An extended stochastic pseudo-spectral Galerkin finite element method (XS-PS-GFEM) for elliptic equations with hybrid uncertainties

Nouy and Clement introduced the stochastic extended finite element method to solve linear elasticity problem defined on random domain. The material properties and boundary conditions were assumed to be deterministic. In this work, we extend this framework to account for multiple independent input uncertainties, namely, material, geometry, and external force uncertainties. The stochastic field is represented using the polynomial chaos expansion. The challenge in numerical integration over multidimensional probabilistic space is addressed using the pseudo-spectral Galerkin method. Thereafter, a sensitivity analysis based on Sobol indices using the derived stochastic extended Finite Element Method solution is presented. The efficiency and accuracy of the proposed novel framework against conventional Monte Carlo methods is elucidated in detail for a few one and two dimensional problems.     

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.     

基于第三方物流的家具配送开放式车辆路径问题

为了降低家具配送成本,提高物流效率,基于第三方物流配送模式,构建了以总行驶距离最短和车辆数最少为最优目标的开放式车辆路径问题(open vehicle routing problem,OVRP)数学模型,并设计了一个改进的两阶段禁忌搜索算法进行求解,第1阶段求解包含所有客户的TSP(traveling salesman problem)路径来作为第2阶段划分OVRP路径的基础.设计了一个随机动态禁忌表,并将"邻域算子编号"和"邻域交换点对"同时作为禁忌对象,避免了过度禁忌的情况.另外,对5个邻域算子进行了测试,表明采用由点交换、分序点插入、点逆序和前点前向插入这4个算子组成的多邻域结构体效果最佳.经算例测试和文献对比,验证了设计算法的有效性,采用第三方物流配送比自营物流配送更节省成本.     

融合语义标签和噪声先验的图像生成

针对现有生成模型难以直接从复杂语义标签生成高分辨率图像的问题,提出了融合语义标签和噪声先验的生成对抗网络(SLNP-GAN)。首先,直接输入语义标签(包含形状、位置和类别等信息),使用全局生成器对其进行编码,并结合噪声先验来学习粗粒度的全局属性,初步合成低分辨率图像;然后,基于注意力机制,使用局部细化生成器来查询低分辨率图像子区域对应的高分辨率子标签,获取细粒度信息,从而生成纹理清晰的复杂图像;最后,采用改进的引入动量的Adam算法(AMM)算法来优化对抗训练。实验结果表明,与现有方法text2img相比,所提方法的像素精确度(PA)在COCO_Stuff和ADE20K数据集上分别提高了23.73%和11.09%;相较于Adam算法,AMM算法收敛速度提升了约一倍,且损失值波幅较小。可见,SLNP-GAN能高效地获取全局特征和局部纹理,生成细粒度、高质量的图像。     

水文水资源学家陈守煜先生学术研究的知识图谱分析

为探索水文水资源学家陈守煜先生学术研究历程及其演变规律,以其335篇文献作为数据基础,采用共现分析等文献计量可视化分析方法,通过文献发表的时间分布、学科和期刊分布、高被引文献分布和研究主题等方面的知识图谱分析,系统分析了陈先生的学术研究历程。结果显示:陈守煜先生学术研究生命周期历时达62年,在其60岁后出现了2个学术创新高峰期;其学术研究特色荟萃于其河渠非恒定流计算、模糊集理论的水文水资源应用、模糊水文水资源学基本理论、模糊聚类-识别-优选统一理论、可变模糊集理论和可变模糊集理论的水文水资源应用6个研究主题,其中主题1为后续的5个模糊水文水资源学创新主题奠定了数值计算的研究方法论,主题2为模糊集现有理论的应用探索,主题3至主题5为面向工程应用的模糊集理论不断深入创新,主题6为可变模糊集理论的应用探索,形成了"紧密结合应用-持续理论创新-创新理论再应用"这种理论与实践互馈的研究特色。他每一阶段的研究目标比较集中、明确,与其研究主题密切对应,显著提高了研究效率;他的学术研究影响广泛,合计被引8554次,缘于他的源头理论创新。陈先生的研究,理论与应用不断互馈,贵在厚积薄发、深入钻研、持续创新,其研究理念和方法论,对水利科学的学术创新研究具有重要的启示作用和深远影响。     

A framework for improving the efficiency of operational testing through Bayesian adaptive design

When developing a product, it is important to consider product performance from a user perspective. This type of evaluation can be done through operational testing—assessing the ability of representative users to satisfactorily accomplish tasks with the product in operationally representative environments. This process can be expensive and time-consuming, but is critical to understanding whether the product can adequately do the job for which it was designed. We show how an existing design of experiments (DOEs) process for operational testing can be leveraged to construct a Bayesian adaptive design. This design, nested within the larger design created by the DOE process, allows interim analyses to stop testing early for success or futility. Representative simulations are presented to demonstrate how these interim analyses can be used in an operational test setting, and reductions in necessary test events are shown. The application of Bayesian-adaptive design methods will allow future operational testing to be conducted in less time and at less expense, on average, without compromising the ability of the existing process to verify the product meets the user's needs.     

Generally weighted moving average monitoring schemes: Overview and perspectives

An overview of monitoring schemes from a class called generally weighted moving average (GWMA) is provided. A GWMA scheme is an extended version of the exponentially weighted moving average (EWMA) scheme with an additional adjustment parameter that introduces more flexibility in the GWMA model as it adjusts the kurtosis of the weighting function so that the GWMA scheme can be designed such that it has an advantage over the corresponding EWMA scheme in the detection of certain shift values efficiently. The parametric and distribution-free GWMA schemes to monitor various quality characteristics and its existing enhanced versions (ie, double GWMA, composite Shewhart-GWMA, mixed GWMA-CUSUM, and mixed CUSUM-GWMA) have better performance than their corresponding EWMA counterparts in many situations; hence, all such existing research works discussing GWMA-related schemes (ie, 66 publications in total) are documented and categorized in such a manner that it is easy to identify research gaps. Finally, a number of possible future research ideas are provided.     

Progressive mean control chart is not a special case of an exponentially weighted moving average control chart

The progressive mean (PM) statistic is based on a simple idea of accumulating information of each subgroup by calculating the average progressively. Its weighting structure is based on a subgroup number that changes arithmetically, which makes the PM chart unique and efficient compared with the existing classical memory control charts. In a recent article (see reference 1), it was claimed that the PM chart is a special case of the exponentially weighted moving average (EMWA) chart. In this article, it is shown that even though the PM statistic can be written in the form of an EWMA statistic, the variance of the PM statistic is different from that of the EWMA statistic. Consequently, the limits of the PM chart are different from that of the EWMA chart. Therefore, it is found that the PM chart is not a special case of the EWMA chart; hence, the claim in reference 1 is incorrect. Furthermore, it is pointed out in this paper that no adaptive property in the weighting parameter of the PM statistic exists, further contradicting the claim in reference 1.     

An evolved VIKOR method for multiple-criteria compromise ranking modeling under T-spherical fuzzy uncertainty

The development of T-spherical fuzzy (T-SF) sets in qualitative multiple-criteria choice analysis has been well investigated, but little is explored about how the VIKOR (i.e., VIšekriterijumsko KOmpromisno Rangiranje in Serbian) mechanism is generalized into intricate T-SF environments. This paper has the objective of propounding a creative T-SF VIKOR methodology for compromise ranking modeling in multiple criteria analysis. The exploitation of the T-SF configuration brings about superior information space representation in response to intricate realistic environments. However, because of the increased complexity of decision situations involving T-spherical fuzziness, treatment with the T-SF information arises a pragmatic difficulty in developing methodological approaches to T-SF VIKOR. In light of this concern, this paper unfolds an analytical framework for the T-SF VIKOR method predicated on new notions of an evolved T-SF score function and the Minkowski-type T-SF distance measure along with its special cases of the Manhattan-, Euclidean-, and Chebyshev-type distances. The aspired and despised T-SF characteristics can be identified supported by the evolved T-SF score function of T-SF performance ratings. This paper gives a new delineation of the group utility, individual regret, and joint generalized measures with the aid of the Minkowski-type T-SF distance measure relative to the aspired/despised T-SF characteristics. A synthetic mechanism is built to validate the standards of acceptable advantage and stability and yield the ultimate compromise choices for multiple criteria decision aiding. The practicality of the T-SF VIKOR methodology in down-to-earth decision situations has been well demonstrated through the selection issues of warehouse locations and advertisement strategies. Moreover, the developed approach has shown better application outcomes than the past methods did. The comparative study with parametric analysis has revealed the steadiness and effectiveness of the compromise choice results in T-SF circumstances.     

移动机器人同步定位与建图技术综述

同步定位与建图技术(SLAM)一直是移动机器人领域比较热门的研究方向,它可以给机器人提供强大的环境感知能力;传统的依靠外部位置参考来定位的方法如果无法获得时,移动机器人需要即时定位自身位置来构建增量式地图,因此SLAM技术也就应运而生;对激光SLAM和视觉SLAM的研究现状及最新标志性成果进行了介绍,重点对以相机与激光雷达融合、相机与IMU融合、激光雷达与IMU融合为代表的多传感器融合SLAM技术展开讨论、系统地梳理了几种融合方式的优势与不足,同时介绍了该领域的研究热点语义SLAM,最后讨论了SLAM技术在该领域未来的发展方向以及存在的挑战。