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arXiv 2021年

作者： Zhang, Yang Yu, Xin Lu, Xiaobo Liu, Ping The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Australian Institute of Artificial Intelligence University of Technology Sydney UltimoNSW2007 Australia The School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Singapore138634 Singapore

In this paper, we study the task of hallucinating an authentic high-resolution (HR) face from an occluded thumbnail. We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN, which exploits facial geometry priors to replenish and upsample (8×) the occluded and tiny faces (16×16 pixels). Pro-UIGAN iteratively (1) estimates facial geometry priors for low-resolution (LR) faces and (2) acquires non-occluded HR face images under the guidance of the estimated priors. Our multi-stage hallucination network upsamples and inpaints occluded LR faces via a coarse-to-fine fashion, significantly reducing undesirable artifacts and blurriness. Specifically, we design a novel cross-modal attention module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively. Such a design encourages joint feature learning across the input facial and landmark features, and deep feature correspondences will be discovered by attention. Thus, facial appearance features and facial geometry priors are learned in a mutually beneficial manner. Extensive experiments show that our Pro-UIGAN attains visually pleasing completed HR faces, thus facilitating downstream tasks, i.e., face alignment, face parsing, face recognition as well as expression classification. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Modified weak-value-amplification technique for measuring a mirror's velocity based on the Vernier effect

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Physical Review A 2022年第1期105卷 013718-013718页

作者： Jing-Hui Huang Fei-Fan He Xue-Ying Duan Guang-Jun Wang Xiang-Yun Hu Institute of Geophysics and Geomatics China University of Geosciences Lumo Road 388 430074 Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education 430074 Wuhan China School of Automation China University of Geosciences Lumo Road 388 430074 Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems 430074 Wuhan China

A modified weak-value-amplification (MWVA) technique to measure a mirror's velocity based on the Vernier effect is proposed. To demonstrate its enhanced sensitivity and higher signal-to-noise ratio (SNR), we use two cascaded Michelson interferometers with similar optical structures. One has a fixed mirror and acts as a fixed part of the Vernier scale, while the other, with a moving mirror, acts as a sliding part of the Vernier scale for velocity sensing. The envelope of the cascaded interferometers shifts much more than a single one with a certain enhancement factor, which is related to the free space range difference between them. In addition, we calculate the SNR based on the Fisher information with both the MWVA and traditional weak-value-amplification (TWVA) techniques. The results show that both the SNR and the sensitivity with our MWVA technique is greater than that of the TWVA technique within the range of our time measurement window. In particular, MWVA can present a viable and effective alternative to the TWVA technique out of the limit of resolution. Furthermore, by using the principles of the Vernier effect, it is applicable and convenient to improve the sensitivity and SNR in measuring other quantities with the TWVA technique.

关键词： Quantum measurements Quantum optics

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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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Modulated-Virtual-Vector-Based Predictive Current control for Dual Three-Phase PMSM With Enhanced Steady-State Performance

Modulated-Virtual-Vector-Based Predictive Current Control fo...

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Annual Conference of Industrial Electronics Society

作者： Ze Li Xingyu Zheng Jinhui Xia Liling Wang Yuanbo Guo Xiaonan Gao Zhiheng Liu Xiaohua Zhang College of Electronic and Information Engineering Hebei University Baoding China School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education Southeast University Nanjing China School of Electrical Engineering Dalian University of Technology Dalian China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The School of Electrical Engineering Dalian University of Technology Dalian China

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predictive current control (SV-PCC) for DTP-PMSM presents high torque ripple and current harmonics, and high computational burden. To solve those issues, a modulated-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM is proposed in this paper. Wherein, twenty-four VVs are synthesized by the inherent voltage vectors, and two VVs and one zero voltage vector with optimal duty cycles are determined and applied in each sampling period to improve the steady-state performance. The selection of optimal VVs and the calculation of the optimal duty cycles are simplified by integrating the deadbeat control and modulation scheme. Various comparisons are carried out to validate the effectiveness and superiority of the proposed MVV-PCC strategy.

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The parameters identification of magnetic core using fruit fly optimization algorithm

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Chemical engineering Transactions 2016年 51卷 169-174页

作者： Jiang, Wenjuan Shi, Yunbo Zhao, Wenjie Wang, Xiangxin Higher Educ. Key Lab. for Measuring and Contr. Technol. and Instrumentations of Heilongjiang Province School of Measurement Control Technology and Communications Engineering Harbin University of Science and Technology Harbin Heilongjiang Province150080 China School of Automation Engineering Northeast Dianli University Jilin Jilin Province132012 China

ISBN: (纸本)9788895608433

This paper is concerned about the parameters identification of the Jiles-Atherton hysteresis loop model using Fruit Fly Optimization Algorithm (FOA). An improved Fruit Fly optimization algorithm (IFOA) is proposed to overcome the drawback of FOA, such as easily falling into local optimum, low precision, and poor stability. The IFOA has been applied to identify Jiles-Atherton model parameters of conventional non-oriented electrical steel. The simulation results are compared with those of FOA and Particle Swarm Optimization (PSO), which shows the modelled M - H curve obtained with IFOA is in good agreement with the measured M - H curve and IFOA method has the advantages of better global searching ability, higher precision and stability. Copyright © 2016, AIDIC Servizi S.r.l.

关键词： Particle swarm optimization (PSO)

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Robust and Scalable Power System State Estimation via Composite Optimization

arXiv

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arXiv 2017年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center and the Electrical and Computer Engineering Department University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200072 China

—In today’s cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) one. However, due to nonconvexity and nonsmoothness, existing LAV solvers based on linear programming are typically slow, hence inadequate for real-time system monitoring. This paper develops two novel algorithms for efficient LAV estimation, which draw from recent advances in composite optimization. The first is a deterministic linear proximal scheme that handles a sequence of (5 ∼ 10 in general) convex quadratic problems, each efficiently solvable either via off-the-shelf toolboxes or through the alternating direction method of multipliers. Leveraging the sparse connectivity inherent to power networks, the second scheme is stochastic, and updates only a few entries of the complex voltage state vector per iteration. In particular, when voltage magnitude and (re)active power flow measurements are used only, this number reduces to one or two, regardless of the number of buses in the network. This computational complexity evidently scales well to large-size power systems. Furthermore, by carefully mini-batching the voltage and power flow measurements, accelerated implementation of the stochastic iterations becomes possible. The developed algorithms are numerically evaluated using a variety of benchmark power networks. Simulated tests corroborate that improved robustness can be attained at comparable or markedly reduced computation times for medium- or large-size networks relative to existing alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Electric power transmission networks

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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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Learning ReLU networks on linearly separable data: Algorithm, optimality, and generalization

arXiv

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arXiv 2018年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

Neural networks with REctified Linear Unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being e.g. Gaussian, or require the network size and/or training size to be sufficiently large. In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data are linearly separable and a hinge loss criterion is adopted. Leveraging the power of random noise perturbation, this paper presents a novel stochastic gradient descent (SGD) algorithm, which can provably train any single-hidden-layer ReLU network to attain global optimality, despite the presence of infinitely many bad local minima, maxima, and saddle points in general. This result is the first of its kind, requiring no assumptions on the data distribution, training/network size, or initialization. Convergence of the resultant iterative algorithm to a global minimum is analyzed by establishing both an upper bound and a lower bound on the number of non-zero updates to be performed. Moreover, generalization guarantees are developed for ReLU networks trained with the novel SGD leveraging classic compression bounds. These guarantees highlight a key difference (at least in the worst case) between reliably learning a ReLU network as well as a leaky ReLU network in terms of sample complexity. Numerical tests using both synthetic data and real images validate the effectiveness of the algorithm and the practical merits of the theory. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Adaptive fuzzy control of output-constrained switched nonlinear systems

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ICIC Express Letters 2015年第12期9卷 3355-3362页

作者： Niu, Ben Liu, Yanli Shen, Mouquan School of Control Science and Engineering Dalian University of Technology No. 2 Linggong Road Ganjingzi District Dalian China College of Mathematics and Physics Bohai University No. 19 Keji Road New Songshan District Jinzhou China Automation Research Institute Bohai University No. 19 Keji Road New Songshan District Jinzhou China College of Electrical Engineering and Control Science Nanjing Technology University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

In this paper, the problem of adaptive fuzzy tracking control is considered of uncertain switched nonlinear systems with an output constraint. First, a barrier Lyapunov function (BLF) is employed to deal with the output constraint. Then fuzzy logic systems are used to approximate the unknown nonlinearities and backstepping technique is employed to construct an adaptive fuzzy controller. The designed controller guarantees all the closed-loop signals remain bounded while the system output converges to a small neighborhood of the desired reference signal without violating the constraint. An example is given to demonstrate the validity of the developed result. © 2015 ICIC International.

关键词： Lyapunov functions

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