High-resolution FIB-TEM-STEM structural characterization of grain boundaries in the high dielectric constant perovskite CaCu3Ti4O12

In this work, the grain boundaries composition of the polycrystalline CaCu₃Ti₄O₁₂ (CCTO) was investigated. A Focused Ion Beam (FIB)/lift-out technique was used to prepare site-specific thin samples of the grain boundaries interface of CCTO ceramics. Scanning transmission electron microscopy (STEM) coupled with energy dispersive X-ray spectrometry (EDXS) and Electron Energy Loss Spectroscopy (EELS) systems were used to characterize the composition and nanostructure of the grain and grain boundaries region. It is known that during conventional sintering, discontinuous grain growth occurs and a Cu-rich phase appears at grain boundaries. This Cu-rich phase may affect the final dielectric properties of CCTO but its structure and chemical composition remained unknown. For the first time, this high-resolution FIB-TEM-STEM study of CCTO interfacial region highlights the composition of the phases segregated at grain boundaries namely CuO, Cu₂O and the metastable phase Cu₃TiO_4.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

基于组合双向拍卖的云资源调度方法

针对跨数据中心的资源调度问题，提出了一种基于组合双向拍卖（PCDA）的资源调度方案。首先，将云资源拍卖分为三个部分：云用户代理报价、云资源提供商要价、拍卖代理组织拍卖；其次，在定义用户的优先级及任务紧迫度的基础上，在拍卖过程中估算每一个工作发生的服务等级协议（SLA）违规并以此计算云提供商的收益，同时每轮竞拍允许成交多项交易；最终达到根据用户等级合理分配云资源调度的效果。仿真实验结果表明该算法保证了竞拍成功率，与传统一次拍卖成交一项的组合双向拍卖方案相比，PCDA在竞拍时间段产生的能耗降低了35.00%，拍卖云提供商的利润提高了约38.84%。     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

Multi-resource scheduling for FPGA systems

In modern cloud data centers, reconfigurable devices (FPGAs) are used as an alternative to Graphics Processing Units to accelerate data-intensive computations (e.g., machine learning, image and signal processing). Currently, FPGAs are configured to execute fixed workloads, repeatedly over long periods of time. This conflicts with the needs, proper to cloud computing, to flexibly allocate different workloads and to offer the use of physical devices to multiple users. This raises the need for novel, efficient FPGA scheduling algorithms that can decide execution orders close to the optimum in a short time. In this context, we propose a novel scheduling heuristic where groups of tasks that execute together are interposed by hardware reconfigurations. Our contribution is based on gathering tasks around a high-latency task that hides the latency of tasks, within the same group, that run in parallel and have shorter latencies. We evaluated our solution on a benchmark of 37500 random workloads, synthesized from realistic designs (i.e., topology, resource occupancy). For this testbench, on average, our heuristic produces optimum makespan solutions in 47.4% of the cases. It produces acceptable solutions for moderately constrained systems (i.e., the deadline falls within 10% of the optimum makespan) in 90.1% of the cases.     

Demand response for home energy management system

The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. Demand response (DR) program is expected to be vital in home energy management system (HEMS) which aims to schedule the operation of appliances to save energy costs by considering customer convenience as well as characteristics of electric appliances. The DR program is a challenging optimization problem especially when the formulations are non-convex or NP-hard problems. In order to solve this challenging optimization problem efficiently, an effective heuristic approach is proposed to achieve a near optimal solution with low computational costs. Different from previously proposed methods in literatures which are not suitable to be run in embedded devices such as a smart meter. The proposed algorithm can be implemented in an embedded device which has severe limitations on memory size and computational power, and can get an optimal value in real-time. Numerical studies were carried out with the data simulating practical scenarios are provided to demonstrate the effectiveness of the proposed method.     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.     

电场对竖直矩形微槽群润湿及表面温度的影响

微槽群在热流密度较大时会达到其毛细极限，可通过主动换热方式之一——电水动力学效应对其进行强化。本文为了研究电场对微槽群表面润湿特性和温度分布的影响，采用平板电极提供电场，蒸馏水作为工质，使用高速相机拍摄微槽内液体润湿长度，测量误差为2.97%～7.46%；使用红外热像仪拍摄电场作用下微槽群表面温度分布，测量误差为2.1%～2.39%。热流密度测量误差范围是9.66%～11.11%。结果表明：电场通过驱动微槽内流体向加热区域流动而提升其润湿性能，且较低热流密度下提升更好。因润湿性能的提升，微槽表面温度得以下降。随着电场增强，微槽横向温度分布的“波峰”、“波谷”差别加大，微槽纵向温度明显降低。当热流密度加大时，温降更为显著，1.4W/cm²热流密度、6kV电压下温降可达到30℃以上。温降的增加反映了电场对微槽的强化润湿进一步提升了微槽换热性能，且电场对较高热流情形下的微槽换热有着更为显著的强化效果。     

太赫兹波计算鬼成像:原理和展望

本文立足于太赫兹波成像领域近年来备受关注的研究热点—太赫兹波计算鬼成像,首先回顾了鬼成像从量子到经典再到计算的历史过程,然后阐述了计算鬼成像的数学原理,随后综述了计算鬼成像在太赫兹波段的发展历程,及其在超衍射分辨成像、石墨烯光电导成像、太赫兹光谱成像等方面的应用,并在最后展望了太赫兹波计算鬼成像的发展前景:计算鬼成像作为一种成像手段,可以绕开在太赫兹频段缺乏经济高效的焦面阵列式探测器的难题,但目前的成像帧率还难以满足快速成像的应用需求,相信在未来随着器件性能的提升和成像算法的优化,其成像帧率可以得到大幅提升。     

太赫兹脉冲焦平面成像技术的发展与应用

作为太赫兹技术中的重要组成部分,太赫兹脉冲焦平面成像一经问世就引起了行业内的广泛关注,人们引入了各种方法去提升此成像技术的测量性能,同时也尝试将此成像技术应用于不同的工业和基础研究领域。本文综述了近年来人们对太赫兹脉冲焦平面成像的技术改良和应用研究,包括提升成像系统的空间分辨率、信噪比、信息获取能力,以及将此成像技术应用于光谱识别检测、超表面器件功能验证、太赫兹特殊光束测量、太赫兹表面波观测等,希望该综述能够推动太赫兹脉冲焦平面成像的进一步技术革新和应用拓展。