. The results demonstrated that at least one-channel capacitance signal strongly correlated to the muscle morphological parameters (R 2 > 0.5, quadratic regression). The average R 2 s of the most correlated channels were up to 0.86 for pinnation angles and 0.83 for the muscle fascicle length changes. The interesting findings in this preliminary study suggest the biological physical significance of the capacitance signals during human locomotion. Future efforts are worth being paid in this new research direction for more promising results.

关键词： Muscles Robot sensing systems Capacitance Legged locomotion Electrodes Ultrasonic variables measurement

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Copy and Paste GAN: Face Hallucination From Shaded Thumbnails

Copy and Paste GAN: Face Hallucination From Shaded Thumbnail...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang Zhang Ivor W. Tsang Yawei Luo Chang-Hui Hu Xiaobo Lu Xin Yu School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China Centre for Artificial Intelligence University of Technology Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology China Southeast University Nanjing China Australian Centre for Robotic Vision Australian National University Australia

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details; while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternatively in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively.

关键词： Face Lighting Gallium nitride Image resolution Feature extraction Generative adversarial networks automation

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An Enhanced Fault Diagnosis Method with Uncertainty Quantification Using Bayesian Convolutional Neural Network

An Enhanced Fault Diagnosis Method with Uncertainty Quantifi...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Qihang Fang Gang Xiong Xiuqin Shang Sheng Liu Bin Hu Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Fault diagnosis is a vital technique to pinpoint the machine malfunctions in manufacturing systems. In recent years, the deep learning techniques greatly improve the fault detection accuracy, but there still remain some problems. If one fault is absent in the training data or the fault signal is disturbed by severe noise interference, the fault classifier may misjudge the health state. This problem limits the reliability of the fault diagnosis in real applications. In this paper, we enhance the fault diagnosis method by using Bayesian Convolutional Neural Network (BCNN). A Shannon entropy-based method is presented to quantify the prediction uncertainty. The BCNN turns the deterministic predictions to probabilistic distributions and enhances the robustness of the fault diagnosis. The uncertainty quantification method helps to indicate the wrong predictions, detect unknown faults, and discover the strong disturbances. Then, a fine-tuning strategy is applied to enhance the model performance further. The potential usability of the proposed method in monitoring the motors of 3D printers is studied. And the experiment is conducted on a motor bearing dataset provided by Case Western Reserve University. The proposed BCNN achieves 99.82% fault classification accuracy over nine health conditions. Its robustness is verified by comparing the testing accuracy with three other methods on the noisy datasets. And the uncertainty quantification method successfully detects the outlier inputs.

关键词： Fault diagnosis Uncertainty Bayes methods Circuit faults Robustness Predictive models Noise measurement

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Adaptive tracking a linear system with unknown periodic signal in multi-agent systems

Adaptive tracking a linear system with unknown periodic sign...

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Chinese Intelligent systems Conference, CISC 2017

作者： Li, Feifei Zhang, Ya School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9789811064982

This paper studies the tracking control problem of multi-agent systems where each agent has homogeneous sensor and heterogeneous dynamic system, the moving target has unknown periodic input signal and the unknown periodic input can be modelled as a finite dimensional Fourier decomposition. Since some agents can not detect the target, a distributed estimation based tracking control algorithm is applied. We first design a consensus based distributed observer to estimate the state and the unknown periodic input of the system from the available measurement outputs. Leader-follower consensus protocol is applied, and the stability condition of the estimation errors is given. Then, based on the estimations, a model reference adaptive control (MRAC) algorithm is adopted to design the tracking controller. It is proved that under the proposed distributed estimation based tracking control algorithm, each agent can asymptotically track the target. A numerical simulation is given to prove the feasibility of the algorithm in this paper. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Position tracking control of magnetic levitation system based on continuous sliding mode control*

Position tracking control of magnetic levitation system base...

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Chinese automation Congress (CAC)

作者： Lei Zhao Linjie Chen Junxiao Wang Li Yu College of Information Zhejiang University of Technology Hangzhou China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing Chian

In this paper, aiming at the problem of position tracking control of magnetic levitation system under parameter uncertainties and unknown external disturbance, a continuous sliding mode control(CSMC) method based on extended state observer(ESO) is proposed. Firstly, in order to suppress the adverse effects of disturbance and system unmeasurable states on control performance, an ESO for the system was designed to estimate system disturbances and unmeasured states. Meanwhile, in order to improve the robustness of the system and eliminate the chattering phenomenon caused by the high-frequency switching function in the sliding mode control law, a position tracking controller based on continuous sliding mode control is designed. Then combined with disturbance estimation technology and continuous sliding mode control technology, a composite position tracking controller(CSMC+ESO) is designed for the magnetic levitation system. Simulation and experimental results shows that compared with the general continuous sliding mode control(CSMC), the proposed control method improves the tracking accuracy, convergence rate, obtain better anti-interference capability and noise filtering performance for control input.

关键词： Magnetic levitation Observers Sliding mode control Mathematical model Uncertain systems Robustness

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Mobile Energy Transfer in Internet of Things

arXiv

引用

arXiv 2020年

作者： Zhang, Qingqing Wang, Gang Giannakis, Georgios B. Liu, Qingwen Chen, Jie College of Electronic and Information Engineering Tongji University Shanghai201800 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN 55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

—Internet of things (IoT) is powering up smart cities by connecting all kinds of electronic devices. The power supply problem of IoT devices constitutes a major challenge in current IoT development, due to the poor battery endurance as well as the troublesome cable deployment. The wireless power transfer (WPT) technology has recently emerged as a promising solution. Yet, existing WPT advances cannot support free and mobile charging like Wi-Fi communications. To this end, the concept of mobile energy transfer (MET) is proposed, which relies critically on an resonant beam charging (RBC) technology. The adaptive (A) RBC technology builds on RBC, but aims at improving the charging efficiency by charging devices at device preferred current and voltage levels adaptively. A mobile ARBC scheme is developed relying on an adaptive source power control. Extensive numerical simulations using a 1,000mAh Li-ion battery show that the mobile ARBC outperforms simple charging schemes such as the constant power charging, the profile-adaptive charging, and the distance-adaptive charging in saving energy. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy transfer

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System identification of time-delay with single fractional pole heating process considering rate limit effect

System identification of time-delay with single fractional p...

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第三十八届中国控制会议

作者： Jie Yuan YangQuan Chen Shumin Fei School of Automation Southeast University Mechatronics Embedded Systems and Automation Lab University of California Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University

The heat transfer dynamic is extremely common in industry process. The time-delay with single fractional pole(TDSFP) model gives higher accuracy than first-order plus time-delay(FOPTD) model for the ideal heat transfer process. Actuator rate limit is a common and important phenomenon in heating process control and is becoming a stumbling block in precision process control. The actuator will get saturated for a certain duration when the identification test signal is a step function. The time duration when actuator rate is saturated can be misidentified as the delay of the process model, which misleads the controller design and generates unsatisfactory performance. This paper proposes a new model structure in identifying a TDSFP process under the step test signal and this model structure considers actuator rate limit. Parameters sensitivity is analyzed to access the effect of identification accuracy resulting from parameters estimation mismatch. An illustrative example is given to show the effectiveness of the proposed identification method and the sensitivity of system parameters.

关键词： Process control fractional-order heat transfer actuator rate limit parameter estimation parameter sensitivity

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