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Advances in Deep Learning for Intelligent Sensing Systems

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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 November 2022) | Viewed by 34750

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Special Issue Editors

Dr. Min Xia

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Guest Editor

Department of Engineering, Lancaster University, Lancaster LA1 4YW, UK
Interests: machine condition monitoring; smartning machine condition monitoring; smart sensing; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Teng Li

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Guest Editor

School of Control Science and Engineering, Shandong University, Jinan, China
Interests: machine learning; intelligent systems; wind farms, robotics; their applications in structural and machine health monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Clarence de Silva

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Guest Editor

Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: mechatronics; sensors and actuators; intelligent control; robotics

Special Issue Information

Dear Colleagues,

Recent advances in deep learning techniques have led to significant progress in sensing systems. Today, intelligent sensing can benefit sensing processes in many research and application fields. For example, inherent and complex time-series or spatiotemporal correlations among sensing data can be exploited using deep learning to characterize the studied objectives of interest. In many real-world sensing scenarios, the high-level features within sensing data imply underlying or unknown interactions leading to useful information or knowledge, which may outperform traditional analytical tools when learning and interpreting studied problems.

The Special Issue focuses on advanced deep learning methods, e.g., deep neural networks, to address a broad view of problems about sensor, sensing, and sensory issues in intelligent sensing systems. Contributions are encouraged to design and develop novel deep learning frameworks, particularly concentrating on their effectiveness, intelligence, and reliability in solving challenging sensing issues or achieving superior sensing performance. This Special Issue is dedicated to both theoretical innovations and real-world applications with field implementation and experiments. The topics of this issue include but are not limited to the following:

Data acquisition, analysis, and decision making in sensors, robotics, and intelligent systems;
Deep neural networks, deep attention mechanisms, and other representative learning techniques;
Classification, regression, interpretation, and prediction for intelligent sensing;
Machine condition monitoring, environmental monitoring, structural health monitoring, and other monitoring programs;
Comparison studies between traditional methods and advanced deep learning methods in sensing problems.

Dr. Min Xia
Prof. Dr. Teng Li
Prof. Dr. Clarence de Silva
Guest Editors

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Keywords

Deep learning
Deep neural network
Sensor and sensing system
Intelligent sensing
Big data analysis
Decision making

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Published Papers (9 papers)

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Research

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13 pages, 1602 KiB

Open AccessArticle

Text Summarization Method Based on Gated Attention Graph Neural Network

by Jingui Huang, Wenya Wu, Jingyi Li and Shengchun Wang

Sensors 2023, 23(3), 1654; https://doi.org/10.3390/s23031654 - 2 Feb 2023

Cited by 6 | Viewed by 2594

Abstract

Text summarization is an information compression technology to extract important information from long text, which has become a challenging research direction in the field of natural language processing. At present, the text summary model based on deep learning has shown good results, but how to more effectively model the relationship between words, more accurately extract feature information and eliminate redundant information is still a problem of concern. This paper proposes a graph neural network model GA-GNN based on gated attention, which effectively improves the accuracy and readability of text summarization. First, the words are encoded using a concatenated sentence encoder to generate a deeper vector containing local and global semantic information. Secondly, the ability to extract key information features is improved by using gated attention units to eliminate local irrelevant information. Finally, the loss function is optimized from the three aspects of contrastive learning, confidence calculation of important sentences, and graph feature extraction to improve the robustness of the model. Experimental validation was conducted on a CNN/Daily Mail dataset and MR dataset, and the results showed that the model in this paper outperformed existing methods. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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22 pages, 931 KiB

Open AccessArticle

Novel Meta-Learning Techniques for the Multiclass Image Classification Problem

by Antonios Vogiatzis, Stavros Orfanoudakis, Georgios Chalkiadakis, Konstantia Moirogiorgou and Michalis Zervakis

Sensors 2023, 23(1), 9; https://doi.org/10.3390/s23010009 - 20 Dec 2022

Cited by 1 | Viewed by 3174

Abstract

Multiclass image classification is a complex task that has been thoroughly investigated in the past. Decomposition-based strategies are commonly employed to address it. Typically, these methods divide the original problem into smaller, potentially simpler problems, allowing the application of numerous well-established learning algorithms that may not apply directly to the original task. This work focuses on the efficiency of decomposition-based methods and proposes several improvements to the meta-learning level. In this paper, four methods for optimizing the ensemble phase of multiclass classification are introduced. The first demonstrates that employing a mixture of experts scheme can drastically reduce the number of operations in the training phase by eliminating redundant learning processes in decomposition-based techniques for multiclass problems. The second technique for combining learner-based outcomes relies on Bayes’ theorem. Combining the Bayes rule with arbitrary decompositions reduces training complexity relative to the number of classifiers even further. Two additional methods are also proposed for increasing the final classification accuracy by decomposing the initial task into smaller ones and ensembling the output of the base learners along with that of a multiclass classifier. Finally, the proposed novel meta-learning techniques are evaluated on four distinct datasets of varying classification difficulty. In every case, the proposed methods present a substantial accuracy improvement over existing traditional image classification techniques. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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20 pages, 1384 KiB

Open AccessArticle

Defect Severity Identification for a Catenary System Based on Deep Semantic Learning

by Jian Wang, Shibin Gao, Long Yu, Dongkai Zhang and Lei Kou

Sensors 2022, 22(24), 9922; https://doi.org/10.3390/s22249922 - 16 Dec 2022

Cited by 1 | Viewed by 1911

Abstract

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and

F_{1}

-score value. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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13 pages, 1450 KiB

Open AccessArticle

Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains

by Piotr Boniecki, Agnieszka Sujak, Agnieszka A. Pilarska, Hanna Piekarska-Boniecka, Agnieszka Wawrzyniak and Barbara Raba

Sensors 2022, 22(17), 6578; https://doi.org/10.3390/s22176578 - 31 Aug 2022

Cited by 4 | Viewed by 1627

Abstract

The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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18 pages, 5598 KiB

Open AccessArticle

Few-Shot Text Classification with Global–Local Feature Information

by Depei Wang, Zhuowei Wang, Lianglun Cheng and Weiwen Zhang

Sensors 2022, 22(12), 4420; https://doi.org/10.3390/s22124420 - 11 Jun 2022

Cited by 3 | Viewed by 2034

Abstract

Meta-learning frameworks have been proposed to generalize machine learning models for domain adaptation without sufficient label data in computer vision. However, text classification with meta-learning is less investigated. In this paper, we propose SumFS to find global top-ranked sentences by extractive summary and improve the local vocabulary category features. The SumFS consists of three modules: (1) an unsupervised text summarizer that removes redundant information; (2) a weighting generator that associates feature words with attention scores to weight the lexical representations of words; (3) a regular meta-learning framework that trains with limited labeled data using a ridge regression classifier. In addition, a marine news dataset was established with limited label data. The performance of the algorithm was tested on THUCnews, Fudan, and marine news datasets. Experiments show that the SumFS can maintain or even improve accuracy while reducing input features. Moreover, the training time of each epoch is reduced by more than 50%. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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18 pages, 4301 KiB

Open AccessArticle

Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side

by Jinsong Sang, Hongbin Sun and Lei Kou

Sensors 2022, 22(6), 2256; https://doi.org/10.3390/s22062256 - 14 Mar 2022

Cited by 21 | Viewed by 3781

Abstract

As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor–critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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21 pages, 32597 KiB

Open AccessArticle

Using Voice Activity Detection and Deep Neural Networks with Hybrid Speech Feature Extraction for Deceptive Speech Detection

by Serban Mihalache and Dragos Burileanu

Sensors 2022, 22(3), 1228; https://doi.org/10.3390/s22031228 - 6 Feb 2022

Cited by 12 | Viewed by 5905

Abstract

In this work, we first propose a deep neural network (DNN) system for the automatic detection of speech in audio signals, otherwise known as voice activity detection (VAD). Several DNN types were investigated, including multilayer perceptrons (MLPs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs), with the best performance being obtained for the latter. Additional postprocessing techniques, i.e., hysteretic thresholding, minimum duration filtering, and bilateral extension, were employed in order to boost performance. The systems were trained and tested using several data subsets of the CENSREC-1-C database, with different simulated ambient noise conditions, and additional testing was performed on a different CENSREC-1-C data subset containing actual ambient noise, as well as on a subset of the TIMIT database. An accuracy of up to 99.13% was obtained for the CENSREC-1-C datasets, and 97.60% for the TIMIT dataset. We proceed to show how the final VAD system can be adapted and employed within an utterance-level deceptive speech detection (DSD) processing pipeline. The best DSD performance is achieved by a novel hybrid CNN-MLP network leveraging a fusion of algorithmically and automatically extracted speech features, and reaches an unweighted accuracy (UA) of 63.7% on the RLDD database, and 62.4% on the RODeCAR database. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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13 pages, 2372 KiB

Open AccessArticle

Real-Time Closed-Loop Detection Method of vSLAM Based on a Dynamic Siamese Network

by Quande Yuan, Zhenming Zhang, Yuzhen Pi, Lei Kou and Fangfang Zhang

Sensors 2021, 21(22), 7612; https://doi.org/10.3390/s21227612 - 16 Nov 2021

Cited by 5 | Viewed by 2379

Abstract

As visual simultaneous localization and mapping (vSLAM) is easy disturbed by the changes of camera viewpoint and scene appearance when building a globally consistent map, the robustness and real-time performance of key frame image selections cannot meet the requirements. To solve this problem, a real-time closed-loop detection method based on a dynamic Siamese networks is proposed in this paper. First, a dynamic Siamese network-based fast conversion learning model is constructed to handle the impact of external changes on key frame judgments, and an elementwise convergence strategy is adopted to ensure the accurate positioning of key frames in the closed-loop judgment process. Second, a joint training strategy is designed to ensure the model parameters can be learned offline in parallel from tagged video sequences, which can effectively improve the speed of closed-loop detection. Finally, the proposed method is applied experimentally to three typical closed-loop detection scenario datasets and the experimental results demonstrate the effectiveness and robustness of the proposed method under the interference of complex scenes. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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Review

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36 pages, 5109 KiB

Open AccessReview

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

by Lei Kou, Yang Li, Fangfang Zhang, Xiaodong Gong, Yinghong Hu, Quande Yuan and Wende Ke

Sensors 2022, 22(8), 2822; https://doi.org/10.3390/s22082822 - 7 Apr 2022

Cited by 67 | Viewed by 9565

Abstract

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Intelligent Sensing Systems)

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