Time of Arrival and Angle of Arrival Statistics for Distant Circular Scattering Model

General scattering model is local scattering process assumed that the mobile station is located inside the scattering region, in order to study distant scattering model in suburban or hilly propagation environment, a geometrically based statistical distant circular scattering mode in macrocell environment was proposed, the closed-form expressions of the joint probability density function of the time of arrival /the angle of arrival, the marginal probability density function of the angle of arrival and the angle of departure, the marginal probability density function of the time of arrival were derived, this probability density function’s provided insight into the properties of the spatial distant scattering channel model

Generalized Synchronization of Different Chaotic Systems Based on Nonnegative Off-Diagonal Structure

The generalized synchronization problem is studied in this paper for different chaotic systems with the aid of the direct design method. Based on Lyapunov stability theory and matrix theory, some sufficient conditions guaranteeing the stability of a nonlinear system with nonnegative off-diagonal structure are obtained. Then the control scheme is designed from the stable system by the direct design method. Finally, two numerical simulations are provided to verify the effectiveness and feasibility of the proposed method

Experimental study of the Couple Characteristics of the Refrigerants and Vortex Tube

Vortex tube is a simple energy separation device, also known as Ranque tube or Hilsch tube, which can separate a high-pressure stream into two different hot and cold streams. Since its simple structure and unique temperature separation characteristics, vortex tube has been widely used in various industries. In recent years, with the in-depth study of the vortex tube, it has been found that compared with the conventional expansion expander and the throttle valve, the vortex tube is much more structurally simple and efficient, respectively. Researchers have proposed the use of the vortex tube in the refrigeration system in order to reduce the throttling loss and improve system efficiency. This has important implications for improving the performance of the system, to achieve energy saving and emission reduction. However, due to the different physical properties of the different working fluid, energy separation in the vortex tube are not the same. In the existing studies on the vortex tube, the working fluid mainly used air, nitrogen, carbon dioxide and other natural refrigerants, the research about the influence of refrigerants is few. Due to the fact that the vortex tube is increasingly used in refrigeration and heating system, it is urgent to study the coupling characteristics between vortex tube and refrigerants and find optimal conditions in different systems. The different temperature separation effect of the refrigerants in the vortex tube in the low inlet pressure(300kPa) have been studied in our previous study and three fluid characteristics (specific heat ratio, kinematic viscosity, thermal conductivity) were considered as main influencing factors of energy separation. The influence of different working fluid in high pressure conditions has not been considered ,which is part of research work in this paper. The coupling characteristic between vortex tube and refrigerants wais studied and the closed loop system was constructed. R134a, R744, R32, R227ea were selected as the working fluids, experiments were carried out in different inlet pressure(500kPa?850kPa), different inlet temperature (308.15K?333.15K), different cold flow ratio (20%?97%). The temperature separation of different working fluids under different conditions were explored and the influences of different characteristics of the working fluids on the temperature separation process were discussed. These studies can help more profound understanding of the vortex tube temperature separation process, and also has certain significance on the applications of the vortex tube in the refrigeration system

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community (https://ccnp.scidb.cn) at the Science Data Bank

Time of Arrival and Angle of Arrival Statistics for Distant Circular Scattering Model

General scattering model is local scattering process assumed that the mobile station is located inside  the scattering region, in order to study distant scattering model in suburban or hilly propagation environment, a geometrically based statistical distant circular scattering mode in macrocell environment was proposed, the closed-form expressions of the joint probability density function of the time of arrival /the angle of arrival, the marginal probability density function of the angle of arrival and the angle of departure, the marginal probability density function of the time of arrival were derived, this probability density function’s provided insight into the properties of the spatial distant scattering channel model. DOI: http://dx.doi.org/10.11591/telkomnika.v10i3.619

Second-Order Consensus in Multiagent Systems via Nonlinear Protocol

This paper focuses on theoretical analysis of second-order consensus in multiagent system. As an extension of the general linear protocol, a nonlinear protocol is designed for multiagent system with undirected communication topology. The nonlinear protocol is also applied to achieve reference velocity consensus. Through choosing the appropriate Lyapunov functions and using LaSalle’s invariance principle, some consensus conditions are derived. Simulation examples are provided to demonstrate the effectiveness of the proposed results

Second-Order Consensus in Multiagent Systems via Nonlinear Protocol

This paper focuses on theoretical analysis of second-order consensus in multiagent system. As an extension of the general linear protocol, a nonlinear protocol is designed for multiagent system with undirected communication topology. The nonlinear protocol is also applied to achieve reference velocity consensus. Through choosing the appropriate Lyapunov functions and using LaSalle’s invariance principle, some consensus conditions are derived. Simulation examples are provided to demonstrate the effectiveness of the proposed results

Novel Iterative Learning Controls For Linear Discrete-Time Systems Based On A Performance Index Over Iterations

An optimal iterative learning control (ILC) is proposed to optimize an accumulative quadratic performance index in the iteration domain for the nominal dynamics of linear discrete-time systems. Properties of stability, convergence, robustness, and optimality are investigated and demonstrated. In the case that the system under consideration contains uncertain dynamics, the proposed ILC design can be applied to yield a guaranteed-cost ILC whose solution can be found using the linear matrix inequality (LMI) technique. Simulation examples are included to demonstrate feasibility and effectiveness of the proposed learning controls. © 2008 Elsevier Ltd. All rights reserved

Distributed power optimization of large wind farms using ADMM for real-time control

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Cooperative control among the turbines has the potential to improve the power output. However, existing centralized power optimization methods are computationally expensive and does not scale well for large wind farms, limiting their practical use in real-time control for time-varying wind conditions and turbine configuration (with adding or maintaining of turbines). To address this problem, this paper proposes a fully distributed power optimization method for wind farms using alternating direction method of multipliers (ADMM). The proposed method allows the wind farm power output to be optimized in fully distributed manner with turbine-toturbine message passing over a mesh network, guarantees the implemented control actions satisfy the control constraints of all turbines, and provably converges to a stationary point of the wind farm power optimization problem. Simulation results demonstrate that the proposed method can significantly reduce the computation time with hardly sacrificing the power gain compared with centralized method and thus is computationally efficient for real-time power optimization of large wind farms

Model-guided learning for wind farm power optimization

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Accurately modeling the interactions is challenging due to the highly complex nature of the wakes and this limits the performance of model-based wind farm power optimization methods. There are also data-driven approaches, which do not require a system model. However, they generally require a large number of measurement data and the convergence speed can be slow. To address these limitations, this article proposes a model-guided learning (MGL) method for wind farm to improve its power output by leveraging the knowledge of the available simplified power generation model and learning from the real-time power generation data. The proposed method can quickly increase the power output of the wind farm, guarantee implemented control actions to satisfy the control constraints of all turbines, and have the ability to find the optimal solution of the power optimization problem. The presented method is then extended to deal with time-varying wind conditions using a hierarchical framework. Simulation results indicate that the proposed scheme can efficiently improve the power output of the wind farm in different wind conditions compared with some benchmarks. It shows a power efficiency gain of 2.5%over greedy policy and 1.2% than the model-based gradient method in given complex wind conditions, which are substantial improvements in the performance for the considered wind farm power optimization problem