Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Wireless sensor networks (WSNs) offer the potential to significantly improve the efficiency of existing transportation systems. Currently, collecting traffic data for traffic planning and management is achieved mostly through wired sensors. The equipment and maintenance cost and time-consuming installations of existing sensing systems prevent large-scale deployment of real-time traffic monitoring and control. Small wireless sensors with integrated sensing, computing, and wireless communication capabilities offer tremendous advantages in low cost and easy installation. In this paper, we first survey existing WSN technologies for intelligent transportation systems (ITSs), including sensor technologies, energy-efficient networking protocols, and applications of sensor networks for parking lot monitoring, traffic monitoring, and traffic control. Then, we present new methods on applying WSNs in traffic modeling and estimation and traffic control, and show their improved performance over existing solutions. Copyright as China and India as they are experiencing fast economic growth.Intelligent traffic control is very important in addressing traffic congestion. The emerging technology of wireless sensor networks (WSNs) is poised to revolutionize traffic management and control. Batterypowered low-cost traffic sensor nodes with integrated computing and wireless communication capabilities will change the landscape of real-time traffic data acquisition. As these wireless sensors become widely available and their costs come down, a tremendous challenge is the design of sensor-based traffic control systems that are large-scale, efficient, and high

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Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios (SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing (CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing (CS) and time-correspondence imaging (CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale "double-slit" image, the mean square error (MSE) by GI and the normalized GI (NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021 by CCNGI scheme with 2500 measurements. For the eight-grayscale "lena" object, the peak signal-to-noise rates (PSNRs) are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale "double-slit" object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints (GISC) with a shorter reconstruction time.

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