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This paper proposes a novel wide-range soft-switching pulse-width modulation three-level (TL) combined dc-dc converter characterized with no added primary clamping devices and reduced output inductance value. It is well suited for high input voltage dc-dc industrial applications due to following desirable features: all the switches sustain half of the input voltage; off state voltage of the switches is directly clamped by the bulky input capacitors, and no added primary clamping devices such as clamping diodes, flying capacitors, are required; TL waveform before output filter can be achieved, which significantly reduces the value of the output inductance; the modified phase-shift control method can be utilized to achieve wide output range. The converter can obtain zero-voltage switching (ZVS) for all power switches in wide load range; furthermore, less conduction loss is added to achieve ZVS. The operation and theoretical analysis of the proposed converter are presented. Experiments are carried out to validate the proposed converter, and efficiency curves are tested and given. [ABSTRACT FROM AUTHOR]

Existing data analysis techniques have difficulty in handling multidimensional data. Multidimensional data has been a challenge for data analysis because of the inherent sparsity of the points. In this paper, we first present a novel data preprocessing technique called shrinking which optimizes the inherent characteristic of distribution of data. This data reorganization concept can be applied in many fields such as pattern recognition, data clustering, and signal processing. Then, as an important application of the data shrinking preprocessing, we propose a shrinking-based approach for multidimensional data analysis which consists of three steps: data shrinking, cluster detection, and cluster evaluation and selection. The process of data shrinking moves data points along the direction of the density gradient, thus generating condensed, widely-separated clusters. Following data shrinking, clusters are detected by finding the connected components of dense cells (and evaluated by their compactness). The data-shrinking and cluster-detection steps are conducted on a sequence of grids with different cell sizes. The clusters detected at these scales are compared by a cluster- wise evaluation measurement, and the best clusters are selected as the final result. The experimental results show that this approach can effectively and efficiently detect clusters in both low and high-dimensional spaces. [ABSTRACT FROM AUTHOR]

A new multilevel matrix converter that can be applied to medium or high voltage ac drives is presented to alleviate harmonic components in the output voltage. The proposed converter contains six flying capacitors to balance the voltage distribution of series connection bidirectional switch modules and provide middle voltage levels. Stable flying capacitors voltage must be maintained to facilitate the operation of the converter. When the converter is working, the voltage of flying capacitors can be controlled by swapping two switching modes with opposite charging current corn sponding to each middle voltage levels. A simple output voltage vector synthesis method is described and utilized. The operation and commutation strategies are discussed. Simulations and experiments are carried out to validate the proposed converter. Comparisons are made between proposed converter and conventional matrix converter. [ABSTRACT FROM AUTHOR]

Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

Mining communities or clusters in networks is valuable in analyzing, designing, and optimizing many natural and engineering complex systems, e.g., protein networks, power grid, and transportation systems. Most of the existing techniques view the community mining problem as an optimization problem based on a given quality function(e.g., modularity), however none of them are grounded with a systematic theory to identify the central nodes in the network. Moreover, how to reconcile the mining efficiency and the community quality still remains an open problem. In this paper, we attempt to address the above challenges by introducing a novel algorithm. First, a kernel function with a tunable influence factor is proposed to measure the leadership of each node, those nodes with highest local leadership can be viewed as the candidate central nodes. Then, we use a discrete-time dynamical system to describe the dynamical assignment of community membership; and formulate the serval conditions to guarantee the convergence of each node's dynamic trajectory, by which the hierarchical community structure of the network can be revealed. The proposed dynamical system is independent of the quality function used, so could also be applied in other community mining models. Our algorithm is highly efficient: the computational complexity analysis shows that the execution time is nearly linearly dependent on the number of nodes in sparse networks. We finally give demonstrative applications of the algorithm to a set of synthetic benchmark networks and also real-world networks to verify the algorithmic performance. [ABSTRACT FROM PUBLISHER]