https://elib.vku.udn.vn/handle/123456789/2387 2026-04-02T22:30:25Z https://elib.vku.udn.vn/handle/123456789/2709 Title: Kỷ yếu Hội thảo Khoa học Quốc gia về Công nghệ thông tin và Ứng dụng trong các lĩnh vực - Lần thứ 12 (CITA 2023) Authors: VKU 2023-06-01T00:00:00Z https://elib.vku.udn.vn/handle/123456789/2706 Title: AECWT-3DR-Net: Damage Localization Network for Concrete Structures Using Acoustic Emission Sensors Authors: Van, Vy; Hyungchul, Yoon Abstract: In the construction industry, the deterioration of structures is a significant concern. To detect cracks in concrete structures, acoustic emission sensors are commonly used. The traditional approach relies on measuring the time of arrival, time difference of arrival, and received signal strength indicator. However, conventional methods are prone to error in the presence of inhomogeneous materials. In this research, we introduce a new method that employs deep learning techniques to detect cracks using acoustic emission sensors. The aim of this approach is to enhance the accuracy of crack detection while automating the process. The proposed method entails the following steps: capturing signals from acoustic emission sensors and then converting them into a time-frequency representation using continuous wavelet transform. These representations are fed into a convolutional neural network that is specifically designed to locate the crack. Finally, the convolutional neural network is trained to predict the coordinates of the crack. The proposed method's effectiveness and advancements were confirmed through experiments conducted on a concrete block that had a crack artificially created by pencil-lead breaks. Description: Proceeding of The 12th Conference on Information Technology and It's Applications (CITA 2023); pp: 2-11. 2023-06-01T00:00:00Z https://elib.vku.udn.vn/handle/123456789/2704 Title: Evolutionary Generative Adversarial Network for Missing Data Imputation Authors: Vi, Bao Ngoc; Tran, Cao Truong; Nguyen, Chi Cong Abstract: Generative adversarial networks (GAN) have been a compelling method for generating new data in data science industry. This generative model has been accepted for data imputation in specific areas. However, existing GANs (GAN and its variants) are likely to suffer from training problems such as instability and mode collapse. This paper proposes a new novel method for imputing missing data by adapting GAN and Evolutionary Computation framework. Therefore, the new methods is named Evolutionary Generative Adversarial for Imputation Data (EGAIN). EGAIN utilises the different training observations with mutation, selection, and evolving process among a population of generator G. In this experiment, three different loss functions is used to validate the output of G and the training process of discriminator D. EGAIN is also tested on various datasets and is compared with state-of-the-art imputation method for illustrating its performance. Description: Proceeding of The 12th Conference on Information Technology and It's Applications (CITA 2023); pp: 12-22. 2023-06-01T00:00:00Z https://elib.vku.udn.vn/handle/123456789/2703 Title: Implementation of Convolutional Neural Network on a Microcontroller for Classification of Audio Signals Authors: Tran, Dinh Hoang Long; Le, Quoc Huy Abstract: The goal of this work is to develop a compact and low-cost device to detect dangerous and suspicious sounds in a sensitive area. The proposed solution uses an STM32 microcontroller embedded with a deep learning model and equipped with various peripherals. For the demo purpose we used the STM32F746NGH6 Discovery Kit and built the convolutional neural network embedded in this microcontroller with the popular Keras API. We illustrated that the deep learning model using convolutional neural networks algorithm can be implemented on STM32F746NGH6 microcontroller kit and the device can classify the labeled audio with an accuracy of about 97%. We also found that signals from real sound sensors or real microphones have noises which affects strongly on the model accuracy and thus it is necessary to build the dataset based on the available hardware. Our in-progress work is to record audio using ADMP401 analog MEMS microphone available on the STM32F746NGH6 microcontroller and then using these datasets for a second experimental model. Description: Proceeding of The 12th Conference on Information Technology and It's Applications (CITA 2023); pp: 23-32. 2023-06-01T00:00:00Z