Taiwan Association of Engineering and Technology Innovation: E-Journals
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Effect of Silicon Dioxide-Reinforcement on the Mechanical Properties of AZ91 Alloy through Equal Channel Angular Pressing
Full text linkThis study aims to investigate the enhancement of AZ91 magnesium-aluminum alloy by reinforcing it with 3 wt.% silicon dioxide and processing it through Equal Channel Angular Pressing (ECAP). Specimens with 0 wt.% and 3 wt.% silicon dioxide are fabricated using gravity casting and mechanical stirring, followed by T4 heat treatment and ECAP. Microstructural analysis using scanning electron microscopy (SEM) and X-ray diffraction (XRD) reveals that silicon dioxide is uniformly dispersed, refining the grain structure and dissolving the β-phase, leading to improved ductility. Mechanical testing shows that adding 3 wt.% silicon dioxide increases the yield strength (YS) by 15.26% and the ultimate tensile strength (UTS) by 23.65% after T4 treatment. ECAP further enhances these values by 19.92% and 41.35%, respectively, while increasing hardness by 15.95%. The improved strength-to-weight ratio makes this alloy suitable for automotive, aerospace, and electronics applications, particularly for lightweight structural components
Enhanced Sample-Based Online Fault Identification for Electric Energy Meter Verification Devices
Full text linkTo solve the issues of low accuracy and difficulty of online fault identification for Automatic Verification Devices (AVDs) of Electric Energy Meters (EEMs), a method based on Installed Standard Energy Meters (ISEMs) is proposed. ISEMs are tested concurrently with EEMs undergoing verification, and test data from meter positions are collected online without disrupting AVD operation. The features of the meter positions are constructed, and their principal components are extracted to reduce feature dimensionality. Unlabeled samples are categorized into typical fault categories using the K-means clustering algorithm. A Multi-Class Support Vector Machine model is trained and optimized by Bayesian optimization based on the labeled samples. The model is then employed for AVD online fault identification. Enhanced with Monte Carlo samples augmentation, the proposed approach achieves a 0.35% error rate, a 94.40% accuracy improvement compared to the model without sample enhancement. This method provides a reliable and cost-effective solution for online fault identification of AVDs
Review of Biomedical Signal-Based Control Systems for Electric Wheelchairs
Full text linkMobility impairments significantly challenge independence and quality of life, especially for individuals who rely on wheelchairs. Recent advances in intelligent control systems for electric wheelchairs aim to address these challenges by enabling hands-free operation using biomedical signals. This review aims to provide a comprehensive overview of control strategies that utilize physiological and biological signals—such as head movements, voice commands, electroencephalogram, electrooculography, and electromyography—for wheelchair navigation. The study categorizes and compares these systems based on input modality, signal type, adaptability, and integration with soft computing techniques. Key findings highlight the strengths of multimodal approaches, the challenges posed by signal noise and user fatigue, and the need for improved real-world validation. By synthesizing the current research landscape, this review identifies future research directions focused on enhancing usability, safety, and accessibility in smart wheelchair technologies
Enhanced Human-Computer Interaction: A Unified Pipeline for Classification and Gesture Analysis
Full text linkThe purpose of this study is to develop a unified framework that combines object classification with vision-based gesture recognition. The proposed approach integrates YOLOv3 object detection enhanced by Z-Score Propensity Normalization to minimize false positives in Non-Maximum Suppression. Gesture recognition is performed using geometric contour detection and a Support Vector Machine classifier trained with Principal Component Analysis, which hierarchically refines detected bounding boxes and classifies hand gestures using spatial-temporal distance metrics. Experimental results show an average accuracy of 96.70%, a precision of 0.968, and an F1-score of 0.9671 for recognizing three gestures: hands down, one hand up, and hands up. This integrated method significantly improves computational efficiency and robustness, demonstrating strong potential for practical applications in augmented reality, assistive technologies, and immersive computing
A Review of Emerging Airport Technologies from a Passenger-Centric Perspective
Full text linkThis study examines the transformative role of emerging technologies, particularly the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), in modernizing airport operations and enhancing passenger experiences. Using a PRISMA-guided systematic review of 51 peer-reviewed publications, the study integrates bibliometric and content analyses to evaluate applications across passenger navigation, baggage handling, cargo management, and security operations. The findings reveal substantial progress in automation, real-time data analytics, and AI-driven personalization, which collectively improve efficiency, reduce congestion, and elevate service quality. However, challenges persist, including high costs, integration complexity, cybersecurity risks, and regulatory constraints. Emerging but underexplored areas, such as Autonomous Guided Vehicles (AGVs) and IoT-enabled baggage management, highlight critical opportunities for future research and practice. By consolidating insights from diverse studies, this review provides both scholarly and practical contributions, offering a roadmap for airports to evolve into efficient, resilient, and passenger-centric ecosystems aligned with global digital transformation trends
Design of AlN-Based PMUT with High Electromechanical Coupling Efficiency for Breast Cancer Diagnosis
Full text linkThe study aims to develop a piezoelectric micromachined ultrasonic transducer (PMUT) with high transmission capability to achieve greater detection depth. The structural design of the sensing cell based on aluminum nitride (AlN) thin film is provided, and a mathematical theoretical model is derived. Static pressure, static displacement, vibration modes, resonant frequency, sensitivity, and acoustic impedance are analyzed using the finite element analysis. The areas of the upper and lower electrodes and the thicknesses of the piezoelectric and vibrating films are optimized. Simulation results indicate that when the ratio of the upper and lower electrodes of the sensing cells is 0.7, the electromechanical coupling efficiency of the transducer is enhanced, and its transmission performance is further improved. Mainly the first-order resonant frequency of PMUT is 7.62 MHz, with a sensitivity of -198 dB and an effective electromechanical coupling coefficient of 10.2%, meeting the design requirements for breast cancer detection
Non-Invasive Monitoring of Knee Osteoarthritis Severity Using Vibration Stimulation
Full text linkThis study aims to explore the application of vibration stimulation for the early detection and assessment of knee osteoarthritis severity, using a porcine knee joint. Accelerometers are attached to the femurs and tibias to measure vibratory responses under simulated osteoarthritic conditions. Frequency response functions are generated from the acceleration data and quantified using the root mean square deviation (RMSD) relative to baseline conditions. To ensure the reliability of the results, a coherence filter is applied, confirming significant differences across various stages of joint injury. The RMSD analysis demonstrates the technique's ability to detect phase differences, particularly within the 1000 Hz frequency range. These findings suggest that vibration stimulation could be a feasible non-invasive diagnostic method for assessing osteoarthritis severity in humans. This study highlights the potential of vibration-based diagnostics as an innovative approach for the early detection of osteoarthritis
Risk-Aware Multi-Agent Advantage Actor-Critic Based Resource Allocation for C-V2X Communication in Cellular Networks
Full text linkIntelligent transportation systems have emerged promisingly for industries to enable automated and safe driving. However, to satisfy reliability, environmental sustainability, and overall performance, deep reinforcement learning requires massive energy consumption with its computational demands. In this research, the risk-aware multi-agent advantage actor-critic (RA-MA-A2C)-based resource allocation (RA) is proposed for the cellular-vehicle-to-everything (C-V2X) network. The RA-MA-A2C considers collision risk when allocating resources such as frequency and power. By integrating risk assessment into the decision-making process, the RA-MA-A2C adjusts RA to mitigate collision risks and thereby increases the system’s effectiveness. The RA-MA-A2C’s performance is evaluated in terms of the success rate, completion time, vehicle-to-infrastructure link sum rate, and vehicle-to-vehicle links probability. The RA-MA-A2C demands 108 ms completion time with a 98.81% success rate, surpassing the performance of the existing offloading resource allocation-based deep reinforcement learning (ORAD) method
A Novel Traveling Wave Fault Location Method Based on ICEEMDAN-NTEO for Distribution Network
Full text linkThe traditional traveling wave fault location method is easily disturbed by load-side noise, which leads to low accuracy of traveling wave head identification and further leads to large location errors. This study aims to propose a new traveling wave fault location method based on ICEEMDAN-NTEO for distribution networks. By analyzing the frequency characteristics in the faulty traveling wave signal, the ICEEMDAN method is used to decompose the faulty signal and effectively filter out the noise components in the signal. The NTEO method is then used to calculate the energy values of the obtained modal components, enhancing the transient characteristics of the traveling wave. This method solves the problems of decomposition scale or modal aliasing that exist in traditional location methods and has higher anti-noise characteristics. Based on the accurate identification of the traveling wave head, this method further improves the accuracy of the traveling wave location in the distribution network
Enhancement of Properties of Fly Ash Geopolymer Paste with Low NaOH Concentrations Using a Pressing Approach
Full text linkGeopolymers are widely recognized as an eco-friendly alternative material. However, the impact of pressing stresses and low NaOH concentrations on their properties remains underexplored. This research aims to investigate the effects of pressing stresses on unit weight, porosity, water absorption, and compressive strength of high-calcium fly ash geopolymer paste with low NaOH concentrations. The low NaOH concentrations of 0.5, 1.0, and 2.0 M, pressing stresses of 10, 20, and 30 MPa, and liquid-to-binder ratios of 0.10, 0.12, 0.14, 0.16, 0.18, and 0.20 by weight are used. The specimens of geopolymer paste are oven-dried at 60°C for 24 hours before evaluation. The testing results show that the compressive strength of casted geopolymer paste is between 2 to 15 MPa, with higher compressive strength associated with lower porosity. The water absorption rate is between 11% and 21% by weight, which has a higher water absorption rate as the porosity increases