Adaptive backstepping control for ship nonlinear active fin system based on disturbance observer and neural network

Adaptive backstepping control based on disturbance observer and neural network for ship nonlinear active fin system is proposed. One disturbance observer is given to observe the disturbances of the system, by this way, the response time is shorten and the negative impact of disturbance and uncertain elements of the system is reduced. In addition, radial basic function neural network (RBFNN) is proposed to approach the unknown elements in the ship nonlinear active fin system, therefor the system can obtain good roll reduction effectiveness and overcome the uncertainties of the model, the designed controller can maintain the ship roll angle at desired value. Finally, the simulation results are given for a supply vessel to verify the successfulness of the proposed controller

RESEARCH ON NEARSHORE WAVE CONDITIONS AT NHAT LE COASTAL AREA (QUANG BINH PROVINCE) BY USING MIKE21-SW

Research on marine dynamics, including coastal wave motions, is a concern of countries in the world in general and Vietnam in particular. Coastal wave dynamics has a direct impact on human activities including coastal construction, shipping, irrigation, aquatic resources exploitation, etc. The coastal area of Nhat Le, Quang Binh is one of the areas strongly influenced by the coastal wave regime which increases the risk of coastal erosion, estuarine sedimentation, destroys the economic life, affects marine fishing and directly affects the tourist beach area. This article aims to introduce some research results based on the application of MIKE21-SW model of the Danish Hydraulic Institute (DHI) to simulate coastal wave regime in Nhat Le coastal zone, Quang Binh province. The model results are verified by real-time wave data in long-term from the WaMoS® II Radar System at Quang Binh station. The results show that there are many similarities in wave height and direction between the computational model and the actual observation data from the radar system. This result will be an important basis for research and application for coastal protection, reduction in river mouth sedimentation, clearing and flood drainage in the study area

When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach

Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes

Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach

Toward user-driven Metaverse applications with fast wireless connectivity and tremendous computing demand through future 6G infrastructures, we propose a Brain-Computer Interface (BCI) enabled framework that paves the way for the creation of intelligent human-like avatars. Our approach takes a first step toward the Metaverse systems in which the digital avatars are envisioned to be more intelligent by collecting and analyzing brain signals through cellular networks. In our proposed system, Metaverse users experience Metaverse applications while sending their brain signals via uplink wireless channels in order to create intelligent human-like avatars at the base station. As such, the digital avatars can not only give useful recommendations for the users but also enable the system to create user-driven applications. Our proposed framework involves a mixed decision-making and classification problem in which the base station has to allocate its computing and radio resources to the users and classify the brain signals of users in an efficient manner. To this end, we propose a hybrid training algorithm that utilizes recent advances in deep reinforcement learning to address the problem. Specifically, our hybrid training algorithm contains three deep neural networks cooperating with each other to enable better realization of the mixed decision-making and classification problem. Simulation results show that our proposed framework can jointly address resource allocation for the system and classify brain signals of the users with highly accurate predictions

Arbitrage and asset market equilibrium in infinite dimensional economies with risk-averse expected utilities

We consider a model with an infinite numbers of states of nature, von Neumann - Morgenstern utilities and where agents have different prob- ability beliefs. We show that no-arbitrage conditions, defined for finite dimensional asset markets models, are not sufficient to ensure existence of equilibrium in presence of an infinite number of states of nature. How- ever, if the individually rational utility set U is compact, we obtain an equilibrium. We give conditions which imply the compactness of U. We give examples of non-existence of equilibrium when these conditions do not hold

Efficient Generation of Coherent Stokes Field in Hydrogen Gas-Filled Hollow Core Photonic Crystal Fibres

In this paper, we study of the coherent Stokes generation in a transient Raman regime by Hydrogen gas-filled hollow-core photonic crystal fibres (HC-PCFs) configuration. The temporal and spatial evolution of the pump and Stokes field envelopes as well as the coherence and population inversion is numerically observed. The influence of the pump pulse width and gas pressure on the energy exchange along fiber and Stokes generation efficiency is investigated