EXTENDING VOCAL RANGE FOR A MEZZO-SOPRANO IN VOCAL TECHING FOR LIGHT MUSIC

Popular music is one of the genres of music loved by the majority of young people. The teaching of popular music has now been included in the professional vocal training program in major art training departments in our country such as: Military University of Culture and Arts, Hanoi College of Art, National University of Art Education, ThanhHoa University of Culture, Sports and Tourism, Hue Academy of Music, Ho Chi Minh City Conservatory... It is very necessary to improve the quality of singing popular music in schools, especially with the expansion of the range for female voices

MODELING AND SIMULATION OF A LEAN SYSTEM. CASE STUDY OF A PAINT LINE IN A FURNITURE COMPANY

Since they were first developed, lean methodologies have grown in importance and scope and have been applied in both manufacturing and service. However, determining how to transform a common manufacturing company into a lean one, as well as how to evaluate the future company, are challenges for both researchers and manufacturers. This paper presents a case study of a lean manufacturing implementation for the paint line system in a furniture company. A systematic method for execution is shown. In addition, a simulation model is constructed to evaluate the new system in comparison with the MRP system. The new system promises much improvement in terms of a resource’s utility and the system’s productivity.Lean Techniques, Simulation Model, Paint Line, Furniture Company.

A new approach to weighted Hardy-Rellich inequalities: improvements, symmetrization principle and symmetry breaking

We investigate necessary and sufficient conditions on the weights for the Hardy-Rellich inequalities to hold, and propose a new way to use the notion of Bessel pair to establish the optimal Hardy-Rellich type inequalities. Our results sharpened earlier Hardy-Rellich and Rellich type inequalities in the literature. We also study several results about the symmetry and symmetry breaking properties of the Rellich type and Hardy-Rellich type inequalities, and then partially answered an open question raised by Ghoussoub and Moradifam. Namely, we will present conditions on the weights such that the Rellich type and Hardy-Rellich type inequalities hold for all functions if and only if the same inequalities hold for all radial functions.Comment: 22 page

Optimization of network traffic anomaly detection using machine learning

In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms

A Generalization Bound of Deep Neural Networks for Dependent Data

Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications such as evolutionary biology, infectious disease epidemiology, and stock price prediction. This work establishes a generalization bound of feed-forward neural networks for non-stationary $\phi$-mixing data