INCREASE STUDENTS' INTEREST THROUGH THE MBT-03 TRAINING MACHINE APPLICATION INTO AK GUN PRACTICE COURSE

This article aims to briefly assess the advantages and limitations of the BT-03 shooting machine when it is applied to the practical teaching of the practical module of National Defense and Security Education (NDSE) at Ho Chi Minh City University of Technology and Education. Through surveying the Likert scale (5 levels) with 340 students and 28 experts, the author conducted quantitative statistical analysis to compare the interest of learners and lecturers after their experience with this device. The results showed that both students and lecturers highly appreciated all 10 observed variables affecting the excitement through the application of the MBT-03 exercise machine. This is the basis for universities and national defense education centers to consult and invest in shooting machines that combine information technology in teaching. Thereby, it helps to innovate teaching methods in a positive way and contributes to improving the quality of NDSE.  Article visualizations

Approximate query processing in a data warehouse using random sampling

Data analysis consumes a large volume of data on a routine basis.. With the fast increase in both the volume of the data and the complexity of the analytic tasks, data processing becomes more complicated and expensive. The cost efficiency is a key factor in the design and deployment of data warehouse systems. Approximate query processing is a well-known approach to handle massive data among different methods to make big data processing more efficient, in which a small sample is used to answer the query. For many applications, a small error is justifiable for the saving of resources consumed to answer the query, as well as reducing the latency. We focus on the approximate query processing using random sampling in a data warehouse system, including algorithms to draw samples, methods to maintain sample quality, and effective usages of the sample for approximately answering different classes of queries. First, we study different methods of sampling, focusing on stratified sampling that is optimized for population aggregate query. Next, as the query involves, we propose sampling algorithms for group-by aggregate queries. Finally, we introduce the sampling over the pipeline model of queries processing, where multiple queries and tables are involved in order to accomplish complicated tasks. Modern big data analyses routinely involve complex pipelines in which multiple tasks are choreographed to execute queries over their inputs and write the results into their outputs (which, in turn, may be used as inputs for other tasks) in a synchronized dance of gradual data refinement until the final insight is calculated. In a pipeline, approximate results are fed into downstream queries, unlike in a single query. Thus, we see both aggregate computations from sampled input and approximate input. We propose a sampling-based approximate pipeline processing algorithm that uses unbiased estimation and calculates the confidence interval for produced approximate results. The key insight of the algorithm calls for enriching the output of queries with additional information. This enables the algorithm to piggyback on the modular structure of the pipeline without having to perform any global rewrites, i.e. no extra query or table is added into the pipeline. Compared to the bootstrap method, the approach described in this paper provides the confidence interval while computing aggregation estimates only once and avoids the need for maintaining intermediary aggregation distributions. Our empirical study on public and private datasets shows that our sampling algorithm can have significantly (1.4 to 50.0 times) smaller variance, compared to the Neyman algorithm, for optimal sample for population aggregate queries. Our experimental results for group-by queries show that our sample algorithm outperforms the current state-of-the-art on sample quality and estimation accuracy. The optimal sample yields relative errors that are 5x smaller than competing approaches, under the same budget. The experiments for approximate pipeline processing show the high accuracy of the computed estimation, with an average error as low as 2%, using only a 1% sample. It also shows the usefulness of the confidence interval. At the confidence level of 95%, the computed CI is as tight as +/- 8%, while the actual values fall within the CI boundary from 70.49% to 95.15% of times

V2V: Vector Embedding of a Graph and Applications

We present V2V, a method for embedding each vertex in a graph as a vector in a fixed dimensional space. Inspired by methods for word embedding such as word2vec, a vertex embedding is computed through enumerating random walks in the graph, and using the resulting vertex sequences to provide the context for each vertex. This embedding allows one to use well-developed techniques from machine learning to solve graph problems such as community detection, graph visualization, and vertex label prediction. We evaluate embeddings produced by V2V through comparing results obtained using V2V with results obtained through a direct application of a graph algorithm, for community detection. Our results show that V2V provides interesting trade-offs among computation time and accuracy

Conception d'antenne intelligente reconfigurable pour la radio cognitive

Reconfigurable antennas offer multiple functions by dynamically changing their properties such as operating frequency, polarization, radiation pattern, and a combination of all these factors. Their agility and diversity create a wide range of different and new applications for radio systems such as local networks, satellites, and especially in cognitive radio. In this thesis, two new frequency reconfigurable antennas are proposed. The frequency reconfiguration is obtained by changing the geometry of radiating patch. Their dimensions have been optimized by genetic algorithm embedded in the electromagnetic simulation software. These antennas operate at the frequency band for IEEE 802.11b/g standard with satisfactory radiation characteristics. This thesis also presents a method of controlling the operation of the frequency reconfigurable antenna by a micro-controller. The operation of electronic switches (PIN diodes) are carried out through programs which allows an adaptive operating system like smart antennas and work well in cognitive radio environment.Les antennes reconfigurables offrent de multiples fonctions en changeant dynamiquement leurs propriétés telles que la fréquence de fonctionnement, la polarisation, le diagramme de rayonnement ou toute combinaison de ces trois paramètres. Leur agilité et leur diversité créent de nouvelles possibilités d'applications pour les systèmes radio tels que les réseaux locaux, les liaisons par satellite et notamment la radio cognitive. Dans cette thèse, deux antennes reconfigurables en fréquence fonctionnant dans les bandes des standards sans fil actuels ont été proposées. Elles sont basées sur la modification de la géométrie du patch rayonnant. Leurs dimensions ont été optimisées par algorithmes génétiques embarqués et combinés à un logiciel de simulation électromagnétique. La commande de la reconfiguration de ces antennes est réalisée à l'aide d'un microcontrôleur qui pilote l'état des commutateurs (des diodes PIN). De ce fait, un système d'antenne reconfigurable intelligent dédié à la radio cognitive a été développé

Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots

This paper aims at designing and implementing an intelligent controller for the orientation control of a two-wheeled mobile robot. The controller is designed in LabVIEW and based on analyzed image parameters from cameras. The image program calculates the distance and angle from the camera to the object. The fuzzy controller will get these parameters as crisp input data and send the calculated velocity as crisp output data to the right and left wheel motor for the robot tracking the target object. The results show that the controller gives a fast response and high reliability and quickly carries out data recovery from system faults. The system also works well in the uncertainties of process variables and without mathematical modeling

Approximation of mild solutions of the linear and nonlinear elliptic equations

In this paper, we investigate the Cauchy problem for both linear and semi-linear elliptic equations. In general, the equations have the form $$\frac{\partial^{2}}{\partial t^{2}}u\left(t\right)=\mathcal{A}u\left(t\right)+f\left(t,u\left(t\right)\right),\quad t\in\left[0,T\right],$$ where $\mathcal{A}$ is a positive-definite, self-adjoint operator with compact inverse. As we know, these problems are well-known to be ill-posed. On account of the orthonormal eigenbasis and the corresponding eigenvalues related to the operator, the method of separation of variables is used to show the solution in series representation. Thereby, we propose a modified method and show error estimations in many accepted cases. For illustration, two numerical examples, a modified Helmholtz equation and an elliptic sine-Gordon equation, are constructed to demonstrate the feasibility and efficiency of the proposed method.Comment: 29 pages, 16 figures, July 201

A new stability results for the backward heat equation

In this paper, we regularize the nonlinear inverse time heat problem in the unbounded region by Fourier method. Some new convergence rates are obtained. Meanwhile, some quite sharp error estimates between the approximate solution and exact solution are provided. Especially, the optimal convergence of the approximate solution at t = 0 is also proved. This work extends to many earlier results in (f2,f3, hao1,Quan,tau1, tau2, Trong3,x1).Comment: 13 page