Learning shape and texture progression for young child face aging

Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.     

化学微发泡成型外观表面技术研究

从汽车内饰轻量化方向考虑,研究了化学微发泡技术,从成核和气泡长大阶段进行研究,通过特殊的柔性后退core-back工艺,在充模过程中控制气泡的变形和破裂以保证成型制品的表面质量,实现化学微发泡成型制品无外观缺陷、质量减轻20%以上的目的。     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Association of TaD14-4D,a Gene Involved in Strigolactone Signaling,with Yield Contributing Traits in Wheat

Tillering is a crucial agronomic trait of wheat; it determines yield and plant architecture. Strigolactones (SLs) have been reported to inhibit plant branching. D14, a receptor of SLs, has been described to affect tillering in rice, yet it has seldomly been studied in wheat. In this study, three TaD14 homoeologous genes, TaD14-4A, TaD14-4B, and TaD14-4D, were identified. TaD14-4A, TaD14-4B, and TaD14-4D were constitutively expressed, and TaD14-4D had a higher expression level in most tissues. TaD14 proteins were localized in both cytoplasm and nucleus. An SNP and a 22 bp insertion/deletion (Indel) at the exon regions of TaD14-4D were detected, forming three haplotypes, namely 4D-HapI, 4D-HapII, and 4D-HapIII. Due to the frameshift mutation in the coding region of 4D-HapII, the interaction of 4D-HapII with TaMAX2 and TaD53 was blocked, which led to the blocking of SL signal transduction. Based on the two variation sites, two molecular markers, namely dCAPS-250 and Indel-747, were developed. Association analysis suggested that haplotypes of TaD14-4D were associated with effective tillering number (ETN) and thousand kernel weight (TKW) simultaneously in four environments. The favorable haplotype 4D-HapIII underwent positive selection in global wheat breeding. This study provides insights into understanding the function of natural variations of TaD14-4D and develops two useful molecular markers for wheat breeding.     

基于熵权物元模型的长三角幸福河层次评价

将幸福河概念与长江三角洲区域一体化发展战略相结合，基于自然、人类社会、人水关系3个系统构建了包含24个指标的长三角幸福河层次评价指标体系；引入需求层次理论，以基础Ⅰ层次、基础Ⅱ层次、提升Ⅰ层次、提升Ⅱ层次、幸福层次5个层次作为幸福河的层次评价等级，运用熵权物元模型，建立了长三角幸福河层次评价模型，并对2018年长三角三省一市幸福河层次等级进行了评价。结果表明，三省一市均处于转化中的中间状态，其中江苏省向提升Ⅰ层次转化，浙江省、安徽省向提升Ⅱ层次转化，上海市向幸福层次转化，说明区域整体幸福河层次处于较高水平，但稳定性较差。     

钻井机器人的连续油管钻压和钻速控制模型

连续油管钻井机器人利用机身内外的钻井液压力差作为动力源，可在牵引连续油管的同时加载钻压。以钻井机器人为基础，建立连续油管钻柱动力学模型，并推导出通过钻井液排量控制钻压和钻速的单参数控制数学模型；对钻井机器人引入调速回路，建立具有调速功能的钻柱动力学模型；在溢流阀调定压力大于机身内外压差时，推导出利用钻井液排量和节流阀流通面积两种参数控制钻压、钻速的数学模型，在溢流阀调定压力小于机身内外压差时，推导出利用钻井液排量、节流阀流通面积和溢流阀调定压力3种参数控制钻压、钻速的数学模型；以11.43 cm（4.5英寸）井眼为例，对上述3种数学模型进行了分析。分析结果表明：钻压、钻速随钻井液排量的增加基本呈线性增加，在钻井液排量大于0.005 m³/s时，钻井机器人能够向前爬行，在钻井液排量大于0.005 7 m³/s时，钻头能够正常钻进；调节节流阀流通面积和溢流阀调定压力，可以在一定范围内无级调钻压和钻速；3种控制方法相结合，可以实现小排量、大钻压，及大排量、小钻压等钻井参数的控制。以控制模型为基础，针对不同井下工况建立钻进工艺的专家数据库，以钻井机器人为"大脑"，结合井下随钻测量数据就能够实现闭环控制，自动钻进。     

针对多种处理痕迹的数字语音取证算法

现有的数字语音取证研究主要集中于对单一的某种操作进行检测，无法对不相关的操作进行判断。针对该问题，提出了一种能够同时检测经过变调、低通滤波、高通滤波和加噪这四种操作的数字语音取证方法。首先，计算语音的归一化梅尔频率倒谱系数（MFCC）统计矩特征；然后通过多个二分类器对特征进行训练，并组合投票得到多分类器；最后使用该多分类器对待测语音进行分类。在TIMIT以及UME语音库上的实验结果表明，归一化MFCC统计矩特征在库内实验中均达到了97%以上的检测率，且在对MP3压缩鲁棒性测试的实验中，检测率仍能保持在96%以上。     

多约束下距离加权最优滑模末制导律

为解决多约束下制导炮弹的精确制导问题,采用带有相对距离权函数的最优滑模末制导律,将权函数引入到最优制导律中,通过改变制导炮弹的运动轨迹、运动时间,进而增强制导精度。针对单权函数难以同时满足制导精度与导引头视线角、过载等约束的问题,采用不同权函数的分段加权方法解决加权最优末制导引起的制导问题。结合滑模变结构控制理论,设计分段加权最优滑模末制导律,增强制导系统的抗干扰能力。仿真验证结果表明,该末制导律既能解决过载、导引头视线角、落角等多约束情况下的精确制导问题,同时又具有一定的鲁棒性。