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Graph-guided masked autoencoder for process anomaly detection

process Safety and Environmental Protection 2024年 186卷 1345-1357页

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan

Developing an accurate and reliable anomaly detection model is of great significance for safe operation in the process industry. To minimize false positives, it is crucial to accurately model the intricate topological and nonlinear connections among variables. In this study, a process anomaly detection method based on graph-guided masked autoencoder (GGMAE) is proposed by introducing the concept of the graph to the process industry. GGMAE first constructs a topology graph according to the process flowchart. Then, the patch and mask mechanism forces GGMAE to learn the process intrinsic information to reconstruct the input according to the variable topological relationship and temporal characteristics. Additionally, Kullback-Leibler divergence is used as the loss to ensure that the distribution of input and output is consistent. Experiments on two publicly available anomaly detection benchmarks demonstrate the superiority of GGMAE over existing methods. Visualization of the results demonstrates the physically compliant reconstruction logic. © 2024 The Institution of chemical Engineers

关键词： Anomaly detection

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Observer-based stabilization for discrete nonlinear semi-Markov jump singularly perturbed models with mode-switching delay

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Science China(Information Sciences) 2025年第4期68卷 389-390页

作者： Wenhai QI Runkun LI Ju H.PARK Zheng-Guang WU Huaicheng YAN School of Engineering Qufu Normal University Department of Electrical Engineering Yeungnam University Institute of Cyber-Systems and Control Zhejiang University Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology

Semi-Markov jump systems(S-MJMs) not only characterize hybrid systems but also address the limitations of Markov jump systems(MJMs) [1–3]. Due to their ability to exhibit multi-time-scale features, singularly perturbed models(SPMs) effectively model practical systems influenced by multiple time-scale phenomena [4]. In this study, the observer-based output feedback controller is asynchronous with the original system due to the time delay in the controller mode switching. A nonlinear plant with singularly perturbed parameters(SPPs) is represented using an interval type-2(IT2) fuzzy model [5].

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Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor

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IEEE Sensors Journal 2023年第14期23卷 15762-15772页

作者： Dai, Yun Yang, Chao Liu, Yi Yao, Yuan Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu300044 Taiwan

As the acquisition of variables that measure quality is typically challenging, labeled samples for building a model for soft sensors are often inadequate. Additionally, owing to the installation of redundant sensors, high-dimensional process data with strong correlations are acquired. Therefore, in this study, a sample selection strategy for active learning (AL), referred to as latent-enhanced variational adversarial AL (LVAAL), is developed to enhance quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data. Specifically, a latent-enhanced variational autoencoder (LEVAE) is used to deceive the adversarial network into predicting all samples, both labeled and unlabeled, as labeled. However, the adversarial network attempts to indicate the difference between latent representations. Subsequently, Gaussian process regression (GPR) is adopted as a base model for LVAAL. With the proposed LVAAL selection strategy, informative unlabeled samples are incrementally selected, thereby enhancing the prediction performance. The application results for a numerical case and an industrial example demonstrate the advantages of LVAAL compared with existing AL strategies. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Graph convolutional network soft sensor for process quality prediction

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Journal of process control 2023年第1期123卷 12-25页

作者： Jia, Mingwei Xu, Danya Yang, Tao Liu, Yi Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu30013 Taiwan

The nonlinear time-varying characteristics of the process industry can be modeled using numerous data-driven soft sensor methods. However, the intrinsic relationships among the variables, especially the localized spatial–temporal correlations that shed light on model behavior, have received little attention. In this study, a soft sensor based on a graph convolutional network is constructed by introducing the concept of graph to process modeling. The focus is on obtaining localized spatial–temporal correlations that aid in comprehending the intricate interactions among the variables included in the soft sensor. The model is trained by considering the regularization terms and it learns distinctive localized spatial–temporal correlations in an end-to-end manner. Furthermore, long-term dependence is established via temporal convolution. Thus, both the localized spatial–temporal correlations and time-series properties are captured. The feasibility of the proposed soft sensor is illustrated using two fermentation processes. The localized spatial–temporal correlations of this case study are visualized, and they demonstrate that the soft sensor is not a black-box model;instead, it is consistent with process knowledge. © 2023 Elsevier Ltd

关键词： Convolution

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Defect Detection of Composite Materials Using Channel Attention and Convolutional Autoencoder 13

Defect Detection of Composite Materials Using Channel Attent...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Jiang, Jiahao Jiang, Zhili Wang, Fumin Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

ISBN: (纸本)9798350361674

As an effective non-destructive technology, infrared thermography is often used to detect inner defects in composites. However, non-homogeneous background in the thermal images reduces image quality inevitably. A squeeze and excitation convolutional autoencoder thermography (SE-CAT) technique is introduced for detecting underlying defects. Specifically, the channel attention mechanism assigns weights to different convolutional channels to highlight certain prominent features. As a result, the defect information can be extracted in a targeted manner using the convolutional autoencoder module. Finally, an experiment of carbon fiber reinforced composite materials shows good defect detection performance of the proposed SE-CAT method. © 2024 IEEE.

关键词： Carbon fiber reinforced plastics

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Factor analysis thermography for defect detection of panel paintings

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Quantitative InfraRed Thermography Journal 2023年第1期20卷 25-37页

作者： Liu, Kaixin Huang, Kai-Lun Sfarra, Stefano Yang, Jianguo Yi, Liu Yuan, Yao Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China 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

Active infrared thermography is an important non-destructive testing method used for revealing defect structures in materials. In many applications, thermographic data processing is necessary to extract defect features from a large number of thermal images. This work proposes to use a factor analysis thermography (FAT) method that automatically extracts defect features from thermograms via exploratory factor analysis, in tandem with a fuzzy c-means (FCM) clustering algorithm to segment the defects and background. By means of factor rotation, factor analysis minimises the complexity of factor loadings and makes the results more interpretable. Consequently, the defect information is extracted while large signal-to-noise ratios are obtained. Employing the FCM image segmentation algorithm on factor loading images reduces the interference of background on human visual detection. Additionally, the parameter selection is emphasised and addressed. Experiments on a panel painting illustrate that the proposed method promotes the accuracy and efficiency of thermographic detection of defects, compared with the popular principal component thermography (PCT) method. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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Novel Self-assembled Tellurium Nanoparticles And Their Effects In Myoblastoma Cell Line

Journal of Applied Science and Engineering

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Journal of Applied Science and engineering 2025年第11期28卷 2265-2279页

作者： Chithamparam, Pandian Christyraj, Johnson Retnaraj Samuel Selvan Thiagamani, Senthil Muthu Kumar Sriariyanun, Malinee Muthukumaran, Azhaguchamy Department of Biotechnology Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Regeneration and Stem Cell Biology Lab Centre for Molecular and Nanomedical Sciences International Research Centre Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Anand Nagar Tamil Nadu Krishnankoil626126 India Department of Mechanical Engineering INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai71800 Malaysia UniversitiTeknologi Malaysia Skudai Johor Johor Bahru81310 Malaysia Biorefinery and Process Automation Engineering Center Department of Chemical and Process Engineering The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Harnessing the distinct features of nanoparticles and nanomaterials for biomedical use has seen remarkable progress in recent years. Tellurium nanoparticles (TeNPs) have attracted attention due to their exceptional properties and potential uses in photoconductivity, nonlinear optics, piezoelectricity, and thermal conductivity applications. This study synthesized TeNPs through a novel heat reduction method using ascorbic acid as a reducing agent, followed by various characterization studies. Functional studies, such as the antibacterial activity, antioxidant activity and cytotoxicity assays, AO/EB staining, and live cell imaging were performed using TeNPs. The various concentrations of TeNPs (5 − 20 µg/ml) result in inhibition zones against *** in the 5-13 mm range and against *** in the 6-12 mm range. The produced TeNPs minimum inhibitory concentration (MIC) demonstrated that these nanoparticles had antibacterial properties against the tested gram-positive and gram-negative bacterial strains. Moreover, TeNPs exhibit strong antioxidant properties and demonstrate high toxicity in C2C12 myoblastoma cells. Notably, at a concentration of 5 µg/ml, TeNPs exhibit potential toxicity. Live cell imaging and cell proliferation assay with high and lower concentrations of TeNPs retards cell growth and proliferation of C2C12 myoblastoma cells compared to control. Based on the results, due to the increased toxicity nature of TeNPs, lower concentrations below 5 µg might be used in scaffolds to enhance cell proliferation for regenerative medicine and tissue engineering applications. © The Author(’s).

关键词： Cell proliferation

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Decoding and quantitative detection of antibiotics by a luminescent mixed-lanthanide-organic framework

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Frontiers of Environmental Science & engineering 2022年第12期16卷 55-65页

作者： Yuping Wang Jing Xia Yanxin Gao Department of Environmental Science and Engineering Fuzhou UniversityMinhou350108China State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process School of Resources and Environmental EngineeringEast China University of Science and TechnologyShanghai200237China

Due to the potential risk of antibiotics to the environment,the development of inexpensive,simple,and reliable antibiotic detection methods is significant but also faces *** this work,several lanthanide-organic frameworks(LOFs),constructed from lanthanide ions(Eu3+and/or Tb3+)and 1,3,5-benzene-tricarboxylic acid(BTC),were synthesized by solvothermal ***-S3 with comparable emission peaks of 5D4→7F5(Tb3+,545 nm)and 5D0→7F2(Eu3+,618 nm)was selected as a luminescent *** this system,the highly efficient energy transferred from the organic linker to lanthanide ions and from Tb3+to Eu3+***-S3 sensor was capable of decoding antibiotics by distinguishable emission intensity ***,a two-dimensional decoded map of antibiotics was *** linear relationship between antibiotic concentration and emission intensity ratio indicated the quantitative determination of antibiotics was *** a typical analyte,the response mechanism of nalidixic acid(NA)was investigated in *** competition of NA and BTC for UV light absorption,as well as the binding propensity of NA and Tb,affected the organic linkers-to-lanthanide ions and Tb-to-Eu energy transfer,resulting in the change of fluorescence intensity *** self-calibrating mixed-LOF sensor overcame the uncontrollable errors of the traditional absolute emission intensity method and generated stable luminescent signals in multiple ***,the integration of LOF-S3 with polymer fibers enabled the formation of a LOF-polymer fibrous composite with fluorescence detection capability,which is a promising portable sensor for practical applications.

关键词： Antibiotics Sensor Luminescence Lanthanide-organic frameworks

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A Neural Additive Domain Adaptation Network with Interpretability for Fault Detection in Various Operating Conditions 25

A Neural Additive Domain Adaptation Network with Interpretab...

引用

25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Su, Cheng Zhong, Weimin Yang, Dan Li, Linlin Peng, Xin East China University Of Science And Technology State Key Laboratory Of Industrial Control Technology Shanghai China East China University Of Science And Technology Key Laboratory Of Smart Manufacturing In Energy Chemical Process Shanghai China University Of Science And Technology Beijing Department Of Control Science And Engineering Beijing China

ISBN: (纸本)9798350340266

Domain adaptation (DA) can be used for fault detection under multiple operating conditions. However, the lack of interpretability of DA models prevents potential users from understanding the decisions made by models. To give a solution, a neural additive domain adaptation network (NADAN) is proposed, which is constructed based on a neural additive model (NAM). Benefiting from the structural advantages of NAM, the predictions of NADAN are interpretable. Specifically, NADAN is able to explain how each feature affects the decision of the model. In addition, NADAN consists of two modules, feature learning (FL) and domain adaptation (DA), and achieves domain alignment through the combination of FL and DA. The FL module is pretrained to learn high-order features. The DA module takes learned features as input and achieves domain alignment based on multi-kernel maximum mean discrepancy (MK-MMD). Compared to the raw data, the learned features are more task-relevant, which helps to improve the performance of NADAN on DA. Furthermore, NADAN augments the feature dimensions during DA to measure the inter-domain differences in detail, which in turn achieves better domain alignment. Experiments on datasets generated from wastewater treatment simulation model demonstrate the effectiveness of the proposed NADAN. © 2024 IEEE.

关键词： Additives

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