Effects of upholstery materials on the burning behavior of real‐scale residential upholstered furniture mock‐ups

Fire spread and growth on real‐scale four cushion mock‐ups of residential upholstered furniture (RUF) were investigated with the goal of identifying whether changes in five classes of materials (barrier, flexible polyurethane foam, polyester fiber wrap, upholstery fabric, and sewing thread), referred to as factors, resulted in statistically significant changes in burning behavior. A fractional factorial experimental design plus practical considerations yielded a test matrix with 20 material combinations. Experiments were repeated a minimum of two times. Measurements included fire spread rates derived from video recordings and heat release rates (HRRs). A total of 13 experimental parameters (3 based on the videos and 10 on the HRR results), referred to as responses, characterized the measurements. Statistical analyses based on Main Effects Plots (main effects) and Block Plots (main effects and factor interactions) were used. The results showed that three of the factors resulted in statistically significant effects on varying numbers of the 13 responses. The Barrier and Fabric factors had the strongest main effects with roughly comparable magnitudes. Foam was statistically significant for fewer of the responses and its overall strength was weaker than for Barrier and Fabric. No statistically significant main effects were identified for Wrap or Thread. Multiple two‐term interactions between factors were identified as being statistically significant. The Barrier*Fabric interaction resulted in the highest number of and strongest statistically significant effects. The existence of two‐term interactions means that it will be necessary to consider their effects in approaches designed to predict the burning behavior of RUF.     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

烯丙基聚烷氧基环氧醚的制备及其性能

针对烯丙基聚烷氧基环氧醚中存在催化剂和钾钠离子含量高,导致制备的三元共聚嵌段硅油反应活性差的问题,研究了在无相转移催化剂的条件下制备烯丙基聚烷氧基环氧醚的方法,表征了产物的结构,分析了制备条件对产物pH值与环氧指数的影响;以其为原料制备了三元共聚嵌段硅油,并对棉织物进行柔软整理,分析了整理后织物的亲水性、手感及物理力学性能。结果表明:当原料烯丙基聚氧乙烯醚、环氧氯丙烷和氢氧化钠的量比为1∶2.5∶1.5,冰醋酸的添加量是过量氢氧化钠的1.1倍时,产物烯丙基聚烷氧基环氧醚的环氧指数为1.77 mol/kg,pH值小于7,精制后钾钠离子含量可降低到5 μg/g以下;织物经过硅油整理后具有较好的亲水性,当整理液质量浓度为30 g/L时,毛效达到7.3 cm,手感评级达到4.7级以上,织物整体性能有所提高。     

泛在网在食品安全领域的可行性研究——以建设泛在食品安全网为例

分析了中国当前食品安全现状,并提出建设泛在食品安全网的构想,通过对新技术的融合,设计出泛在食品安全网体系架构,指出泛在食品安全网的建设能够解决消费者、企业、监管机构之间面临的信息不对称等诸多问题,进一步提升中国食品安全管理水平。     

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Side-channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side-channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side-channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short-term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45-nm library.     

四川盆地高磨地区灯影组转向酸类型选择研究及应用

针对四川盆地高磨地区灯影组碳酸盐岩Ⅰ、Ⅱ、Ⅲ储层,通过转向酸变黏峰值黏度、双岩心酸化效果、转向压力等室内实验结果分析表明高酸浓度变黏型转向酸对于Ⅱ、Ⅲ类储层具有更好的转向效果,现场试验最大转向压力10 MPa;低酸浓度变黏型转向酸对于Ⅰ类储层具有更好的转向效果,现场试验最大转向压力12 MPa。     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

无轨辅助运输现状及发展前景

随着现代化矿井的高产高效,井下无轨辅助运输以载重大、速度快、运输灵活等优势替代了有轨辅助运输。经过多年的发展,无轨辅助设备种类多,功能全,适应于井下各种运输需要。但在发展的同时,无轨辅助运输也存在很多不足之处。针对此现状,从设备智能化、自动化和市场规模扩大化的角度阐述了无轨辅助运输的远大发展前景,对无轨辅助运输行业的发展具有一定的借鉴作用。     

浅析小型水电站厂用电节约的优化措施

水电站厂用电指标使用综合厂用率,反映电站厂用电消耗的实际情况,可通过厂用电率合理性分析电站厂用耗电情况以及节能降耗挖潜的空间,促进厂用电的合理管理,提高电站效益。文章结合华能甘肃水电开发有限公司管控的6座小型水电站厂用电管理实践,分析了影响水电厂用电损耗的相关因素,提出了厂用电节能降耗的应对措施。     

Design of energy‐efficient train speed profiles considering fixed‐block signaling system

In dense traffic railway networks, trains may often slow down or stop between stations owing to previous train delays. If preceding train trajectory can be predicted, energy‐efficient driving can be achieved by suppressing unnecessary speed changes. In this paper, we propose an algorithm to find energy‐efficient driving considering fixed‐block signaling (FBS) system by using dynamic programming (DP). DP is suitable for use because it can optimize the control inputs with discrete and state constraints. In this paper, we discuss energy‐efficient driving by considering a FBS system using some case studies of simulation. In the simulation, we examine a technique to drive an express train in an energy‐efficient way when the preceding local train is running toward the station with passing loops. The results show that the proposed method can derive complex speed profiles for energy‐efficient driving and the train can be operated with a maximum reduced energy consumption of 8.3%.