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Machines
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Selected Papers from the 1st International Electronic Conference on Machines...
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Selected Papers from the 1st International Electronic Conference on Machines and Applications (IECMA 2022)

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8567

Special Issue Editors

Prof. Dr. Antonio J. Marques Cardoso

E-Mail Website
Guest Editor
CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P - 6201-001 Covilhã, Portugal
Interests: diagnosis and fault tolerance of electrical machines, power electronics and drives
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dan Zhang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giuseppe Carbone

E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, Università della Calabria, 87036 Rende, Italy
Interests: robotics; robot design; mechatronics; walking hexapod; design procedure; mechanics of machinery; leg–wheel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will comprise extended and expanded versions of proceedings papers from the 1st International Electronic Conference on Machines and Applications (IECMA 2022), which will be held on 15–30 September 2022, on sciforum.net.

In this 1st edition of the e-conference, contributors are invited to provide papers and presentations from the field of machines and their applications at large. Selected papers that are expected to attract the most interest on the web, or that will provide a particularly innovative contribution, will be gathered for publication. These papers will be subjected to peer review and published in the Special Issue, with the aim of rapid and wide dissemination of the research results, developments, and applications.

We hope that this conference series will further expand in the future, and become recognized as a new avenue by which to (electronically) present novel developments related to the field of machines and their applications.

Prof. Dr. Antonio J. Marques Cardoso
Prof. Dr. Dan Zhang
Prof. Dr. Giuseppe Carbone
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. Machines 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 2400 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, 10533 KiB  
Article
Robotic System for Blood Serum Aliquoting Based on a Neural Network Model of Machine Vision
by Sergey Khalapyan, Larisa Rybak, Vasiliy Nebolsin, Dmitry Malyshev, Anna Nozdracheva, Tatyana Semenenko and Dmitry Gavrilov
Machines 2023, 11(3), 349; https://doi.org/10.3390/machines11030349 - 03 Mar 2023
Cited by 3 | Viewed by 1761
Abstract
The quality of the diagnostic information obtained in the course of laboratory studies depends on the accuracy of compliance with the regulations for the necessary work. The process of aliquoting blood serum requires immersing the pipette to different depths depending on the boundary [...] Read more.
The quality of the diagnostic information obtained in the course of laboratory studies depends on the accuracy of compliance with the regulations for the necessary work. The process of aliquoting blood serum requires immersing the pipette to different depths depending on the boundary level between blood phases. A vision system can be used to determine this depth during automated aliquoting using various algorithms. As part of the work, two recognition algorithms are synthesized, one of which is based on the use of the HSV color palette, the other is based on the convolutional neural network. In the Python language, software systems have been developed that implement the ability of a vision system to recognize blood in test tubes. The developed methods are supposed to be used for aliquoting biosamples using a delta robot in a multirobotic system, which will increase the productivity of ongoing biomedical research through the use of new technical solutions and principles of intelligent robotics. The visualized results of the work of the considered programs are presented and a comparative analysis of the quality of recognition is carried out. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Electronic Conference on Machines and Applications (IECMA 2022))
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17 pages, 9379 KiB  
Article
A Study on the Effectiveness of Partial Discharge Models for Various Electrical Machines’ Insulation Materials
by Dimosthenis Verginadis, Tryfon Iakovidis, Athanasios Karlis, Michael Danikas and Jose-Alfonso Antonino-Daviu
Machines 2023, 11(2), 230; https://doi.org/10.3390/machines11020230 - 04 Feb 2023
Cited by 1 | Viewed by 1405
Abstract
A vital component of electrical machines (EMs), which plays the most significant role in their reliable and proper operation, is their insulation system. Synchronous generators (SGs) are the most commonly used EMs in energy production and industry. Epoxy resin and mica are the [...] Read more.
A vital component of electrical machines (EMs), which plays the most significant role in their reliable and proper operation, is their insulation system. Synchronous generators (SGs) are the most commonly used EMs in energy production and industry. Epoxy resin and mica are the predominant insulation materials for the SGs’ windings because their characteristics and properties are suitable for extending the lifetime of the insulation. Partial discharges (PDs) are both a symptom of insulation degradation, as they cause serious problems for insulation, and a means to identify possible insulation faults with offline and/or online PD tests and measurements. A comparison of three different equivalent circuit models of PDs occurring in different insulation materials (epoxy resin, mica, and a combination of these two) is presented in this paper. Different applied voltages and/or various geometries of voids are the factors investigated through simulations. The number of PDs, PD activity, and flashover voltages are examined in order to evaluate which of the aforementioned materials has the best reaction against PD activity. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Electronic Conference on Machines and Applications (IECMA 2022))
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16 pages, 8610 KiB  
Article
Obstacle Detection by Autonomous Vehicles: An Adaptive Neighborhood Search Radius Clustering Approach
by Wuhua Jiang, Chuanzheng Song, Hai Wang, Ming Yu and Yajie Yan
Machines 2023, 11(1), 54; https://doi.org/10.3390/machines11010054 - 02 Jan 2023
Cited by 1 | Viewed by 1910
Abstract
For autonomous vehicles, obstacle detection results using 3D lidar are in the form of point clouds, and are unevenly distributed in space. Clustering is a common means for point cloud processing; however, improper selection of clustering thresholds can lead to under-segmentation or over-segmentation [...] Read more.
For autonomous vehicles, obstacle detection results using 3D lidar are in the form of point clouds, and are unevenly distributed in space. Clustering is a common means for point cloud processing; however, improper selection of clustering thresholds can lead to under-segmentation or over-segmentation of point clouds, resulting in false detection or missed detection of obstacles. In order to solve these problems, a new obstacle detection method was required. Firstly, we applied a distance-based filter and a ground segmentation algorithm, to pre-process the original 3D point cloud. Secondly, we proposed an adaptive neighborhood search radius clustering algorithm, based on the analysis of the relationship between the clustering radius and point cloud spatial distribution, adopting the point cloud pitch angle and the horizontal angle resolution of the lidar, to determine the clustering threshold. Finally, an autonomous vehicle platform and the offline autonomous driving KITTI dataset were used to conduct multi-scene comparative experiments between the proposed method and a Euclidean clustering method. The multi-scene real vehicle experimental results showed that our method improved clustering accuracy by 6.94%, and the KITTI dataset experimental results showed that the F1 score increased by 0.0629. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Electronic Conference on Machines and Applications (IECMA 2022))
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27 pages, 15266 KiB  
Article
Performance Enhancement of Direct Torque and Rotor Flux Control (DTRFC) of a Three-Phase Induction Motor over the Entire Speed Range: Experimental Validation
by Mussaab M. Alshbib, Mohamed Mussa Elgbaily, Ibrahim Mohd Alsofyani and Fatih Anayi
Machines 2023, 11(1), 22; https://doi.org/10.3390/machines11010022 - 25 Dec 2022
Cited by 6 | Viewed by 2117
Abstract
This paper proposes a robust and effective method of direct torque and rotor flux control (DTRFC) strategy for an induction motor (IM). The described scheme ensures the elimination of uncontrollable angles (UCAs) over the entire speed range. This means that each voltage vector [...] Read more.
This paper proposes a robust and effective method of direct torque and rotor flux control (DTRFC) strategy for an induction motor (IM). The described scheme ensures the elimination of uncontrollable angles (UCAs) over the entire speed range. This means that each voltage vector (VV) produces the required effect of both torque and flux without any counteracting effect. First, the behaviour of the DTRFC algorithm was analysed at low and high speeds in terms of determining the values of UCAs. Through the analysis, it was found that the basic scheme suffered from UCAs at medium and high speeds. Accordingly, a special strategy for medium and high speeds with 18 sub-sectors (SSs) was proposed while maintaining the basic 6 sectors strategy for low speed. The transition speed between the two strategies was determined to ensure the absence of UCAs over the whole speed range. The simulation results of the proposed method were obtained in the MATLAB/Simulink environment. Furthermore, to verify the effectiveness of this method, a dSPACE-based experimental induction motor DTRFC drive system was accomplished. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Electronic Conference on Machines and Applications (IECMA 2022))
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