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Advanced Intelligent Sensor Based on Deep Learning
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Advanced Intelligent Sensor Based on Deep Learning

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

Deadline for manuscript submissions: closed (1 January 2024) | Viewed by 3212

Special Issue Editors

Prof. Dr. Chung-Hong Lee

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Interests: Artificial Intelligence; sensor array; blockchain; electronic nose; sensor fusion
Prof. Dr. Chun-Lien Su

E-Mail Website
Guest Editor
Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan
Interests: microgrids
Special Issues, Collections and Topics in MDPI journals
Dr. Mahmoud Elsisi

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Interests: Artificial Intelligence; automation; renewable energy; model predictive control; wind energy conversion systems; autonomous vehicle control
Special Issues, Collections and Topics in MDPI journals
Dr. Chien-Chang Chen

E-Mail Website
Guest Editor
National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Interests: intelligent control and optimized computing system; electromagnetic engineering system design; power electronics engineering system; Artificial Intelligence and smart manufacturing system

Special Issue Information

Dear Colleagues,

Sensors incorporated with dedicated signal processing functions are called “Smart Sensors” (or “Intelligent Sensors”). Applications of advanced intelligent sensing and sensors are a rapidly developing, interdisciplinary, and challenging field with great practical importance and potential. In particular, when connected to advanced deep learning algorithms, these advances are relevant to enabling smarter and broader new technologies, such as state-of-the-art updates in gas sensors and biosensor-based applications, intelligent control, ambient computing systems, automation, and smart power systems.

Smart sensors use advanced signal processing techniques, sensor fusion methods, feature extraction techniques, and machine-learning/deep-learning algorithms to better understand sensor data, better integrate sensors, and better extract features to form measures that can be used in intelligent sensing applications. The goal of this Special Issue is to publish both recent innovative research results, as well as review papers on the Smart sensor applications.

Potential topics include, but are not limited to, the following:

  • Smart sensors with ambient computing systems
  • Smart health systems
  • Smart sensors for sustainable systems
  • Electric nose applications based on smart sensors
  • Smart sensors with automation
  • Intelligent control systems
  • Blockchain based systems
  • Smart power system
  • Supply chain systems

Prof. Dr. Chung-Hong Lee
Prof. Dr. Chun-Lien Su
Dr. Mahmoud Elsisi
Dr. Chien-Chang Chen
Guest Editors

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.

Keywords

  • intelligent smart sensors
  • sensor array
  • deep learning
  • automation
  • electric nose
  • intelligent control
  • ambient computing
  • sensor fusion
  • energy forcasting
  • blockchain

Published Papers (2 papers)

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Research

15 pages, 8584 KiB  
Article
Design of a Soft Sensor Based on Long Short-Term Memory Artificial Neural Network (LSTM) for Wastewater Treatment Plants
by Roxana Recio-Colmenares, Elizabeth León Becerril, Kelly Joel Gurubel Tun and Robin F. Conchas
Sensors 2023, 23(22), 9236; https://doi.org/10.3390/s23229236 - 17 Nov 2023
Viewed by 1113
Abstract
Assessment of wastewater effluent quality in terms of physicochemical and microbial parameters is a difficult task; therefore, an online method which combines the variables and represents a final value as the quality index could be used as a useful management tool for decision [...] Read more.
Assessment of wastewater effluent quality in terms of physicochemical and microbial parameters is a difficult task; therefore, an online method which combines the variables and represents a final value as the quality index could be used as a useful management tool for decision makers. However, conventional measurement methods often have limitations, such as time-consuming processes and high associated costs, which hinder efficient and practical monitoring. Therefore, this study presents an approach that underscores the importance of using both short- and long-term memory networks (LSTM) to enhance monitoring capabilities within wastewater treatment plants (WWTPs). The use of LSTM networks for soft sensor design is presented as a promising solution for accurate variable estimation to quantify effluent quality using the total chemical oxygen demand (TCOD) quality index. For the realization of this work, we first generated a dataset that describes the behavior of the activated sludge system in discrete time. Then, we developed a deep LSTM network structure as a basis for formulating the LSTM-based soft sensor model. The results demonstrate that this structure produces high-precision predictions for the concentrations of soluble X1 and solid X2 substrates in the wastewater treatment system. After hyperparameter optimization, the predictive capacity of the proposed model is optimized, with average values of performance metrics, mean square error (MSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), of 23.38, 0.97, and 1.31 for X1, and 9.74, 0.93, and 1.89 for X2, respectively. According to the results, the proposed LSTM-based soft sensor can be a valuable tool for determining effluent quality index in wastewater treatment systems. Full article
(This article belongs to the Special Issue Advanced Intelligent Sensor Based on Deep Learning)
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17 pages, 2567 KiB  
Article
Arabic Captioning for Images of Clothing Using Deep Learning
by Rasha Saleh Al-Malki and Arwa Yousuf Al-Aama
Sensors 2023, 23(8), 3783; https://doi.org/10.3390/s23083783 - 7 Apr 2023
Cited by 2 | Viewed by 1606
Abstract
Fashion is one of the many fields of application that image captioning is being used in. For e-commerce websites holding tens of thousands of images of clothing, automated item descriptions are quite desirable. This paper addresses captioning images of clothing in the Arabic [...] Read more.
Fashion is one of the many fields of application that image captioning is being used in. For e-commerce websites holding tens of thousands of images of clothing, automated item descriptions are quite desirable. This paper addresses captioning images of clothing in the Arabic language using deep learning. Image captioning systems are based on Computer Vision and Natural Language Processing techniques because visual and textual understanding is needed for these systems. Many approaches have been proposed to build such systems. The most widely used methods are deep learning methods which use the image model to analyze the visual content of the image, and the language model to generate the caption. Generating the caption in the English language using deep learning algorithms received great attention from many researchers in their research, but there is still a gap in generating the caption in the Arabic language because public datasets are often not available in the Arabic language. In this work, we created an Arabic dataset for captioning images of clothing which we named “ArabicFashionData” because this model is the first model for captioning images of clothing in the Arabic language. Moreover, we classified the attributes of the images of clothing and used them as inputs to the decoder of our image captioning model to enhance Arabic caption quality. In addition, we used the attention mechanism. Our approach achieved a BLEU-1 score of 88.52. The experiment findings are encouraging and suggest that, with a bigger dataset, the attributes-based image captioning model can achieve excellent results for Arabic image captioning. Full article
(This article belongs to the Special Issue Advanced Intelligent Sensor Based on Deep Learning)
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