Advances in Technology Innovation

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.

Leila Troudi

Khaled Jelassi

Quantitative and Qualitative Characterization of Coatings Added to Low Voltage Switches

低压开关涂层的定量和定性表征

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.

Te-Kwei Wang

Yu-Hsun Lin

Jian-Yuan Shen

Developing and Implementing an AI-Based Leak Detection System in a Long-Distance Gas Pipeline

开发和实施基于人工智能的长距离天然气管道泄漏检测系统

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 (KT) value is reduced as the relative hole height (hL/d) value is decreasing and the relative hole length (B/L) and wave steepness (H/L) values are increasing. The energy dissipation coefficient (KD) value increases with an increment in hL/d, H/L, and B/L but starts to decrease after reaching the maximum, which is the optimum H × B/L2 value. This optimum value is found to be 0.0034(hL/d)2.618 depending on the (hL/d) value, while the maximum KD value is recorded to be 0.70.

None Nastain

None Suripin

Nur Yuwono

Ignatius Sriyana

Wave Transmission and Energy Dissipation in a Box Culvert-Type Slotted Breakwater

箱涵式开孔防波堤中的波浪透射与能量耗散

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.

Satoshi Hayakawa

Yoshikatsu Hoshi

Hidetoshi Oya

Observer-Based Quadratic Guaranteed Cost Control for Linear Uncertain Systems with Control Gain Variation

基于观测器的线性不确定系统的二次保代价控制与控制增益变化

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.

Che-Yu Lin

Yi-Cheng Chen

Chin Pok Pang

Tung-Han Yang

Measurement Accuracy of Ultrasound Viscoelastic Creep Imaging in Measuring the Viscoelastic Properties of Heterogeneous Materials

超声粘弹性蠕变成像测量异质材料粘弹性特性的测量精度

Effective analysis helps players evaluate their performance, make necessary adjustments, and develop diverse game strategies. Moreover, the analysis provides viewers with different perspectives, enhancing their understanding of the game. This study aims to develop a basketball player detection and analysis system to assist in analyzing on-court situations. The system uses perspective transformation to obtain player tracking information on the top view image in basketball games. The system uses a modified you only look once (YOLO) v5 model that replaces the backbone of YOLOv5s with the MobileNetv3-small architecture for player detection. Compared to the original YOLOv5, the modified YOLOv5 reduces parameters from 7.02 × 106 to 3.5 × 106, a decrease of 49.8%. The number of frames obtainable per second increases from 12.4 to 17.5, an improvement of about 41.1%. Finally, the system performs perspective transformation and tracks the detected player positions onto the top-view court image using the YOLOv5 model.

None Jia-Shing Sheu

None Sheng-Ju Lin

An Efficient Application of Modified YOLOv5 in Basketball Player Detection and Analysis

改进YOLOv5在篮球运动员检测与分析中的高效应用

Named entity recognition (NER) is critical for language understanding and text mining systems, such as event extraction and automatic question-and-answer systems. However, NER from automatic speech recognition (ASR) outputs remains challenging due to errors and lack of textual cues. This study aims to provide a comprehensive bibliometric analysis of research on NER from ASR, focusing on publications indexed in the Scopus database before 2024 to understand the research field. Using Biblioshiny and VOSviewer tools, this research identifies the key trends, prominent authors, and international collaborations in the research network. The results show steady growth in this research area, while conference papers are the predominant source type. Additionally, the study highlights the increasing intervention of deep learning approaches to enhance NER accuracy, suggesting potential research directions to reduce error rates, and developing more robust NER algorithms. Finally, the findings underscore the importance of cross-disciplinary collaborations to document any current challenges.

None Thu Hien Nguyen

None Tuan Linh Nguyen

None Thanh Binh Nguyen

Current Trends in Named Entity Recognition from Automatic Speech Recognition: A Bibliometric Analysis Using Scopus Database

自动语音识别中命名实体识别的当前趋势：基于Scopus数据库的文献计量分析

This research aims to utilize an organized grid-based strategy to make the development of complex Finite Automata easier. Product Automata are used to merge many automata into a single automaton to integrate various computing processes. Combining these methodologies provides novel methods of improving the scalability and efficiency of automata building, broadening the field of research for automata theory and its applications. The scalability of this developed automated system will benefit sectors such as automotive production and logistics. The results indicate considerable improvements in construction time, memory usage, scalability, and resilience compared to older approaches. The performance of the developed method, as measured by construction time, memory utilization, scalability, robustness, application range, and complexity, is 25% to 50% higher than that of traditional methodologies in the literature.

None Rupam Nag

None Dibyendu Barman

None Abul Hasnat

A Novel Approach to Construct Finite Automata Using Grid and Product Automata

一种利用网格和乘积自动机构建有限自动机的新方法

The same-end-inlet-outlet (SEIO) muffler, also referred to as a flow reversal muffler under flow conditions, features inlet and outlet pipes positioned on the same side of the chamber. Recently, a parametric expression has been developed to determine the end correction for double tuning of the SEIO muffler. This study extends the development of the SEIO muffler by experimentally validating the derived end correction expressions. Additionally, the tuning of the muffler is assessed with a mean flow using 3-D computational fluid dynamics, solving the linearized Navier-Stokes equation. This investigation explores the impact of flow conditions (Mach number 0.05 and 0.1) and temperature conditions (T = 733 K and 953 K) on the transmission loss (TL) of a doubly tuned muffler. The findings reveal that the muffler maintains its double tuning, even in the presence of mean flow at elevated temperatures, albeit with somewhat of a reduction in performance.

None Chetan Dayanand Gaonkar

None Thappaganadoddi Nagabushnasharma Sreenivasa

Effect of Mean Flow on the Transmission Loss of a Doubly Tuned Flow Reversal Muffler

平均流对双调谐流动反转消声器传递损失的影响

This study aims to optimize photovoltaic systems by developing a novel hybrid metaheuristic approach for maximum power point tracking (MPPT). The proposed method eclectically combines particle swarm optimization (PSO) and gravitational search algorithm (GSA) to overcome individual limitations and leverage complementary strengths. PSO, while surpassing in exploration, may suffer from premature convergence. GSA demonstrates strong exploitation capabilities but can struggle with slow convergence. A simulation model is developed to evaluate the hybrid algorithm’s performance in optimizing PV systems’ duty cycle. The approach utilizes the exploitation capabilities of PSO and GSA to navigate the search space effectively. Results demonstrate that the hybrid algorithm outperforms traditional techniques and standalone metaheuristics, achieving improved convergence time, faster settling time, and enhanced MPPT tracking efficiency. Under varying irradiance conditions, the proposed method consistently delivers higher power generation and improved overall PV system efficiency, offering a promising solution for optimizing PV systems and maximizing energy generation.

None Aditya Sharma

None Dheeraj Kumar Palwalia

Synergistic Application of Particle Swarm Optimization and Gravitational Search Algorithm for Solar PV Performance Improvement

粒子群优化与引力搜索算法的协同应用以提升太阳能光伏性能

涉及主题	工程类计算机科学物理数学量子力学材料科学生物热力学人工智能机械工程地理复合材料经济操作系统地质学电气工程化学结构工程有机化学统计
出版信息	出版商: Taiwan Association of Engineering and Technology Innovation，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2020，原创研究文献占比: 0.00%，自引率:N.A.， Gold OA占比: 0.00%
平均审稿周期	网友分享经验：12 Weeks

Advances in Technology Innovation

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