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Special Issue "Smart Sensors and Devices in Artificial Intelligence"

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

Deadline for manuscript submissions: 30 October 2019

Special Issue Editors

Guest Editor
Prof. Dr. Dan Zhang

Department of Mechanical Engineering, Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
Website | E-Mail
Interests: robotics and mechatronics; high performance parallel robotic machine development; sustainable/green manufacturing systems; micro/nano manipulation and MEMS devices (sensors), micro mobile robots and control of multi-robot cooperation, intelligent servo control system for the MEMS based high-performance micro-robot; web-based remote manipulation; rehabilitation robot and rescue robot
Guest Editor
Prof. Dr. Xuechao Duan

Institute on Mechatronics, Xidian University, 710071, No.2 Taibai Rd, Xi’an, China
Website | E-Mail
Interests: parallel robots; mechatronics; intelligent control; design optimization

Special Issue Information

Dear Colleagues,

Sensors are eyes or/and ears of an intelligent system, such as UAV, AGV and robots. With the development of material, signal processing and multidisciplinary interactions, more and more smart sensors are proposed and fabricated under increasing demands for homes, industry and military fields. Networks of sensors will be able to enhance the ability to obtain huge amounts of information (big data) and improve precision, which also mirrors the developmental tendency of modern sensors. Moreover, artificial intelligence is a novel impetus for sensors and networks, which gets sensors to learn and think and feed more efficient results back.

This Special Issue welcomes new research results from academia and industry, on the subject of “Smart Sensors and Networks”, especially sensing technologies utilizing Artificial Intelligence. The Special Issue topics include, but are not limited to:

  • smart sensors
  • biosensors
  • sensor network
  • sensor data fusion
  • artificial intelligence
  • deep learning
  • mechatronics devices for sensors
  • applications of sensors for robotics and mechatronics devices

The Special Issue also welcome excellent extended papers invited from the 2018 2nd International Conference on Artificial Intelligence Applications and Technologies (AIAAT 2018) and 2019 3rd International Conference on Artificial Intelligence Applications and Technologies (AIAAT 2019).

Prof. Dr. Dan Zhang
Prof. Dr. Xuechao Duan
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 papers will be 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 monthly 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 1800 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

  • smart sensors
  • biosensor
  • sensor network
  • sensor data fusion
  • artificial intelligence
  • deep learning
  • robotics
  • mechatronics devices

Published Papers (2 papers)

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Research

Open AccessArticle A Hybrid CNN–LSTM Algorithm for Online Defect Recognition of CO2 Welding
Sensors 2018, 18(12), 4369; https://doi.org/10.3390/s18124369
Received: 11 October 2018 / Revised: 6 December 2018 / Accepted: 6 December 2018 / Published: 10 December 2018
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Abstract
At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes
[...] Read more.
At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion. This process realizes the implicit mapping from molten pool images to welding defects. The test results on the self-made molten pool image dataset show that CNN contributes to the overall feasibility of the CNN–LSTM algorithm and LSTM network is the most superior in the feature hybrid stage. The algorithm converges at 300 epochs and the accuracy of defects detection in CO2 welding molten pool is 94%. The processing time of a single image is 0.067 ms, which fully meets the real-time monitoring requirement based on molten pool image. The experimental results on the MNIST and FashionMNIST datasets show that the algorithm is universal and can be used for similar image recognition and classification tasks. Full article
(This article belongs to the Special Issue Smart Sensors and Devices in Artificial Intelligence)
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Graphical abstract

Open AccessArticle A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)
Sensors 2018, 18(10), 3470; https://doi.org/10.3390/s18103470
Received: 29 September 2018 / Revised: 11 October 2018 / Accepted: 13 October 2018 / Published: 15 October 2018
Cited by 1 | PDF Full-text (3166 KB) | HTML Full-text | XML Full-text
Abstract
Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone
[...] Read more.
Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Shijiazhuang, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application. Full article
(This article belongs to the Special Issue Smart Sensors and Devices in Artificial Intelligence)
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