International Journal of Engineering and Technology Innovation 2021-05-05T17:30:07+00:00 Wen-Hsiang Hsieh Open Journal Systems <p><strong><em>International Journal of Engineering and Technology Innovation</em></strong> (IJETI), ISSN 2223-5329 (Print), ISSN 2226-809X (Online), is an international, multidiscipline, open access, peer-reviewed scholarly journal, published quarterly for researchers, developers, technical managers, and educators in the field of engineering and technology innovation. The official abbreviated title is <strong><em>Int. j. eng. technol. innov</em>.</strong></p> <p>IJETI is indexed by:&nbsp;</p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;"><img style="width: 136px; height: 38px;" src="" alt="" width="171" height="38"> &nbsp;&nbsp; <span style="font-family: 'Noto Sans'; font-size: 10.5pt;"><img style="width: 244px; height: 58px;" src="/public/site/images/ijeti/ESCI3.png"> &nbsp;&nbsp; <img style="width: 136px; height: 38px;" src="/public/site/images/ijeti/EBSCO-1.png" width="170" height="35"> &nbsp; </span></span><span style="font-family: 'Noto Sans'; font-size: 10.5pt;"><img style="width: 136px; height: 38px;" src="/public/site/images/ijeti/image0031.jpg" width="116" height="38"> &nbsp; </span><img style="font-family: 'Noto Sans'; font-size: 10.5pt;" src="/public/site/images/ijeti/DOAJ4.png" alt=""></p> <p><img style="font-family: 'Noto Sans'; font-size: 10.5pt;" src="/public/site/images/ijeti/google5.png" alt=""> &nbsp; <img src="" alt=""> &nbsp; <img src="/public/site/images/allen/ProQuest-41.png"> &nbsp;&nbsp;<img src="/public/site/images/ijeti/Resarch_Bible5.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/ijeti/WorldCat5.png" alt="" width="118" height="40">&nbsp;&nbsp;<img src="/public/site/images/allen/academia-12.png" width="136" height="27"> &nbsp;<img src="/public/site/images/ijeti/TOCs5.jpg" alt=""> &nbsp; <img src="/public/site/images/allen/Publons-22.5_1.png"> &nbsp; <img src="/public/site/images/allen/crossref3.png" width="92" height="42"></p> <p style="margin: 0cm 0cm 0pt;"><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;">Under evaluation of SCI, EI(Compendex), etc.</span></p> <p style="margin: 0cm 0cm 0pt;">&nbsp;</p> A Robust Formation Control Strategy for Multi-Agent Systems with Uncertainties via Adaptive Gain Robust Controllers 2021-03-29T06:05:16+00:00 Shun Ito Kaoru Ohara Yoshikatsu Hoshi Hidetoshi Oya Shunya Nagai <p>This paper deals with a design problem of an adaptive gain robust controller which achieves consensus for multi-agent system (MAS) with uncertainties. In the proposed controller design approach, the relative position between the leader and followers are considered explicitly, and the proposed adaptive gain robust controller consisting of fixed gains and variable ones tuned by time-varying adjustable parameters can reduce the effect of uncertainties. In this paper, we show that sufficient conditions for the existence of the proposed adaptive gain robust controller are reduced to solvability of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed robust formation control system is verified by simple numerical simulations. A main result of this study is that the proposed adaptive gain robust controller can achieve consensus and formation control giving consideration to relative distance in spite of uncertainties.</p> 2021-04-01T00:00:00+00:00 Copyright (c) 2021 Shun Ito, Kaoru Ohara, Yoshikatsu Hoshi, Hidetoshi Oya, Shunya Nagai Investigation into the Thermal Behavior and Loadability Characteristic of a YASA-AFPM Generator via an Improved 3-D Coupled Electromagnetic-Thermal Approach 2021-04-27T04:18:09+00:00 Saadat Jamali Arand Amir Akbari Mohammad Ardebili <p>The objective of this paper is to investigate the thermal behaviour and loadability characteristic of a yokeless and segmented armature axial-flux permanent-magnet (YASA-AFPM) generator, which uses an improved 3-D coupled electromagnetic-thermal approach. Firstly, a 1-kW YASA-AFPM generator is modelled and analysed by using the proposed approach; the transient and steady-state temperatures of different parts of the generator are determined. To improve the modelling accuracy, the information is exchanged between the thermal and electromagnetic models at each step of the co-simulation, considering both the accurate calculation of losses and the impacts of temperature rise on the temperature-dependent characteristics of the materials. Then, by using the proposed approach, the impact of the slot opening width and the turn number of stator segments on the generator loadability are investigated. After that, the experimental tests are performed. The results reveal the effectiveness and accuracy of the approach to predict the machine loadability and thermal behavior.</p> 2021-04-01T00:00:00+00:00 Copyright (c) 2021 Saadat Jamali Arand, Amir Akbari, Mohammad Ardebili A Systematic Review on Automatic Detection of Plasmodium Parasite 2021-03-29T06:05:16+00:00 Amin Siddiq Sumi Hanung Adi Nugroho Rudy Hartanto <p>Plasmodium parasite is the main cause of malaria which has taken many lives. Some research works have been conducted to detect the Plasmodium parasite automatically. This research aims to identify the development of current research in the area of Plasmodium parasite detection. The research uses a systematic literature review (SLR) approach comprising three stages, namely planning, conducting, and reporting. The search process is based on the keywords which were determined in advance. The selection process involves the inclusion and exclusion criteria. The search yields 45 literatures from five different digital libraries. The identification process finds out that 28 methods are applied and mainly categorizes as machine learning algorithms with performance achievements between 60% and 95%. Overall, the research of Plasmodium parasite detection today has focused on the development with artificial intelligence specifically related to machine and deep learning. These approaches are believed as the most effective approach to detect Plasmodium parasites.</p> 2021-04-01T00:00:00+00:00 Copyright (c) 2021 Amin Siddiq Sumi, Hanung Adi Nugroho, Rudy Hartanto Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients 2021-03-30T09:19:07+00:00 Saleh Alshehri <p>This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients.</p> 2021-04-01T00:00:00+00:00 Copyright (c) 2021 Saleh Alshehri Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model 2021-03-29T06:05:16+00:00 Ying-Heng Yeo Kin-Sam Yen <p>As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate.</p> 2021-04-01T00:00:00+00:00 Copyright (c) 2021 Ying-Heng Yeo, Kin-Sam Yen Voltage Differencing Current Conveyor Based Voltage-Mode and Current-Mode Universal Biquad Filters with Electronic Tuning Facility 2021-05-05T17:30:07+00:00 Suvajit Roy Tapas Kumar Paul Saikat Maiti Radha Raman Pal <p>The objective of this study is to present four new universal biquad filters, two voltage-mode multi-input-single-output (MISO), and two current-mode single-input-multi-output (SIMO). The filters employ one voltage differencing current conveyor (VDCC) as an active element and two capacitors along with two resistors as passive elements. All the five filter responses, i.e., high-pass, low-pass, band-pass, band-stop, and all-pass responses, are obtained from the same circuit topology. Moreover, the pole frequency and quality factor are independently tunable. Additionally, they do not require any double/inverted input signals for response realization. Furthermore, they enjoy low active and passive sensitivities. Various regular analyses support the design ideas. The functionality of the presented filters are tested by PSPICE simulations using TSMC 0.18 µm technology parameters with ± 0.9 V supply voltage. The circuits are also justified experimentally by creating the VDCC block using commercially available OPA860 ICs. The experimental and simulation results agree well with the theoretically predicted results.</p> 2021-04-01T00:00:00+00:00 Copyright (c) 2021 Suvajit Roy, Tapas Kumar Paul, Saikat Maiti, Radha Raman Pal