Article
Chemistry, Analytical
Gang Li, Dan Wang, Kang Wang, Ling Lin
Summary: This paper proposes a two-dimensional sample selection method based on spectral data quality and variable correlation for modeling. The experimental results show that this method significantly improves the accuracy and prediction performance of the model.
ANALYTICA CHIMICA ACTA
(2022)
Article
Engineering, Mechanical
Dattar Singh Aulakh, Suresh Bhalla
Summary: This study evaluates a scaled-down bridge model under pedestrian motions for damage monitoring using piezo sensors under operational modal analysis (OMA). The results show that strain-based damage sensitive features are superior to displacement-based ones, especially for incipient damage states.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Remote Sensing
Yehan Sun, Lijun Jiang, Jun Pan, Shiting Sheng, Libo Hao
Summary: The purpose of this study is to establish a smoke detection framework. The smoke spectral characteristics and variation pattern were studied and analyzed, and smoke identification and concentration inversion were carried out using the Mahalanobis distance. The extraction of the smoke concentration center and fire source positioning were realized based on the Laplace operator. The proposed method was applied and verified on forest smoke satellite data in Daxing'anling, China, and British Columbia, Canada, and achieved high accuracy in smoke recognition and fire source location.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Computer Science, Artificial Intelligence
Honghu Pan, Yang Bai, Zhenyu He, Chunkai Zhang
Summary: This paper proposes an adjacency-aware Graph Convolutional Network (AAGCN) to smooth intra-class features and reduce intra-class variance in person re-identification (ReID). The AAGCN establishes connections between intra-class features and applies low-pass filtering to smooth adjacent nodes. Two methods, Mahalanobis Neighborhood Adjacency (MNA) and Non-Linear Mapping (NLM), are proposed to learn adjacency relations for intra-class features. Experimental results demonstrate the effectiveness of the proposed method on visible and visual-infrared ReID datasets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Geosciences, Multidisciplinary
Andree M. Nenkam, Alexandre M. J. -C. Wadoux, Budiman Minasny, Alex B. McBratney, Pierre C. S. Traore, Anthony M. Whitbread
Summary: Many areas in the world lack sufficient soil data, leading to ineffective soil-related studies and inadequate soil management strategies. This paper demonstrates how to find "homosoils", which are geographically distant but share similar soil-forming factors, in order to obtain new soil data for a study area. By clustering the study area into homogeneous areas and identifying a homosoil for each area using distance metrics, this approach provides a solution to the problem of sparse soil data. The concept of homosoils shows promise for future applications such as transferring soil models and agronomic experimental results between areas.
Article
Multidisciplinary Sciences
Mauricio Diazgranados, Carolina Tovar, Thomas R. Etherington, Paula A. Rodriguez-Zorro, Carolina Castellanos-Castro, Manuel Galvis Rueda, Suzette G. A. Flantua
Summary: This study assesses the impact of future climate change on ecosystem services provided by the critically important paramos of Boyaca, Colombia. Results indicate a general trend of reduction in area for all ecosystem services under future climate conditions, with some increases and differing intensities of loss for different services.
Article
Chemistry, Multidisciplinary
Le Du, Wenhao Jin, Yang Wang, Qingchao Jiang
Summary: This paper proposes a data-driven time-slice latent variable correlation analysis-based model predictive fault detection framework to ensure accurate fault detection in dynamic batch processes. The proposed framework unfolds the batch process data into time slices, maps the process data to latent variables and residual subspaces, and generates prediction-based residuals to identify the characteristics of detected faults.
Article
Computer Science, Artificial Intelligence
Hongyuan Wang, Linyu Wu, Fuhua Chen, Zongyuan Ding, Yuchang Yin, Chenchao Dai
Summary: This paper proposes a person re-identification model based on covariance matrix, which reduces computational cost and achieves higher efficiency in accuracy and time cost by finding the optimal covariance matrix through learning.
PATTERN RECOGNITION LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Khongorzul Dashdondov, Mi-Hye Kim
Summary: This study improves the prediction of hypertension detection using machine learning methods and identifies various risk factors associated with chronic hypertension. The proposed method achieves high accuracy and can be applied to the detection of other diseases.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Maria Jose Gomez-Silva, Arturo de la Escalera, Jose Maria Armingol
Summary: The automatic re-identification of individuals across video surveillance cameras is challenging due to the learning of discriminative features and distance metrics affected by appearance variations. This article focuses on finding discriminative descriptors to reflect appearance differences independently of acquisition point variations through Mahalanobis distance learning in a Deep Neural Re-Identification model.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2021)
Article
Humanities, Multidisciplinary
Shuyu Wu, Jingyang Zhong, Hui Ye, Xusheng Kang
Summary: It is important to identify the category of cultural relics through chemical composition analysis. This study used distance discriminant analysis to classify glass artifacts into two categories, based on their chemical composition distribution. Key feature factors such as SiO2, K2O, PbO, and the presence of weathering on the surface were selected through regression analysis. Using Mahalanobis distance discriminant modeling, the study successfully differentiated unknown glass artifacts, with the Spearman-Mahalanobis method outperforming the stepwise regression-Mahalanobis method.
Article
Spectroscopy
Juan Huo, Yuping Ma, Changtong Lu, Chenggang Li, Kun Duan, Huaiqi Li
Summary: This paper introduces a novel chemometrics technique for estimating the quality similarity rate in the tobacco industry, which can accurately predict the quality similarity score at high speed.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Computer Science, Artificial Intelligence
Bin Li, Qiyu He, Xiaopeng Liu, Yajun Jiang, Zhigang Hu
Summary: The person re-identification problem in computer vision is a valuable research direction. Existing methods have some issues, so a new approach based on joint distance measure is proposed, which achieves better performance.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ricardo Fuentes-Fino, Saul Calderon-Ramirez, Enrique Dominguez, Ezequiel Lopez-Rubio, David Elizondo, Miguel A. Molina-Cabello
Summary: In the field of medical imaging, limited number of observations for each class within a labeled dataset can negatively impact prediction algorithms. To increase sample size, a second set of unlabeled images is often used, but this introduces the challenges of finding observations with different pathologies and different distributions from the labeled dataset. This study focuses on a mathematical model called Feature Density to estimate predictive uncertainty in supervised classification algorithms and improve performance when presented with out-of-distribution data.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
M. Khosravi, R. S. Smith
Summary: In this article, the problem of system identification with available side-information on the steady-state gain is considered. A nonparametric identification method is formulated as a constrained convex program over the reproducing kernel Hilbert space. The proposed formulation has a unique solution obtained through finite-dimensional convex optimization, and it has a closed-form solution in certain cases. Extensive numerical comparisons are performed to verify the efficiency of the proposed methodology.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Haibin Wu, Yu-Han Lo, Le Zhou, Yuan Yao
Summary: In the era of big data, small data problems still exist in many industrial sectors, including high-value process industries. It is necessary to integrate small data measured in different tasks and utilize deep embedding neural network for process modeling.
JOURNAL OF PROCESS CONTROL
(2022)
Article
Polymer Science
Wei Qi, Tzu-Heng Chiu, Yi-Kai Kao, Yuan Yao, Yu-Ho Chen, Hsun Yang, Chen-Chieh Wang, Chia-Hsiang Hsu, Rong-Yeu Chang
Summary: Resin transfer molding (RTM) is a promising method for manufacturing fiber-reinforced plastics (FRPs) with light weight, high strength, and multifunctional features. The permeability and porosity of fiber reinforcements are critical properties for controlling resin flow and numerical simulations of RTM. Previous measurement methods have limitations, but this study proposes a new measurement system and algorithm that can simultaneously estimate in-plane permeability and porosity.
Review
Chemistry, Analytical
Vasiliki Dritsa, Noemi Orazi, Yuan Yao, Stefano Paoloni, Maria Koui, Stefano Sfarra
Summary: There has been a growing interest in using pulsed infrared thermography (PT) for the non-destructive evaluation of Cultural Heritage (CH) recently. PT allows the depth-resolved detection of subsurface features, providing valuable information for scholars and restorers. Ongoing research activities aim to further enhance the effectiveness of PT. This manuscript reviews the specific use of PT in the analysis of three types of CH, including documentary materials, panel paintings-marquetry, and mosaics.
Article
Polymer Science
Kaixin Liu, Fumin Wang, Yuxiang He, Yi Liu, Jianguo Yang, Yuan Yao
Summary: In this article, a novel generative manifold learning thermography (GMLT) method is proposed for defect detection and evaluation of composites. The method utilizes spectral normalized generative adversarial networks as an image augmentation strategy, to learn the thermal image distribution and generate virtual images to enrich the dataset. Manifold learning is employed for unsupervised dimensionality reduction, and partial least squares regression is used for defect visualization. Probability density maps and quantitative metrics are introduced to evaluate and explain the defect detection performance. Experimental results demonstrate the superiority of GMLT compared to other methods.
Article
Automation & Control Systems
Kaixin Liu, Mingkai Zheng, Yi Liu, Jianguo Yang, Yuan Yao
Summary: In this article, a deep autoencoder thermography (DAT) method is proposed for detecting subsurface defects in composite materials. The multilayer network structure of DAT can handle nonlinear temperature profiles, and the output of the intermediate hidden layer is visualized to highlight defects. The layer-by-layer feature visualization reveals how the model extracts defect features.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Chemical
Mingwei Jia, Junhao Hu, Yi Liu, Zengliang Gao, Yuan Yao
Summary: Faults in the process industry can be diagnosed using various data-driven methods, but little attention has been given to the physical consistency of model prediction logic. To address this, we propose a graph learning fault diagnosis framework that combines graphs with process physics. Our framework focuses on knowledge embedding and explanation, and includes components such as a topology graph, self-attention mechanism, graph convolution, graph pooling, and a gating mechanism. We also use a graph explainer to assess the physical consistency of the model's prediction logic. The feasibility of our method is demonstrated using the Tennessee Eastman process.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Automation & Control Systems
Mingwei Jia, Danya Xu, Tao Yang, Yi Liu, Yuan Yao
Summary: In this study, a soft sensor based on a graph convolutional network is developed to model the nonlinear time-varying characteristics of the process industry. The focus is on obtaining localized spatial-temporal correlations to understand the intricate interactions among variables. The model is trained with regularization terms and learns distinctive localized spatial-temporal correlations in an end-to-end manner, capturing both the localized spatial-temporal correlations and time-series properties.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Chemistry, Analytical
Yi Liu, Yuxin Jiang, Zengliang Gao, Kaixin Liu, Yuan Yao
Summary: To ensure safe and efficient operation of packed columns, we proposed a machine vision approach using a convolutional neural network (CNN) to detect flooding in real time. Real-time images of the packed column were captured and analyzed with a CNN model trained on a dataset of recorded images. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding events.
Article
Materials Science, Multidisciplinary
Yi-Ting Tsai, Yu-Kai Huang, Zhen-Feng Jiang, Yuan Yao, Pei-Hsuan Lo, Yu-Chiang Chao, Bi-Hsuan Lin, Chun Che Lin
Summary: This study modified the partial inversion of a spinel structure through cation substitution, resulting in the creation of MgGa2O4 with a local structure. Increasing the concentration of Sn led to a decrease in the vibrational mode of GaO4 and a retention of the vibrational mode of MgO4. The long SWIR emission achieved through Sn substitution enabled the development of an SWIR light-emitting diode that improved the accuracy of an artificial intelligence based image recognition system.
ACS MATERIALS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Yun Dai, Chao Yang, Yi Liu, Yuan Yao
Summary: This study develops a sample selection strategy for active learning called latent-enhanced variational adversarial AL (LVAAL) to improve quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data and deceives the adversarial network into predicting all samples as labeled. Then, Gaussian process regression (GPR) is used for prediction. The experimental results show that LVAAL outperforms existing AL strategies.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Jian-Guo Wang, Rui Chen, Xiang-Yun Ye, Yuan Yao, Zhong-Tao Xie, Shi-Wei Ma, Li-Lan Liu
Summary: This study proposes a novel root cause diagnosis framework based on Granger causality analysis, which utilizes both normal data and disturbance data to obtain compact and useful disturbance-related information.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Engineering, Chemical
Jian-Guo Wang, Rui Chen, Jing-Ru Su, Hui-Min Shao, Yuan Yao, Shi-Wei Ma, Li-Lan Liu
Summary: In this study, the use of kernel multivariate Granger causality (KMGC) test as an analytical tool for diagnosing the root cause of plant-wide oscillations is proposed. The kernel function allows for easier processing of nonlinear data by mapping it to a higher-dimensional space. The combination of KMGC with a fuzzification function, creating fuzzy kernel multivariate Granger causality (FKMGC), improves computational efficiency and accurately identifies the root cause of oscillations. FKMGC reduces analysis time by 95% compared to Gaussian kernel-based algorithms.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
(2023)
Article
Instruments & Instrumentation
Yi Liu, Fumin Wang, Kaixin Liu, Miranda Mostacci, Yuan Yao, Stefano Sfarra
Summary: Infrared thermography is a cost-effective non-destructive evaluation technique that is critical for detecting defects in cultural heritage. However, there is a lack of in-depth studies on using infrared thermography for contemporary artworks. This study proposes a deep convolutional autoencoder thermography method for defect detection in contemporary artworks, which significantly improves accuracy compared to commonly used methods, as shown in a case study on a handmade replica of Picasso's 'La Bouteille de Suze'.
QUANTITATIVE INFRARED THERMOGRAPHY JOURNAL
(2023)
Article
Chemistry, Analytical
Yi Liu, Fumin Wang, Zhili Jiang, Stefano Sfarra, Kaixin Liu, Yuan Yao
Summary: Infrared thermography is a widely used nondestructive testing technique for artwork inspection. However, raw thermograms often have limitations in quantity and background noise. To overcome these challenges, this study proposes a defect inspection method for artwork based on principal component analysis, incorporating two deep learning approaches: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting thermal images, while the CAE strategy emphasizes enhancing their quality. Principal component thermography (PCT) is then used to analyze the processed data and improve defect detectability. The integration of the SNGAN strategy led to a 1.08% enhancement in signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.
Article
Engineering, Electrical & Electronic
Junhua Zheng, Yangxuan Liu, Yi Liu, Beiping Hou, Yuan Yao, Le Zhou
Summary: This article investigates the problem of modeling regression with labeled and unlabeled data samples commonly found in industrial processes. By incorporating additional information on unlabeled data samples, a new semi-supervised latent factor analysis model is developed. The semi-supervised model can efficiently extract useful information for improving regression performance compared to purely supervised regression models. The proposed basic semi-supervised model has also been extended to a mixture form, capable of describing data from more complex processes. Two soft sensors are constructed based on the semi-supervised models for predictive modeling of key/mass variables. Three case studies are used to evaluate the performance of the proposed soft sensing methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
