Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows
Sign Up to like & getrecommendations! Published in 2017 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2017.08.2334
Abstract: Abstract An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To… read more here.
Keywords: soft sensor; weighted supervised; based weighted; adaptive soft ... See more keywords
Ensemble deep relevant learning framework for semi-supervised soft sensor modeling of industrial processes
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.07.086
Abstract: Abstract Deep learning has been growing in popularity for soft sensor modeling of nonlinear industrial processes, infeuality-related variables. However, applications may be highly nonlinear, and the quantity of labeled samples is considerably limited. The extraction… read more here.
Keywords: industrial processes; sensor modeling; ensemble deep; soft sensor ... See more keywords
Siamese Neural Network and Multimodal Data Fusion Approach for Small-Sample Learning in Industrial Soft Sensor Modeling
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Sensors Journal"
DOI: 10.1109/jsen.2024.3451148
Abstract: In industrial scenarios, soft sensor modeling often faces the challenge of underfitting due to limited available data. Therefore, in the field of industrial soft sensing, it is crucial to develop solutions for regression problems based… read more here.
Keywords: industrial soft; multimodal data; small sample; sample learning ... See more keywords
Process Industries Soft Sensor Modeling Based on Semi-Supervised Growing Echo States Gaussian Process
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Sensors Journal"
DOI: 10.1109/jsen.2025.3550875
Abstract: The echo state Gaussian process (ESGP) is an efficient method for modeling dynamical systems and has been successfully employed in soft sensor modeling within the process industry. However, the ESGP operates as a supervised learner,… read more here.
Keywords: process; gaussian process; semi supervised; sensor modeling ... See more keywords
Electromechanical Characterization and Experimental Sensor Modeling of Thermoformed FEP Piezoelectrets for Dynamic Force Environments
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Sensors Journal"
DOI: 10.1109/jsen.2025.3581027
Abstract: This study explores the design, fabrication, and electromechanical characterization of thermoformed tubular Teflon piezoelectrets for force measurement applications. Piezoelectrets, a subclass of electrets, leverage engineered dipole configurations within charged internal cavities to exhibit piezoelectric properties.… read more here.
Keywords: force; electromechanical characterization; sensor modeling; experimental sensor ... See more keywords
A Novel Soft Sensor Modeling Approach Based on Difference-LSTM for Complex Industrial Process
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2021.3110507
Abstract: The main purpose of soft sensor modeling is to capture the dynamic nonlinear features between the easy-to-measure auxiliary variables and the difficult-to-measure key variables. However, in complex industrial process, it is a challenging work due… read more here.
Keywords: complex industrial; industrial process; process; soft sensor ... See more keywords
Adaptive Wavelet Normalization Network for Soft Sensor Modeling in Nonstationary Industrial Processes
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2025.3609103
Abstract: In industrial processes, accurate, real-time soft sensor modeling of key product indices is essential for optimal process control and improved product quality. However, the nonstationary characteristics inherent in industrial data, i.e., data distribution drift over… read more here.
Keywords: normalization; industrial processes; sensor; sensor modeling ... See more keywords
A Supervised Bidirectional Long Short-Term Memory Network for Data-Driven Dynamic Soft Sensor Modeling
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2022.3152856
Abstract: Data-driven soft sensors have been widely adopted in industrial processes to learn hidden knowledge automatically from process data, then to monitor difficult-to-measure quality variables. However, to extract and utilize useful dynamic latent features accurately for… read more here.
Keywords: sensor modeling; supervised bidirectional; soft sensor; data driven ... See more keywords
Soft Sensor Modeling Method Based on Target-Guided Related Feature Learning and Its Application
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DOI: 10.1109/tim.2022.3219482
Abstract: Currently, soft sensors have been extensively used in complex industrial processes. How to learn effective features from nonlinear process data is the core task of building precise soft sensors. Deep learning, a type of feature… read more here.
Keywords: feature; sensor modeling; soft sensor; target guided ... See more keywords
A Temporal Convolutional-Based Kolmogorov-Arnold Network for Industrial Soft Sensor Modeling
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2025.3578182
Abstract: Multitimescale characteristics, nonlinearity, and dynamic features exist extensively in modern industrial processes, impairing the predictive performance and accuracy of soft sensor modeling for primary variables. To address this challenge, a multitimescale neural network-based soft sensor… read more here.
Keywords: temporal convolutional; kolmogorov arnold; sensor modeling; network ... See more keywords