Novel techniques for electrical impedance metrology

L'abstract è presente nell'allegato / the abstract is in the attachmen

NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT

Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. The findings of this study offer promising directions for future breakthroughs in mobile robot navigatio

SOUND TRANSMISSION ACROSS A FINITE SIMPLY SUPPORTED DOUBLE-LAMINATED COMPOSITE PLATE WITH ENCLOSED AIR CAVITY

ABSTRACTSound transmission across a finite orthotropic laminated double-composite plate with enclosed air cavity on an infinite acoustic rigid baffle is investigated analytically. Sound velocity potential method combined with simply supported boundary conditions is used instead of traditional methods, has good scalability and is important for studies of acoustic vibration of structures. The sound transmission loss is calculated from the ratio of incident to transmitted acoustic powers. Specifically, the focus is placed on the effects of several key system parameters on sound transmission including the plate dimensions, the laminate configurations, the boundary conditions, and the composite materials are systematically examined

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

VIETNAM'S ELECTRONICS INDUSTRY: THE RISE AND PROBLEMS OF FURTHER DEVELOPMENT

Purpose: To evaluate the Vietnamese electronics industry (EI) by analyzing its successes and shortcomings in recent years in comparison with other Southeast Asian countries, as well as the problems faced by the young industries in Vietnam in the course of its development and identify their abilities to participate in the global value chain. Methodology: Systems approach was used to analyze the status of Vietnam’s EI. The advantages and the results achieved by EI are assessed and described with the help of objective data from statistical and officially published sources. Main Findings: The position of Vietnam’s EI in the context of growing industry 4.0 was recognized. Its dependence on imports and other reasons that affect its efficiency and competitiveness were identified in the study. The Government should provide adequate support for the development of priority area including EI within the framework of national policies and encourage import substitution program of the industrial enterprises. Applications: Research results could be used for providing recommendations for making Vietnam's electronic industry development policy and import substitution program in the future. Novelty/ Originality: Study considered the effects of inconsistent structure, import dependence of production, lack of unified government control, quality of labor, etc. in gross output and export revenue