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arXiv 2020年

作者： Zhang, Yang Tsang, Ivor W. Li, Jun Liu, Ping Lu, Xiaobo Yu, Xin The School of Automation Southeast University Nanjing210096 China The Australian Artificial Intelligence Institute University of Technology Sydney UltimoNSW2007 Australia The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Singapore138634 Singapore The Research School of Engineering Australian National University Canberra Australia

Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both tasks seamlessly for daily-life unconstrained face images. In this paper, we present a novel Vivid Face Hallucination Generative Adversarial Network (VividGAN) for simultaneously super-resolving and frontalizing tiny non-frontal face images. VividGAN consists of coarse-level and fine-level Face Hallucination Networks (FHnet) and two discriminators, i.e., Coarse-D and Fine-D. The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i.e., fine-grained facial components, to attain a frontal HR face image with authentic details. In the fine-level FHnet, we also design a facial component-aware module that adopts the facial geometry guidance as clues to accurately align and merge the frontal coarse HR face and prior information. Meanwhile, two-level discriminators are designed to capture both the global outline of a face image as well as detailed facial characteristics. The Coarse-D enforces the coarsely hallucinated faces to be upright and complete while the Fine-D focuses on the fine hallucinated ones for sharper details. Extensive experiments demonstrate that our VividGAN achieves photo-realistic frontal HR faces, reaching superior performance in downstream tasks, i.e., face recognition and expression classification, compared with other state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Complex systems Modelling of UK Winter Wheat Yield

SSRN

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SSRN 2022年

作者： Pearson, Simon Hall, R.J. Wei, H.-L. Ma, Y. Fang, Shibo Hanna, Edward School of Geography and Lincoln Centre for Water and Planetary Health University of Lincoln LincolnLN6 7FL United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Lincoln Institute of Agri-Food Technology University of Lincoln Riseholme Campus LincolnLN2 2NG United Kingdom Institute of Ecological and Agricultural Meteorology Chinese Academy of Meteorological Science No. 46 Zhongguancun South Road Beijing100081 China

Wheat is one of the most important global crops and understanding the drivers of wheat yield has important societal benefits. Climate variables are particularly important in determining interannual variations in wheat yield, either as primary factors which directly influence the stages of wheat growth, or as secondary factors through their influence on pests, diseases and soil conditions. Here we present a new approach to model wheat yield modelling;an empirical method based on nonlinear complex systems identification, known as NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs). We demonstrate the NARMAX analytical approach for a specific site, Rothamsted, UK, where detailed meteorological variables are available, together with specific information on site conditions and crop growth stages. An illustrative comparison of NARMAX yield forecasts is made with the WOFOST crop model. We also demonstrate how regional wheat yield forecasts might be derived from a new gridded meteorological data product. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Distributed Adaptive Observer for Discrete-Time LTI SISO System with Completely Unknown Periodic Input

Distributed Adaptive Observer for Discrete-Time LTI SISO Sys...

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第三十八届中国控制会议

作者： Yaoyao Wen Donghua Chen Ya Zhang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Architecture Engineering Nanjing Institute of Technology

This paper focuses on designing a distributed observer for multiple sensors to estimate the input and state of a linear-time-invariant(LTI) single-input-single-output(SISO) discrete-time system with completely unknown periodic input. A consensus based adaptive observer to estimate the unknown input. The observer assumes the form of an adaptive finite impulse response(FIR) filter, which requires the agent to get the model and output of the target. In this paper, an adaptive distributed estimation algorithm is designed for some agents that cannot get the target model and output to estimate the target state, output and unknown input simply by relying on the information of its neighbors. Experimental results are provided to further verify and testify the effectiveness of the algorithm.

关键词： distributed estimation adaptive FIR unknown input sensor network

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Delays in Model Reduction of Chemical Reaction Networks

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IFAC-PapersOnLine 2018年第14期51卷 100-105页

作者： Lipták, György Hangos, Katalin M. Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences P.O. Box 63 BudapestH-1518 Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary

A novel engineering model reduction method is proposed in this paper that can be applied to a chemical reaction network (CRN) with chains of linear reactions. The reduced model is a delayed CRN with possibly different delays but with less state variables than the original model. As the first step of the model reduction, a decomposition method is also developed to transform chains with joint reactions into independent chains of linear reactions. The well known example of McKeithan's network is used as a case study to illustrate the basic concepts and the design method. © 2018

关键词： Reduction

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Enabling predictive analytics for smart manufacturing through an IIoT platform ⁎

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IFAC-PapersOnLine 2020年第3期53卷 179-184页

作者： T. Cerquitelli N. Nikolakis P. Bethaz S. Panicucci F. Ventura E. Macii S. Andolina A. Marguglio K. Alexopoulos P. Petrali A. Pagani P. van Wilgen M. Ippolito Department of Control and Computer Engineering Politecnico di Torino Turin ITALY Laboratory for Manufacturing Systems & Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras GREECE COMAU S.p.A. Turin ITALY Interuniversity Department of Regional and Urban Studies and Planning Politecnico di Torino Turin ITALY SynArea Consultants S.r.l. Turin ITALY Engineering Ingegneria Informatica S.p.A. Palermo ITALY Whirlpool EMEA S.p.A. Biandronno ITALY VDL Weweler Apeldoorn NETHERLANDS

In the last few years, manufacturing systems are getting gradually transformed into smart factories. In this context, an increasing number of information and communication technologies is incorporated towards facilitating management, production, and control processes. The introduction of advanced embedded systems with enhanced connectivity produces a vast amount of data, posing a challenge in terms of data analytics. However, the in-time collection and analysis of acquired data can create insight into the manufacturing process as well as its assets. One aspect of major importance for every production system is preserving its equipment in operational condition, and within those limits that could minimize unplanned breakdowns and production stoppages. This paper details the predictive analytics methodology integrated into the SERENA platform able to: (i) streamline the prognostics of the industrial components, (ii) characterize the health status of the monitored equipment, (iii) generate an early warning related to the condition of the equipment, and (iv) forecast the future evolution of the monitored equipment’s degradation. To demonstrate the effectiveness of the proposed methodology, different use cases are discussed with results obtained on real-data collected in real-time from the industrial environments.

关键词： Data analytics data management analytics architecture predictive analytics production systems

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Hybrid protocol for distributed non-differentiable extended monotropic optimization

Hybrid protocol for distributed non-differentiable extended ...

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International Conference on control and automation (ICCA)

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Advanced Innovation Center for Intelligent Robots and Systems (Beijing Institute of Technology) Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education Beijing China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper presents a hybrid protocol design for distributed non-differentiable extended monotropic optimization problems, which have various applications in large-scale optimization and evolutionary computation. The considered objective function is the sum of local non-differentiable objective functions, which are assigned to different agents in multi-agent networks, with local set constraints and affine equality constraints. Each agent can only access to its local non-differentiable objective function, local set constraint and equality constraint, and exchange information with its neighbors to obtain the global optimal solution. For this type of optimization problems, we propose a distributed state-dependent hybrid method over multi-agent networks. In addition, we properly design the jump map and jump set to improve the transient performance and accelerate the consensus process of existing distributed continuous-time methods. With invariance principle of hybrid dynamical systems, we prove that the proposed hybrid protocol converges to the global optimal solution. We verify that the proposed hybrid method has a good convergence property and transient performance through numerical experiments.

关键词： Optimization Protocols Linear programming Dynamical systems Heuristic algorithms Convergence Transient analysis

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Analysis of the Advantages of Millimeter Waves for Video Traffic Transmission in 5G Networks

Analysis of the Advantages of Millimeter Waves for Video Tra...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Mohammed Saleh Ali Muthanna Abdulfattah Mohammed Obadi Musaeed Ammar Muthanna Maria Filimonova Yaqoob Ahmed Hazzaa Esmail Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Faculty of Infocommunication Networks and Systems Saint -Petersburg State University of Telecommunications Saint Petersburg Russia Department of Information and Communication Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

The growing interest in video-based applications has resulted in a rapid increase in wireless data traffic and meeting the stringent quality-of-experience (QoE) demands for such type of traffic poses a great challenge given the scarce spectrum. Millimeter waves suffers from the huge losses during the propagation in comparison with other communications. However, with smaller cell sizes, spectral efficiency increases, atmospheric absorption and rain do not yield additional losses on the track for cells of 200 m size. Therefore, mmWave communication is mainly used for the internal environment, as well as access to small cells and feedback of data with cell sizes of 200 m. To this end, this paper presents models for transmission of video traffic in 5G networks based on millimeter waves in conjunction with cloud units on RAN boundary to offload the underlaying network. The system achieves higher efficiency in terms of unloading, overloading, coverage and delay. The system is modeled in terms of the probability of blocking for three scenarios i.e. 1) Using mmWave radio interface 2) Using D2D technology 3) Without using D2D technology; each of them is a separate radio interface. The simulation results show that the worst-case scenario is to use a network without D2D and cloud layers.

关键词： Device-to-device communication Millimeter wave communication 5G mobile communication Millimeter wave technology Relays Task analysis Cloud computing

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Research and development of a stereo encoder of a FM-transmitter based on FPGA 1st

Research and development of a stereo encoder of a FM-transmi...

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1st International Conference on Computer Science, engineering and Education Applications, ICCSEEA2018

作者： Artem, Volokyta Volodymyr, Shymkovych Ivan, Volokyta Vladyslav, Vasyliev Department of Computer Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 37 Prosp. Peremohy Kyiv Ukraine Department of Automation and Control in Technical Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 37 Prosp. Peremohy Kyiv Ukraine Royal Caribbean International FL United States

ISBN: (纸本)9783319910079

This paper describes the research and development of the digital stereo encoder FM transmitter including its implementation on Field Programmable Gate Array (FPGA). The development of structural schemes of the digital part of the FM transmitter exciter, the stereo encoder and the digital synthesizer of the sine wave on the FPGA is also described. Paper includes a description of development of a digital synthesizer of a sinusoidal signal on a FPGA, which uses a tabular method to generate the sinusoidal wave, fourth part of its period is written to memory and the rest of the period is calculated using the first part. To generate five sinusoids of different frequencies in a stereo encoder, only one table in memory used. The simulation of the stereocoder on the FPGA in carried out in the specialized Computer-Aided Design (CAD). The graphs of the left and right balance signals, the sum and difference of these signals, the signal of difference of the two sine waves which have their amplitude modulated and with a carrier signal of 19 kHz and the complex stereo signal are constructed. After receiving a complex stereo signal its frequency modulation is carried out with a carrier signal of 100 MHz, the FPGA chip outputs the frequency code of modulated signal. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Digital signal processing

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Affinely Parametrized State-space Models: Ways to Maximize the Likelihood Function

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IFAC-PapersOnLine 2018年第15期51卷 718-723页

作者： Wills, Adrian Yu, Chengpu Ljung, Lennart Verhaegen, Michel School of Electrical Engineering and Computer Science University of Newcastle Australia School iof Automation Beijing Institute of Technology China Division of Automatic Control Department of Electrical Engineering Linkoping University Sweden Delft Center for Systems and Control Delft University Delft2628CD Netherlands

Using Maximum Likelihood (or Prediction Error) methods to identify linear state space model is a prime technique. The likelihood function is a nonconvex function and care must be exercised in the numerical maximization. Here the focus will be on affine parameterizations which allow some special techniques and algorithms. Three approaches to formulate and perform the maximization are described in this contribution: (1) The standard and well known Gauss-Newton iterative search, (2) a scheme based on the EM (expectation-maximization) technique, which becomes especially simple in the affine parameterization case, and (3) a new approach based on lifting the problem to a higher dimension in the parameter space and introducing rank constraints. © 2018

关键词： Maximum likelihood estimation

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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