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Special Issue "Deep Learning-Based Soft Sensors"

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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 14233

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Special Issue Editors

Prof. Dr. Salvatore Graziani

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Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Interests: artificial intelligence; neural networks; soft sensors; ionic polymeric transducers; sensor modelling and characterization; mechanical sensors; energy harvesting; smart materials; smart sensing systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Maria Gabriella Xibilia

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Dipartimento di Ingegneria, University of Messina, Contrada di Dio, S. Agata, 98166 Messina ME, Italy
Interests: automatic control; system identification; nonlinear control; industrial automation; soft sensors; soft computing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The implementation of Industry 4.0 enforces the need for processes monitoring. Plant monitoring, control policies, and asset management require the estimation of plant working conditions. Efficient measurement and data elaboration systems are, therefore, required. A key role will be played by sensing systems with somewhat contrasting constraints on sensing capability, efficiency, redundancy, and costs. Physical or economic reasons, e.g., can limit the nature, number, and location of measuring systems.

Soft Sensors (SSs) are becoming a penetrating solution in the industry, capable of overcoming physical limitations that can be involved with the problems mentioned above. SSs are software tools that elaborate data on easy-to-measure process variables (SS inputs) and estimate hard-to-measure quantities (SS outputs). They are used for many purposes, such as hardware sensing back-up, real-time estimation for monitoring and control, sensor validation, and fault detection. Notwithstanding the interest in SSs, unsolved problems have so far hindered the full success of the data-driven designing approach. Deep learning has emerged as a valuable approach for alleviating some such problems. Nevertheless, the complex and nonlinear nature of industrial processes still poses interesting challenges that are the focus of the Special Issue. Real case studies will be a valuable contribution to the significativity of the Issue.

Contributes are invited on the following topics:

-Data-driven methods for SS design;
-Deep belief networks for SS design;
-Autoencoders for SS design;
-Long- and short-term memory networks for SS design;
-Convolution neural networks for SS design;
-Ensemble methods;
-Model structure selection;
-Small and large dataset;
-Semisupervised learning;
-Model validation;
-Hyperparameters design;
-Learning methods;
-Computational complexity;
-Feature selection in SS design;
-SSs for industrial applications;
-SSs for inferential control;
-SSs for monitoring and fault detection.

Prof. Dr. Salvatore Graziani
Prof. Dr. Maria Gabriella Xibilia
Guest Editors

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Published Papers (6 papers)

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Research

Open AccessArticle

2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting

Calvin Janitra Halim

and

Kazuhiko Kawamoto

Sensors 2020, 20(15), 4195; https://doi.org/10.3390/s20154195 - 28 Jul 2020

Cited by 1 | Viewed by 1946

Abstract

Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal sequences that arise in the real world are noisy and chaotic, modeling approaches that utilize probabilistic temporal models, such as deep Markov models (DMMs), are favorable because of their ability to model uncertainty, increasing their robustness to noise. However, approaches based on DMMs do not maintain the spatial characteristics of spatiotemporal sequences, with most of the approaches converting the observed input into 1D data halfway through the model. To solve this, we propose a model that retains the spatial aspect of the target sequence with a DMM that consists of 2D convolutional neural networks. We then show the robustness of our method to data with large variance compared with naive forecast, vanilla DMM, and convolutional long short-term memory (LSTM) using synthetic data, even outperforming the DNN models over a longer forecast period. We also point out the limitations of our model when forecasting real-world precipitation data and the possible future work that can be done to address these limitations, along with additional future research potential. Full article

(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)

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Open AccessArticle

Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments

and

Sensors 2020, 20(12), 3348; https://doi.org/10.3390/s20123348 - 12 Jun 2020

Cited by 5 | Viewed by 1587

Abstract

The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities. Full article

(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)

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Open AccessArticle

Deep Generative Models-Based Anomaly Detection for Spacecraft Control Systems

Hyojung Ahn

Dawoon Jung

and

Han-Lim Choi

Sensors 2020, 20(7), 1991; https://doi.org/10.3390/s20071991 - 02 Apr 2020

Cited by 13 | Viewed by 3143

Abstract

A spacecraft attitude control system provides mechanical and electrical control to achieve the required functions under various mission scenarios. Although generally designed to be highly reliable, mission failure can occur if anomalies occur and the attitude control system fails to properly orient and stabilize the spacecraft. Because accessing spacecraft to directly repair such problems is usually infeasible, developing a continuous condition monitoring model is necessary to detect anomalies and respond accordingly. In this study, a method for detecting anomalies and characterizing failures for spacecraft attitude control systems is proposed. Herein, features are extracted from multidimensional time-series data of a simulation of the attitude control system. Then, the artificial neural network learning algorithms based on two types of generation models are applied. A Bayesian optimization algorithm with a Gaussian process is used to optimize the hyperparameters for the neural network to improve the performance. The performance is evaluated based on the reconstruction error through the algorithm using the newly generated data not used for learning as input data. Results show that the detection performance depends on the operating characteristics of each submode in the operation scenarios and type of generation model. The diagnostic results are monitored to detect anomalies in operation modes and scenarios. Full article

(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)

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Open AccessArticle

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

and

Sensors 2020, 20(3), 695; https://doi.org/10.3390/s20030695 - 27 Jan 2020

Cited by 15 | Viewed by 1987

Abstract

Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR. Full article

(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)

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Open AccessArticle

VaryBlock: A Novel Approach for Object Detection in Remote Sensed Images

and

Sensors 2019, 19(23), 5284; https://doi.org/10.3390/s19235284 - 30 Nov 2019

Cited by 10 | Viewed by 2498

Abstract

In recent years, the research on optical remote sensing images has received greater and greater attention. Object detection, as one of the most challenging tasks in the area of remote sensing, has been remarkably promoted by convolutional neural network (CNN)-based methods like You Only Look Once (YOLO) and Faster R-CNN. However, due to the complexity of backgrounds and the distinctive object distribution, directly applying these general object detection methods to the remote sensing object detection usually renders poor performance. To tackle this problem, a highly efficient and robust framework based on YOLO is proposed. We devise and integrate VaryBlock to the architecture which effectively offsets some of the information loss caused by downsampling. In addition, some techniques are utilized to facilitate the performance and to avoid overfitting. Experimental results show that our proposed method can enormously improve the mean average precision by a large margin on the NWPU VHR-10 dataset. Full article

(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)

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Open AccessArticle

Dual Model Medical Invoices Recognition

and

Sensors 2019, 19(20), 4370; https://doi.org/10.3390/s19204370 - 10 Oct 2019

Cited by 8 | Viewed by 2438

Abstract

Hospitals need to invest a lot of manpower to manually input the contents of medical invoices (nearly 300,000,000 medical invoices a year) into the medical system. In order to help the hospital save money and stabilize work efficiency, this paper designed a system to complete the complicated work using a Gaussian blur and smoothing–convolutional neural network combined with a recurrent neural network (GBS-CR) method. Gaussian blur and smoothing (GBS) is a novel preprocessing method that can fix the breakpoint font in medical invoices. The combination of convolutional neural network (CNN) and recurrent neural network (RNN) was used to raise the recognition rate of the breakpoint font in medical invoices. RNN was designed to be the semantic revision module. In the aspect of image preprocessing, Gaussian blur and smoothing were used to fix the breakpoint font. In the period of making the self-built dataset, a certain proportion of the breakpoint font (the font of breakpoint is 3, the original font is 7) was added, in this paper, so as to optimize the Alexnet–Adam–CNN (AA-CNN) model, which is more suitable for the recognition of the breakpoint font than the traditional CNN model. In terms of the identification methods, we not only adopted the optimized AA-CNN for identification, but also combined RNN to carry out the semantic revisions of the identified results of CNN, meanwhile further improving the recognition rate of the medical invoices. The experimental results show that compared with the state-of-art invoice recognition method, the method presented in this paper has an average increase of 10 to 15 percentage points in recognition rate. Full article

(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)

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