空中爆炸载荷下梯度波纹夹层板抗爆性能仿真研究

通过有限元软件Autodyn模拟了梯度波纹夹层板在空中爆炸载荷下的动态响应,分析了芯层排列顺序对其响应模式和抗爆性能的影响;在此基础上,选择抗爆性能最优的芯层组合填充聚氯乙烯泡沫,研究了填充方式对其抗爆性能的影响;分析了夹层结构的吸能特性。结果表明：芯层壁板厚度从迎爆面到背爆面逐渐减小的组合具有最优的抗爆性能,且只在第一层填充泡沫的梯度波纹夹层板的下面板变形最小;从迎爆面到背爆面单层填充时,聚氯乙烯泡沫的吸能不断下降;随着填充层数增加,下面板变形以及聚氯乙烯泡沫和下面板的吸能逐渐增大。     

光子多普勒测速仪与压杆相结合的冲击波反射压力测试技术

为在强电磁干扰环境下测量爆炸冲击波的壁面反射压力,将光子多普勒测速仪(PDV)与压杆相结合,设计了一套压力测试系统。采用滑动安装结构消除探头在应力波作用下产生的轴向速度,根据杆中弹性波多次反射的压杆自由面速度曲线,提取起跳点,拟合得到了压杆声速。对压力测试系统进行了响应特性、有效测量时间和测量下限分析,并采用该系统测量了球形炸药爆炸的冲击波反射压力。研究结果表明：该系统频响低于杆表面粘贴的应变片电测系统,测量下限优于应变片电测系统;测得的球形炸药爆炸的冲击波最大反射压力为463.5 MPa,上升沿为5 μs;PDV和压杆相结合的冲击波壁面反射压力测试技术可行。     

高炉开炉装料计算软件的设计

高炉开炉装料计算工作繁琐、复杂,手工计算耗费时间长、容易出错,难以得到最佳方案,影响高炉顺利开炉。通过高炉开炉装料计算软件的开发实践,介绍了装料计算模型的建模步骤;讨论了软件的功能需求;阐述了选用三层网络结构的原因,从而描述了装料计算软件的设计方法。软件投入运行后,能够快速、准确地计算、分析开炉数据,取得了满意的效果。     

假捻变形的交络技术

德国Ｅｎｋａｔｅｃｎｉｃａ公司制造的ＴＤ喷嘴系列适用于假捻变形工艺的新进展,本文介绍了该喷嘴的结构,以及变形纱的质量测定方法。     

纳米电子器件中的噪声及其应用研究

对多种纳米材料和纳米器件的噪声进行了比较详细的研究,对纳米器件中几种常用的噪声测量方法进行了探讨。讨论了利用噪声(主要是散粒噪声)进行介观电学机制探测的方法,并提出了用于解决单电子晶体管中背景电荷噪声的有效方法,揭示了双势垒共振隧穿二极管在负微分电阻区的散粒噪声大于Poisson值的本质。     

激光喷丸强化ZK60变形镁合金的疲劳性能研究

为研究激光喷丸对高强度变形镁合金ZK60疲劳性能的影响机理,使之适应交变应力结构件的工作需要,进行了激光喷丸强化表面改性和拉伸疲劳性能实验。实验结果表明,在激光喷丸强化区域获得有益的残余压应力,产生硬化效应,表面塑性变形的微小区域,变形轮廓曲线波动范围较基体小,增加喷丸次数可增强激光喷丸的强化效应。经3次激光喷丸后,中心孔疲劳试样的平均疲劳寿命由未喷丸试样的78023次提高到125641次,寿命增益达61%。从断口来看,激光喷丸强化主要是改变了裂纹源萌生位置,延缓了裂纹扩展速率,对瞬断区无明显影响。激光喷丸强化为镁合金的抗疲劳制造提供了新的途径。     

Large‐area epitaxial silicon solar cells based on industrial screen‐printing processes

Thin‐film epitaxial silicon solar cells are an attractive future alternative for bulk silicon solar cells incorporating many of the process advantages of the latter, but on a potentially cheap substrate. Several challenges have to be tackled before this potential can be successfully exploited on a large scale. This paper describes the points of interest and how IMEC aims to solve them. It presents a new step forward towards our final objective: the development of an industrial cell process based on screen‐printing for > 15% efficient epitaxial silicon solar cells on a low‐cost substrate. Included in the discussion are the substrates onto which the epitaxial deposition is done and how work is progressing in several research institutes and universities on the topic of a high‐throughput epitaxial reactor. The industrial screen‐printing process sequence developed at IMEC for these epitaxial silicon solar cells is presented, with emphasis on plasma texturing and improvement of the quality of the epitaxial layer. Efficiencies between 12 and 13% are presented for large‐area (98 cm²) epitaxial layers on highly doped UMG‐Si, off‐spec and reclaim material. Finally, the need for an internal reflection scheme is explained. A realistically achievable internal reflection at the epi/substrate interface of 70% will result in a calculated increase of 3 mA/cm² in short‐circuit current. An interfacial stack of porous silicon layers (Bragg reflectors) is chosen as a promising candidate and the challenges facing its incorporation between the epitaxial layer and the substrate are presented. Experimental work on this topic is reported and concentrates on the extraction of the internal reflection at the epi/substrate interface from reflectance measurements. Initial results show an internal reflectance between 30 and 60% with a four‐layer porous silicon stack. Resistance measurements for majority carrier flow through these porous silicon stacks are also included and show that no resistance increase is measurable for stacks up to four layers. Copyright © 2005 John Wiley & Sons, Ltd.     

Video Shot Boundary Detection in MPEG Compressed Sequences Using SVM Learning

A number of automated video shot boundary detection methods for indexing a videosequence to facilitate browsing and retrieval have been proposed in recent years.Among these methods,the dissolve shot boundary isn't accurately detected because it involves the camera operation and objectmovement.In this paper,a method based on support vector machine (SVM) is proposed to detect thedissolve shot boundary in MPEG compressed sequence.The problem of detection between the dissolveshot boundary and other boundaries is considered as two-class classification in our method.Featuresfrom the compressed sequences are directly extracted without decoding them,and the optimal classboundary between two classes are learned from training data by using SVM.Experiments,whichcompare various classification methods,show that using proposed method encourages performance ofvideo shot boundary detection.     

Learning discriminative and meaningful samples for generalized zero shot classification

Generalized zero shot classification aims to recognize both seen and unseen samples in test sets, which has gained great attention. Recently, many works consider using generative adversarial network to generate unseen samples for solving generalized zero shot classification problem. In this paper, we study how to generate discriminative and meaningful samples. We propose a method to learn discriminative and meaningful samples for generalized zero shot classification tasks (LDMS) by generative adversarial network with the regularization of class consistency and semantic consistency. In order to make the generated samples discriminative, class consistency is used, such that the generated samples of the same classes are near and of different classes are far away. In order to make the generated samples meaningful, semantic consistency is used, such that the semantic representations of the generated samples are close to their class prototypes. It encodes the discriminative information and semantic information to the generator. In order to alleviate the bias problem, we select some confident unseen samples. We use the seen samples, the generated unseen samples and the selected confident unseen samples to train the final classifier. Extensive experiments on all datasets demonstrate that the proposed method can outperform state-of-the-art models on generalized zero shot classification tasks.     

引入自上向下特征融合的小目标检测算法

针对原始SSD(Single Shot Multibox Detector)算法未充分利用各特征层之间关系导致浅层特征层缺乏小目标语义信息的问题,为了提高对小目标的检测能力,提出了一种结合PANet多尺度特征融合网络和自上向下特征融合路径的TTB-SSD(Top to Bottom SSD)改进算法。首先,使用PANet多尺度特征融合网络对特征进行反复提取,从而获得丰富的多尺度语义信息;然后,使用一种深层特征融合模块将浅层特征层的空间信息传递到深层特征层,进而更准确地对小目标进行定位;最后,为了增强浅层特征层的语义信息,构造了自上向下的特征融合路径,从而强化浅层对小目标检测的准确率。实验结果表明,在PASCAL VOC2007测试集检测的mAP(Mean Average Precision)值达到80.5%,对目标的mAP较原始SSD提高了5.7%,证明了该算法对小目标检测的有效性。