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Sensing Technology in Artificial Intelligence and Intelligent Control Systems
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Sensing Technology in Artificial Intelligence and Intelligent Control Systems

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 4318

Special Issue Editor

Prof. Dr. Wai Lun Lo

E-Mail Website
Guest Editor
Department of Computer Science, Chu Hai College of Higher Education, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, Hong Kong 999077, China
Interests: adaptive control; fuzzy control; applications of computer vision; intelligent control; application of artificial intelligence to the design of power electronic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensing technologies, artificial intelligence and intelligent control are popular research areas in the field of science and engineering. Intelligent control is a control technique which uses various artificial intelligence (AI) and intelligent computing approaches to solve complex control problems under different operating conditions and environments. These approaches utilize some AI technologies such as the neural network, machine learning, reinforcement learning, fuzzy and neuro-fuzzy system and evolutionary computation method. All these intelligent computing approaches can help us to find an optimal control solution for a linear or nonlinear system with changing parameters, operating conditions and outside disturbances.

With the rapid development of sensor technology, control systems nowadays are able to give optimal performances for changing parameters and environments. By using advanced sensing technologies, various environmental physical parameters can be measured by different types of sensors. After collecting the signals from the sensors, signal processing or data analysis can be carried out. Digital images, videos and audio signals are among the most common forms of collected data. Computer vision algorithms and speech processing algorithms can be applied to the collected data so that knowledge of the data can be extracted. Computer vision involves methods for acquiring, processing, analyzing and understanding digital images, as well as the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information. Artificial intelligence, neural network and data-mining algorithms can be applied to the signals obtained from sensing devices and knowledge or estimated parameters can be obtained. Finally, the results could be used for system control or system state monitoring.

This Special Issue calls for high-quality, up-to-date research related to innovative sensor technologies for Sensing Technology in Artificial Intelligence and Intelligent Control. In particular, the Special Issue is going to be a sharing platform for the most recent achievements and developments in sensing technology and intelligent control. All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this Special Issue. 

We would like to invite authors to submit articles related to the utilization of new sensor technology for advanced intelligent control systems and parameter estimations to this Special Issue.

Prof. Dr. Wai Lun Lo
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

19 pages, 4895 KiB  
Article
Novel Straightening-Machine Design with Integrated Force Measurement for Straightening of High-Strength Flat Wire
by Lukas Bathelt, Maximilian Scurk, Eugen Djakow, Christian Henke and Ansgar Trächtler
Sensors 2023, 23(22), 9091; https://doi.org/10.3390/s23229091 - 10 Nov 2023
Viewed by 1107
Abstract
In a punch-bending machine, wire products are manufactured for a wide range of industrial sectors, such as the electronics industry. The raw material for this process is flat wire made of high-strength steel. During the manufacturing process of the flat wire, residual stresses [...] Read more.
In a punch-bending machine, wire products are manufactured for a wide range of industrial sectors, such as the electronics industry. The raw material for this process is flat wire made of high-strength steel. During the manufacturing process of the flat wire, residual stresses and plastic deformations are induced into the wire. These residual stresses and deformations fluctuate over the length of the semi-finished product and have a negative effect on the final product quality. Straightening machines are used to reduce this influence to a minimum. So far, the adjustment of a straightening machine has been performed manually, which is a lengthy and complex task even for an experienced worker. This inevitably leads to the use of inefficient straightening strategies and causes high rejection rates in the entire production process. Due to a lack of sensor information from the straightening operation, application of modern feedback control methods has not been practicable. This paper presents a novel design for a straightening machine with an integrated, precise straightening force measurement. By simultaneously monitoring the position of the straightening rollers, state variables of the straightening operation can be derived. Additionally, a tension control for feeding the flat wire is introduced. This is implemented to mitigate the disturbing effects caused by irregularities in the wire-feeding process. In the results of this article, the high precision of the developed force measurement design and its possible applications are shown. Full article
(This article belongs to the Special Issue Sensing Technology in Artificial Intelligence and Intelligent Control Systems)
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19 pages, 9241 KiB  
Article
Leak State Detection and Size Identification for Fluid Pipelines with a Novel Acoustic Emission Intensity Index and Random Forest
by Tuan-Khai Nguyen, Zahoor Ahmad and Jong-Myon Kim
Sensors 2023, 23(22), 9087; https://doi.org/10.3390/s23229087 - 10 Nov 2023
Cited by 1 | Viewed by 740
Abstract
In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed [...] Read more.
In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity. In addition, it shows the capability to not just discriminate between normal and leaking states, but also to distinguish different leak sizes. To calculate the probability of a state change from normal condition to leakage, a changepoint detection method, using a Bayesian ensemble, was utilized. After the leak is detected, size identification is performed by feeding the AIIC to the RF. The experimental results were compared with two cutting-edge methods under different scenarios with various pressure levels and leak sizes, and the proposed method outperformed both the earlier algorithms in terms of accuracy. Full article
(This article belongs to the Special Issue Sensing Technology in Artificial Intelligence and Intelligent Control Systems)
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12 pages, 5627 KiB  
Article
Optimized Dropkey-Based Grad-CAM: Toward Accurate Image Feature Localization
by Yiwei Liu, Luping Tang, Chen Liao, Chun Zhang, Yingqing Guo, Yixuan Xia, Yangyang Zhang and Sisi Yao
Sensors 2023, 23(20), 8351; https://doi.org/10.3390/s23208351 - 10 Oct 2023
Cited by 1 | Viewed by 942
Abstract
Regarding the interpretable techniques in the field of image recognition, Grad-CAM is widely used for feature localization in images to reflect the logical decision-making information behind the neural network due to its high applicability. However, extensive experimentation on a customized dataset revealed that [...] Read more.
Regarding the interpretable techniques in the field of image recognition, Grad-CAM is widely used for feature localization in images to reflect the logical decision-making information behind the neural network due to its high applicability. However, extensive experimentation on a customized dataset revealed that the deep convolutional neural network (CNN) model based on Gradient-weighted Class Activation Mapping (Grad-CAM) technology cannot effectively resist the interference of large-scale noise. In this article, an optimization of the deep CNN model was proposed by incorporating the Dropkey and Dropout (as a comparison) algorithm. Compared with Grad-CAM, the improved Grad-CAM based on Dropkey applies an attention mechanism to the feature map before calculating the gradient, which can introduce randomness and eliminate some areas by applying a mask to the attention score. Experimental results show that the optimized Grad-CAM deep CNN model based on the Dropkey algorithm can effectively resist large-scale noise interference and achieve accurate localization of image features. For instance, under the interference of a noise variance of 0.6, the Dropkey-enhanced ResNet50 model achieves a confidence level of 0.878 in predicting results, while the other two models exhibit confidence levels of 0.766 and 0.481, respectively. Moreover, it exhibits excellent performance in visualizing tasks related to image features such as distortion, low contrast, and small object characteristics. Furthermore, it has promising prospects in practical computer vision applications. For instance, in the field of autonomous driving, it can assist in verifying whether deep learning models accurately understand and process crucial objects, road signs, pedestrians, or other elements in the environment. Full article
(This article belongs to the Special Issue Sensing Technology in Artificial Intelligence and Intelligent Control Systems)
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16 pages, 5816 KiB  
Article
Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment
by Ying-Qing Guo, Mengjiao Gu and Zhao-Dong Xu
Sensors 2023, 23(15), 6901; https://doi.org/10.3390/s23156901 - 03 Aug 2023
Cited by 2 | Viewed by 1049
Abstract
The low light conditions, abundant dust, and rocky terrain on the lunar surface pose challenges for scientific research. To effectively perceive the surrounding environment, lunar rovers are equipped with binocular cameras. In this paper, with the aim of accurately detect obstacles on the [...] Read more.
The low light conditions, abundant dust, and rocky terrain on the lunar surface pose challenges for scientific research. To effectively perceive the surrounding environment, lunar rovers are equipped with binocular cameras. In this paper, with the aim of accurately detect obstacles on the lunar surface under complex conditions, an Improved Semi-Global Matching (I-SGM) algorithm for the binocular cameras is proposed. The proposed method first carries out a cost calculation based on the improved Census transform and an adaptive window based on a connected component. Then, cost aggregation is performed using cross-based cost aggregation in the AD-Census algorithm and the initial disparity of the image is calculated via the Winner-Takes-All (WTA) strategy. Finally, disparity optimization is performed using left–right consistency detection and disparity padding. Utilizing standard test image pairs provided by the Middleburry website, the results of the test reveal that the algorithm can effectively improve the matching accuracy of the SGM algorithm, while reducing the running time of the program and enhancing noise immunity. Furthermore, when applying the I-SGM algorithm to the simulated lunar environment, the results show that the I-SGM algorithm is applicable in dim conditions on the lunar surface and can better help a lunar rover to detect obstacles during its travel. Full article
(This article belongs to the Special Issue Sensing Technology in Artificial Intelligence and Intelligent Control Systems)
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