Journal Articles - Computer and Information Technology - 2020

Permanent URI for this collection

http://repository.vlu.edu.vn:443/handle/123456789/101

Browse

Recent Submissions

Now showing 1 - 5 of 8
  • Publication
    Extended network and algorithm finding maximal flows
    ( 2020)
    Trần Ngọc Việt
    Graph is a powerful mathematical tool applied in many fields as transportation, communication,informatics, economy, in ordinary graph the weights of edges and vertexes are considered independentlywhere the length of a path is the sum of weights of the edges and the vertexes on this path. However, inmany practical problems, weights at a vertex are not the same for all paths passing this vertex, but dependon coming and leaving edges. The paper develops a model of extended network that can be applied tomodelling many practical problems more exactly and effectively. The main contribution of this paper isalgorithm finding maximal flows on extended networks
  • Publication
    Nghiên cứu các kỹ thuật trích rút thuộc tính trong bài toán nhận dạng khuôn mặt
    ( 2020)
    Nguyễn Thu Nguyệt Minh
    Bài toán nhận dạng khuôn mặt có vai trò vô cùng quan trọng trong nhiều ứng dụng thực tiễn, có thể kể đến là chức năng facw tagging trên facebook. Nhận dạng khuôn mặt cũng được ứng dụng trong một số hệ thống về quản lý nhân sự và các hệ thống phát hiện tội phạm. Mục tiêu của bài viết là nghiên cứu về các phương pháp trích chọn thuộc tính nổi tiếng với các đặc trưng HOG, Eigenfaces, Fisherfaces, LBPH ... và đánh giá hiệu quả của các phương pháp này trong bài toán nhận dạng khuôn mặt.
  • Publication
    Deep learning convolutional neural network in rainfall–runoff modelling
    ( 2020)
    Song Pham Van
    ;
    Hoang Minh Le
    ;
    Dat Vi Thanh
    ;
    Thanh Duc Dang
    ;
    Ho Huu Loc
    ;
    Duong Tran Anh
    Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models
  • Publication
    Enabling Wireless Power Transfer and Multiple Antennas Selection to IoT Network Relying on NOMA
    ( 2020)
    "Si-Phu Le
    ;
    Minh-Sang Van Nguyen
    ;
    Dinh-Thuan Do
    ;
    Hong-Nhu Nguyen
    ;
    Ngoc-Long Nguyen
    ;
    Nhat-Tien Nguyen
    ;
    Miroslav Voznak
    Wireless Power Transfer (WPT) is a significant technique for Internet of Things (IoT) networks. Recently, more interest has been focused on multiple access technique without orthogonal signals for wireless communication. Non-orthogonal Multiple Access (NOMA) scheme is proposed to allow users the access point in IoT network. In this paper, we propose the power beacon which is able to feed energy to power-constraint relay node to further support transmission from the source to destinations in IoT networks. In this article, a NOMA system is benefited by with WPT and antenna selection technique. The system improvement can be achieved through the exact closed-form expressions of outage probability (OP). The performance gap among two users is evaluated using model of the Rayleigh fading channels. Furthermore, we compare NOMA with traditional scheme to highlight advantage of such IoT system.