Nb和时效时间对热轧中锰TRIP钢中P偏聚的影响

热轧态中锰TRIP钢首先经650 ℃退火2 h,随后在550 ℃进行等温时效热处理,采用场发射扫描电镜(FE-SEM)研究该钢中P的偏聚和时效析出行为的变化情况。结果表明,中锰TRIP钢中P在晶界的偏聚是一种非平衡偏聚现象,临界时间约为50 h,与理论计算结果48 h较为吻合。在局部偏聚区域内,C与P存在共偏聚的关系,即P偏聚量高的地方,C含量也高。而合金元素Nb具有抑制P偏聚的效果,在20~70 h时效时间内,可以相对降低6.57%~19.5%的最大P偏聚量。根据电子背散射衍射(EBSD)菊池线分析,P偏聚量低于2.28at%时,P为固溶状态,高于2.28at.%时,P为析出状态。     

两相区配分时间对IQ&P钢组织与性能的影响

以含Cu低碳钢为研究对象,利用SEM、EPMA和拉伸试验研究了两相区配分时间对其组织演变、元素配分以及经IQ&P处理后力学性能的影响,并利用Dictra软件对元素配分行为进行了动力学计算。结果表明,IQ处理后试验钢中的块状马氏体形成于原铁素体区域;随两相区配分时间延长,粒状马氏体数量减少,板条状马氏体之间的间距减小。检测和计算结果的对比显示,C、Mn、Cu 3种元素的相对配分速率与计算结果一致,但实际配分速率低于各自的计算结果。随两相区配分时间延长,经IQ&P处理后试验钢的抗拉强度先增加后减小,而伸长率持续减小;在600 s时达到较好的强塑性匹配,强塑积为16 963.24 MPa·%。     

New insight to the oxidation kinetics of silicon-containing steel at high temperature

This study investigated the oxidation kinetics of silicon-containing steel at 1150–1300 °C using a Simultaneous Thermal Analyzer under atmospheric conditions similar to that of an industry reheating furnace. There is a critical time point for the oxidation kinetics at an oxygen concentration of 4·0 vol.-%., following which the oxidation rate constant increases with the increasing oxidation temperature. The model coefficient A in the kinetic oxidation equation was found to be constant. However, before the critical time point, the oxidation rate constant remained unchanged; the model coefficient A decreased with the increasing temperature. Therefore, the kinetic model of silicon-containing steel for isothermal oxidation was observed to be a modified one on the basis of the experimental data. In addition, the critical time point was prolonged with the increasing isothermal oxidation temperature. Moreover, the oxidation activation energy of the tested silicon-containing steel was 366·16 kJ mol^?1.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

大规模多状态计算系统的容错性能分析

针对异构计算节点组成的大规模多状态计算系统的容错性能分析问题，提出了一种计算系统容错性能的评估方法。该方法采用自定义的两级容错性能形式化描述框架进行系统描述，通过构造多值决策图（Multi-value Decision Diagram，MDD）模型对系统进行容错性能建模，并基于构造的模型高效地计算出部件故障的条件下计算系统在特定性能水平上运行的概率，减少了计算的冗余性。实验结果表明，该方法在模型的大小和构建时间上均优于传统方法。该方法的提出将对系统操作员或程序设计者具有重要意义，使其确保系统适合预期应用。     

Modeling managerial behavior in real options valuation for project-based environments

Project valuation, as a decision-making tool for initiating investments in projects, should be able to value project flexibilities and incorporate reasonable risk preferences of relevant decision makers. Real options valuation methods are the available approaches for valuing project flexibilities, whereas they have shortcomings in considering managers’ reasonable risk preferences in project decisions. Therefore, researchers have suggested approximating the perspective on risk of real options methods and practitioners in project management. This study proposes a fair real options valuation for project-based environments by a behavioral economic approach, which adopts binomial lattice method, Monte-Carlo simulation, and cumulative prospect theory. The results show that behavioral factors such as ‘risk attitude’ and ‘loss aversion’ should be accepted in project investment decisions while limited to an acceptable amount depending on the project conditions (e.g. uniqueness of decision-making scenarios). This research contributes to the project management domain by enhancing project investment decisions that include project flexibilities.     

Robust predictive extended state observer for a class of nonlinear systems with time-varying input delay

ABSTRACT

This paper deals with asymptotic stabilisation of a class of nonlinear input-delayed systems via dynamic output feedback in the presence of disturbances. The proposed strategy has the structure of an observer-based control law, in which the observer estimates and predicts both the plant state and the external disturbance. A nominal delay value is assumed to be known and stability conditions in terms of linear matrix inequalities are derived for fast-varying delay uncertainties. Asymptotic stability is achieved if the disturbance or the time delay is constant. The controller design problem is also addressed and a numerical example with an unstable system is provided to illustrate the usefulness of the proposed strategy.     

Domestic demand-side response with heat pumps: controls and tariffs

ABSTRACT

Electric heat pumps feature prominently in projected energy transitions in the UK and elsewhere. Owing to their high electricity consumption, heat pumps are viewed as important targets for demand-side response (DSR). Findings are presented from a field trial of a new control system that aims to optimize heat pump performance, including under time-varying tariff conditions. The trial involved monitoring 76 properties with heat pumps, but without dedicated heat storage; 31 of these received the control system. Interviews were conducted with a subsample of 12 participants. The controller successfully evened out electricity demand over the day (reducing the evening peak), but this was associated with increased late night and daytime temperatures. Interview participants reported some disturbance owing to overnight heating and noise, as well as usability issues with the controller interface and hardware. These issues present risks to the future acceptability of such systems. While the system delivered short-term demand reductions successfully, longer-term demand shifting risked causing unacceptable disturbance to occupants. Future control systems could overcome some of the issues identified in this pioneering trial through more effective zoning, using temperature caps or installing dedicated heat storage, but these may either limit the available flexibility or be challenging to achieve.     

Robust orientation control of multi‐DOF cell based on uncertainty and disturbance estimation

Multiple degrees‐of‐freedom (multi‐DOF) cell orientation control is a vital important technique involved in single cell surgery applications. Currently, few studies have been performed toward automation of multi‐DOF cell orientation control using robotically controlled optical tweezers. In this paper, a robust control framework is developed to perform multi‐DOF cell rotational control with consideration of model uncertainties and external disturbances. Both simulation and experimental studies are presented to illustrate the performance of the proposed control strategy. The main contributions of this work lie in that this is the first time to develop a unified framework to achieve multi‐DOF cell orientation control without the need for accurate dynamic model parameters and/or any knowledge about uncertainty characteristic, which greatly enhances the robustness of the overall system.