Advances in Technology Innovation https://ojs.imeti.org/index.php/AITI <p style="margin: 0cm 0cm 0pt;"><strong><em>Advances in Technology Innovation</em></strong> (AITI), ISSN 2518-2994 (Online), ISSN 2415-0436 (Print), is an international, multidiscipline, peer-reviewed scholarly journal. The official abbreviated title is <em><strong>Adv. technol. innov.</strong></em> It is dedicated to provide a platform for fast communication between the newest research works on the innovations of Technology &amp; Engineering. A paper will be online shortly once it is accepted and typeset. Currently, there is no publication charge, including article processing and submission charges. AITI is an open access journal which means that all contents are freely available without charge to the user or his/her institution.</p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;">AITI is indexed by:</span></p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;"><img style="width: 136px; height: 26px;" src="http://ojs.imeti.org/public/site/images/allen/image001.png" alt="" width="171" height="53">&nbsp; </span><img src="/public/site/images/ijeti/DOAJ4.png" alt=""> &nbsp;&nbsp; <img src="/public/site/images/ijeti/google5.png" alt=""> &nbsp; <img src="http://ojs.imeti.org/public/site/images/ijeti/CNKI.png" alt="">&nbsp; <img src="/public/site/images/allen/ProQuest-41.png"> <img src="/public/site/images/ijeti/CAB_ABSTRACTS4.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/ijeti/Resarch_Bible5.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/ijeti/WorldCat5.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/allen/academia-12.png"> &nbsp;<img src="/public/site/images/ijeti/TOCs5.jpg" alt=""> &nbsp; <img src="/public/site/images/allen/Publons-22.5_1.png"> &nbsp;&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;">&nbsp;Under evaluation of SCI, EI(Compendex) , INSPEC, etc.</span></p> <p style="margin: 0cm 0cm 0pt;">&nbsp;</p> Taiwan Association of Engineering and Technology Innovation en-US Advances in Technology Innovation 2415-0436 <hr style="font: 12px/normal Verdana, Arial, Helvetica, sans-serif; color: rgb(0, 0, 0); text-transform: none; text-indent: 0px; letter-spacing: normal; word-spacing: 0px; white-space: normal; cursor: default; widows: 1; font-size-adjust: none; font-stretch: normal; -webkit-text-stroke-width: 0px;"> <p style="line-height: 150%;"><span style="font-family: Times New Roman;">Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. is accepted for publication. Authors can retain copyright of their article with no restrictions.</span></p> <p style="line-height: 150%;">&nbsp;</p> <p style="line-height: 150%;"><img alt="" src="data:image/png;base64,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"></p> <p style="line-height: 150%;"><span style="font-family: Times New Roman, Times, serif;">Since Jan. 01, 2019, AITI will publish new articles with Creative Commons Attribution Non-Commercial License, under <a href="https://creativecommons.org/licenses/by-nc/4.0/">The Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License</a>.<br>The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.</span></p> <p style="line-height: 150%;"><span style="font-family: Times New Roman;">&nbsp;</span></p> Measurement Accuracy of Ultrasound Viscoelastic Creep Imaging in Measuring the Viscoelastic Properties of Heterogeneous Materials https://ojs.imeti.org/index.php/AITI/article/view/9592 <p>Ultrasound viscoelastic creep imaging (UVCI) is a newly developed technology aiming to measure the viscoelastic properties of materials. The purpose of this study is to investigate the accuracy of UVCI in measuring the viscoelastic properties of heterogeneous materials that mimic pathological lesions and normal tissues. The finite element simulation is used to investigate the measurement accuracy of UVCI on three material models, including a homogeneous material, a single-inclusion phantom, and a three-layer structure. The measurement accuracy for a viscoelastic property is determined by the difference between the simulated measurement result of that viscoelastic property and its true value defined during the simulation process. The results show that UVCI in general cannot accurately measure the true values of the viscoelastic properties of a heterogeneous material, demonstrating the need to further improve the theories and technologies relevant to UVCI to improve its measurement accuracy on tissue-like heterogeneous materials.</p> Che-Yu Lin Yi-Cheng Chen Chin Pok Pang Tung-Han Yang Copyright (c) 2022 Che-Yu Lin, Yi-Cheng Chen, Chin Pok Pang, Tung-Han Yang http://creativecommons.org/licenses/by-nc/4.0 2022-06-14 2022-06-14 7 3 10.46604/aiti.2021.9592 Observer-Based Quadratic Guaranteed Cost Control for Linear Uncertain Systems with Control Gain Variation https://ojs.imeti.org/index.php/AITI/article/view/9252 <p>This study proposes a method for designing observer-based quadratic guaranteed cost controllers for linear uncertain systems with control gain variations. In the proposed approach, an observer is designed, and then a feedback controller that ensures the upper bound on the given quadratic cost function is derived. This study shows that sufficient conditions for the existence of the observer-based quadratic guaranteed cost controller are given in terms of linear matrix inequalities. A sub-optimal quadratic guaranteed cost control strategy is also discussed. Finally, the effectiveness of the proposed controller is illustrated by a numerical example. The result shows that the proposed controller is more effective than conventional methods even if system uncertainties and control gain variations exist.</p> Satoshi Hayakawa Yoshikatsu Hoshi Hidetoshi Oya Copyright (c) 2022 Satoshi Hayakawa, Yoshikatsu Hoshi, Hidetoshi Oya http://creativecommons.org/licenses/by-nc/4.0 2022-06-09 2022-06-09 7 3 155 168 10.46604/aiti.2022.9252 Wave Transmission and Energy Dissipation in a Box Culvert-Type Slotted Breakwater https://ojs.imeti.org/index.php/AITI/article/view/9080 <p>This research is conducted to examine the transmission wave and energy dissipation of a box culvert-type slotted breakwater, which is designed as a breakwater structure with a watertight wall at the top and a box culvert type hole at the bottom. The process involves physical modeling of this structure in the laboratory. The hole and wave parameters are varied to determine the breakwater performance. The results show that the transmission coefficient (<em>K<sub>T</sub></em>) value is reduced as the relative hole height (<em>h<sub>L</sub></em>/<em>d</em>) value is decreasing and the relative hole length (<em>B</em>/<em>L</em>) and wave steepness (<em>H</em>/<em>L</em>) values are increasing. The energy dissipation coefficient (<em>K<sub>D</sub></em>) value increases with an increment in <em>h<sub>L</sub></em>/<em>d</em>, <em>H</em>/<em>L</em>, and <em>B</em>/<em>L</em> but starts to decrease after reaching the maximum, which is the optimum <em>H</em><em> × B</em>/<em>L<sup>2</sup></em> value. This optimum value is found to be 0.0034(<em>h<sub>L</sub></em>/<em>d</em>)<sup>2.618</sup> depending on the (<em>h<sub>L</sub></em>/<em>d</em>) value, while the maximum <em>K<sub>D</sub></em> value is recorded to be 0.70.</p> Nastain Suripin Nur Yuwono Ignatius Sriyana Copyright (c) 2022 Nastain, Suripin, Nur Yuwono, Ignatius Sriyana http://creativecommons.org/licenses/by-nc/4.0 2022-06-08 2022-06-08 7 3 10.46604/aiti.2021.9080 Developing and Implementing an AI-Based Leak Detection System in a Long-Distance Gas Pipeline https://ojs.imeti.org/index.php/AITI/article/view/8904 <p>This research proposes an artificial intelligence (AI) detection model using convolutional neural networks (CNN) to automatically detect gas leaks in a long-distance pipeline. The change of gap pressure is collected when leakage occurs in the pipeline, and thereby the feature of gas leakage is extracted for building the CNN model. The gas leak patterns in the long-distance pipeline are analyzed. A pipeline detection model based on AI technology for automatically monitoring the leaks is proposed by extracting the feature of gas leakage. This model is tested by collecting gas pressure data from an existing natural gas pipeline system starting from Mailiao to Taoyuan in Taiwan. The testing result shows that the reduced model of leak detection can be used to detect the leaks from the upstream and downstream pipelines, and the AI-based pipeline leak detection system can obtain a satisfactory result.</p> Te-Kwei Wang Yu-Hsun Lin Jian-Yuan Shen Copyright (c) 2022 Te-Kwei Wang, Yu-Hsun Lin, Jian-Yuan Shen http://creativecommons.org/licenses/by-nc/4.0 2022-06-06 2022-06-06 7 3 169 180 10.46604/aiti.2022.8904 Quantitative and Qualitative Characterization of Coatings Added to Low Voltage Switches https://ojs.imeti.org/index.php/AITI/article/view/8971 <p>Electroplating is one of the most important processes in the manufacturing of switches. Coating the conductive parts of switches improves their appearance and increases their durability, even in severe environments. This study proposes a non-destructive testing method to qualitatively and quantitatively characterize coatings added to the conductive parts of low voltage switches (contacts and terminals). The method is based on the injection of a high-frequency signal into a switch using the vector network analyzer (VNA). An in-depth analysis of the reflected signal is conducted to characterize the coatings. For the quantitative characterization, a comparison is made between switches that are plated with different coating thicknesses. As for the qualitative characterization, a comparison is made between switches that are manufactured with different types of metals. The results show that each switch type has an electromagnetic signature that varies according to the conductivity and the thickness of the metals used for coating.</p> Leila Troudi Khaled Jelassi Copyright (c) 2022 Leila Troudi, Khaled Jelassi http://creativecommons.org/licenses/by-nc/4.0 2022-05-13 2022-05-13 7 3 206 215 10.46604/aiti.2022.8971 An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA https://ojs.imeti.org/index.php/AITI/article/view/8538 <p>In image processing, developing efficient, automated, and accurate techniques to classify images with varying intensity level, resolution, aspect ratio, orientation, contrast, sharpness, etc. is a challenging task. This study presents an integrated approach for image classification by employing transfer learning for feature selection and using principal component analysis (PCA) for feature reduction. The PCA algorithm is employed for reducing the dimensionality of the features extracted by the VGG16 model to obtain a handful of features for speeding up image reorganization. For multilayer perceptron classifiers, support vector machine (SVM) and random forest (RF) algorithms are used. The performance of the proposed approach is compared with other classifiers. The experimental results establish the supremacy of the VGG16-PCA-Multilayer perceptron model integrated approach and achieve a reorganization accuracy of 91.145%, 95.0%, 92.33%, and 98.59% on Fashion-MNIST dataset, ORL dataset of faces, corn leaf disease dataset, and rice leaf disease datasets, respectively.</p> Rahul Sharma Amar Singh Copyright (c) 2022 Rahul Sharma, Amar Singh http://creativecommons.org/licenses/by-nc/4.0 2022-03-14 2022-03-14 7 3 105 117 10.46604/aiti.2022.8538