Electromgnetic-gravitational cross-sections in external elctromagnetic fields

The classical processes: the conversion of photons into gravitons in the static electromagnetic fields are considered by using Feynman perturbation techniques. The differential cross sections are presented for the conversion in the electric field of the flat condesor and the magnetic field of the selenoid. A numerical evaluation shows that the cross sections may have the observable value in the present technical scenario.Comment: 11 pages, LATEX, no figure

THE QUALITY WATER ENVIRONMENT HAPPENING OF THE HUONG RIVER IN THE HUE CITY, PERIOD OF 2003-2006

Joint Research on Environmental Science and Technology for the Eart

Multimedia applications and network management support in Video Dialtone ATM network

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 63-64).by Dang Van Tran.M.S

Development of deep reinforcement learning for inverted pendulum

This paper presents a modification of the deep Q-network (DQN) in deep reinforcement learning to control the angle of the inverted pendulum (IP). The original DQN method often uses two actions related to two force states like constant negative and positive force values which apply to the cart of IP to maintain the angle between the pendulum and the Y-axis. Due to the changing of too much value of force, the IP may make some oscillation which makes the performance system could be declined. Thus, a modified DQN algorithm is developed based on neural network structure to make a range of force selections for IP to improve the performance of IP. To prove our algorithm, the OpenAI/Gym and Keras libraries are used to develop DQN. All results showed that our proposed controller has higher performance than the original DQN and could be applied to a nonlinear system

On Throughput for UAV Relay Assisted for Use in Disaster Communications

In this paper, the system performance of an energy harvesting (EH) unmanned aerial vehicle (UAV) system for use in disasters was investigated. The communication protocol was divided into two phases. In the first phase, a UAV relay (UR) harvested energy from a power beacon (PB). In the second phase, a base station (BS) transmitted the signal to the UR using non-orthogonal multiple access (NOMA); then, the UR used its harvested energy from the first phase to transfer the signal to two sensor clusters, i.e., low-priority and high-priority clusters, via the decode-and-forward (DF) technique. A closed-form expression for the throughput of the cluster heads of these clusters was derived to analyze the system performance. Monte Carlo simulations were employed to verify our approach

Autonomic virtual resource management for service hosting platforms

International audienceCloud platforms host several independent applications on a shared resource pool with the ability to allocate com- puting power to applications on a per-demand basis. The use of server virtualization techniques for such platforms provide great flexibility with the ability to consolidate sev- eral virtual machines on the same physical server, to resize a virtual machine capacity and to migrate virtual machine across physical servers. A key challenge for cloud providers is to automate the management of virtual servers while taking into account both high-level QoS requirements of hosted applications and resource management costs. This paper proposes an autonomic resource manager to con- trol the virtualized environment which decouples the provi- sioning of resources from the dynamic placement of virtual machines. This manager aims to optimize a global utility function which integrates both the degree of SLA fulfillment and the operating costs. We resort to a Constraint Pro- gramming approach to formulate and solve the optimization problem. Results obtained through simulations validate our approach