Special Issue "Smart Systems (SmaSys2019&2020)"

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Innovations in Materials Processing".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 2777

Special Issue Editors

Prof. Dr. Hidemitsu Furukawa
E-Mail Website
Guest Editor
Department of Mechanical Systems Engineering, Yamagata University, Yonezawa, Yamagata 992-8510, Japan
Interests: 3D printing; soft robotics; gels; food; light scattering
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Prof. Dr. Bungo Ochiai
E-Mail Website
Guest Editor
Graduate School of Science and Engineering, Yamagata University, Yamagata 992-8510, Japan
Interests: polymer chemistry; organic–inorganic hybrid materials; green chemistry
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Dr. Masato Makino
E-Mail Website
Guest Editor
Graduate School of Science and Engineering, Yamagata University, Yamagata 992-8510, Japan
Interests: microhydrodynamics; soft matter physics; 3D printing
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Dr. Ajit Khosla
E-Mail Website
Guest Editor
Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Yamagata University, Yamagata 992-8510, Japan
Interests: flexible sensors and electronics; 3D-printed sensors and systems; 3D and 4D printing; hybrid materials; MEMS/NEMS; nanocomposites
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are planning to publish a Special Issue on "Smart Systems" related to the International Conference of Smart Systems Engineering (SmaSys2019&2020). The Special Issue on “Smart Systems” provides opportunities for collaboration across a wide range of fields and technologies related to emerging smart systems. Smart systems regard broad scientific and engineering fields. They include organic materials, organic electronics, organic devices, biomaterials, biomedical and biosystem engineering, electrical engineering and informatics, mechanical systems engineering, smart flexible structure and systems, green materials and their processing, tourism engineering with agriculture and foods, and new engineering education.

All the participants of SmaSys2019&2020 and their colleagues, especially their students, are encouraged to submit their works to this Special Issue.

Prof. Dr. Hidemitsu Furukawa
Prof. Dr. Bungo Ochiai
Dr. Masato Makino
Dr. Ajit Khosla
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. Technologies 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 1400 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 (3 papers)

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Research

Article
Investigation of AgI-Based Solid Solutions with Ag2CO3
Technologies 2021, 9(3), 54; https://doi.org/10.3390/technologies9030054 - 25 Jul 2021
Viewed by 750
Abstract
The formation phenomena of silver carbonate (Ag2CO3)–silver iodide (AgI) solid solutions were investigated by X-ray diffraction, thermogravimetry-differential thermal analysis, and electrical conductivity measurement. Results revealed that AgI and Ag2CO3 reacted with each other when mixed at [...] Read more.
The formation phenomena of silver carbonate (Ag2CO3)–silver iodide (AgI) solid solutions were investigated by X-ray diffraction, thermogravimetry-differential thermal analysis, and electrical conductivity measurement. Results revealed that AgI and Ag2CO3 reacted with each other when mixed at room temperature. The reaction products were classified into three types: (1) AgI-based solid solutions in the AgI-rich region for x = 10% or less in x Ag2CO3–(1 − x) AgI; (2) Ag2CO3-based solid solutions in the Ag2CO3-rich region for x = 60% or more; and (3) silver carbonate iodides in the intermediate range for x between 10% and 60%. For the AgI-based solid solutions, the incorporation of Ag2CO3 into the AgI lattice expanded the unit cell and enhanced electrical conductivity. The solubility limit of Ag2CO3 into the AgI lattice estimated from the differential thermal analysis was x ≈ 5%. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2019&2020))
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Article
Adaptive Deep Learning for Soft Real-Time Image Classification
Technologies 2021, 9(1), 20; https://doi.org/10.3390/technologies9010020 - 10 Mar 2021
Cited by 1 | Viewed by 1022
Abstract
CNNs (Convolutional Neural Networks) are becoming increasingly important for real-time applications, such as image classification in traffic control, visual surveillance, and smart manufacturing. It is challenging, however, to meet timing constraints of image processing tasks using CNNs due to their complexity. Performing dynamic [...] Read more.
CNNs (Convolutional Neural Networks) are becoming increasingly important for real-time applications, such as image classification in traffic control, visual surveillance, and smart manufacturing. It is challenging, however, to meet timing constraints of image processing tasks using CNNs due to their complexity. Performing dynamic trade-offs between the inference accuracy and time for image data analysis in CNNs is challenging too, since we observe that more complex CNNs that take longer to run even lead to lower accuracy in many cases by evaluating hundreds of CNN models in terms of time and accuracy using two popular data sets, MNIST and CIFAR-10. To address these challenges, we propose a new approach that (1) generates CNN models and analyzes their average inference time and accuracy for image classification, (2) stores a small subset of the CNNs with monotonic time and accuracy relationships offline, and (3) efficiently selects an effective CNN expected to support the highest possible accuracy among the stored CNNs subject to the remaining time to the deadline at run time. In our extensive evaluation, we verify that the CNNs derived by our approach are more flexible and cost-efficient than two baseline approaches. We verify that our approach can effectively build a compact set of CNNs and efficiently support systematic time vs. accuracy trade-offs, if necessary, to meet the user-specified timing and accuracy requirements. Moreover, the overhead of our approach is little/acceptable in terms of latency and memory consumption. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2019&2020))
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Article
Thermo-Reversible Gelation of Aqueous Hydrazine for Safe Storage of Hydrazine
Technologies 2020, 8(4), 53; https://doi.org/10.3390/technologies8040053 - 12 Oct 2020
Viewed by 931
Abstract
A reversible gelation–release system was developed for safe storage of toxic hydrazine solution based on gelation at lower critical solution temperature (LCST). Poly(N-isopropylacrylamide) (PNIPAM) and its copolymer could form gels of 35wt% hydrazine by dissolution under low temperature and storage at [...] Read more.
A reversible gelation–release system was developed for safe storage of toxic hydrazine solution based on gelation at lower critical solution temperature (LCST). Poly(N-isopropylacrylamide) (PNIPAM) and its copolymer could form gels of 35wt% hydrazine by dissolution under low temperature and storage at ambient temperatures. For example, PNIPAM gelled a 63 fold heavier amount of 35wt% hydrazine. Aqueous hydrazine was released from the gels by compression or heating, and the gelation–release cycles proceeded quantitatively (> 95%). The high gelation ability and recyclability are suitable for rechargeable systems for safe storage of hydrazine fuels. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2019&2020))
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