SiC MOSFET短路失效与退化机理研究综述及展望

SiC MOSFET可以大幅提升变流器的效率和功率密度,在高频、高温、高压等领域有较好的应用前景。但是,由于其短路耐受时间短、特性退化现象严重以及失效机理模糊等因素,致使SiC MOSFET的普及应用受到了限制。因此,探究SiC MOSFET短路失效与特性退化的机理,可以为SiC MOSFET器件的应用及其保护电路的设计提供指导,具有重要的研究价值。该文首先归纳SiC MOSFET的短路故障类型,并针对其中一种典型的短路故障进行详细的特性分析。在此基础上,论述SiC MOSFET单次短路故障后存在的两种典型失效模式,综述其在两种失效模式下的失效机理以及影响因素。其次,对SiC MOSFET经历重复短路应力后器件特性退化机理的研究现状进行系统的总结。最后指出当前SiC MOSFET短路失效与特性退化的研究难点,展望SiC MOSFET短路特性研究的发展趋势。     

活塞车直线伺服刀架系统对于幅值变化的周期性扰动抑制的研究

在中凸变椭圆活塞车削的过程中,系统的扰动抑制是一个关键问题.文章针对直线伺服刀架系统对变化负载的扰动抑制进行了分析,指出切削力的干扰是按变椭圆规律周期变化的,对重复控制器在周期参考输入信号的情况下,分别对幅值不变和幅值变化的周期性负载扰动抑制的效果进行了研究,指出重复控制器对于幅值变化的周期性扰动信号没有对于幅值不变的周期性扰动信号抑制的效果好.为解决此问题提出了一种PID反馈补偿控制策略,仿真结果表明该控制方法可以达到较好的控制效果,增加了系统的抗扰性,为提高中凸变椭圆活塞加工精度提供了一条途径.     

Supervised Multi‐Rejector of Periodic Disturbances for Nonlinear Systems Using Decoupled State Multimodel

In this paper, a multi‐rejector of periodic disturbances is proposed for discrete‐time nonlinear systems represented by a decoupled state multimodel. We report a decoupled state multimodel repetitive‐predictive control based on a supervised algorithm to ensure reference trajectory tracking and periodic disturbances rejection. Partial predictors associated to the local controllers make the best choice of the most valid partial controller that meets the desired closed loop performances. The effectiveness of the supervised multi‐rejector is shown via a simulation example. The obtained results are satisfactory and show a good rejection of periodic disturbances and reference trajectory tracking.     

基于块稀疏快速重构的MISO活跃用户集与信道联合估计

针对多用户多输入单输出（Multiple input single output, MISO)系统的用户选择与信道估计问题,引入基于用户分布式自选择的信道接入策略,设计一种新的结合该策略的时分双分复用（Time division duplex, TDD)模式数据传输帧结构。利用用户活跃模式自然稀疏性和信道冲激响应时延域稀疏性,将基站接收上行随机导频序列建模为块稀疏线性模型。基于凸松弛的l2/l1模型提出一种快速的块稀疏重构算法求解问题模型。算法首先对目标函数进行变量分裂,然后利用交替方向法对各变量进行交替更新,直至满足收敛条件。交替更新中,对于无法获得闭式解的信号变量项,采取块坐标下降法求解。计算机仿真表明,与块正交匹配追踪和块压缩采样匹配追踪比较,新算法能够在保持高重构精度的前提下获得更快的计算速度。     

Observer based repetitive learning control for a class of nonlinear systems with non‐parametric uncertainties

In this paper, a repetitive learning control (RLC) scheme is developed for a class of nonlinear systems to handle an output tracking problem, where two state observers are introduced concurrently to estimate the unavailable control system and reference states information. The estimation of reference state information is because of the lack of reference internal model in the RLC design. By virtue of the periodicity of reference signals and the associated learning capability in control mechanism, the involved unstructured nonlinear uncertainties can be handled. The Lyapunov‐like energy function method is adopted to facilitate the learning control design as well as property analysis thus achieve the asymptotical convergence of errors in state observation and output tracking simultaneously. Moreover, owing to the robustification of the learning controller that is addressed by incorporating projection, the proposed control scheme would be applicable in practice. In the end, an illustrative example is simulated to demonstrate the efficacy of the proposed RLC law. Copyright © 2014 John Wiley & Sons, Ltd.     

Modeling and robust discrete LQ repetitive control of electrically driven robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.     

CLARA算法聚类蛋白质序列的实现

随着基因工程产生大量新序列,导致蛋白质序列数据库的迅速增长,巨量蛋白质数据的功能组和族谱分析使蛋白质序列聚类分析成为结构和功能基因组学重要的研究目标,应用数据挖掘技术对生物数据进行聚类分析成为生物信息学研究的热点。聚类分析算法中的CLARA划分算法已广泛应用于其它领域,但在大数据量蛋白质序列聚类分析中应用很少,文章应用CLARA算法对在基准数据库中选取的蛋白质序列进行聚类分析,并将结果与其它几种蛋白质聚类算法进行了比较。     

The weak aggregating algorithm and weak mixability

This paper resolves the problem of predicting as well as the best expert up to an additive term of the order o(n), where n is the length of a sequence of letters from a finite alphabet. We call the games that permit this weakly mixable and give a geometrical characterisation of the class of weakly mixable games. Weak mixability turns out to be equivalent to convexity of the finite part of the set of superpredictions. For bounded games we introduce the Weak Aggregating Algorithm that allows us to obtain additive terms of the form .     

A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms. Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases. Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing, text mining, XML, and parallel and distributed processing.     

Variational Segmentation of Image Sequences Using Region-Based Active Contours and Deformable Shape Priors

In this paper we address the problem of segmentation in image sequences using region-based active contours and level set methods. We propose a novel method for variational segmentation of image sequences containing nonrigid, moving objects. The method is based on the classical Chan-Vese model augmented with a novel frame-to-frame interaction term, which allow us to update the segmentation result from one image frame to the next using the previous segmentation result as a shape prior. The interaction term is constructed to be pose-invariant and to allow moderate deformations in shape. It is expected to handle the appearance of occlusions which otherwise can make segmentation fail. The performance of the model is illustrated with experiments on synthetic and real image sequences.