含铌和钛的微合金热轧带钢中碳氮化物析出的定量研究

为了弄清带钢在热轧生产中碳氮化物产生的机理和数量，对一种微合金钢进行了光学显微镜和透射电镜检验。结果发现，几乎总合金加入量（0．06％Ti和0．02％Nb）的一半在均热后都以共晶体或不溶解的微粒形式存在。细粒碳氨化物的晶内形核只在奥氏体相中出现。卷取期间，在铁素体中没有再形成碳氮化物微粒，并且均热时已溶解的微合金元素，到轧制过程结束时，几乎近一半仍留在固溶体中。按照已有的组织一性能的良好对应关系，在奥氏体中已形核的碳氢化物微粒所产生的析出强化作用为60～80MN／m2。1引言为了提高微合金化钢的强度，多年来，一…     

第23届APCOM会议上有关矿业计算机模拟、自动化、优化和争论(一)

本文内容取材于第23届国际APCOM会议论文集,它对矿业计算机模拟、自动化、优化以及争论要点进行了综述。     

聚焦离子束技术最近进展

本文评述了聚焦离子（ＦＩＢ）技术的最新进展。这些技术包括离子植入，离子束辅助蚀刻和沉积以及就地制造，由于ＦＩＢ的无掩模能力和高电流密度，发现ＦＩＢ作为一种显微外科手术工具的在超大规模集成（ＵＬＳＩ）器件或材料分析以及就地制造方面有重要应用，最近的聚焦离子束系统能产生直径小于１０ｎｍ的聚焦离子束，而且作为纳米制造工具的重要性也日益增加，使用ＦＩＢ主要在ＧａＡｓ／ＧａＡｌＡｓ异质结构中制造了各种小尺寸     

Design of internal model control based fractional order PID controller

This article presents a design of the internal model control(IMC)based single degree of freedom(SDF) fractional order(FO)PID controller with a desired bandwidth specification for a class of fractional order system(FOS). The drawbacks of the SDF FO-IMC are eliminated with the help of the two-degree of freedom(TDF)FO PID controller. The robust stability and robust performance of the designed controller are analyzed using an example.     

On the method-independent lower bound (MILB) to the variance of attenuation estimation in random reflection

Based on the Cramer-Rao inequality, the MILB is calculated for the problem of estimating the frequency slope of the attenuation coefficient of tissue from random reflections of ultrasonic waves. Under typical signal conditions, this bound for a 1-cm x 1-cm region was found to be about 0.08 db/(MHz-cm), rather close to the clinical requirement of 0.1 dB/(MHz-cm). Comparison to existing methods (including an autoregressive deconvolution method) shows room for further improvement.     

Using previous models to bias structural learning in the hierarchical BOA

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step toward the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in the future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.     

A deformable surface model for real-time water drop animation

A water drop behaves differently from a large water body because of its strong viscosity and surface tension under the small scale. Surface tension causes the motion of a water drop to be largely determined by its boundary surface. Meanwhile, viscosity makes the interior of a water drop less relevant to its motion, as the smooth velocity field can be well approximated by an interpolation of the velocity on the boundary. Consequently, we propose a fast deformable surface model to realistically animate water drops and their flowing behaviors on solid surfaces. Our system efficiently simulates water drop motions in a Lagrangian fashion, by reducing 3D fluid dynamics over the whole liquid volume to a deformable surface model. In each time step, the model uses an implicit mean curvature flow operator to produce surface tension effects, a contact angle operator to change droplet shapes on solid surfaces, and a set of mesh connectivity updates to handle topological changes and improve mesh quality over time. Our numerical experiments demonstrate a variety of physically plausible water drop phenomena at a real-time rate, including capillary waves when water drops collide, pinch-off of water jets, and droplets flowing over solid materials. The whole system performs orders-of-magnitude faster than existing simulation approaches that generate comparable water drop effects.     

A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments

This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines.