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Summary Under the comprehensive influence of material and loads, dam structural behavior presents the time‐varying nonlinear characteristics. To forecast the dam structural behavior (displacement, stress, seepage, etc.), the models monitoring dam safety are often built according to the prototype observations on dam safety. However, the modeling process is usually fulfilled with the offline and static pattern. As time goes on, the fitting and forecasting ability of built static model will decline gradually. The article is focused on the support vector machine (SVM)‐based model monitoring dam safety. The methods are studied to advance the adaptability of SVM model and reduce the modeling time. By implementing the impact analysis for SVM parameters and input vector, the optimization method of SVM parameters and input vector is presented to enhance the efficiency of building the SVM‐based static model monitoring dam safety. To describe dynamically the time‐varying mapping relationship between dam structural behavior (effect‐quantity) and its cause (influence‐quantity), the way is developed to update in real time above model by making the most use of new observations. The displacement of one actual dam is taken as an example to verify the modeling efficiency and forecasting ability. Copyright © 2015 John Wiley & Sons, Ltd.

Deformation monitoring is the main program in the area of dam safety. Because statistical model is simple and intuitive, it is widely used in dam safety monitoring. However, in dam's displacement statistic model, there is a high degree of linear relationship between influence factors. Due to the influence of multicollinearity, models calculated with traditional methods are not accurate and stable. Besides, because of dam integrity, each part of dam is interrelated and interactive. Currently, single point or multipoints displacement monitoring models cannot accurately reflect the actual dam running state. In this paper, the theory of panel data is introduced to dam deformation analysis. Panel data contain time series data and cross section data, which is able to solve serious multicollinearity problem of traditional regression method. Moreover, all measuring points are classified into several groups according to their similar deformation law. Based on the random-coefficient model of panel data, potential relationship between different measuring points is built. Take 1 hydropower station, for example, to examine that random-coefficient model is able to improve the modeling situation that estimators are not significant and simultaneously provide a stable model, which explores a new approach for the research of dam displacement monitoring. [4] used statistical regression method to research dam prototype observation data. He set deformation measured value as explained variable and set appropriate explanation variables at the same time. The unbiased efficient estimator of each explanation variable was obtained through the traditional least squares method (ordinary least squares, OLS).After that, influence factors of deformation-measured values were analyzed quantitatively. On the basis of Chen JY's study,

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This paper presents an efficient method for optimizing power/ground (P/G) networks by widening wires and adding decoupling capacitors (decaps). It proposes a structured skeleton that is intermediate to the conventional method that uses full meshes (which are hard to analyze efficiently), and treestructured networks (which provide poor performance). As an example, we consider a P/G network structure modeled as an overlying mesh with underlying trees originating from the mesh, which eases the task of analysis with acceptable performance sacrifices. A fast and efficient event-driven P/G network simulator is proposed, which hierarchically simulates the P/G network with an adaptation of PRIMA to handle non-zero initial conditions. An adjoint network that incorporates the variable topology of the original P/G network, as elements switch in and out of the network, is constructed to calculate the transient adjoint sensitivity over multiple intervals. The gradients of the most critical node with respect to each wire width and decap are used by a sensitivity-based heuristic optimizer that minimizes a weighted sum of the wire and the decap area. Experimental results show that this procedure can be used to efficiently optimize large networks.

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