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A Singular Spectrum Analysis-Based data-driven technique for the Removal of Cardiogenic Oscillations in Esophageal Pressure Signals

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2020年 8卷 3300211页

作者： Mukhopadhyay, Sourav Kumar Zara, Michael Telias, Irene Chen, Lu Coudroy, Remi Yoshida, Takeshi Brochard, Laurent Krishnan, Sridhar Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada Ryerson Univ Inst Biomed Engn Sci & Technol iBEST Toronto ON M5B 2K3 Canada St Michaels Hosp Li Ka Shing Knowledge Inst Keenan Res Ctr Toronto ON M5B 1T8 Canada Osaka Univ Hosp Intens Care Unit Suita Osaka 5650871 Japan

Objective: Assessing the respiratory and lung mechanics of the patients in intensive care units is of utmost need in order to guide the management of ventilation support. The esophageal pressure (Peso) signal is a minimally invasive measure, which portrays the mechanics of the lung and the pattern of breathing. Because of the close proximity of the lung to the beating heart inside the thoracic cavity, the P-eso signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation (CGO) signal. However, the area of research addressing the removal of CGO from Peso signal is still lagging behind. Methods and results: This paper presents a singular spectrum analysis-based high-efficient, adaptive and robust technique for the removal of CGO from P-eso signal utilizing the inherent periodicity and morphological property of the P-eso signal. The performance of the proposed technique is tested on P-eso signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic P-eso signals. The efficiency of the proposed technique in removing CGO from the Peso signal is quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised P-eso signal fall under the categories `very good' as per the subjective measure. Conclusion and clinical impact: The proposed technique: (1) does not follow any predefined mathematical model and hence, it is data-driven, (2) is adaptive to the sampling rate, and (3) can be adapted for denoising other biomedical signals which exhibit periodic or quasi-periodic nature.

关键词： Matrix decomposition Biomedical measurement Pollution measurement Lung Noise reduction Oscillators Bandwidth Cardiogenic oscillation data-driven technique esophageal pressure signal mean opinion score mechanical ventilation singular spectrum analysis

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Performance of a data-driven technique applied to changes in wave height and its effect on beach response

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Water Science and Engineering 2016年第1期9卷 42-51页

作者： José M.Horrillo-Caraballo Harshinie Karunarathna Shun-qi Pan Dominic Reeve Energy&Environment Research Group Zienkiewicz Centre for Computational EngineeringSwansea UniversityBay CampusSwansea SA1 8ENUK Hydro-environmental Research Centre School of EngineeringCardiff UniversityCardiff CF24 3AAUK

In this study the medium-term response of beach profiles was investigated at two sites： a gently sloping sandy beach and a steeper mixed sand and gravel beach. The former is the Duck site in North Carolina, on the east coast of the USA, which is exposed to Atlantic Ocean swells and storm waves, and the latter is the Milford-on-Sea site at Christchurch Bay, on the south coast of England, which is partially sheltered from Atlantic swells but has a directionally bimodal wave exposure. The data sets comprise detailed bathymetric surveys of beach profiles covering a period of more than 25 years for the Duck site and over 18 years for the Milford-on-Sea site. The structure of the data sets and the data-driven methods are described. Canonical correlation analysis （CCA） was used to find linkages between the wave characteristics and beach profiles. The sensitivity of the linkages was investigated by deploying a wave height threshold to filter out the smaller waves incrementally. The results of the analysis indicate that, for the gently sloping sandy beach, waves of all heights are important to the morphological response. For the mixed sand and gravel beach, filtering the smaller waves improves the statistical fit and it suggests that low-height waves do not play a primary role in the medium-term morohological resoonse, which is primarily driven by the intermittent larger storm waves.

关键词： Beach profile Canonical correlation analysis data-driven technique Empirical orthogonal function Forecast Statistical model Wave height threshold

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Constrained robust model predictive control embedded with a new data-driven technique

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IET CONTROL THEORY AND APPLICATIONS 2020年第16期14卷 2395-2405页

作者： Yang, L. Lu, J. Xu, Y. Li, D. Xi, Y. Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

In the control field, the adaptive model predictive control (AMPC) has the capability of taking effective control actions on unknown-but-bounded time-independent or slowly time-varying systems coupling with constraints. In essence, AMPC estimates the uncertain parameters or uncertainty set online by utilising historian data to extract model information. The model estimation procedure imposes some specific conditions on data and these extra conditions have restricted its practical use. To overcome these problems, a new data-driven control methodology is presented that integrates the data-driven concept into robust model predictive control (RMPC) architecture for unknown-but-bounded time-independent or slowly time-varying plant. The key novelty is to employ historian data to derive control policy and make a prediction in replacement with the complicated procedure of utilising data to estimate model parameters. A data-driven RMPC algorithm is developed within the robust model predictive control framework with the fulfilment of recursive feasibility and stability. The authors display the highlights of the data-driven model predictive control controller through two simulation examples. The resulting controller is verified to reduce conservativeness and increase the closed-loop performance of the system.

关键词： closed loop systems robust control control system synthesis predictive control time-varying systems uncertain systems constrained robust model predictive control data-driven technique control field adaptive model predictive control AMPC effective control actions unknown-but-bounded time-independent time-varying systems historian data model information model estimation procedure data-driven control methodology data-driven concept robust model predictive control architecture time-varying plant control policy utilising data model parameters data-driven RMPC algorithm robust model predictive control framework data-driven model predictive control controller

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A data-driven technique for Investment Strategy of Distribution Systems Upgrade

A Data-Driven Technique for Investment Strategy of Distribut...

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China International Conference on Electricity Distribution (CICED)

作者： Ning, Shichao Lv, Lin Liu, Tingjian Su, Yunche Xiao, Wanfang Sichuan Univ Dept Elect Informat Chengdu 610065 Sichuan Provinc Peoples R China 2Changping Power Supply Co State Grid Changping Dist 102200 Beijing Provinc Peoples R China

ISBN: (纸本)9781467390682

This paper presents an investment funds allocation model for distribution system upgrade based on data-driven technique. First, a bilevel programming evaluation system is established, which takes upgrade measures as element layer indices and comprehensive evaluation parameters as performance layer indices. In order to mine the correlation between these indices, enormous indices data generated by a virtual distribution system program is analyzed through artificial neutral network (ANN). Second, the trained ANN is embedded in the distribution network upgrade optimization model, which is solved by genetic algorithm (GA). Finally, effectiveness and feasibility of model are verified by a city distribution system.

关键词： data-driven technique distribution system upgrade artificial neutral network genetic algorithm

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Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2007年第2期21卷 795-808页

作者： Antory, David Univ Warwick Mfg Grp Int Automot Res Ctr Elect Test Adv Architectures Coventry CV4 7AL W Midlands England

This paper presents a case study of the application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine. Using measurement signals taken from the sensors/actuators which are present in a modern automotive vehicle, a data-driven diagnostic model is built for condition monitoring purposes. Detailed investigations have shown that measured signals taken from the experimental test-bed often contain redundant information and noise due to the nature of the process. In order to deliver a clear interpretation of these measured signals, they therefore need to undergo a 'compression' and an 'extraction' stage in the modelling process. It is at this stage that the proposed data-driven monitoring technique plays a significant role by taking only the important information of the original measured signals for fault diagnosis purposes. The status of the engine's performance is then monitored using this diagnostic model. This condition monitoring process involves two separate stages of fault detection and root-cause diagnosis. The effectiveness of this diagnostic model was validated using an experimental automotive 1.9L four-cylinder diesel engine embedded in a chassis dynamometer in an engine test-bed. Two joint diagnostics plots were used to provide an accurate and sensitive fault detection process. Using the proposed model, small air leaks in the inlet manifold plenum chamber with a diameter size of 2-6 turn were accurately detected. Further analyses using contribution to T-2 and Q statistics show the effect of these air leaks on fuel consumption. It was later discovered that these air leaks may contribute to emissions fault. In comparison to the existing model-based approaches, the proposed method has several benefits: (i) it makes no simplifying assumptions, as the model is built entirely from the measured signals;(ii) it is simple and straight-forward;(iii) there is no additional hardware required for modelling;(iv) it is a time and cost-

关键词： application data-driven technique condition monitoring diagnosis air leaks automotive diesel engine

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data-driven soft sensor development based on deep learning technique

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JOURNAL OF PROCESS CONTROL 2014年第3期24卷 223-233页

作者： Shang, Chao Yang, Fan Huang, Dexian Lyu, Wenxiang Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

In industrial process control, some product qualities and key variables are always difficult to measure online due to technical or economic limitations. As an effective solution, data-driven soft sensors provide stable and reliable online estimation of these variables based on historical measurements of easy-to-measure process variables. Deep learning, as a novel training strategy for deep neural networks, has recently become a popular data-driven approach in the area of machine learning. In the present study, the deep learning technique is employed to build soft sensors and applied to an industrial case to estimate the heavy diesel 95% cut point of a crude distillation unit (CDU). The comparison of modeling results demonstrates that the deep learning technique is especially suitable for soft sensor modeling because of the following advantages over traditional methods. First, with a complex multi-layer structure, the deep neural network is able to contain richer information and yield improved representation ability compared with traditional data-driven models. Second, deep neural networks are established as latent variable models that help to describe highly correlated process variables. Third, the deep learning is semi-supervised so that all available process data can be utilized. Fourth, the deep learning technique is particularly efficient dealing with massive data in practice. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Deep neural network Nonlinear regression Soft sensor data-driven technique

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A data-driven Motion Compensation Scheme for Compressed Sensing SAR Image Restoration

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Chen, Chengzhi Yang, Huizhang Chen, Shengyao Xi, Feng Liu, Zhong Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

Synthetic aperture radar (SAR) can produce well-focused images based on accurate observation models. However, motion errors in the data acquisition process often introduce inaccuracies in the models and degrade the image quality. Classical motion compensation (MOCO) methods can mitigate this problem, but they are not applicable to compressed sensing (CS) SAR imaging. Existing CS SAR imaging methods can jointly estimate and compensate the motion error from the data by iterative optimization, but they incur a high computational cost. To solve these problems, in this article, we propose an efficient data-driven MOCO strategy for CS SAR imaging. Specifically, we develop a two-step measurement estimation scheme followed by a fitting and filtering procedure to extract the motion error from the data. Then, we use the estimated motion error to correct the CS SAR observation model and reformulate a sparse SAR reconstruction problem based on the corrected model. This strategy significantly reduces the computational cost compared with existing CS SAR MOCO methods. To further expedite the image recovery, we design a fast imaging algorithm that exploits the feature of the observation matrix to accelerate the matrix-vector products and the interpolation operations involved in the recovery problem. Experimental results show that the proposed method can efficiently reconstruct SAR images from CS SAR data with motion errors and offer favorable imaging performance.

关键词： Radar polarimetry Radar imaging Imaging Radar Image reconstruction Motion measurement Measurement uncertainty Electronics packaging Computational modeling Motion compensation Compressed sensing (CS) data-driven technique fast imaging motion compensation (MOCO) synthetic aperture radar (SAR)

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data-driven Fuzzy Constant Voltage Regulation of Inductive Power Transfer Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Xu, Donghui Tian, Engang Chen, Huwei Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Jiangsu Aerosp Power Machinery & Elect Co Ltd Taizhou 225300 Peoples R China

This paper concerns the problem of constant voltage regulation for an unknown inductive power transfer (IPT) system with phase shift modulation (PSM). Different from mainstream model-based approaches, the proposed data-driven fuzzy method not only formulates a controller solely based on input-output data, but also guarantees the stability of nonlinear inductive power transfer systems. First, a data-based closed-loop fuzzy representation is derived to parameterize the nonlinear system. Subsequently, a sufficient data-based condition is proposed to ensure the stability of the nonlinear system and the asymptotically tracking characteristic without identifying the system parameters. Finally, an experimental example is proposed to demonstrate the accuracy of the proposed method and the robustness of suggested controllers.

关键词： Regulation Circuit stability Asymptotic stability Voltage control Stability criteria Nonlinear systems data models Topology Phase modulation PD control data-driven technique output regulation inductive power transfer (IPT) system Takagi-Sugeno model linear matrix inequality (LMI)

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Integrating physics of the problem into data-driven methods to enhance elastic full-waveform inversion with uncertainty quantification

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PHYSICA D-NONLINEAR PHENOMENA 2025年 472卷

作者： Negahdari, Vahid Moghadasi, Seyed Reza Razvan, Mohammad Reza Sharif Univ Technol Dept Math Sci Tehran 111559415 Iran

Full-Waveform Inversion (FWI) is a nonlinear iterative seismic imaging technique that, by reducing the misfit between recorded and predicted seismic waveforms, can produce detailed estimates of subsurface geophysical properties. Nevertheless, the strong nonlinearity of FWI can trap the optimization in local minima. This issue arises due to factors such as improper initial values, the absence of low frequencies in the measurements, noise, and other related considerations. To address this challenge and with the advent of advanced machine-learning techniques, data-driven methods, such as deep learning, have attracted significantly increasing attention in the geophysical community. Furthermore, the elastic wave equation should be included in FWI to represent elastic effects accurately. The intersection of data-driven techniques and elastic scattering theories presents opportunities and challenges. In this paper, by using the knowledge of elastic scattering (physics of the problem) and integrating it with machine learning techniques, we propose methods for the solution of time- harmonic FWI to enhance accuracy compared to pure data-driven and physics-based approaches. Moreover, to address uncertainty quantification, by modifying the structure of the Variational Autoencoder, we introduce a probabilistic deep learning method based on the physics of the problem that enables us to explore the uncertainties of the solution. According to the limited availability of datasets in this field and to assess the performance and accuracy of the proposed methods, we create a comprehensive dataset close to reality and conduct a comparative analysis of the presented approaches to it.

关键词： Full waveform inversion Inverse elastic scattering data-driven technique Deep learning Variational autoencoder Seismic imaging Uncertainty quantification

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data-driven Event-Triggered Output Regulation of Networked Inductive Power Transfer Systems: An Input-Output data Approach

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Xu, Donghui Tian, Engang Xie, Xiangpeng Chen, Jin Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Peoples R China

This article introduces a data-driven output regulation approach for networked inductive power transfer (IPT) systems considering communication delays and sampling noises. Diverging from mainstream identification-based methods that often disregard noise information, our methodology provides a model-free controller design approach exploiting noisy input-output (I/O) data. The main contributions are threefold. First, we propose an I/O data-driven parameterization framework tailored for scenarios with noisy data. Second, an I/O data-based event-triggering mechanism (ETM) is proposed to alleviate communication burdens. Finally, a concise co-designing event-triggered control procedure is derived to ensure the stability of delayed I/O systems in terms of data-based linear matrix inequalities. The robustness and efficacy of our proposal are validated through experiments involving constant voltage regulation of IPT systems, considering communication delays and parameter fluctuations.

关键词： Noise Event detection Regulation Noise measurement Delays System dynamics Proposals Linear matrix inequalities Delay effects Vectors data-driven technique event-triggered control inductive power transfer (IPT) networked control systems robust control

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