基于漂移扩散模型方程的IGCT电路模型

现有基于双极扩散方程(ADE)的功率半导体器件模型只能准确描述准中性区载流子分布,应用于高压器件建模时精度有限。提出一种基于漂移扩散模型(DDM)的集成门极换流晶闸管(IGCT)电路模型的建模方法以提高模型精度。通过分析发现,求解变量数量级相差较大,以及由高掺杂浓度决定的特征参数是影响模型特性的主要因素。由此,通过边界高掺杂区域的解析建模、简化基区求解的边界条件以及应用移动网格法求解DDM提高了模型的稳定性和计算速度。最后,IGCT双脉冲测试仿真与实验对比结果表明在仿真耗时接近的前提下,该方法相对于现有基于ADE模型可以更好地仿真器件在关断振荡以及穿通时的特性。     

Dissipative Particle Dynamics Simulation: A Review on Investigating Mesoscale Properties of Polymer Systems

Polymer systems have typical multiscale characteristics, both in space and time. The mesoscopic properties of polymers are difficult to describe through traditional experimental approaches. Dissipative particle dynamics (DPD) is a simulation method used for solving mesoscale problems of complex fluids and soft matter. The mesoscopic properties of polymer systems, such as conformation, dynamics, and transport properties, have been studied extensively using DPD. This paper briefly summarizes the application of DPD to research involving microchannel flow, electrospinning, free-radical polymerization, polymer self-assembly processes, polymer electrolyte fuel cells, and biomedical materials. The main features and possible development avenues of DPD are described as well.     

高功率锂离子电池研究进展

高功率快放型锂离子电池是目前锂离子电池领域研究的重点方向之一。为了获得具有高功率密度的锂离子电池,正极材料须具有较高的电压和较高的电子与离子导电率,正极材料主要包括高电压钴酸锂、镍锰酸锂和高电压三元材料,负极材料包括碳系材料、钛基材料和金属氧化物材料,以及为提高首效和降低负极电位而采用的预嵌锂方法,并对锂离子电池电解液用锂盐、溶剂和添加剂进行了综述。最终总结了功率密度测试方法,并对高功率锂离子电池的研究进行展望。      

Global software testing under deadline pressure: Vendor-side experiences

ContextIn the era of globally-distributed software engineering, the practice of global software testing (GST) has witnessed increasing adoption. Although there have been ethnographic studies of the development aspects of global software engineering, there have been fewer studies of GST, which, to succeed, can require dealing with unique challenges.ObjectiveTo address this limitation of existing studies, we conducted, and in this paper, report the findings of, a study of a vendor organization involved in one kind of GST practice: outsourced, offshored software testing.MethodWe conducted an ethnographically-informed study of three vendor-side testing teams over a period of 2 months. We used methods, such as interviews and participant observations, to collect the data and the thematic-analysis approach to analyze the data.FindingsOur findings describe how the participant test engineers perceive software testing and deadline pressures, the challenges that they encounter, and the strategies that they use for coping with the challenges. The findings reveal several interesting insights. First, motivation and appreciation play an important role for our participants in ensuring that high-quality testing is performed. Second, intermediate onshore teams increase the degree of pressure experienced by the participant test engineers. Third, vendor team participants perceive productivity differently from their client teams, which results in unproductive-productivity experiences. Lastly, participants encounter quality-dilemma situations for various reasons.ConclusionThe study findings suggest the need for (1) appreciating test engineers’ efforts, (2) investigating the team structure’s influence on pressure and the GST practice, (3) understanding culture’s influence on other aspects of GST, and (4) identifying and addressing quality-dilemma situations.     

Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach

In this paper, we investigate the problem of time series forecasting using single hidden layer feedforward neural networks (SLFNs), which is optimized via multiobjective evolutionary algorithms. By utilizing the adaptive differential evolution (JADE) and the knee point strategy, a nondominated sorting adaptive differential evolution (NSJADE) and its improved version knee point-based NSJADE (KP-NSJADE) are developed for optimizing SLFNs. JADE aiming at refining the search area is introduced in nondominated sorting genetic algorithm II (NSGA-II). The presented NSJADE shows superiority on multimodal problems when compared with NSGA-II. Then NSJADE is applied to train SLFNs for time series forecasting. It is revealed that individuals with better forecasting performance in the whole population gather around the knee point. Therefore, KP-NSJADE is proposed to explore the neighborhood of the knee point in the objective space. And the simulation results of eight popular time series databases illustrate the effectiveness of our proposed algorithm in comparison with several popular algorithms.     

Novel associative classifier based on dynamic adaptive PSO: Application to determining candidates for thoracic surgery

Association rule mining is a data mining technique for discovering useful and novel patterns or relationships from databases. These rules are simple to infer and intuitive and can be easily used for classification in any domain that requires explanation for and investigation into how the classification works. Examples of such areas are medicine, agriculture, education, etc. For such a system to find wide adoptability, it should give output that is correct and comprehensible. The amount of data has been growing very fast and so has the search space of these problems. So we need to change traditional methods. This paper discusses a rule mining classifier called DA-AC (dynamic adaptive-associative classifier) which is based on a Dynamic Particle Swarm Optimizer. Due to its seeding method, exemplar selection, adaptive parameters, dynamic reconstruction of regions and velocity update, it avoids premature convergence and provides a better value in every dimension. Quality evaluation is done both for individual rules as well as entire rulesets. Experiments were conducted over fifteen benchmark datasets to evaluate performance of proposed algorithm in comparison with six other state-of-the-art non associative classifiers and eight associative classifiers. Results demonstrate competitive performance of proposed DA-AC while considering predictive accuracy and number of mined patterns as parameters. The method was then applied to predict life expectancy of post operative thoracic surgery patients.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

高斯PLDA在说话人确认中的应用及其联合估计

近年来,基于总变化因子的说话人识别方法成为说话人识别领域的主流方法.其中,概率线性鉴别分析（Probabilistic linear discriminant analysis,PLDA）因其优异的性能而得到学者们的广泛关注.然而,在估计PLDA模型时,传统的因子分析方法只更新模型空间,因此,模型均值不能很好地与更新后的模型空间耦合.提出联合估计法对模型均值和模型空间同时估计,得到更为严格的期望最大化更新公式,在美国国家标准与技术局说话人识别评测2010扩展测试数据库以及2012核心测试数据库上,等错率得到一定提升.     

面向口语统计语言模型建模的自动语料生成算法

在资源相对匮乏的自动语音识别(Automatic speech recognition, ASR)领域, 如面向电话交谈的语音识别系统中, 统计语言模型(Language model, LM)存在着严重的数据稀疏问题. 本文提出了一种基于等概率事件的采样语料生成算法, 自动生成领域相关的语料, 用来强化统计语言模型建模. 实验结果表明, 加入本算法生成的采样语料可以缓解语言模型的稀疏性, 从而提升整个语音识别系统的性能. 在开发集上语言模型的困惑度相对降低7.5%, 字错误率(Character error rate, CER)绝对降低0.2个点; 在测试集上语言模型的困惑度相对降低6%, 字错误率绝对降低0.4点.