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Micromachines
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Artificial Intelligence on MEMS/Microdevices/Microsystems
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Artificial Intelligence on MEMS/Microdevices/Microsystems

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (15 April 2021) | Viewed by 14263

Special Issue Editors

Prof. Dr. Yeonwoo Lee

E-Mail Website
Guest Editor
Department of Artificial Intelligence Engineering, Mokpo National University, Cheonggye-myeon, Muan-gun, Jeollanam-do, Republic of Korea
Interests: cognitive radio; smart grid; artificial intelligence algorithm; nature-inspired algorithm; 6G communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bhanu Shrestha

E-Mail Website
Guest Editor
Department of Electronic Engineering, Kwangwoon University, Bima Build. #525, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea
Interests: RFIC/MMIC/IPD device and system design; wireless communication; design and fab-rication of device and systems; RF biosensors; ICT convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will be focused on micro-electromechanical systems (MEMS)/microdevices and systems with artificial intelligence (AI). Artificial intelligence (AI), through those devices and systems, makes it possible for machines to learn from experience, adjust to new inputs, and perform human-like tasks. Making microstructures is being challenging task day by day. Therefore, in order to improve and optimize device and system performance, AI is a significant candidate that can mitigate the problem occurring in those devices and systems. This Special Issue seeks research papers and review articles that focus on novel methodological developments of AI on MEMs/microdevices and systems for various communication systems.

We look forward to receiving your submissions!

Prof. Dr. Yeonwoo Lee
Prof. Dr. Bhanu Shrestha
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. Micromachines 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 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

  • Microdevice
  • Microsystem
  • Artificial intelligence (AI)
  • AI on microstructures
  • RF MEMS
  • Application of microstructures

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Published Papers (3 papers)

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17 pages, 8883 KiB  
Article
Application of Machine Learning Algorithm on MEMS-Based Sensors for Determination of Helmet Wearing for Workplace Safety
by Yan Hao Tan, Agarwal Hitesh and King Ho Holden Li
Micromachines 2021, 12(4), 449; https://doi.org/10.3390/mi12040449 - 16 Apr 2021
Cited by 7 | Viewed by 3311
Abstract
Appropriate use of helmets as industrial personal protective gear is a long-standing challenge. The dilemma for any user wearing a helmet is thermal discomfort versus the chances of head injuries while not wearing it. Applying helmet microclimate psychrometry, we propose a logistic regression- [...] Read more.
Appropriate use of helmets as industrial personal protective gear is a long-standing challenge. The dilemma for any user wearing a helmet is thermal discomfort versus the chances of head injuries while not wearing it. Applying helmet microclimate psychrometry, we propose a logistic regression- (LR) based machine learning (ML) algorithm coupled with low-cost and readily available MEMS sensors to determine if a helmet was worn (W) or not worn (NW) by a human user. Experiment runs involving human subject (S) and mannequin experiment control (C) groups were conducted across no mask (NM) and mask (M) conditions. Only ambient-microclimate humidity difference (AMHD) was a feasible parameter for helmet wearing determination with 71 to 85% goodness of fit, 72 to 76% efficacy, and distinction from control group. Ambient-microclimate humidity difference’s rate of change (AMHDROC) had high correlation to helmet wearing and removal initiations and was quantitatively better in all measures. However, its feasibility was doubtful for continuous use beyond 1 min due to plateauing AMHD response. Experiments with control groups and temperature measurement showed invariant response to helmet worn or not worn with goodness of fit and efficacy consolidation to 50%. Results showed the algorithm can make helmet-wearing determinations with combination of analysis and use of data that was individually authentic and non-identifiable. This is an improvement as compared to state of the art skin-contact mechanisms and image analytics methods in enabling safety enhancements through data-driven worker safety ownership. Full article
(This article belongs to the Special Issue Artificial Intelligence on MEMS/Microdevices/Microsystems)
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13 pages, 8891 KiB  
Article
Image Pre-Processing Method of Machine Learning for Edge Detection with Image Signal Processor Enhancement
by Keumsun Park, Minah Chae and Jae Hyuk Cho
Micromachines 2021, 12(1), 73; https://doi.org/10.3390/mi12010073 - 11 Jan 2021
Cited by 32 | Viewed by 6273
Abstract
Even though computer vision has been developing, edge detection is still one of the challenges in that field. It comes from the limitations of the complementary metal oxide semiconductor (CMOS) Image sensor used to collect the image data, and then image signal processor [...] Read more.
Even though computer vision has been developing, edge detection is still one of the challenges in that field. It comes from the limitations of the complementary metal oxide semiconductor (CMOS) Image sensor used to collect the image data, and then image signal processor (ISP) is additionally required to understand the information received from each pixel and performs certain processing operations for edge detection. Even with/without ISP, as an output of hardware (camera, ISP), the original image is too raw to proceed edge detection image, because it can include extreme brightness and contrast, which is the key factor of image for edge detection. To reduce the onerousness, we propose a pre-processing method to obtain optimized brightness and contrast for improved edge detection. In the pre-processing, we extract meaningful features from image information and perform machine learning such as k-nearest neighbor (KNN), multilayer perceptron (MLP) and support vector machine (SVM) to obtain enhanced model by adjusting brightness and contrast. The comparison results of F1 score on edgy detection image of non-treated, pre-processed and pre-processed with machine learned are shown. The pre-processed with machine learned F1 result shows an average of 0.822, which is 2.7 times better results than the non-treated one. Eventually, the proposed pre-processing and machine learning method is proved as the essential method of pre-processing image from ISP in order to gain better edge detection image. In addition, if we go through the pre-processing method that we proposed, it is possible to more clearly and easily determine the object required when performing auto white balance (AWB) or auto exposure (AE) in the ISP. It helps to perform faster and more efficiently through the proactive ISP. Full article
(This article belongs to the Special Issue Artificial Intelligence on MEMS/Microdevices/Microsystems)
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13 pages, 3626 KiB  
Article
Optimization of Machine Learning in Various Situations Using ICT-Based TVOC Sensors
by Jae Hyuk Cho and Hayoun Lee
Micromachines 2020, 11(12), 1092; https://doi.org/10.3390/mi11121092 - 10 Dec 2020
Cited by 2 | Viewed by 2661
Abstract
A computational framework using artificial intelligence (AI) has been suggested in numerous fields, such as medicine, robotics, meteorology, and chemistry. The specificity of each AI model and the relationship between data characteristics and ground truth, allowing their guidance according to each situation, has [...] Read more.
A computational framework using artificial intelligence (AI) has been suggested in numerous fields, such as medicine, robotics, meteorology, and chemistry. The specificity of each AI model and the relationship between data characteristics and ground truth, allowing their guidance according to each situation, has not been given. Since TVOCs (total volatile organic compounds) cause serious harm to human health and plants, the prevention of such damages with a reduction in their occurrence frequency becomes not an optional process but an essential one in manufacturing, as well as for chemical industries and laboratories. In this study, with consideration of the characteristics of the machine learning technique and ICT (information and communications technology), TVOC sensors are explored as a function of grounded data analysis and the selection of machine learning models, determining their performance in real situations. For representative scenarios, considering features from an ICT semiconductor sensor and one targeting TVOC gas, we investigated suitable analysis methods and machine learning models such as LSTM (long short-term memory), GRU (gated recurrent unit), and RNN (recurrent neural network). Detailed factors for these machine learning models with respect to the concentration of TVOC gas in the atmosphere are compared with original sensory data to obtain their accuracy. From this work, we expect to significantly minimize risk in empirical applications, i.e., maintaining homeostasis or predicting abnormal situations to construct an opportune response. Full article
(This article belongs to the Special Issue Artificial Intelligence on MEMS/Microdevices/Microsystems)
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