RESEARCH ON FACTORS INFLUENCING THE INTENT TO USE NETFLIX MOVIES IN VIETNAM

Abstract The Netflix movies market is steadily growing, especially during the complex COVID-19 pandemic. Consumers, instead of opting for free movie streaming services with potential risks and copyright violations, are choosing to pay for a better experience while emphasizing responsibility for protecting copyrights and supporting authors and producers. This research aims to examine the factors influencing the intent to use Netflix movie streaming services among surveyed individuals, primarily focusing on employees aged 18 to 22 in Vietnam. Participants were surveyed through online and offline questionnaires. The author conducted logistic regression analysis, treating the use of Netflix movies as the dependent variable, with five independent variables sourced from a literature review. Through online and offline survey questionnaires and multivariate regression models, the study identified and concluded the factors influencing employees' intent to use Netflix movie streaming services in Vietnam. Data were quantitatively analyzed using IBM SPSS 20.0. The research results identified five positively influencing factors on the intent to use Netflix movie streaming services: Price perception, Risk perception, Attitude, Ethical awareness, Subjective norms. Among these factors, Price perception had the strongest influence on the intent to use Netflix movies, while the Subjective norms factor was found to be insignificant. Consequently, the article suggests managerial implications for businesses to attract customers and promote the Netflix movies market

USING AN INTELLIGENT ANFIS-ONLINE CONTROLLER FOR STATCOM IN IMPROVING DYNAMIC VOLTAGE STABILITY

This research has introduced the intelligent ANFIS-Online controller of STATCOM for improving the dynamic voltage on the power network under a 3-phase short circuit fault. The ANFIS-Online is made using an artificial neural network identifier. And based on the identifier, the premise and consequent parameters of ANFIS are adjusted timely. To demonstrate the performance of the suggested controller, the transient waves are shown to describe the effectiveness of the intelligent ANFIS-Online controller to enhance the transient response of the research system under a 3-phase short circuit fault. It's shown that the suggested intelligent ANFIS-Online controller has provided waves better than the other controllers such as ANFIS controller, ANFIS-PSO controller, ANFIS-GA controller for STATCOM equipment to enhance transient voltage stability

Coderivatives at infinity of set-valued mappings

In this paper, the concept of coderivatives at infinity of set-valued mappings is introduced. Well-posedness properties at infinity of set-valued mappings as well as Mordukhovich's criterion at infinity are established. Fermat's rule at infinity in set-valued optimization is also provided. The obtained results, which give new information even in the classical cases of smooth single-valued mappings, provide complete characterizations of the properties under consideration in the setting at infinity of set-valued mappings

Learning Symmetrization for Equivariance with Orbit Distance Minimization

We present a general framework for symmetrizing an arbitrary neural-network architecture and making it equivariant with respect to a given group. We build upon the proposals of Kim et al. (2023); Kaba et al. (2023) for symmetrization, and improve them by replacing their conversion of neural features into group representations, with an optimization whose loss intuitively measures the distance between group orbits. This change makes our approach applicable to a broader range of matrix groups, such as the Lorentz group O(1, 3), than these two proposals. We experimentally show our method's competitiveness on the SO(2) image classification task, and also its increased generality on the task with O(1, 3). Our implementation will be made accessible at https://github.com/tiendatnguyen-vision/Orbit-symmetrize.Comment: 16 pages, 1 figur

Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System

A surrogate model that accurately predicts distribution system voltages is crucial for reliable smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate modeling method that employs a limited dataset to learn the power-voltage relationship of an unbalanced three-phase distribution system. The proposed surrogate model is designed using a fixed-point load-flow equation, and the stochastic gradient descent method with an automatic differentiation technique is employed to update the parameters of the surrogate model using complex power and voltage samples. Numerical examples in IEEE 13-bus, 37-bus, and 123-bus systems demonstrate that the proposed surrogate model can outperform surrogate models based on the deep neural network and Gaussian process regarding prediction accuracy and sample efficiencyComment: Under review in IEEE PES Lette

Energy Consumption Minimization for Autonomous Mobile Robot: A Convex Approximation Approach

In this paper, we consider a trajectory design problem of an autonomous mobile robot working in industrial environments. In particular, we formulate an optimization problem that jointly determines the trajectory of the robot and the time step duration to minimize the energy consumption without obstacle collisions. We consider both static and moving obstacles scenarios. The optimization problems are nonconvex, and the main contribution of this work proposing successive convex approximation (SCA) algorithms to solve the nonconvex problems with the presence of both static and moving obstacles. In particular, we first consider the optimization problem in the scenario with static obstacles and then consider the optimization problem in the scenario with static and moving obstacles. Then, we propose two SCA algorithms to solve the nonconvex optimization problems in both the scenarios. Simulation results clearly show that the proposed algorithms outperform the A* algorithm, in terms of energy consumption. This shows the effectiveness of the proposed algorithms