Haiphong, Vietnam

Vietnam Maritime University

Haiphong, Vietnam

Vietnam Maritime University is a university in Haiphong operated by the Ministry of Transport. The university was established on 1 April 1956 as Haiphong Maritime University. As of 2007, this is the only maritime university in Vietnam. The university has over 1000 staff, of which, 450 are faculty staff. The university provides undergraduate and graduate education of shipbuilding, maritime navigation, nautical technology. It has a second campus in Vung Tau city. Wikipedia.

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Tran T.A.,Vietnam Maritime University
IEEE International Conference on Sustainable Energy Technologies, ICSET | Year: 2017

Recently, The World is being challenged by energy crisis and global climate changing. By this reasons, it is necessary to give the methods so as to gain the development and utilization of renewable energy, improve energy structure, ensure energy security, protect environment, and achieve sustainable economic and social development. This article concentrates on researching of the biomass densification technology by analyzing technical economy of large scale application depending on Fuzzy synthetic evaluation methodology creates Weaver-Thomas model with the aim of reducing the Greenhouse Gases Emission. © 2016 IEEE.

Nguyen X.S.,Hubei Engineering University | Nguyen X.S.,Vietnam Maritime University | Zhang G.,Hubei Engineering University | Yang X.,South China University of Technology
ACS Applied Materials and Interfaces | Year: 2017

Uniform and magnetic recyclable mesocrystalline Zn-doped Fe3O4 hollow submicrospheres (HSMSs) were successfully synthesized via a simple one-pot solvothermal route and were used for efficient heterogeneous photo-Fenton catalyst. XRD, XPS, Raman spectroscopy, Mössbauer spectroscopy, SEM, HRTEM, and EDX analyses revealed that the shell of HSMSs is highly porous and assembled by oriented attachment of magnetite nanocrystal building blocks with Zn-rich surfaces. Furthermore, a possible formation mechanism of mesocrystalline hollow materials was proposed. First, Fe3O4 mesocrystals were assembled by oriented nanocrystals, and a Zn-rich amorphous shell grew on the surfaces. Then, Zn gradually diffused into Fe3O4 crystals to form Zn-doped Fe3O4 due to the Kirkendall effect with increasing the reaction time. Meanwhile, the inner nanocrystals would be dissolved, and outer particles would grow larger owing to the Ostwald ripening process, leading to the formation of a hollow structure with porous shell. The Zn-doped Fe3O4 HSMSs exhibited high and stable photo-Fenton activity for degradation of rhodamine B (RhB) and cephalexin under visible-light irradiation in the presence of H2O2, which results from their hollow mesocrystal structure and Zn doping. It could be easily separated and reused by an external magnetic field. The results suggested that the as-obtained magnetite hollow mesocrystals could be a promising catalyst in the photo-Fenton process. © 2017 American Chemical Society.

Nam N.D.,Vietnam Maritime University | ThiChieu L.,Sudan University of Science and Technology | Khanh P.M.,Sudan University of Science and Technology
Key Engineering Materials | Year: 2017

This article studies the mechanism of work hardening of austenitic high manganese steel alloyed with chromium and vanadium. The steel was annealed at 650°C before austenitizing at 1100°C, and then was quenched with water. We have observed that after the heat treatment, the size of austenite grain was small (1,950μm2 - level 6). The hardness of the steel was 223HB and the toughness was 115J/cm2. After impact loading, there was no martensite but twinning and sliding in the microstructure of the steel. The nano austenite was found in the microstructure. The steel was also hardened by small austenite grain and the carbide particles were finely dispersed in the microstructure. © 2017 Trans Tech Publications.

Ha Q.P.,University of Technology, Sydney | That N.D.,University of Technology, Sydney | That N.D.,Vietnam Maritime University | Nam P.T.,Quynhon University | Trinh H.,Deakin University
ISA Transactions | Year: 2014

This paper deals with the problem of partial state observer design for linear systems that are subject to time delays in the measured output as well as the control input. By choosing a set of appropriate augmented Lyapunov-Krasovskii functionals with a triple-integral term and using the information of both the delayed output and input, a novel approach to design a minimal-order observer is proposed to guarantee that the observer error is ε-convergent with an exponential rate. Existence conditions of such an observer are derived in terms of matrix inequalities for the cases with time delays in both the output and input and with output delay only. Constructive design algorithms are introduced. Numerical examples are provided to illustrate the design procedure, practicality and effectiveness of the proposed observer. © 2013 ISA.

Nguyen H.-T.,Vietnam Maritime University | Caplier A.,CNRS GIPSA Laboratory
IEEE Transactions on Information Forensics and Security | Year: 2015

We present a novel feature extraction method named local patterns of gradients (LPOGs) for robust face recognition. LPOG uses block-wised elliptical local binary patterns (BELBP), a refined variant of ELBP, and local phase quantization (LPQ) operators directly on gradient images for capturing local texture patterns to build up a feature vector of a face image. From one input image, two directional gradient images are computed. A symmetric pair of BELBP and a LPQ operator are then separately applied upon each gradient image to generate local patterns images. Histogram sequences of local patterns images' nonoverlapped subregions are finally concatenated to form the LPOG vector for the given image. Based on LPOG descriptor, we propose a novel face recognition system which exploits whitened principal component analysis (WPCA) for dimension reduction and weighted angle-based distance for classification. Experimental results on three large public databases (FERET, AR, and SCface) prove that LPOG WPCA system is robust against a wide range of challenges, such as illumination, expression, occlusion, pose, time-lapse variations, and low resolution. In addition, comparison with other systems shows that LPOG WPCA significantly outperforms the state-of-the-art methods. Computationally, timing benchmarks also demonstrate that our LPOG method is faster than many advanced feature extraction algorithms and can be applied in real-world applications. © 2015 IEEE.

Tran V.L.,Vietnam Maritime University | Im N.,Mokpo Maritime University
International Journal of Naval Architecture and Ocean Engineering | Year: 2012

The recent researches on the automatic berthing control problems have used various kinds of tools as a control method such as expert system, fuzzy logic controllers and artificial neural network (ANN). Among them, ANN has proved to be one of the most effective and attractive options. In a marine context, the berthing maneuver is a complicated procedure in which both human experience and intensive control operations are involved. Nowadays, in most cases of berthing operation, auxiliary devices are used to make the schedule safer and faster but none of above researches has taken into account. In this study, ANN is applied to design the controllers for automatic ship berthing using assistant devices such as bow thruster and tug. Using back-propagation algorithm, we trained ANN with set of teaching data to get a minimal error between output values and desired values of four control outputs including rudder, propeller revolution, bow thruster and tug. Then, computer simulations of automatic berthing were carried out to verify the effectiveness of the system. The results of the simulations showed good performance for the proposed berthing control system.

Nguyen D.T.,Vietnam Maritime University | Jung J.E.,Chung - Ang University
Future Generation Computer Systems | Year: 2016

Social networking services are becoming increasingly popular during the daily lives of Internet citizens, especially since the advent of smart mobile devices with integrated utility modules such as 4G/WIFI connectivity, global positioning services, cameras, and heart beat sensors. Many devices are available for sharing information at any time, which can be listed by posting a photo, sharing a status, or narrating an event. The behavior of users means that the flow of data (or a social data stream) has real-time characteristics, which actually comprise notifications about your friends' posts after a short delay for diffusion over the network. The data stream contains news pieces related to real social facts as well as unfocused information. In addition, important information (or events) attracts more public attention, which is demonstrated by the number of relevant messages or communication interactions between people interested in specific topics. From a technical perspective, the characteristics of data in the aforementioned scenario provide us with an opportunity to construct a model that can automatically determine the occurrence of events based on a social data stream. In this study, we propose an approach to solve the problem of early event identification, which requires appropriate approaches for processing incoming data in terms of the processing performance and number of data. © 2016 Elsevier B.V.

Nguyen M.Y.,Thai Nguyen University | Nguyen D.M.,Vietnam Maritime University
Electric Power Components and Systems | Year: 2016

This article presents a new approach to the energy efficiency of households based on smart grid technologies applied to the existing power grid. The idea is to make the electrical appliances capable of actively responding to the condition of the upstream grid, e.g., the electricity price, which, under market environments, is passed to end-use customers through retailers or load aggregators. This both saves on the electricity bill for customers and improves the energy efficiency of the system as a whole. The key element is advanced metering infrastructure, which performs two main functions: (1) computing the so-called control policy for electrical appliances with anticipated data and (2) real-time control based on the above control policy and actual measurements. By stochastic optimization, the proposed scheme is capable of monitoring the uncertainty related to the electricity consumption in households, e.g., temperature and/or time and quantity demanded, etc. In addition, with the physical model taken into account, the problem can be applied to various appliances, including what were considered critical loads. The proposed scheme was tested in heat, ventilation, and air-conditioning systems; the simulation result showed the effectiveness in comparison with the traditional (on/off) scheme based on thermostats. © 2016 Taylor & Francis Group, LLC.

Pham D.V.,Vietnam Maritime University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

This paper presents a library written by C# language for the online handwriting recognition system using UNIPEN-online handwritten training set. The recognition engine based on convolution neural networks and yields recognition rates to 99% to MNIST training set, 97% to UNIPEN's digit training set (1a), 89% to a collection of 44022 capital letters and digits (1a,1b) and 89% to lower case letters (1c). These networks are combined to create a larger system which can recognize 62 English characters and digits. A proposed handwriting segmentation algorithm is carried out which can extract sentences, words and characters from handwritten text. The characters then are given as the input to the network. © 2012 Springer-Verlag.

Nguyen H.-T.,Vietnam Maritime University
Proceedings of 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science, NICS 2015 | Year: 2015

In this work, we present a new approach for gender classification problem by combining two different types of local features extracted from face images. Given one input image, a Elliptical Local Binary Patterns (ELBP) operator and a Local Phase Quantization (LPQ) operator are applied to generate two pattern images. Then, each pattern image is divided into disjoint rectangular sub-regions to compute their histograms. Finally, all the histograms are concatenated to construct a global representation referred to as Combined Local Patterns (CLP) vector that contains both ELBP and LPQ patterns. In the classification stage, the binary SVM classifier is used to determine the genders of the test images. Experiments carried out upon two public databases, AR and FERET, show that our method achieves good results when dealing with gender recognition problem under facial expressions, illumination, occlusion and time-lapse variations. © 2015 IEEE.

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