检索结果-内蒙古大学图书馆

A Review of Lithium-Ion Battery State of Health Estimation and Prediction methods

WORLD ELECTRIC VEHICLE JOURNAL 2021年第3期12卷

作者： Yao, Lei Xu, Shiming Tang, Aihua Zhou, Fang Hou, Junjian Xiao, Yanqiu Fu, Zhijun Zhengzhou Univ Light Ind Henan Engn Res Ctr New Energy Vehicle Lightweight Zhengzhou 450000 Peoples R China Chongqing Univ Technol Vehicle Engn Inst Chongqing 400054 Peoples R China Chongqing Univ Technol Key Lab Adv Mfg Technol Automobile Parts Minist Educ Chongqing 400054 Peoples R China

Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.

关键词： lithium-ion power battery state of health data-driven methods fusion technology

来源：评论

学校读者我要写书评

暂无评论

data-driven verification of stochastic linear systems with signal temporal logic constraints

引用

AUTOMATICA 2021年 131卷

作者： Salamati, Ali Soudjani, Sadegh Zamani, Majid Ludwig Maximilians Univ Munchen Dept Comp Sci Munich Germany Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England Univ Colorado Dept Comp Sci Boulder CO 80309 USA

Cyber-physical systems usually have complex dynamics and are required to fulfill complex tasks. In recent years, formal methods from Computer Science have been used by control theorists for both describing the required tasks and ensuring that they are fulfilled by the systems. The crucial drawback of formal methods is that a complete model of the system often needs to be available. The main goal of this paper is to study formal verification of linear time-invariant systems with respect to a fragment of temporal logic specifications when only a partial knowledge of the model is available, i.e., a parameterized model of the system is known but the exact values of the parameters are unknown. We provide a probabilistic measure for the satisfaction of the specification by trajectories of the system under the influence of uncertainty. We assume these specifications are expressed as signal temporal logic formulae and provide an approach that relies on gathering input-output data from the system and employs Bayesian inference on the collected data to associate a notion of confidence to the satisfaction of the specification. The main novelty of our approach is to combine both data-driven and model-based techniques in order to have a two-layer probabilistic reasoning over the behavior of the system. The inner layer is with respect to the uncertainties in dynamics and observed data while the outer layer is with respect to the distribution over the parameter space. The latter is updated using Bayesian inference on the collected data. The proposed approach is demonstrated in two case studies. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Bayesian inference data-driven methods Verification Synthesis Signal temporal logic Parameterized models

来源：评论

学校读者我要写书评

暂无评论

data-driven Design of Model-based Fault Diagnosis Systems

引用

IFAC Proceedings Volumes 2012年第15期45卷 840-847页

作者： S.X. Ding Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen Duisburg 47057 Germany

In this paper, recent development of data-driven design of fault detection and isolation (FDI) systems is presented. The major attention and focus are on the design schemes for observer-based FDI systems.

关键词： Fault detection and diagnosis data-driven methods observer-based methods subspace identification methods parity space methods

来源：评论

学校读者我要写书评

暂无评论

data-driven Stability Analysis of Switched Linear Systems Using Adaptive Sampling ⁎

引用

IFAC-PapersOnLine 2024年第11期58卷 31-36页

作者： Alexis Vuille Guillaume O. Berger Raphaël M. Jungers UCLouvain Louvain-la-Neuve Belgium

In the realm of system analysis, data-driven methods have gained a lot of attention in recent years. We introduce a new innovative approach for the data-driven stability analysis of switched linear systems which is adaptive sampling. Our aim is to address limitations of existing approaches, in particular, the fact that these methods suffer from ill-conditioning of the optimal Lyapunov function, which is a direct consequence of the way the data is collected by sampling uniformly the state space. Our adaptive-sampling approach consists in a two-step procedure, in which an optimal sampling distribution is estimated in the first step from data collected with a non-optimal distribution, and then, in the second step, new data points are sampled according to the identified distribution to establish the final probabilistic guarantee for the convergence rate of the system. Numerical experiments show the efficiency of our approach, namely, in terms of the total number of data points needed to guarantee stability of the system with given confidence.

关键词： data-driven methods chance-constrained optimization stability analysis switched linear systems

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of acute hyperglycemia based on data-driven prediction models

引用

SLAS TECHNOLOGY 2024年第5期29卷 100182页

作者： Dong, Xinxin Dong, Wenping Guo, Xueshan Taiyuan Iron Steel Grp Co Ltd Dept Geriatr Gen Hosp Taiyuan 030003 Shanxi Peoples R China Taiyuan Iron Steel Grp Co Ltd Gen Hosp Dept Operat Management Taiyuan 030003 Shanxi Peoples R China

Acute hyperglycemia is a common endocrine and metabolic disorder that seriously threatens the health and life of patients. Exploring effective diagnostic methods and treatment strategies for acute hyperglycemia to improve treatment quality and patient satisfaction is currently one of the hotspots and difficulties in medical research. This article introduced a method for diagnosing acute hyperglycemia based on data-driven prediction models. In the experiment, clinical data from 1000 patients with acute hyperglycemia were collected. Through data cleaning and feature engineering, 10 features related to acute hyperglycemia were selected, including BMI (Body Mass Index), TG (triacylglycerol), HDL-C (High-density lipoprotein cholesterol), etc. The support vector machine (SVM) model was used for training and testing. The experimental results showed that the SVM model can effectively predict the occurrence of acute hyperglycemia, with an average accuracy of 96 %, a recall rate of 84 %, and an F1 value of 89 %. The diagnostic method for acute hyperglycemia based on data-driven prediction models has a certain reference value, which can be used as a clinical auxiliary diagnostic tool to improve the early diagnosis and treatment success rate of acute hyperglycemia patients.

关键词： Acute hyperglycemia data-driven methods Predictive models Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Convolutional-Generative Adversarial Network: data-driven Mechanical Inverse Method for Intelligent Tactile Perception

ADVANCED INTELLIGENT SYSTEMS

引用

ADVANCED INTELLIGENT SYSTEMS 2022年第9期4卷

作者： Li, Yiwen Zhang, Jingyi Yi, Jixuan Zhang, Kai Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China

Inverse problem method uses the results of observations to infer the model parameters of a given system, which can be used in the area of tactile perception. By integrating tactile perception in robotic systems, reconstructing structural parameters of target object can be achieved. However, with insufficient information, how to evaluate complex structural parameters accurately remains a challenge. A data-driven method is proposed for the structural perception based on convolutional-generative adversarial network (CGAN), which can precisely evaluate the structural parameters, notwithstanding missing a large quantity of sampled strains randomly in space domain. The CGAN model has been verified on a reconfigurable structure. Both the numerical calculations and experiments indicate that the structural accuracy rate can reach above 90% in spite of the strain loss ratio being 50%. Through inpainting the observations and discretizing the model parameters, a complete process is proposed to deal with the inverse problem of predicting continuous structure from the incomplete strain, which provides a new solution for applying machine learning method into intelligence tactile robot.

关键词： convolutional-generative adversarial networks data-driven methods intelligence tactile robots inverse problems structural inverse predicting

来源：评论

学校读者我要写书评

暂无评论

Probing quantum correlations in many-body systems: a review of scalable methods

引用

REPORTS ON PROGRESS IN PHYSICS 2023年第11期86卷 114001-114001页

作者： Frerot, Irenee Fadel, Matteo Lewenstein, Maciej CNRS Inst Neel Grenoble France Swiss Fed Inst Technol Dept Phys Zurich Switzerland Barcelona Inst Sci & Technol ICFO Inst Ciencies Foton Castelldefels 08860 Barcelona Spain ICREA Pg Lluis Co 23 Barcelona 08010 Spain

We review methods that allow one to detect and characterize quantum correlations in many-body systems, with a special focus on approaches which are scalable. Namely, those applicable to systems with many degrees of freedom, without requiring a number of measurements or computational resources to analyze the data that scale exponentially with the system size. We begin with introducing the concepts of quantum entanglement, Einstein-Podolsky-Rosen steering, and Bell nonlocality in the bipartite scenario, to then present their multipartite generalization. We review recent progress on characterizing these quantum correlations from partial information on the system state, such as through data-driven methods or witnesses based on low-order moments of collective observables. We then review state-of-the-art experiments that demonstrate the preparation, manipulation and detection of highly-entangled many-body systems. For each platform (e.g. atoms, ions, photons, superconducting circuits) we illustrate the available toolbox for state preparation and measurement, emphasizing the challenges that each system poses. To conclude, we present a list of timely open problems in the field.

关键词： quantum entanglement Bell nonlocality many-body systems entanglement detection quantum gases atomic ensembles data-driven methods

来源：评论

学校读者我要写书评

暂无评论

Gene Regulatory Network Inference: An Introductory Survey

Gene Regulatory Network Inference: An Introductory Survey

引用

作者： Vân Anh Huynh-Thu Guido Sanguinetti

Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 1990s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorization which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialized chapters of this book. less

关键词： Gene regulatory networks Network inference Network reverse-engineering Unsupervised inference data-driven methods Probabilistic models Dynamical models

来源：评论

学校读者我要写书评

暂无评论

data-driven Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs

Data-driven Forecasting of Subsurface Inclusion Defects for ...

引用

第32届中国过程控制会议(CPCC2021)

作者： Chihang Wei Lihua Lyu Weiming Shao Zhihuan Song State Key Laboratory of Industrial Control Technology Zhejiang University Institute of Intelligent Manufacturing Baoshan Iron and Steel Co.Ltd. College of New Energy China University of Petroleum

Continuous casting slab is a kind of indispensable foundation material of national economic construction, whose quality is a crucial guarantee of safety and quality of the final products. Subsurface inclusion is one of the most frequent defects that affects the inner quality of continuous casting slab. Specifically, subsurface inclusion defect refers to the irregular and discontinuous chunk slags embedded in the surface or 2 mm～10 mm under the surface. It can cause serious defects in subsequent hot rolling or cold rolling products, increasing defective index, breakout accident, and difficult realization of hot charge rolling [1,2]. It is however hard to be detected online by traditional mechanism modelbased methods and physics-based methods.

关键词： Real-time forecasting subsurface inclusion defects data-driven methods discriminant analysis reproducing kernel Hilbert space(RKHS)

来源：评论

学校读者我要写书评

暂无评论

Hybrid Model Based Approaches for Systems Health Management and Prognostics

Hybrid Model Based Approaches for Systems Health Management ...

引用

作者： Kulkarni, C. S Wyle (United States) El Segundo California United States

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybri

关键词： Hybrid modeling Systems health management Prognostics data-driven methods

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：