A Novel Method Of Transport Barrier Formation

A novel technique is proposed for creating transport barriers by the rf waves. This is that the transport barriers can be created by the rf induced ponderomotive force itself and no rf induced flow shear generation is necessary. It is demonstrated that the ponderomotive force of the rf waves can stabilize the ion temperature gradient (ITG) mode, considered nowadays as the dominant source of anomalous energy losses in the low confinement (L) mode. It is also shown that this stabilization can be achieved for rather modest values of the rf power and hence should be easily obtained in actual experiments.      

电流变流体的研究进展

电流变流体是近年来逐渐受人们重视的一种功能材料，本文介绍其国内、外发展过程及研究现状。     

Analog and Digital Speed Control of DC Drives Using Proportional-Integral and Integral-Proportional Control Techniques

A comparative study of proportional-integral (P-I) and integral-proportional (I-P) control schemes, for the speed control of a dc drive, using both analog-and microprocessor-based digital circuits, is presented. The often-neglected current response is discussed and results are presented. The speed response to step changes in speed reference and load torque, using both the control schemes, is compared to evaluate the merits of I-P control. A brief discussion of sensitivity to controller gains is also given. It is shown that the I-P control scheme offers some distinctive advantages over P-I control. Experimental and simulation results are also presented.     

基于SOI的硅微谐振式压力传感器芯片制作

采用SOI硅片,基于MEMS技术,设计并加工了一种新型三明治结构的硅微谐振式压力传感器,根据传感器敏感单元的结构设计,制定了相应的制备工艺步骤,并且针对湿法深刻蚀过程中谐振子的刻蚀保护等问题,提出了一种基于氮化硅、氧化硅和氮化硅三层薄膜的保护工艺,实验表明,在采用三层薄膜保护工艺下进行湿法刻蚀10 h后,谐振子被完全释放,三层薄膜保护工艺对要求采用湿法刻蚀镂空释放可动结构具有较高的实用价值。最后对加工完成的谐振式压力传感器进行了初步的性能测试,结果表明,在标准大气压力下谐振子的固有频率为9.932 kHz,品质因数为34。     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Ensuring reliability in B2B services: Fault tolerant inter-organizational workflows

In the age of Business-to-Business (B2B) collaboration, ensuring reliability of workflows underlying inter-organizational business processes is of significant importance. There are, however, quite a few challenges towards achieving seamless operation. Such challenges arise from heterogeneity in infrastructure and coordination mechanism at participant organizations, as well as time and cost associated with recovery from failure. Our research presents foundations for a reliable scheme for recovery from failure of workflow processes spanning through multiple business entities. First, a system model is adapted from the mobile computing literature that serves to establish the requirements to be enforced by each participating organization. In our model, we adopt the Maximal Sequence Path (MSP) approach from Yoo et al. (Lecture Notes in Artificial Intelligence 2132:222–236, 2001), as a means of decomposing workflows into mobile agent-driven processes that communicate via web services at each organization. This decomposition ensures defining logical points within the dynamics of a workflow instance for locating accurate and consistent states of the system for recovery in case of a failure. Then, a set of algorithms for various business scenarios are developed and presented as practical solutions. These algorithms are shown to create checkpoints such that the system is always in a globally consistent state. As such, these algorithms constitute a set of standards that can be incorporated in business process management suites that support reliable inter-organizational collaboration.     

I-convergence in probabilistic n-normed space

In this article we introduce the notion of I-Cauchy sequence and I-convergent sequence in probabilistic n-normed space. The concept of I*-Cauchy sequence and I*-convergence in probabilistic n-normed space are also introduced and some of their properties related to these notions have been established.     

Entanglement mean field theory: Lipkin–Meshkov–Glick Model

Entanglement mean field theory is an approximate method for dealing with many-body systems, especially for the prediction of the onset of phase transitions. While previous studies have concentrated mainly on applications of the theory on short-range interaction models, we show here that it can be efficiently applied also to systems with long-range interaction Hamiltonians. We consider the (quantum) Lipkin–Meshkov–Glick spin model, and derive the entanglement mean field theory reduced Hamiltonian. A similar recipe can be applied to obtain entanglement mean field theory reduced Hamiltonians corresponding to other long-range interaction systems. We show, in particular, that the zero temperature quantum phase transition present in the Lipkin–Meshkov–Glick model can be accurately predicted by the theory.     

红外弱小目标的匹配方法研究与仿真

研究红外图像中的目标匹配问题。针对传统的红外图像中目标匹配效果不理想,匹配不准确。当红外图像中是弱小目标时,背景灰度值与目标灰度值非常相近,灰度直方图均衡化无法实现目标与背景的完整分离,导致漏匹配率较高的问题。为提高目标的匹配精度,提出一种形态学预处理的红外目标匹配方法,通过形态学算法提取出图像中的形状信息,利用形状信息将红外图像中的目标与背景完整分离,避免了只依靠灰度图像信息造成的不能完整分离目标与背景带来的漏匹配问题。进行仿真的结果证明,采用的形态学与处理的匹配方法能够完整将目标与背景分离,成功完成目标匹配,取得了满意的结果。