Spontaneous fluctuations in MRI signals from gray matter (GM) in the brain are interpreted as originating from variations in neural activity, and their inter-regional correlations may be analyzed to reveal functional connectivity. However, most studies of intrinsic neuronal activity have ignored the spontaneous fluctuations that also arise in white matter (WM). In this work, we explore spontaneous fluctuations in resting state MRI signals in WM based on spatial independent component analyses (ICA), a data-driven approach that separates signals into independent sources without making specific modeling assumptions. ICA has become widely accepted as a valuable approach for identifying functional connectivity within cortex but has been rarely applied to derive equivalent structures within WM. Here, BOLD signal changes in WM of a group of subjects performing motor tasks were first detected using ICA, and a spatial component whose time course was consistent with the task was found, demonstrating the analysis is sensitive to evoked BOLD signals in WM. Secondly, multiple spatial components were derived by applying ICA to identify those voxels in WM whose MRI signals showed similar temporal behaviors in a resting state. These functionally-related structures are grossly symmetric and coincide with corresponding tracts identified from diffusion MRI. Finally, functional connectivity was quantified by calculating correlations between pairs of structures to explore the synchronicity of resting state BOLD signals across WM regions, and the experimental results revealed that there exist two distinct groupings of functional correlations in WM tracts at rest. Our study provides further insights into the nature of activation patterns, functional responses and connectivity in WM, and support previous suggestions that BOLD signals in WM show similarities with cortical activations and are characterized by distinct underlying structures in tasks and at rest.

In order to improve the robust stability of the grid-connected inverter of wind power or photovoltaic power generation while connected to a weak power-grid, the robust model of grid-connected inverter under weak power-grid is established, and the influence mechanism of power-grid impedance and voltage distortion on the stability and current quality of grid-connected inverter is analyzed. On this basis, an adaptive robust H_∞ control method is proposed, in which there is no need to online measure the power-grid impedance, and the control parameters do not change with the change of the power-grid impedance. Then, the calculation method of each reference quantity and the design method of control parameters are introduced. Finally, an experimental platform is built, and the correctness and effectiveness of the theoretical analysis and the proposed control method are verified by experiments. Compared with other control methods, the adaptive robust H_∞ control method realizes the stability of the system when the power-grid impedance varies in a wide range, which optimally restrains the power-grid voltage distortion and improves the quality of grid-connected current.

This paper proposes a model predictive controller (MPC) design based on the optimal tip-speed ratio method for maximum power point tracking (MPPT) of a direct-driven permanent magnet synchronous generator (D-PMSG)-based wind energy conversion system (WECS). To eliminate system nonlinearity and time-varying characteristics, a control variable was added at the wind turbine and the system model was feedback-linearized to create a linear time-invariant system, reducing the computational burden of the MPC and improving system performance. MATLAB/Simulink simulations were performed and the results show that the linearized system has high fidelity. Compared to traditional MPC that use an operating point to linearize the system, it has better adaptability to turbulent wind speeds, improving the stability and rapidity of the system.

The coordinated use of electricity and a heat energy system can effectively improve the energy structure during winter heating in the northern part of China and improve the environmental pollution problem. In this paper, an economic scheduling model of an electric–thermal integrated energy system, including a wind turbine, regenerative electric boiler, solar heat collection system, biomass boiler, ground source heat pump and battery is proposed, and a biomass boiler was selected as the auxiliary heat source of the solar heat collection system. A mixed integer linear programming model was established to take the operating cost of the whole system as the target. A day-ahead optimization scheduling strategy considering the demand side response and improving new energy consumption is proposed. In order to verify the influence of the coordinated utilization of the flexible load and energy storage equipment on the optimal scheduling in the model built, three scenarios were set up. Scenario 3 contains energy storage and a flexible load. Compared with scenario 1, the total cost of scenario 3 was reduced by 51.5%, and the abandonment cost of wind energy was reduced by 43.3%. The use of a flexible load and energy storage can effectively reduce the cost and improve new energy consumption. By increasing the capacity of the energy-storage device, the wind power is completely absorbed, but the operation and maintenance cost is increased, so the capacity of energy storage equipment is allocated reasonably according to the actual situation.

In this paper, we investigate the stochastic averaging method for neutral stochastic delay differential equations driven by fractional Brownian motion with Hurst parameter H∈1/2,1. By using the linear operator theory and the pathwise approach, we show that the solutions of neutral stochastic delay differential equations converge to the solutions of the corresponding averaged stochastic delay differential equations. At last, an example is provided to illustrate the applications of the proposed results.