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For the complex dynamical network (CDN) with uncertainties, this paper defines the links synchronization and synthesizes the adaptive control scheme to realize it. Generally speaking, a CDN can be considered as the composition system with the two coupled dynamic subsystems: the nodes subsystem (NS) and the links subsystem (LS). We observed that there are many results in the existing literature on the NS synchronization, which implies that the state at each node tends to be the same when the NS synchronization happens. However, the LS synchronization is rarely discussed in the existing literature due to its unknown meaning and engineering practice background. Inspired by the ego-networks, this paper employs the time-varying outgoing links at each node to geometrically describe the changing of network topologic structure and regards the values-weighted of outgoing links as the state variables of LS, by which the LS synchronization is introduced, the corresponding control scheme for its implementation is synthesized under the condition that the state of NS is available and the state of LS is unavailable. The control scheme includes the adaptive controller for NS and the coupling strategy for LS. Finally, the effectiveness of proposed control scheme in this paper is verified by a numerical example. [ABSTRACT FROM AUTHOR]

This paper mainly discusses exponential synchronization of nonlinear CDN via intermittent pinning control on T. By using Lyapunov and time scales theory, a novel sufficient condition to guarantee the synchronization of CDNs is obtained. The method of intermittent pinning control is adopted in designing controllers to ensure the realization of synchronization of CDNs. Unlike most existing results, our results are an extension of previous results, integral delay and coupling disturbance are considered in the time-delay nonlinear CDNs. Therefore, our results are less conservative but more persuasive. Moreover, our model can be applied not only to continuous/discrete CDNS, but also to mixed CDNs. Finally, we give numerical simulations to demonstrate the feasibility of our theory. [ABSTRACT FROM AUTHOR]

• CSI-SVQR is proposed for wind power probability density forecasting. • Cubic spline interpolation is used to reduce the noisy of wind power data. • The optimal CWC criterion is used to acquire the best PIs. • The SSE of mode criterion is used to acquire the best point forecasting results. • Wilcoxon test verifies that CSI improves the accuracy of forecasting methods. Accurate forecasting of wind power plays an important role in an effective and reliable power system. However, the fact of non-schedulability and fluctuation of wind power significantly increases the uncertainty of power systems. The output power of a wind farm is usually mixed with uncertainties, which reduce the effectiveness and accuracy of wind power forecasting. In order to handle the uncertainty of wind power, this paper first proposes to conduct outlier detection and reconstruct data before the prediction. Then, a wind power probability density forecasting method is proposed, based on cubic spline interpolation and support vector quantile regression (CSI-SVQR), which can better estimate the whole wind power probability density curve. However, the probability density prediction method can not acquire the optimal point prediction and interval prediction results at the same time. In order to analyze the uncertainty of wind power, the present study considers the prediction results from the perspective of probabilistic point prediction and interval prediction respectively. Three sets of real-world wind power data from Canada and China are used to validate the CSI-SVQR method. The results show that the proposed method not only efficiently eliminates the outliers of wind power but also provides the probability density function, offering a complete description of wind power generation fluctuation. Furthermore, more accurate point prediction and prediction interval (PI) can be obtained compared to existing methods. Wilcoxon signed rank test is used to verify that CSI can improve the performance of forecasting methods. [ABSTRACT FROM AUTHOR]

Segmentation of cardiac MRI images plays a key role in clinical diagnosis. In the traditional diagnostic process, clinical experts manually segment left ventricle (LV), right ventricle (RV) and myocardium (Myo) to get the guideline for cardiopathy diagnosis. However, manual segmentation is time-consuming and labor-intensive. In this paper, we propose automatic cardiac MRI segmentation and cardiopathy classification based on deep neural networks and point clouds. The cardiac MRI segmentation consists of two steps: (i) We use a detector based on you only look once (YOLO) to obtain region of interest (ROI) from the sequential diastolic and systolic MRI. (ii) We obtain the segmentation masks from the ROI automatically by a fully convolutional neural network (FCN). Subsequently, we reconstruct 3D surfaces by a simple linear interpolation method, then randomly sample uniform 3D point clouds from the 3D surfaces. From the cardiac point clouds, we perform cardiopathy classification using a cardiopathy diagnosis network (CDN). Experimental results show that the proposed method successfully segments LV, RV, and Myo from cardiac MRI images and achieves comparable results against several existing ones. Moreover, the CDN successfully classifies heart diseases based on point clouds and achieves 92% accuracy on the testing dataset. [ABSTRACT FROM AUTHOR]