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Two-dimensional Hilbert-Huang transform-based thermographic data processing for non-destructive material defect detection

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Quantitative InfraRed Thermography Journal 2024年

作者： Hsiao, Tung-Yu Sfarra, Stefano Liu, Yi Yao, Yuan Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China

Previous research has established the Hilbert-Huang transform (HHT) as a powerful tool for processing and analysing one-dimensional time series signals. Its extension into multi-dimensional Euclidean spaces further demonstrates its versatility. In this study, we leverage a two-dimensional (2D) HHT to enhance non-destructive testing, utilising active infrared thermographic data to improve the detection of material defects. The proposed methodology incorporates a 2D HHT, which synergises multidimensional ensemble empirical mode decomposition with the Riesz transform (RT). This combination is adept at uncovering both global (i.e. intrinsic mode functions) and local (i.e. phase angle and spatial frequency) information. In particular, RT is integrated with a Gaussian filter, which allows for a more thorough analysis of the inherent characteristics of defects through the monogenic signal produced by RT. The experimental application of this method on a mosaic sample, intentionally embedded with defects, has yielded promising results. The approach effectively distinguishes critical defect signals from the surroundings, leading to more accurate and clearer results. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Hilbert-Huang transform

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Event-Triggered SMC for Networked Markov Jumping Systems With Channel Fading and Applications: Genetic Algorithm

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IEEE Transactions on Cybernetics 2023年第10期53卷 6503-6515页

作者： Qi, Wenhai Zhang, Ning Zong, Guangdeng Su, Shun-Feng Yan, Huaicheng Yeh, Ruey-Huei Qufu Normal University School of Engineering Rizhao276826 China Chengdu University School of Information Science and Engineering Chengdu610106 China Tiangong University School of Control Science and Engineering Tianjin300387 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education Shanghai200237 China National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan

The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model. © 2013 IEEE.

关键词： Markov processes

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Application of Image processing and Machine Learning Technologies for Home Care Bed

Application of Image Processing and Machine Learning Technol...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Suponenkovs, Artjoms Lacis, Matiss Markovics, Zigurds Kovalovs, Mihails Platkajis, Ardis Kalnins, Andrejs Process Engineering and Equipment Manufacturing Company PERUZA Riga Latvia Riga Technical University Department of Computer Control and Computer Networks Riga Latvia Riga Technical University Faculty of Computed Science and Information Technology Riga Latvia Riga Stradins University Department of Radiology Riga Latvia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

The problem of analysing a bed bound patient (that is, a patient who is confined to their bed) is a relevant factor in whether or not they will receive timely medical care. This paper aims to investigate the challenges and possible solutions to such problems. It describes an approach and methods for such analysis, as well as for home care bed control. This paper also addresses the challenges of Image processing and Machine Learning. The experimental part describes important aspects regarding analysing brain activity, facial expression, blood flow, and human body pose. These experimental results and the proposed approach based upon them can be helpful for ensuring timely medical care. In addition, the results of this work can be useful for home care bed development. © 2022 IEEE.

关键词： Machine learning

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Deep convolutional autoencoder thermography for artwork defect detection

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Quantitative InfraRed Thermography Journal 2024年第6期21卷 367-383页

作者： Liu, Yi Wang, Fumin Liu, Kaixin Mostacci, Miranda Yao, Yuan Sfarra, Stefano Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan China Professional Restorer Celano Italy Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Infrared thermography is a cost-effective non-destructive evaluation technique that plays a critical role in extracting information about defects in cultural heritage such as works of art. However, in-depth studies on the internal structure of contemporary artworks using infrared thermography remain lacking, and therefore, a deep convolutional autoencoder thermography (DCAT) data analysis method is proposed for defect detection of contemporary artworks. In the proposed method, original data are enhanced by convolution to reduce the noise and inhomogeneous background in the original thermal images;subsequently, a deep autoencoder is used to extract nonlinear features from the enhanced thermographic data, and the results of the hidden layer are visualised. These visualised images highlight information regarding the internal structure and defects of the artwork. The case study on a handmade replica of Picasso’s ‘La Bouteille de Suze’, reproduced exactly as the original, has shown that DCAT significantly improved the accuracy of defect detection when compared with that of commonly used methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Iterative Learning control for Ramp Metering on Service Station On-ramps

arXiv

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arXiv 2024年

作者： Xiang, Hongxi Cenedese, Carlo Balta, Efe C. Lygeros, John Department of Mechanical and Process Engineering ETH Zürich Zürich Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland Delft Center for Systems and Control Delft University of Technology Delft Netherlands Inspire AG Zürich Switzerland

Congestion on highways has become a significant social problem due to the increasing number of vehicles, leading to considerable waste of time and pollution. Regulating the outflow from the Service Station can help alleviate this congestion. Notably, traffic flows follow recurring patterns over days and weeks, allowing for the application of Iterative Learning control (ILC). Building on these insights, we propose an ILC approach based on the Cell Transmission Model with service stations (CTM-s). It is shown that ILC can effectively compensate for potential inaccuracies in model parameter estimates by leveraging historical data. Copyright © 2024, The Authors. All rights reserved.

关键词： Traffic congestion

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Layer-to-Layer Closed-Loop Feedback control Application for Inter-Layer Temperature Stabilization in Laser Powder Bed Fusion

SSRN

引用

SSRN 2023年

作者： Kavas, Barış Balta, Efe C. Tucker, Michael Rupenyan, Alisa Lygeros, John Bambach, Markus Department of Mechanical and Process Engineering Advanced Manufacturing Laboratory ETH Zürich Zürich8092 Switzerland Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland

In laser powder bed fusion (LPBF), part quality and process conditions are highly dependent on the interlayer temperature (ILT) of the exposure surface of printed parts. State-of-the-art closed-loop control applications do not stabilize ILT, which has a decisive influence on the resulting part properties. This paper presents a closed-loop control strategy for LPBF that stabilizes the ILT. The thermal balance is defined with respect to exposure area change in overhanging parts and a control strategy is proposed to maintain the thermal balance by actuating energy input. The proposed control strategy uses an off-axis thermal camera for interlayer temperature acquisition to update the laser power layer to layer. An online optimization-based controller that leverages the repetitive nature of the LPBF process is implemented for the task. The developed controller is validated by experiments with different overhanging geometries. Successful ILT stabilization for various overhanging angles shows the robustness of the controller for an extended number of layers. However, modulation of laser power alone is shown to be insufficient to maintain the ILT within the stable processing window due to excessive heat accumulation in the long term. Nevertheless, the results of this study demonstrate the feasibility of ILT stabilization via of closed-loop control in LPBF and the potential for significant improvements in stabilizing the properties of the printed parts. © 2023, The Authors. All rights reserved.

关键词： Temperature control

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In-situ controller Autotuning by Bayesian Optimization for Closed-loop Feedback control of Laser Powder Bed Fusion process

arXiv

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arXiv 2024年

作者： Kavas, Barış Balta, Efe C. Tucker, Michael R. Krishnadas, Raamadaas Rupenyan, Alisa Lygeros, John Bambach, Markus Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland Control and Automation Group Inspire AG Zürich8005 Switzerland Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland ZHAW Centre for Artificial Intelligence Zürich University of Applied Sciences Switzerland

Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the industrial production of complex and high-criticality parts for aerospace, power generation, medical, transportation, and other industries. This approach relies on static parameter sets obtained through extensive experimentation and a priori simulation on analog parts, with the hope that they remain stable and defect-free once transferred to the production parts. Closed-loop control of LPBF has the potential to enhance process stability further and reduce defect formation in the face of complex thermal histories, stochastic process noise, hardware drift, and unexpected perturbations. The controllers can be classified based on the spatial and temporal scales in which they operate, designated as layer-to-layer and in-layer controllers. However, the performance and effectiveness of controllers largely depend on the tuning of their parameters. Traditionally, controller tuning has been a manual, expertise-driven process that does not guarantee optimal controller performance and is often constrained by the non-transferability of settings between different systems. This study proposes the use of Bayesian Optimization (BO), a sample-efficient algorithm, to automate the tuning of an in-layer controller by leveraging the layer-to-layer repetitive nature of the LPBF process. Two alternative approaches are introduced: online tuning, which adjusts parameters iteratively during the process, and offline tuning, conducted in a representative setup such as laser exposures on a bare metal plate. The proposed methods are experimentally implemented on an in-layer PI controller and the performance of the resulting tuned controllers is investigated on two different wedge geometries that are prone to overheating. The results demonstrate that BO effectively tunes controllers using either method, where both significantly reduced overheating in controlled wedge specimens compared to those uncontro

关键词： controllers

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Slow Feature Thermography for Non-Destructive Evaluation of Defects in Composite Materials

Slow Feature Thermography for Non-Destructive Evaluation of ...

引用

Data Driven control and Learning Systems (DDCLS)

作者： Kaixin Liu Xin Zhong Ping Chen Yun Dai Yi Liu The State Key Lab for Electronic Testing Technology North University of China China Department of Information and Communication Engineering North University of China Taiyuan China College of Mechanical Engineering Nantong Vocational University Nantong China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality reduction methods (e.g. principal component analysis) have achieved good results in thermographic data analysis. In this work, a slow feature thermography (SFT) method for non-destructive evaluation of defects in composite materials. Thermographic data can be regarded as a continuous time series generated by an infrared imager at different moments. Slow feature analysis algorithm is subsequently used to extract slow features of the thermal image sequence. Slow features refer to features that are unchanged or slowly changing in time-series data, such as the background of a thermal image. By extracting the slow features of data, the inhomogeneous background, noise and defect features contained in thermal images can be effectively distinguished. Test results on carbon fiber reinforced polymer specimens demonstrate the feasibility and effectiveness of the SFT method.

关键词： Image recognition Composite materials Time series analysis Infrared imaging Feature extraction Thermal noise Thermal analysis

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Employing Different Hydrodynamic Models (Mike & Hec-Rac) to Predict the Extreme Flood in the Hron River, Slovakia and Provide the Adequate Mitigation Measures

SSRN

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SSRN 2025年

作者： Zeleňáková, Martina Kaya, Yunus Ziya Abd-Elhamid, Hany F. Garaj, Miroslav Fijko, Rastislav Gergeľová, Marcela Bindzárová Department of Environmental Engineering Faculty of Civil Engineering Technical University of Kosice Košice04001 Slovakia Department of Civil Engineering Faculty of Engineering Osmaniye Korkut Ata University Osmaniye80100 Turkey Department of Water and Water Structures Engineering Faculty of Engineering Zagazig University Zagazig44519 Egypt Department of Geodesy Cartography and Geographical Information Systems Faculty of Faculty of Mining Ecology Process Control and Geotechnologies Technical University of Kosice Košice04001 Slovakia

In this study, the abilities of two of the most popular flood modeling software, namely, HEC-RAS and MIKE+, were used for flood modeling in the Hron River in East Slovakia. For modeling purposes, various discharge values of the River from one year to hundred years peak discharge were considered. The vulnerability of the catchment for each period was modeled and visualized using both software. The HEC-RAS and MIKE+ outputs showed that the study area is vulnerable to flood for high-frequency peak discharge values. The outputs of both 2D hydrodynamic approaches were found to be close to each other in various flow conditions. The comparisons of the MIKE+ and HEC-RAS showed that Hron River dimensions for the given catchment are only sufficient to transfer a one-year peak discharge value safely. The outputs of the models exhibited that the study area is not safe in case of ten-year peak discharge value and the risk of the floods getting the highest level in case of hundred-year peak discharge. Due to the models’ findings, it is seen that the area is vulnerable to flood, and three different mitigation scenarios were proposed and analyzed including change in river bed dimensions and rise of the levee on the right and left river bank to protect the area from flood risks. © 2025, The Authors. All rights reserved.

关键词： Floods

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SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor

arXiv

引用

arXiv 2024年

作者： Wang, Han-Guang Hou, Hui-Rang Jin, Li-Cheng Xu, Chen-Yang Zhang, Zhong-Yi Meng, Qing-Hao Zhang, Yong-Hui Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Psychiatry Tianjin Anding Hospital Mental Health Center Tianjin Medical University China

Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods could bring subjective biases and may be denied by subjects. In search of a more objective means of diagnosis, researchers have begun to experiment with deep learning-based methods for identifying depressive disorders in recent years. Methods: In this study, a novel Spatiotemporal-fused network with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor (SAD-TIME) is proposed. SAD-TIME incorporates an automated nodes’ common features extractor (CFE), a spatial sector (SpS), a modified temporal sector (TeS), and a domain adversarial learner (DAL). The CFE includes a multiscale depth-wise 1D-convolutional neural network and a time-interval embedding generator, where the unique information of each channel is preserved. The SpS fuses the functional connectivity with the distance-based connectivity containing spatial position of EEG electrodes. A multi-head-attention graph convolutional network is also applied in the SpS to fuse the features from different EEG channels. The TeS is based on long short-term memory and graph transformer networks, where the temporal information of different time-windows is fused. Moreover, the DAL is used after the SpS to obtain the domain-invariant feature. Results: Experimental results under tenfold cross-validation show that the proposed SAD-TIME method achieves 92.00% and 94.00% depression classification accuracies on two datasets, respectively, in cross-subject mode. Conclusion: SAD-TIME is a robust depression detection model, where the automatedly-generated features, the SpS and the TeS assist the classification performance with the fusion of the innate spatiotemporal information in the EEG signals. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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