This paper describes a K-Means Clustering classification algorithm for the separation of Partial Discharge (PD) signals and pulsating noise due to multiple sources occurring in practical objects. It is based on the comparison of the Auto-Correlation Function (ACF) of the recorded signals assuming that the same source can generate signals having similar ACF while ACF differ when signals with different shapes are compared. The ACF has been selected for its capability of well summarize both time-and frequency-dependent features of the signals. A correlation index that presents the best compromise between strong and weak discrimination among pulses, has been selected out of different distance measurements. The final result of the algorithm is a set of classes containing signals having similar shape which can be processed successively for signal source identification. Meaningful applications of the proposed algorithm are also reported. Improvements in separation effectiveness can enhance the clearness of the PD patterns and, consequently, the quality of the defect identification.

An entirely new algorithm to find all the equilibrium points Of piecewise-linear (PWL) circuits is presented. TO this aim, the new class of the so-called polyhedral circuits, associated to the PWL ones, are defined by replacing the PWL elements a genealogical tree, whose nodes represent specific polyhedral circuits. All the equilibrium points of the original PWL circuit can be captured by the analysis of these nodes. This analysis requires the solution of the Phase I of Linear Programming (LP) problems, one problem for each node. An example shows the capabilities of this algorithm. Complementarity problem [5], [6]. Unfortunately, the number of linear regions explodes because of the diode synthesis. first proposed by Chua and f ( Z ) = 0, where f : TIM + T I M and 2 groups the bmnch variables, in a canonical form based on the absolute value function. The linear regions whose images in f(z) contain the origin as an interior point are determined by of a sign test. Since the corresponding linear systems are solved only if a solution exists, this method is cheaper, even if the sign test A third class of with the polyhedral elements. The algorithm is structured as Ying 171, consists in writing the equations of a PWL circuit

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people's sentiments towards the market and stocks, have been proved to play important roles in the stocks' volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users' sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsic * Corresponding author relations among the events and the investors' sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model.

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An algorithm to capture all the unicursal branches of any one-port characteristic in piecewise-linear (PWL) resistive circuits 'is described. The heart of this algorithm is based on the so-called polyhedral augmented circuits: they are constructed by replacing each PWL element by a suitable polyhedral element and by connecting a norator to the one-port. The structure of the algorithm is based on a genealogical tree, whose nodes represent specific polyhedral augmented circuits. All branches of the PWL one-port characteristic can be captured by checking the solution domains of these circuits. From the numerical point of view, the investigation of the nodes requires the execution of related Linear Programming (LP) problems, one for each node. However, the similar structure of their tableaux allows the reduction of the overall CPU time.

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