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7th International Conference on control, Decision and Information Technologies, CoDIT 2020

作者： Vajpayee, Vineet Becerra, Victor Bausch, Nils Banerjee, Shohan Deng, Jiamei Shimjith, S.R. John Arul, A. University of Portsmouth School of Energy and Electronic Engineering PortsmouthPO1 3DJ United Kingdom Leeds Beckett University School of Built Environment Engineering and Computing LeedsLS6 3QS United Kingdom Bhabha Atomic Research Centre Reactor Control System Design Section Mumbai400 085 India Indira Gandhi Centre for Atomic Research Probabilistic Safety Reactor Shielding and Nuclear Data Section Kalpakkam603 102 India

ISBN: (纸本)9781728159539

This work combines the subspace predictive control technique with the integral sliding mode control strategy to formulate a novel robust subspace predictive control scheme. The subspace predictive controller provides the nominal control whereas the integral sliding mode controller gives the discontinuous control action. The aim is to improve the capability of subspace predictive controller in handling uncertainties and external disturbances. The proposed control scheme is evaluated with a simulated pressurized water nuclear reactor. The effectiveness of the proposed technique is demonstrated for two different load-following operations in the presence of uncertainties. © 2020 IEEE.

关键词： Pressurized water reactors

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Fast Calibration of Agent-Based Model using Mean-Field Approach

Fast Calibration of Agent-Based Model using Mean-Field Appro...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wanze Lu Gang Xiong Xudong Liu Sheng Liu Peijun Ye Liguo Zhang Faculty of Information and Technology Beijing University of Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

Calibration of simulation system is an important step to establish a reliable simulation system. In this paper, based on the existing mean field calibration theory, a new fast calibration method for complex urban traffic system based on mean field method is discussed by establishing the relationship between the macroscopic observation quantity and the proxy microscopic parameters. This method makes use of the machine learning method, and reduces the dimension of the state vector in the discrete time period, so that the dimension of the macroscopic state transition matrix is lower, and then reduces the computational complexity of the system calibration. Finally, the calibration results of the simulation system were verified by SUMO traffic simulation software.

关键词： Visualization Computational modeling Microscopy Digital twin Machine learning Traffic control Reliability theory

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Joint Face Alignment and 3D Face Reconstruction with Efficient Convolution Neural Networks

Joint Face Alignment and 3D Face Reconstruction with Efficie...

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International Conference on Pattern Recognition

作者： Keqiang Li Huaiyu Wu Xiuqin Shang Zhen Shen Gang Xiong Xisong Dong Bin Hu Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

3D face reconstruction from a single 2D facial image is a challenging and concerned problem. Recent methods based on CNN typically aim to learn parameters of 3D Morphable Model (3DMM) from 2D images to render face alignment and 3D face reconstruction. Most algorithms are designed for faces with small, medium yaw angles, which is extremely challenging to align faces in large poses. At the same time, they are not efficient usually. The main challenge is that it takes time to determine the parameters accurately. In order to address this challenge with the goal of improving performance, this paper proposes a novel and efficient end-to-end framework. We design an efficient and lightweight network model combined with Depthwise Separable Convolution and Muti-scale Representation, Lightweight Attention Mechanism, named Mobile-FRNet. Simultaneously, different loss functions are used to constrain and optimize 3DMM parameters and 3D vertices during training to improve the performance of the network. Meanwhile, extensive experiments on the challenging datasets show that our method significantly improves the accuracy of face alignment and 3D face reconstruction. Model parameters and complexity of our method are also improved greatly.

关键词： Training Solid modeling Three-dimensional displays Convolution Face recognition Neural networks Complexity theory

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Disturbance Observer-based Subspace Predictive control of a Pressurized Water-type Nuclear Reactor 7

Disturbance Observer-based Subspace Predictive Control of a ...

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7th International Conference on control, Decision and Information Technologies, CoDIT 2020

ISBN: (纸本)9781728159539

This work presents a disturbance observer-based predictive control strategy using a subspace matrix structure. The aim is to improve the capability of classical predictive controllers in handling external disturbances. A subspace-based predictive controller is designed directly from measurements. Then, a disturbance observer is designed using subspace matrices to estimate the external disturbance. Both of the designs are integrated using a feed-forward plus feed-back strategy to form the proposed control strategy. The proposed scheme is tested with a simulated model of a pressurized water nuclear reactor. The effectiveness of the proposed technique is demonstrated for two different load-following operations. Further, a quantitative analysis is performed to analyse the control performance of the proposed approach. © 2020 IEEE.

关键词： controllers

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Adaptive fuzzy convolution networks for uncertainty-aware image analysis in ambiguous environments

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Expert Systems with Applications 2025年 292卷

作者： Saeed Iqbal Xiaopin Zhong Muhammad Attique Khan Zongze Wu Amir Hussain Shrooq Alsenan Weixiang Liu College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia School of Computing Merchiston Campus Edinburgh Napier University Edinburgh EH10 5DT Scotland UK Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh 11671 Saudi Arabia

In complex and ambiguous situations, effective image analysis is severely hampered by the instability and unpredictability of convolutional frameworks and fuzzy systems, especially when it comes to managing noise-induced uncertainties and nonlinear interactions. The innovative framework Adaptive Fuzzy Convolution Networks (AFCN) is presented in this paper to tackle the problems of nonlinear interactions and noise-induced uncertainties in image processing in ambiguous situations. To improve feature extraction and uncertainty management, the suggested technique combines context-aware fuzzy aggregation operators, adaptive kernel parameterization, and dynamic nonlinearity modulation. Stability in fuzzy systems under different noise situations is ensured by the framework’s use of a feedback-driven method to modify the nonlinearity parameter ( γ k ) depending on noise characterisation. Multiscale fuzzy convolution with adaptive kernel parameters (center ξ s , k ( x ) and spread σ s , k ( x ) ) that react to localized uncertainty gradients are also incorporated into the model, allowing for reliable feature extraction in noisy environments. A hybrid regularization approach that combines fuzzification and L 2 regularization is presented in order to smooth uncertainty propagation over multiscale convolutions and reduce overfitting. The capacity of AFCN to dynamically adjust to noise while maintaining structural integrity and reducing error measures like F-MAE and F-MSE is what makes it innovative. State-of-the-art performance is demonstrated by experimental assessments on several datasets, with an accuracy of 97.2 %, an F1 Score of 97.3 %, and exceptionally low error metrics like F-MAE of 0.005 and F-MSE of 0.010. These developments make AFCN a potent instrument for practical uses like autonomous driving, where resilience to background noise is crucial, and medical imaging, where accurate feature recognition is crucial.

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Bimodal person recognition using dorsal-vein and finger-vein images

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Procedia Computer Science 2020年 176卷 1121-1130页

作者： Amal Abbes Randa Boukhris Trabelsi Yassine Ben Ayed Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL University of Sfax Tunisia Computers Imaging Electronics and Systems Group (CIELS) from Advanced Control and Energy Management Laboratory(CEM-Lab) University of Sfax Sfax Engineering School BP W 3038 Sfax Tunisia

Nowadays, human recognition with biometric characteristic have been investigated in many researchers. However, traditional biometric methods are not always robust against counterfeit and spoof attacks. The challenge of this work is to develop a bimodal biometric system, based on more reliable and robust characteristics against counterfeiting. Indeed, biometric system based on the venous network of the hand gives higher recognition rate compared to other systems. Furthermore, the fusion of two prints minimums the error rate and counterfeits. In our biometric system, the most challenging phase is the feature extraction step. For this, we propose a new feature extraction approach based on the concatenation of the pyramid of Difference of Gaussian (DoG) and Local Line Binary Patterns (LLBP) histograms (H-DoG_LLBP). To evaluate the proposed system, we opted for Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers. The experimental study is based on the dorsal hand vein BOSPHORUS database and finger vein MMCBNU_6000 database. Our system presents an area under curve (AUC) equal to 0.99, and 0.001 mean square error (MSE) with ANN, and 0.0042 equal error rate (EER) with SVM.

关键词： Bimodal Biometric system hand veins

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A Scale Adaptive Kernel Correlation Filter-Based Tracker with Optimized Update Strategy

A Scale Adaptive Kernel Correlation Filter-Based Tracker wit...

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第三十九届中国控制会议

作者： Minghui Shi Yong Heng Liang Han Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing Institute of Electronic System Engineering School of Sino-French Engineer Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Correlation filter-based trackers have achieved competitive results in the field of visual tracking, but the abilities to deal with the multi-scale and keep the tracking stability still need to be improved. In this article, the complex background tracking problems are studied based on the kernel correlation filter. In order to address the fixed size limitation, we present an improved scheme, which can not only realize scale adaption, but also retain the advantages of the original algorithm. In addition, the adaptive update learning rate of the model is achieved by using the peak sidelobe ratio as an indicator to measure the filter performance. Using the index, we can selectively reduce the model interference to low precision information. Moreover, we design a redetection mechanism that runs automatically when the tracking evaluation indicator is low. Compared with the radical model update methods, those conservative update strategies can effectively decrease the model pollution and ensure the tracking effect. Extensive verifications based on OTB-100 benchmark show that the optimized algorithm improves the accuracy, robustness, and generalization ability of target tracking.

关键词： Target tracking Kernel correlation filter Scale adaption Peak sidelobe ratio Learning rate adaption

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Urgency-aware optimal routing in repeated games through artificial currencies

arXiv

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arXiv 2020年

作者： Salazar, Mauro Paccagnan, Dario Agazzi, Andrea Heemels, W.P.M.H. Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology Netherlands Computational Optimization Group Department of Computing Imperial College London United Kingdom Department of Mathematics Duke University United States

When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested agents. How can we reconcile the two? In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish agents in a system-optimal fashion, while accounting for their temporal preferences. We instantiate the framework in a parallel-network whereby agents commute repeatedly (e.g., daily) from a common start node to the end node. Thereafter, we focus on the specific two-arcs case whereby, based on an artificial currency, the agents are charged when traveling on the first, fast arc, whilst they are rewarded when traveling on the second, slower arc. We assume the agents to be rational and model their choices through a game where each agent aims at minimizing a combination of today’s discomfort, weighted by their urgency, and the average discomfort encountered for the rest of the period (e.g., a week). We show that, if prices of artificial currencies are judiciously chosen, the routing pattern converges to a system-optimal solution, while accommodating the agents’ urgency. We complement our study through numerical simulations. Our results show that it is possible to achieve a system-optimal solution whilst reducing the agents’ perceived discomfort by 14-20% when compared to a centralized optimal but urgency-unaware policy. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimal systems

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Unpolarized transverse momentum dependent parton distributions of the nucleon from lattice QCD

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Physical Review D 2024年第11期109卷 114513-114513页

作者： Jin-Chen He Min-Huan Chu Jun Hua Xiangdong Ji Andreas Schäfer Yushan Su Wei Wang Yi-Bo Yang Jian-Hui Zhang Qi-An Zhang Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Department of Physics Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center Institut für Theoretische Physik Southern Center for Nuclear-Science Theory (SCNT) CAS Key Laboratory of Theoretical Physics School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Beijing/Hangzhou China School of Physical Sciences School of Science and Engineering Center of Advanced Quantum Studies Department of Physics School of Physics

We present a first lattice QCD calculation of the unpolarized nucleon’s isovector transverse-momentum-dependent parton distribution functions (TMDPDFs), which are essential to predict observables of multiscale, semi-inclusive processes in the standard model. We use a Nf=2+1+1 MILC ensemble with valence clover fermions on a highly improved staggered quark (HISQ) sea to compute the quark momentum distributions in a large-momentum nucleon on the lattice. The state-of-the-art techniques in renormalization and extrapolation in the correlation distance on the lattice are adopted. The perturbative kernel up to next-to-next-to-leading order is taken into account, and the dependence on the pion mass and the hadron momentum is explored. Our results are qualitatively comparable with phenomenological TMDPDFs, which provide an opportunity to predict high energy scatterings from first principles.

关键词： Lattice QCD Nucleon distribution Parton distribution functions

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Volume control of low-cost ventilator with automatic set-point adaptation

arXiv

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arXiv 2020年

作者： Hewing, Lukas Menner, Marcel Tachatos, Nikolaos Schmid Daners, Marianne du Pasquier, Cosima Lumpe, Thomas S. Shea, Kristina Carron, Andrea Zeilinger, Melanie N. Institute for Dynamic Systems and Control ETH Zurich Zurich8092 Switzerland Product Development Group Zurich ETH Zurich Zurich8092 Switzerland Engineering Design and Computing Laboratory ETH Zurich Zurich8092 Switzerland

This paper considers the control design for a low-cost ventilator that is based on a manual resuscitator bag (also known as AmbuBag) to pump air into the lungs of a patient who is physically unable to breathe. First, it experimentally shows that for accurately tracking tidal volumes, the controller needs to be adapted to the individual patient and the different configurations, e.g., hardware or operation modes. Second, it proposes a set-point adaptation algorithm that uses sensor measurements of a flow meter to automatically adapt the controller to the setup at hand. Third, it experimentally shows that such an adaptive solution improves the performance of the ventilator for various setups. One objective of this paper is to increase awareness of the need for feedback control using sensor measurements in low-cost ventilator solutions in order to automatically adapt to the specific scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Costs

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