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Soft Sensors 2021-2022

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 15365

Special Issue Editor


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Guest Editor
MTA-PE Lendület Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, H-8200 Veszprém, Hungary
Interests: chemical engineering; complex systems; computational intelligence; network science; process engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soft sensors estimate unmeasured variables using computational models. The resulting inferential measurements can be used in fault diagnosis, control and real-time optimization applications and for the validation of online analyzers.

This Special Issue solicits papers that cover the development, validation, application, and maintenance of software sensors. The potential topics include, but are not limited to:

  • Data-driven modeling for soft sensor development (from classical system identification and multivariate chemometric techniques to deep learning);
  • Semi-mechanistic and first-principle models in soft sensor development (including grey box models);
  • Validation and maintenance of soft sensors;
  • Control-oriented soft sensor development (g., inferential control, senseless control);
  • Applications in fault detection and diagnosis and monitoring of complex processes;
  • Applications in state estimation, control, and optimization (e.g., sensorless motor control, nonlinear model predictive control);
  • Special applications in process analytical technology (PAT), manufacturing, chemical, bio, pharmaceutical, oil, and process engineering.

Prof. Dr. Janos Abonyi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

29 pages, 6456 KiB  
Article
Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams
by Yuechen Wang, Huaiping Jin, Xiangguang Chen, Bin Wang, Biao Yang and Bin Qian
Sensors 2023, 23(3), 1520; https://doi.org/10.3390/s23031520 - 30 Jan 2023
Cited by 2 | Viewed by 1994
Abstract
In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue for [...] Read more.
In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue for facilitating efficient process control and monitoring in the process industry. Traditionally, soft sensor models are usually built through offline batch learning, which remain unchanged during the online implementation phase. Once the process state changes, soft sensors built from historical data cannot provide accurate predictions. In practice, industrial process data streams often exhibit characteristics such as nonlinearity, time-varying behavior, and label scarcity, which pose great challenges for building high-performance soft sensor models. To address this issue, an online-dynamic-clustering-based soft sensor (ODCSS) is proposed for industrial semi-supervised data streams. The method achieves automatic generation and update of clusters and samples deletion through online dynamic clustering, thus enabling online dynamic identification of process states. Meanwhile, selective ensemble learning and just-in-time learning (JITL) are employed through an adaptive switching prediction strategy, which enables dealing with gradual and abrupt changes in process characteristics and thus alleviates model performance degradation caused by concept drift. In addition, semi-supervised learning is introduced to exploit the information of unlabeled samples and obtain high-confidence pseudo-labeled samples to expand the labeled training set. The proposed method can effectively deal with nonlinearity, time-variability, and label scarcity issues in the process data stream environment and thus enable reliable target variable predictions. The application results from two case studies show that the proposed ODCSS soft sensor approach is superior to conventional soft sensors in a semi-supervised data stream environment. Full article
(This article belongs to the Special Issue Soft Sensors 2021-2022)
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16 pages, 2015 KiB  
Article
Fusion Methods for Face Presentation Attack Detection
by Faseela Abdullakutty, Pamela Johnston and Eyad Elyan
Sensors 2022, 22(14), 5196; https://doi.org/10.3390/s22145196 - 12 Jul 2022
Cited by 9 | Viewed by 2391
Abstract
Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows [...] Read more.
Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows that both modern, pre-trained, deep learning-based methods, and traditional hand-crafted, feature-engineered methods have been effective in detecting PAs. However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal performance on PA detection tasks. In this paper, we present a simple feature-fusion method that integrates features extracted by using pre-trained, deep learning models with more traditional colour and texture features. Extensive experiments clearly show the benefit of enriching the feature space to improve detection rates by using three common public datasets, namely CASIA, Replay Attack, and SiW. This work opens future research to improve face presentation attack detection by exploring new characterizing features and fusion strategies. Full article
(This article belongs to the Special Issue Soft Sensors 2021-2022)
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24 pages, 3278 KiB  
Article
State Estimation for Coupled Reaction-Diffusion PDE Systems Using Modulating Functions
by David Pumaricra Rojas, Matti Noack, Johann Reger and Gustavo Pérez-Zúñiga
Sensors 2022, 22(13), 5008; https://doi.org/10.3390/s22135008 - 2 Jul 2022
Cited by 1 | Viewed by 1928
Abstract
Many systems with distributed dynamics are described by partial differential equations (PDEs). Coupled reaction-diffusion equations are a particular type of these systems. The measurement of the state over the entire spatial domain is usually required for their control. However, it is often impossible [...] Read more.
Many systems with distributed dynamics are described by partial differential equations (PDEs). Coupled reaction-diffusion equations are a particular type of these systems. The measurement of the state over the entire spatial domain is usually required for their control. However, it is often impossible to obtain full state information with physical sensors only. For this problem, observers are developed to estimate the state based on boundary measurements. The method presented applies the so-called modulating function method, relying on an orthonormal function basis representation. Auxiliary systems are generated from the original system by applying modulating functions and formulating annihilation conditions. It is extended by a decoupling matrix step. The calculated kernels are utilized for modulating the input and output signals over a receding time window to obtain the coefficients for the basis expansion for the desired state estimation. The developed algorithm and its real-time functionality are verified via simulation of an example system related to the dynamics of chemical tubular reactors and compared to the conventional backstepping observer. The method achieves a successful state reconstruction of the system while mitigating white noise induced by the sensor. Ultimately, the modulating function approach represents a solution for the distributed state estimation problem without solving a PDE online. Full article
(This article belongs to the Special Issue Soft Sensors 2021-2022)
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24 pages, 1146 KiB  
Article
Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development
by Pál Péter Hanzelik, Alex Kummer and János Abonyi
Sensors 2022, 22(11), 4268; https://doi.org/10.3390/s22114268 - 3 Jun 2022
Cited by 6 | Viewed by 7847
Abstract
The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our motivation is to take advantage of ML and edge [...] Read more.
The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our motivation is to take advantage of ML and edge computing and offer innovative solutions to the chemical industry for difficult-to-measure laboratory variables. The purpose of software sensor models is to continuously forecast the quality of products to achieve effective quality control, maintain the stable production condition of plants, and support efficient, environmentally friendly, and harmless laboratory work. As a result of the literature review, quite a few ML models have been developed in recent years that support the quality assurance of different types of materials. However, the problems of continuous operation, maintenance and version control of these models have not yet been solved. The method uses ML algorithms and takes advantage of cloud services in an enterprise environment. Industrial 4.0 devices such as the Internet of Things (IoT), edge computing, cloud computing, ML, and artificial intelligence (AI) are core techniques. The article outlines an information system structure and the related methodology based on data from a quality-assurance laboratory. During the development, we encountered several challenges resulting from the continuous development of ML models and the tuning of their parameters. The article discusses the development, version control, validation, lifecycle, and maintenance of ML models and a case study. The developed framework can continuously monitor the performance of the models and increase the amount of data that make up the models. As a result, the most accurate, data-driven and up-to-date models are always available to quality-assurance engineers with this solution. Full article
(This article belongs to the Special Issue Soft Sensors 2021-2022)
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