Probabilistic learning on manifolds (PLoM) with partition

The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number N of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which N is very small and for which the dimension of the diffusion-map basis is close to N. For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion-map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.     

Various density light field image coding based on distortion minimization interpolation

In recent years, the light field (LF) as a new imaging modality has attracted wide interest. The large data volume of LF images poses great challenge to LF image coding, and the LF images captured by different devices show significant differences in angular domain. In this paper we propose a view prediction framework to handle LF image coding with various sampling density. All LF images are represented as view arrays. We first partition the views into reference view (RV) set and intermediate view (IV) set. The RVs are rearranged into a pseudo sequence and directly compressed by a video encoder. Other views are then predicted by the RVs. To exploit the four dimensional signal structure, we propose the linear approximation prior (LAP) to reveal the correlation among LF views and efficiently remove the LF data redundancy. Based on the LAP, a distortion minimization interpolation (DMI) method is used to predict IVs. To robustly handle the LF images with different sampling density, we propose an Iteratively Updating depth image based rendering (IU-DIBR) method to extend our DMI. Some auxiliary views are generated to cover the target region and then the DMI calculates reconstruction coefficients for the IVs. Different view partition patterns are also explored. Extensive experiments on different types LF images also valid the efficiency of the proposed method.     

冰级船板式换热器流动传热分析及结构优化

采用数值模拟方法研究换热板结构对冰晶两相流换热机制和流动特性的影响。研究结果表明:波纹倾角β=60°、波纹深度h=4 mm、波纹间距λ=12 mm为所研究的换热器适用于海水-冰晶两相流的最佳值。利用宽通道对其进行结构优化设计,并采用相关试验数据验证。结果表明:宽通道间距为16 mm时,换热强度最大,流动阻力较小,可有效防止冰晶堵塞板内沟槽,且此时的换热效果和流动特性最佳,适合极地工况下冰晶两相流流动换热。     

Partitioning of PCBs from air to clothing materials in a Danish apartment

Polychlorinated biphenyl (PCB) contamination of buildings continues to pose an exposure threat, even decades after their application in the form of calks and other building materials. In this research, we investigate the ability of clothing to sorb PCBs from contaminated air and thereby influence exposure. The equilibrium concentration of PCB‐28 and PCB‐52 was quantified for nine used clothing fabrics exposed for 56 days to air in a Danish apartment contaminated with PCBs. Fabric materials included pure materials such as cotton and polyester, or blends of polyester, cotton, viscose/rayon, and/or elastane. Air concentrations were fairly stable over the experimental period, with PCB‐28 ranging from 350 to 430 ng/m³ and PCB‐52 ranging from 460 to 550 ng/m³. Mass accumulated in fabric ranged from below detection limits to 4.5 mg/g of fabric. Cotton or materials containing elastane sorbed more than polyester materials on a mass basis. Mass‐normalized partition coefficients above detection limits ranged from 10^5.7 to 10^7.0 L/kg. Clothing acts as a reservoir for PCBs that extends dermal exposure, even when outside or in uncontaminated buildings.     

ATSP:时态网络可视化的自适应时间片划分方法

现有的时态网络可视化方法大多采用等量时间片来可视化网络的演变，不利于时态模式的快速挖掘和发现。为此，根据时态网络固有的特征提出自适应时间片划分方法（Adaptive Time Slice Partition method，ATSP）。在时态网络的两种表示方式（基于事件的表示方式和基于快照的表示方式）的基础上，构建了ATSP的基础模型，同时提出了一种改进模型用来描述事件间隔时间服从长尾分布的时态网络。为了实现时间片的不等量划分，针对探索任务的不同提出了基于时态模式的ATSP规则和基于中心节点的ATSP规则，并提出了实现算法--层次划分算法（Hierarchical Partition algorithm，HP）和增量划分算法（Incremental Partition algorithm，IP）。实验结果表明，ATSP方法比传统的时间片划分方法更能准确地表示网络的时态特征，且该方法应用于可视化时，能有效归纳并展示网络的特征，明显提高了视觉分析的效率。     

A local view on the Hudetz correction of the Yager entropy of dynamical systems

In this paper, we define a local version of Hudetz correction for the Yager entropy of dynamical systems. It is proved that the introduced function is indeed a local entropy map for the Hudetz correction of the Yager entropy, in the sense that, the Hudetz correction of the Yager entropy of a continuous map on a compact metric space is attained by integrating the introduced local entropy.     

Highly Bendable and Rotational Textile Structure with Prestrained Conductive Sewing Pattern for Human Joint Monitoring

Human joints have respective ranges of motion and joint forces corresponding to each kind of joint; this necessitates considerations of the characteristics of human joints to fabricate wearable strain sensors conformable to the human body, and capable of precisely monitoring complex motions of the human body. In the present study, the “all textile‐based highly stretchable structure” that is capable of precisely sensing motions (folding and rotation) of the human joints (finger, wrist, elbow, spine, and knee) is fabricated by optimizing patterns (straight, blind, and zigzag) of conductive yarns employed as the conductive part of the strain sensor, and several textile substrates (braided elastic fabric, knit fabric, and woven fabric), having preferable elasticity and conformability employed for the fabrication of strain sensors suitable for human joints. In particular, the technology, enabling the prestraining of textile substrate, is exploited to fabricate a strain sensor that is capable of outputting selective signals corresponding to the folding motion of the spinal joint over a predetermined angle of motion, and the gait pattern of the wearer of the sensor, attached to his or her knee joint doing folding and rotational motions, is analyzed.