检索结果-内蒙古大学图书馆

Data-driven gradient algorithm for high-precision quantum control

Physical Review A 2018年第4期97卷 042122-042122页

作者： Re-Bing Wu Bing Chu David H. Owens Herschel Rabitz Department of Automation Tsinghua University Beijing 100084 China Center for Quantum Information Science and Technology BNRist Beijing 100084 China School of Electronic and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom Department of Automation Zhengzhou University Zhengzhou 450001 China Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield S1 3JD United Kingdom Department of Chemistry Princeton University Princeton 08544 NJ United States

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

关键词： Quantum control Quantum gates

来源：评论

学校读者我要写书评

暂无评论

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

引用

SENSORS 2020年第3期20卷 726-726页

作者： Lopez-Vazquez, Vanesa Lopez-Guede, Jose Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo DS Labs R+D+I unit of Deusto Sistemas S.A. 01015 Vitoria-Gasteiz Spain Department of System Engineering and Automation Control Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country (UPV/EHU) Nieves Cano 12 01006 Vitoria-Gasteiz Spain se.uhe@zepol.mj Institute of Marine Sciences National Research Council of Italy (CNR) 19032 La Spezia Italy ti.rnc.ramsi.ps@iniram.enomis Stazione Zoologica Anton Dohrn (SZN) 80122 Naples Italy ti.mpvinu@illenaf.e (E.F.) se.cisc.mci@izzugaj (J.A.) Stazione Zoologica Anton Dohrn (SZN) 80122 Naples Italy ti.mpvinu@illenaf.e (E.F.) se.cisc.mci@izzugaj (J.A.) Department of Life and Environmental Sciences Polytechnic University of Marche Via Brecce Bianche 60131 Ancona Italy Institute of Marine Research PO Box 1870 5817 Bergen Norway on.ih@nesnhoj.nepse Stazione Zoologica Anton Dohrn (SZN) 80122 Naples Italy ti.mpvinu@illenaf.e (E.F.) se.cisc.mci@izzugaj (J.A.) Instituto de Ciencias del Mar (ICM) of the Consejo Superior de Investigaciones Científicas (CSIC) 08003 Barcelona Spain

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

关键词： cabled observatories artificial intelligence deep learning machine learning deep-sea fauna

来源：评论

学校读者我要写书评

暂无评论

Study on data-driven PEMFC humidity mechanism soft-sensing model

Study on data-driven PEMFC humidity mechanism soft-sensing m...

引用

International Conference on Applied Machine Learning and Data Science

作者： Baohua Tan Hongbin Chen Rui Quan Shuhai Quan Yuanye Zhou School of Science Hubei University of Technology Hubei Engineering Technology Research Center of Energy Photoelectric Device and System Hubei University of Technology Department of Respiratory Medicine Renmin Hospital of Wuhan University School of Electrical and Electronic Engineering Hubei University of Technology The New Energy Detection and Control Technology Research Centre Wuhan University of Technology

ISBN: (纸本)9781713813279

Proton exchange membrane fuel cell(PEMFC) has a special closed structure,whose humidity cannot be measured *** paper selects the diffusion model as the modelling and analysis basis of the humidity mechanism soft-sensing based on the experimental research *** the study of the basic principles of the diffusion model,a one-dimensional datadriven proton exchange membrane fuel cell humidity mechanism soft-sensing model is analysed and *** last,the actual experimental data drives the humidity mechanism soft-sensing model and the predicted output error based on the humidity mechanism softsensing model can be controlled within 3%.The output results verify the effectiveness of the humidity mechanism soft-sensing model.

关键词： PEMFC Study on data-driven PEMFC humidity mechanism soft-sensing model

来源：评论

学校读者我要写书评

暂无评论

Stability and Performance Analysis of Saturated systems Using an Enhanced Max Quadratic Lyapunov Function

引用

IFAC-PapersOnLine 2017年第1期50卷 11847-11852页

作者： Li Y. Lin Z. Li N. Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 United States

This paper revisits the problem of estimating the domain of attraction and the nonlinear L2 gain of a saturated system with an algebraic loop. A max quadratic Lyapunov function, the maximum function of a group of quadratic Lyapunov functions, is proposed for use in the stability and performance analysis for a saturated system. The max quadratic Lyapunov function involves a partitioning of the state space. Exploiting the special properties of regions of the state space, we propose in this paper an enhanced max quadratic Lyapunov function, which results from adding a term that characterizes the regions of the state space to the max quadratic Lyapunov function. The matrix associated with the enhanced max quadratic Lyapunov function is not required to be positive definite, and thus a set of less conservative stability and performance conditions are established, from which a larger estimate of the domain of attraction and a tighter estimate of the nonlinear L2 gain can be obtained. Simulation results indicate the effectiveness and superiority of the proposed method. © 2017

关键词： Domain of attraction enhanced max quadratic Lyapunov functions input saturation L2 gain

来源：评论

学校读者我要写书评

暂无评论

Corrections to “A Study on the control Method of Lateral Displacement and Yaw Angle in Curved Driving of IRWs system” [Apr 18 Art. no. 0602505]

引用

IEEE Transactions on Applied Superconductivity 2019年第6期29卷

作者： Jun Hui Won Ye Jun Oh Ju Lee Han Woong Ahn Hyung Woo Lee Yonho Cho Department of Electrical Engineering Hanyang University College of Engineering Seoul South Korea Department of Electrical Engineering Hanyang University Seoul South Korea Department of Electrical and Electronic Engineering Hanyang University Seoul South Korea Satellite Control System Team Korea Aerospace Research Institute Daejeon South Korea Department of Railway Vehicle System Engineering Korea National University of Transportation Uiwang South Korea Bogie Development Team Hyundai Rotem Uiwang South Korea

In above paper, the figures were named incorrectly. The correctly named figures and explanations follow. Fig. 1. shows the magnetic flux density distribution of the motor. As shown in the figure, the magnetic flux densities are concentrated on the lip and bridge portions and are modified to show saturation. Fig. 2. is attached (b) to express the system more clearly. In the case of Fig. 4., the proposed algorithm is modified to more clearly represent and emphasize it. In Fig. 5, the longitudinal displacement part is added to graphically show how the information of the laser sensor is received in order to calculate the yaw angle. Fig. 6. shows the algorithm in more detail, making it easier to understand.

关键词： Vehicle dynamics Vehicle routing Torque control Angular velocity control Motion control Hydraulic systems Road vehicles

来源：评论

学校读者我要写书评

暂无评论

A Novel Line Feature Description and Matching Method for Visual-Aided Inertial Navigation system

引用

Journal of Physics: Conference Series 2019年第1期1267卷

作者： Xingxing Guang Yanbin Gao Pan Liu Guangchun Li College of Automation Harbin Engineering University Harbin 150001 China Department of Electrical and Computer Engineering University of Calgary Calgary AB T2N1N4 Canada Beijing Institute of Control and Electronic Technology Beijing 100032 China

With the rapid development of machine vision technology, more and more attention has been paid to the visual-aided inertial navigation system. It is important that to extract and track the line features at the dynamic situation in the visual-aided inertial navigation system which is based on visual line feature information to compensate attitude errors. A novel line feature description is proposed that use the SURF points to mark the LSD lines. Then, through coarse matching and fine matching, the function of continuously tracking the one line features in different images was realized. These line feature description and tracking method are applied in the visual-aided inertial navigation system, and its effectiveness is verified by the vehicle experiment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Finite-time model-assisted active disturbance rejection control with a novel parameters optimizer for hypersonic reentry vehicle subject to multiple disturbances

引用

Aerospace Science and Technology 2018年 79卷 588-600页

作者： Yue Yu Honglun Wang Na Li Huiping Zhang Zikang Su Xingling Shao School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Unmanned System Research Institute Beihang University Beijing 100191 China School of Automation Beijing Institute of Technology Beijing 100081 China Beijing Aerospace Automatic Control Institute Beijing 100854 China Key Laboratory of Instrumentation Science & Dynamic Measurement Ministry of Education North University of China Taiyuan 030051 China National Key Laboratory for Electronic Measurement Technology School of Instrument and Electronics North University of China Taiyuan 030051 China

In this paper, a scheme which combines finite-time model-assisted active disturbance rejection control and novel greedy criterion-based salp swarm algorithm is developed for attitude tracking problem of hypersonic reentry vehicle with multiple disturbances. To simplify control structure, the control scheme is designed within the framework of active disturbance rejection control completely. To lessen tuning parameters, a sigmoid function-based tracking differentiator is employed to generate a more realizable attitude transient profile instead of utilizing conventional nonlinear tracking differentiator. Taking known model information as model-assisted term, finite-time model-assisted extended state observers are constructed to estimate the lumped disturbance in attitude and angular rate loop with employment of function fal . To achieve rapid response and strong robustness, finite-time model-assisted control laws are derived by utilizing function fhan . Finally, a novel greedy criterion-based salp swarm algorithm is used to optimize control parameters in finite-time model-assisted control laws to achieve optimal solution that minimizes the tracking error and energy consumption. Comparative simulations are conducted to illustrate the effectiveness of the proposed scheme.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Elements realization of software-algorithmic system optimization of technological processes based on adaptive methods

引用

IOP Conference Series: Materials Science and engineering 2020年第1期791卷

作者： A L Rutskov E V Sidorenko V L Burkovsky Ya P Fedorov Forecasting department of electricity balance power and loss analysis branch office OJSC «IDGC Centre» - «Voronezhenergo» Arzamaskaya 2 Voronezh 394033 Russia Novovoronezh representation of NR Corporation SA ESKM Moskovsky prospect 179 office building 3 Voronezh 394066 Russia Department of electric drive automation and control in an engineering system Vornezh State Technical University Moscow prospect 179 Voronezh 394066 Russia Corporate finance department and corporate management of the Financial University under the Government of the Russian Federation Leningradsky prospect 49 Mocow 125993 Russia

The paper examines a possibility to optimize complex technological processes of energy resources transfer and consumption. Notes the importance of unidentified and weakly formalized factors consideration, which influence quality of the described object classes functioning. Suggests use of fuzzy neural networks and controllers on their basis for implementation of technological processes optimization of energy resources transfer and consumption at the expense of its dynamic accuracy forecast improvement. Presents algorithmic realization, that later on configured in the MATLAB simulated environment. Quantitative and qualitative parameters, which characterize fuzzy neural controller's usage quality, were received. Later, software environment elements are being described, which constructed on the basis of the suggested adaptive approach for complex technological processes optimization. Points out a possibility to conduct comparative study, and also simulation modeling implementation by way of presented solutions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

State-of-charge estimation for li-ion batteries based on multi-strategy probabilities fusion

State-of-charge estimation for li-ion batteries based on mul...

引用

2017 Chinese automation Congress, CAC 2017

作者： Xiong, Fei Yang, Bo Gao, Yizhao Yang, Jun Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538635247

The estimation of state-of-charge (SOC) is the fundamental technology to improve battery working status and life. But severe external environment, dynamic nonlinearity of battery model, and measurement errors make SOC estimation challengable. In this paper, a multi-strategy probabilities based fusion method is proposed. It can combine the dynamic tracking capability of Proportional-Integral Observer (PIO) and noise tolerance capability of extend Kalman filter (EKF). The different abnormal issues, such as internal resistance change, capacity attenuation, data saturation and initialization error, may have a serious impact on the estimates. While the proposed method can guarantee high accuracy estimation and robustness under these issues. It tracks the changes of residual sequence dynamically, and the weighting coefficient can be real-time adjusted according to Bayesian analysis. In simulation, we test the algorithm performance under a variety of current load conditions and abnormal issues. Compared to other model-based methods, the proposed approach performs higher accuracy and robustness with moderate computational complexity. © 2017 IEEE.

关键词： Lithium-ion batteries

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

引用

Chinese control Conference

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai China The Center for Bioinformatics and Computational Biology East China Normal University Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and high-dimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment decision. However, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model (SAE-SVM), for breast cancer diagnosis. The stacked auto-encoders with the greedy layer-wise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Breast cancer Feature extraction Machine learning Support vector machines electronic mail Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：