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arXiv 2023年

作者： Wiśniewska, Joanna Sawerwain, Marek Institute of Information Systems Faculty of Cybernetics Military University of Technology Gen. S. Kaliskiego 2 Warszawa00-908 Poland Institute of Control & Computation Engineering University of Zielona Góra Licealna 9 Zielona Góra65-417 Poland

The data classification task is broadly utilized in numerous fields of science and it may be realized by different known approaches (e.g. neural networks). However, in this work, quantum computations were harnessed to solve the problem. We take into consideration a quantum circuit which is based on the Variational Quantum Eigensolver (VQE) and so-called SWAP-Test what allows us to solve a classification problem connected with credit sales. More specifically, we cope with a decision problem of determining customer's reliability based on values of selected decision variables (e.g. generated turnover, history of cooperation). The classical data samples are converted into normalized quantum states. After this operation, samples may be processed by a circuit of quantum gates. The VQE approach allows training the parameters of a quantum circuit (so-called ansatz) to output pattern-states for each class. In the utilized data set, two classes may be observed - cases with low and high credit risk. However, the VQE circuit differentiates more classes than two (introduces more detailed cases) and the final results are obtained with the use of aforementioned SWAP-Test. The elaborated solution is compact and requires only logarithmically increasing number of qubits (due to the exponential capacity of quantum registers). Because of the low complexity of the presented quantum circuit, it is possible to perform experiments on currently available quantum computers, including the Noisy Intermediate-Scale Quantum (NISQ) devices. This type of devices, despite the presence of noise, is capable of solving the task analyzed in this work. All calculations, simulations, plots, and comparisons were implemented and conduced in the Python language environment. Source codes for each example of quantum classification can be found in the source code repository. Copyright © 2023, The Authors. All rights reserved.

关键词： Sales

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Koopman-based Kalman Filter for Fault Detection for the Superconducting Radio Frequency Cavities of the European XFEL

Koopman-based Kalman Filter for Fault Detection for the Supe...

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IEEE Conference on Decision and control

作者： Wasif Haider Syed Annika Eichler Ayla Nawaz Bindu Sharan Herbert Werner Institute of Control Systems Hamburg University of Technology Hamburg Germany Machine Beam Control Group Machine Division Deutsches Elektronen-Synchrotron DESY Germany

ISBN: (纸本)9781665436601

The Koopman operator is a novel approach to embed nonlinear dynamics into linear models. This work shows its successful application for fault detection to a large-scale facility with challenging real-time requirements: the European XFEL, which is the worldwide largest linear particle accelerator. We concentrate on the superconducting radio-frequency cavities, from which 808 exist and whose effective operation directly influences the performance of the whole facility. Thus, a proper fault detection scheme is desired. While a nonlinear state-space description of the cavity dynamics is well-known, its usage along with an unscented Kalman filter is not able to cope with the challenging online implementation requirements. Therefore, in this paper, we apply the Koopman operator technique to identify a finite-dimensional linear approximation of the nonlinear system. For the data-driven identification, the model knowledge is exploited by choosing physically motivated basis functions. With the linear approximation at hand, a linear Kalman filter can be applied. Results are presented for real experimental data. Compared to the unscented Kalman filter, the same detection capability but a speed-up of three orders of magnitude in calculation time can be achieved with the Koopman-based Kalman filter, which enables its implementation to the real facility.

关键词： Radio frequency Fault detection Europe Linear approximation Predictive models Superconducting filters Real-time systems

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Incremental dissipativity based control of discrete-time nonlinear systems via the LPV framework

arXiv

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arXiv 2021年

作者： Koelewijn, Patrick J.W. Tóth, Roland Weiland, Siep Control Systems Group Faculty of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 BudapestH-1111 Hungary

Unlike for Linear Time-Invariant (LTI) systems, for nonlinear systems, there exists no general framework for systematic convex controller design which incorporates performance shaping. The Linear Parameter-Varying (LPV) framework sought to bridge this gap by extending convex LTI synthesis results such that they could be applied to nonlinear systems. However, recent literature has shown that naive application of the LPV framework can fail to guarantee the desired asymptotic stability guarantees for nonlinear systems. Incremental dissipativity theory has been successfully used in the literature to overcome these issues for Continuous-Time (CT) systems. However, so far no solution has been proposed for output-feedback based incremental control for the Discrete-Time (DT) case. Using recent results on convex analysis of incremental dissipativity for DT nonlinear systems, in this paper, we propose a convex output-feedback controller synthesis method to ensure closed-loop incremental dissipativity of DT nonlinear systems via the LPV framework. The proposed method is applied on a simulation example, demonstrating improved stability and performance properties compared to a standard LPV controller design. Copyright © 2021, The Authors. All rights reserved.

关键词： Nonlinear systems

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Near-navigation-grade interferometric fiber optic gyroscope with an integrated optical chip

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Chinese Optics Letters 2020年第12期18卷 7-10页

作者： Kejun Shang Ming Lei Qiang Xiang Yonglin Na Lizhe Zhang Huaiyong Yu School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Inertial Technology Beijing Institute of Automatic Control EquipmentBeijing 100074China

We present a near-navigation-grade interferometric fiber optic gyroscope(IFOG)based on an integrated optical *** chip comprises a light source,a photodiode,and a 3 dB coupler within an area of 48 mm^(2).By interrogating with an integrated optical modulator and a small-diameter sensing coil,the IFOG is *** allows for a significant reduction in size,weight,power consumption,and *** performance data of a gyro proto type exhibits 0.018 deg/h bias instability.

关键词： gyroscopes fiber optics fiber optics sensors integrated optics.

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Robust Constraint Satisfaction in Data-Driven MPC

Robust Constraint Satisfaction in Data-Driven MPC

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IEEE Conference on Decision and control

作者： Julian Berberich Johannes Köhler Matthias A. Müller Frank Allgöwer Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Institute of Automatic Control Leibniz University Hannover Hannover Germany

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data. The scheme predicts future trajectories based on data-dependent Hankel matrices, which span the full system behavior if the input is persistently exciting. This paper extends previous work on data-driven MPC by including a suitable constraint tightening which ensures that the closed-loop trajectory satisfies desired pointwise-in-time output constraints. Furthermore, we provide estimation procedures to compute system constants related to controllability and observability, which are required to implement the constraint tightening. The practicality of the proposed approach is illustrated via a numerical example.

关键词： Trajectory Noise measurement Linear systems Stability analysis Numerical models Predictive models Data models

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Back to Science in Searching for SARS-CoV-2 Origins

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China CDC weekly 2023年第14期5卷 315-317页

作者： William J Liu Wenwen Lei Xiaozhou He Peipei Liu Qihui Wang Zhiqiang Wu Yun Tan Shuhui Song Gary Wong Jian Lu Jingkun Jiang Qiang Wei Mingkun Li Juncai Ma Xiaozhong Peng Yixue Li Baoxu Huang Yigang Tong Jun Han Guizhen Wu NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational MedicineRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina Beijing Institute of Genomics Chinese Academy of Sciencesand China National Center for BioinformationBeijingChina Laboratory of Molecular Virology and Immunology Institut Pasteur of ShanghaiChinese Academy of SciencesShanghaiChina State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina School of Environment Tsinghua UniversityBeijingChina National Pathogen Resource Center Chinese Center for Disease Control and Prevention(China CDC)BeijingChina Microbial Resource and Big Data Center Chinese National Microbiology Data Center(NMDC)Institute of MicrobiologyChinese Academy of SciencesBeijingChina State Key Laboratory of Medical Molecular Biology Department of Molecular Biology and BiochemistryInstitute of Basic Medical SciencesMedical Primate Research CenterNeuroscience CenterChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijingChina School of Life Science and Technology ShanghaiTech UniversityShanghaiChina Bio-Med Big Data Center Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghaiChina China Animal Health and Epidemiology Center Qingdao CityShandong ProvinceChi

In recent decades,emerging and re-emerging human-infecting pathogens have been represented as huge threats to public health and have become a global concern(1).After outbreaks of two coronaviruses(CoVs),severe acute respiratory syndrome coronavirus(SARS-CoV)and Middle East respiratory syndrome coronavirus(MERS-CoV),severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)became the first-known pandemic hastening CoV with tremendous wrecking to the world(2).The origin tracing of these emerging pathogens is of great significance in infectious disease prevention and control(3–4).The origin of SARS-CoV-2 remains elusive after the more than 3-year pandemic,though scientists around the world are making great *** the experience of studying many other infectious pathogens,origin tracing is systematic and time-consuming *** supposed origins of many infectious pathogens are still in debate,including SARS-CoV and human immunodeficiency virus,etc(5).

关键词： prevention respiratory acute

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Two Enhanced-rate Power Allocation Strategies for Active IRS-assisted Wireless Network

arXiv

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arXiv 2023年

作者： Cheng, Qiankun Dong, Rongen Cai, Wenlong Liu, Ruiqi Shu, Feng Wang, Jiangzhou The School of Information and Communication Engineering Hainan University Haikou570228 China The School of Information and Communication Engineering Collaborative Innovation Center of Information Technology Hainan University Haikou570228 China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Netwon's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial. Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and appoaches exhaustive search as the number of IRS reflecting elements goes to large-scale. Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

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Graph Attention Network for Word Embeddings 7th

Graph Attention Network for Word Embeddings

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Long, Yunfei Xu, Huosheng Qi, Pengyuan Zhang, Liguo Li, Jun College of Computer Science and Technology Harbin Engineering University Harbin150001 China Wuhan Digital Engineering Institute Wuhan430000 China China Industrial Control Systems Cyber Emergency Research Team Beijing100000 China

ISBN: (纸本)9783030786113

The word embeddings approaches have attracted extensive attention and widely used in many natural language processing (NLP) tasks. Relatedness between words can be reflected in vector space by word embeddings. However, the current word embeddings approaches commonly do not explore the context-specific information of word deeply in the overall corpus. In this paper, we propose to use graph attention network for word embeddings. We build a large single word graph for a corpus based on word order, then learn a word embeddings graph attention network (WEGAT) for the corpus. Our WEGAT is initialized with one-hot representation for word. We propose to use masked language model (MLM) as supervised task. In addition, through the text classification experiment, it is showed that accuracy of the word embeddings represented by WEGAT is higher than the current method for the same classification method. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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An Adaptive Distributed Observer for A Class of Discrete-Time Uncertain Linear systems over Acyclic Digraphs

An Adaptive Distributed Observer for A Class of Discrete-Tim...

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IEEE Conference on Decision and control

作者： Tao Liu Jie Huang Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China Shenzhen Research Institute and Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong

This paper proposes an adaptive distributed observer for a class of discrete-time uncertain linear leader systems. The leader system is assumed to be neutrally stable with unknown parameters in the system matrix. Such a leader system can produce multi-tone sinusoidal signals with unknown frequencies, magnitudes, and phases. Under the assumption that the digraph of the communication network is a spanning tree with the leader system as the root, the proposed adaptive distributed observer is shown to be capable of estimating over the communication network not only the leader's state, but also the unknown parameters of the leader's system matrix.

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