检索结果-内蒙古大学图书馆

Real-Time Battery Temperature Monitoring Using FBG Sensors: A data-driven Calibration method

IEEE SENSORS JOURNAL 2022年第19期22卷 18639-18648页

作者： Zhang, Li Liu, Xuan Li, Kang Du, Dajun Zheng, Min Niu, Qun Yang, Yi Zhou, Qi Sun, Tong Grattan, Kenneth T., V Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England State Grid Jiangsu Elect Power Res Inst Nanjing 211103 Peoples R China City Univ London Sch Mechatron Comp Sci & Engn London EC1V 0HB England

Battery storage has an important role to play in integrating large-scale renewable power generations and in transport decarbonization. Real-time monitoring of battery temperature profiles is indispensable for battery safety management. Due to the advantages of small size, resistance to corrosion, immunity to electromagnetic interference, and multiplexing, fiber Bragg-grating (FBG) sensing has received substantial interest in recent years for battery temperature measurement. However, traditional temperature calibration for FBG sensors often requires a high-standard reference and causes the sensors to fail to be consistent during the calibration or recalibration processes. To tackle the challenges, an ensemble data-driven calibration method is developed in this article for FBG sensors. The calibration model consists of a linear part and a nonlinear part. First, the fuzzy C -means (FCM) algorithm is used to extract the linear relationship between the measured FBG wavelength shift and temperature variation. Then, the empirical mode decomposition (EMD) technique is used to classify the intrinsic mode functions (IMFs) and the remainder of the unmodeled nonlinear information. The unmodeled nonlinear information is further compensated using battery state of charge (SOC) and cycle number information. The experimental results confirm that the proposed temperature calibration method achieves desirable accuracy and reliability, with both the mean absolute error (MAE) and root mean square error (RMSE) being around 0.2 degrees C, respectively. Compared with the traditional temperature calibration method, the proposed approach can be used online in real-life applications.

关键词： data-driven method empirical mode decomposition (EMD) fiber Bragg-grating (FBG) lithium-ion battery temperature calibration

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis in Analog Electrical Circuits: data-driven method

Fault Diagnosis in Analog Electrical Circuits: Data-Driven M...

引用

International Conference on Process Control

作者： A. Zhirabok A. Baranov Far Eastern Federal University Department of Automation and Control

ISBN: (纸本)9781479909261

The paper is devoted to the problem of fault detection in analog electrical circuits described by linear or nonlinear polynomial models. So-called data-driven method for fault detection is considered. This method assumes that parameters of the circuit under consideration may be unknown. It does not use methods of identification and allows checking whether or not parameters of some elements deviate considerably from their nominal values.

关键词： Electrical circuit Linear models Polynomial models Fault detection data-driven method

来源：评论

学校读者我要写书评

暂无评论

A Novel data-driven Robust method Applied to OPF with Uncertain Wind Power 2

A Novel Data-Driven Robust Method Applied to OPF with Uncert...

引用

2nd IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Su, Xiangyang Bai, Xiaoqing Zheng, Yi Song, Zhewei Cao, Yiqi Zhang, Jun Guangxi Univ Key Lab Guangxi Elect Power Syst Optimizat & Ener Nanning Peoples R China

ISBN: (纸本)9781538685495

With the arrival of the big data era, the wind power accommodation has an alternative solution. Meanwhile, the traditional stochastic optimization or robust optimization methods may result in uneconomic or conservative decisions in dealing with the uncertainty problems. Based on this, it is proposed to apply robust optimization of data driven to optimal power flow under the participation of wind power, and the model is improved on the basis of integrated 1-norm and infinity norm. The final example analysis also proves the validity and practicability of the model.

关键词： data-driven method optimal power flow (OPF) robust optimization uncertainty wind power

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimal design of periodically stiffened panels for vibration control using data-driven optimization method

引用

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 160卷 107872-107872页

作者： He, Meng-Xin Lyu, Xiaofei Zhai, Yujia Tang, Ye Yang, Tianzhi Ding, Qian Tianjin Univ Dept Mech Tianjin 300072 Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

Vibration reduction properties of the periodically stiffened panels are studied using the data-driven optimization method and are verified using the 3D printing techniques. The dynamic responses and band gaps of periodically stiffened panels are calculated using the finite element method and periodic boundary conditions. To further develop the potential in vibration mitigation, a data-driven method is used to study the multi-objective structural optimization problem for minimizing the weight and mean square velocity simultaneously. The layout, thickness, and height of the stiffeners are taken as design variables. Numerical results show that the data-driven method is efficient in the optimal design of stiffened panels, and the optimized panels have targeted band gaps for different engineering demands. Moreover, two 3D printed panels are fabricated to study band gap properties of periodically stiffened panels experimentally. CO 2021 Elsevier Ltd. All rights reserved.

关键词： Vibration control Band gap data-driven method Multi-objective optimization 3D printing

来源：评论

学校读者我要写书评

暂无评论

A Novel data-driven Modeling method for the Spatial-Temporal Correlated Complex Sea Clutter

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Zhang, Yanming Jiang, Lijun Ewe, Hong Tat Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Tunku Abdul Rahman UTAR Dept Elect & Elect Engn Kuala Lumpur 43000 Malaysia

The sea clutter, referred to as the time-varying radar backscatter from the ocean surface, plays a significant role in marine radar development. The ocean's complex hydrodynamics cause it to exhibit non-Gaussian and nonstationary characteristics, which brings challenges in the sea clutter modeling, especially for establishing its spatial-temporal correlated and coherent model. In this article, a data-driven method based on the Koopman mode decomposition (KMD) is proposed for modeling spatial-temporal correlated complex sea clutter. The method decomposes the coherent sea clutter dynamic behavior in terms of Koopman modes and corresponding temporal patterns. Then, these spatiotemporal patterns are used to construct the sea clutter state over time according to the approximate solution. Furthermore, this proposed state-of-the-art data-driven approach is benchmarked by the measured sea clutter data from intelligent PIXel processing radar (IPIX). It is demonstrated that the proposed approach accurately models the complex sea clutter with actual statistic characteristics, phase information, and spatial-temporal correlations. The mean absolute error (MAE) and root mean square error (RMSE) between the obtained and actual sea clutter are only 0.1817 and 0.2349, respectively. This work offers a practical approach for modeling sea clutter, especially when the spatial-temporal correlation and coherence information is needed.

关键词： Clutter Sea surface Radar clutter Correlation Surface treatment Surface roughness Rough surfaces data-driven method Doppler spectrum Koopman mode decomposition (KMD) sea clutter spatial-temporal correlated complex signal

来源：评论

学校读者我要写书评

暂无评论

A data driven method for multi-step prediction of ship roll motion in high sea states

引用

OCEAN ENGINEERING 2023年第1期276卷

作者： Zhang, Dan Zhou, Xi Wang, Zi-Hao Peng, Yan Xie, Shao-Rong Shanghai Univ Sch Mechatron Engn & Automation Shanghai 200444 Peoples R China Shanghai Univ Inst Artificial Intelligence Collaborat Innovat Ctr Marine Artificial Intellige Shanghai 200444 Peoples R China Minist Educ Engn Res Ctr Unmanned Intelligent Marine Equipment Shanghai 200444 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200032 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multistep prediction of ship roll motions in high sea states. A hybrid neural network is proposed that combines long short-term memory (LSTM) and convolutional neural network (CNN) in parallel. The motivation is to extract the nonlinear dynamic characteristics and the hydrodynamic memory information through the advantage of CNN and LSTM, respectively. For the feature selection, the time histories of motion states and wave heights are selected to involve sufficient information. Taken a scaled KCS as the study object, the ship motions in sea state 7 irregular long-crested waves are simulated and used for the validation. The results show that at least one period of roll motion can be accurately predicted. Compared with the single LSTM and CNN methods, the proposed method has better performance in predicting the amplitude of roll angles. Besides, the comparison results also demonstrate that selecting motion states and wave heights as feature space improves the prediction accuracy, verifying the effectiveness of the proposed method.

关键词： High sea state Multi-step predictions Ship roll motion data-driven method CNN LSTM

来源：评论

学校读者我要写书评

暂无评论

A data-driven Optimization method for Simulating Arbitrarily Distributed and Spatial-Temporal Correlated Radar Sea Clutter

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Liao, Xingxing Xie, Junhao Zhou, Jie Harbin Inst Technol Key Lab Marine Environm Monitoring & Informat Proc Harbin 150001 Peoples R China

Realistically simulating spatial-temporal correlated complex sea clutter using statistical model-based methods is very challenging because the intricate interplay of various physical mechanisms in both space and time poses significant obstacles for clutter modeling. In this article, to overcome this challenge, we propose a data-driven method based on the 2-D amplitude and phase matching optimization (APMO) with two steps. First, the simulated clutter amplitudes with desired distribution and correlation characteristics are generated by performing the frequency-domain inverse transform and correlation transfer techniques on the measured clutter. Second, an optimization strategy is developed to acquire the well-matched phases for the simulated clutter amplitudes by constraining the phases to the simulated clutter amplitudes and the desired Doppler and range spectra. Two different algorithms are separately adopted to implement the optimization strategy, and their advantages and disadvantages are compared theoretically and experimentally. The APMO method is direct in the analysis of the measured clutter's statistical characteristics and universal to different radar and environmental conditions. It is shown that the simulation results can reproduce the distribution and spatial-temporal correlation features of the real-world X-band complex sea clutter. The relative mean-squared deviation (RMSD) between the measured and simulated clutter's Doppler and range spectra can be notably reduced to 0.0499 and 0.1237, respectively.

关键词： Amplitude and phase matching optimization (APMO) arbitrary distribution complex sea clutter simulation data-driven method spatial-temporal correlation

来源：评论

学校读者我要写书评

暂无评论

A new data driven method for summarising multiple cause of death data

引用

BMC MEDICAL RESEARCH methodOLOGY 2023年第1期23卷 83-83页

作者： Dobson, Annette McElwee, Paul Baneshi, Mohammad Reza Eynstone-Hinkins, James Moran, Lauren Waller, Michael Univ Queensland Sch Publ Hlth Brisbane Qld Australia Australian Bur Stat Brisbane Australia

BackgroundNational mortality statistics are based on a single underlying cause of death. This practice does not adequately represent the impact of the range of conditions experienced in an ageing population in which multimorbidity is *** propose a new method for weighting the percentages of deaths attributed to different causes that takes account of the patterns of associations among underlying and contributing causes of death. It is driven by the data and unlike previously proposed methods does not rely on arbitrary choices of weights which can over-emphasise the contribution of some causes of death. The method is illustrated using Australian mortality data for people aged 60 years or *** to the usual method based only on the underlying cause of death the new method attributes higher percentages of deaths to conditions like diabetes and dementia that are frequently mentioned as contributing causes of death, rather than underlying causes, and lower percentages to conditions to which they are closely related such as ischaemic heart disease and cerebrovascular disease. For some causes, notably cancers, which are usually recorded as underlying causes with few if any contributing causes the new method produces similar percentages to the usual method. These different patterns among groups of related conditions are not apparent if arbitrary weights are *** new method could be used by national statistical agencies to produce additional mortality tables to complement the current tables based only on underlying causes of death.

关键词： Death rates Multiple causes of death Multimorbidity data-driven method

来源：评论

学校读者我要写书评

暂无评论

A Novel data-driven Robust method Applied to OPF with Uncertain Wind Power

A Novel Data-Driven Robust Method Applied to OPF with Uncert...

引用

IEEE Conference on Energy Internet and Energy System Integration

作者： Xiangyang Su Xiaoqing Bai Yi Zheng Zhewei Song Yiqi Cao Jun Zhang Key Laboratory of Guangxi Electric Power System Optimization and Energy-saving Technology Guangxi University Nanning China

With the arrival of the big data era, the wind power accommodation has an alternative solution. Meanwhile, the traditional stochastic optimization or robust optimization methods may result in uneconomic or conservative decisions in dealing with the uncertainty problems. Based on this, it is proposed to apply robust optimization of data driven to optimal power flow under the participation of wind power, and the model is improved on the basis of integrated 1- norm and infinity norm. The final example analysis also proves the validity and practicability of the model.

关键词： data-driven method Optimal power flow (OPF) Robust optimization Uncertainty Wind power

来源：评论

学校读者我要写书评

暂无评论

data-driven methods Applied to Soft Robot Modeling and Control: A Review

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 2241-2256页

作者： Chen, Zixi Renda, Federico Le Gall, Alexia Mocellin, Lorenzo Bernabei, Matteo Dangel, Theo Ciuti, Gastone Cianchetti, Matteo Stefanini, Cesare Scuola Super Sant Anna BioRobot Inst I-56127 Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & AI I-56127 Pisa Italy Khalifa Univ Ctr Autonomous Robot Syst Abu Dhabi U Arab Emirates

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://***/view/23zcb) is built for this review and will be updated frequently. Note to Practitioners-This work is motivated by the need for a review introducing soft robot modeling and control methods in parallel. Modeling and control play significant roles in robot research, and they are challenging especially for soft robots. The nonlinear and complex deformation of such robots necessitates specific modeling and control approaches. We introduce the state-of-the-art data-driven methods and survey three approaches widely utilized. This review also compares the performance of these methods, considering some important features like data amount requirement, control frequency, and target task. The features of each ap

关键词： Soft robot data-driven method physical model Jacobian matrix statistical model neural network reinforcement learning

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：