Statistical Analysis of Human Reliability of Armored Equipment

Human errors of seven types of armored equipment, which occur during the course of field test, are statistically analyzed. The human error-to-armored equipment failure ratio is obtained. The causes of human errors are analyzed. The distribution law of human errors is acquired. The ratio of human errors and human reliability index are also calculated.     

人α_1型基因工程干扰素的中间试制

带有杂交质粒pBV867的大肠杆菌BMH 71-18株,用高密度发酵培养法进行发酵,3批中试的平均菌产量为100.25克/升,粗制干扰素的表达量平均为8.39mg/升。3批粗制干扰素的数量分别为35000,30000和36000 ml。经初步提纯,分别浓缩10.4、10和11.29倍,比活性上升10、33和30倍,初步提纯的得率为34.33％。经高度提纯、浓缩23.8、16.7和26.1倍,比活明显增加至24.38、20.80和21.43×10~6 IU/mg蛋白,平均得率为35.92％,总得率为11.82％,平均提纯倍数为1757倍。 3批中试产品按WHO规程的标准进行检定,经SDS-PAGE银染色法测定,呈一条带,其分子量约为21000,无论在还原或非还原的条件下干扰素纯度均在95％以上。用HPLC检定均为单一峰形,纯度在95％以上。其它检定项目如等电聚焦、效价、热原、残余DNA、鼠IgG含量测定和全波长扫描等均合乎规程要求。     

Human action recognition using multi-layer codebooks of key poses and atomic motions

Taking fully into consideration the fact that one human action can be intuitively considered as a sequence of key poses and atomic motions in a particular order, a human action recognition method using multi-layer codebooks of key poses and atomic motions is proposed in this paper. Inspired by the dynamics models of human joints, normalized relative orientations are computed as features for each limb of human body. In order to extract key poses and atomic motions precisely, feature sequences are segmented into pose feature segments and motion feature segments dynamically, based on the potential differences of feature sequences. Multi-layer codebooks of each human action are constructed with the key poses extracted from pose feature segments and the atomic motions extracted from motion feature segments associated with each two key poses. The multi-layer codebooks represent action patterns of each human action, which can be used to recognize human actions with the proposed pattern-matching method. Three classification methods are employed for action recognition based on the multi-layer codebooks. Two public action datasets, i.e., CAD-60 and MSRC-12 datasets, are used to demonstrate the advantages of the proposed method. The experimental results show that the proposed method can obtain a comparable or better performance compared with the state-of-the-art methods.     

Skeleton-Based Human Action Recognition via Screw Matrices

With the recent advent of low-cost acqui-sition depth cameras, extracting 3D body skeleton has be-come relatively easier, which significantly lighten many dif-ficulties in 2D videos including occlusions, shadows and background extraction, etc. Directly perceived features, for example points, lines and planes, can be easily ex-tracted from 3D videos such that we can employ rigid motions to represent skeletal motions in a geometric way. We apply screw matrices, acquired by using rotations and translations in 3D space, to model single and multi-body rigid motion. Since screw matrices are members of the special Euclidean group SE(3), an action can be repre-sented as a point on a Lie group, which is a differen-tiable manifold. Using Lie-algebraic properties of screw al-gebra, isomorphic to se(3), the classical algorithms of ma-chine learning in vector space can be expanded to man-ifold space. We evaluate our approached on three public 3D action datasets: MSR Action3D dataset, UCF Kinect dataset and Florence3D-Action Dataset. The experimental results show that our approaches either match or exceed state-of-the-art skeleton-based human action recognition approaches.     

基于图正则化与非负组稀疏的自动图像标注

设计一个稳健的自动图像标注系统的重要环节是提取能够有效描述图像语义的视觉特征。由于颜色、纹理和形状等异构视觉特征在表示特定图像语义时所起作用的重要程度不同且同一类特征之间具有一定的相关性,该文提出了一种图正则化约束下的非负组稀疏(Graph Regularized Non-negative Group Sparsity, GRNGS)模型来实现图像标注,并通过一种非负矩阵分解方法来计算其模型参数。该模型结合了图正则化与l2,1-范数约束,使得标注过程中所选的组群特征能体现一定的视觉相似性和语义相关性。在Corel5K和ESP Game等图像数据集上的实验结果表明：相较于一些最新的图像标注模型,GRNGS模型的鲁棒性更强,标注结果更精确。     

Content adaptive video denoising based on human visual perception

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures.     

触摸调光控制芯片的设计与测试

电力作为一种方便的能源广泛应用于我们生活的各个方面,于是电能的节能变得尤为重要。由于家用照明设计可以从控制方面实现节能,因此人们可以根据自己不同时间的不同需要来调节灯光的亮度来达到节约电能的目的。本文提出了一种应用于家用照明亮度的调节控制芯片的设计方案,首先分析顶层模块的功能,再将其功能分类细化,分配到不同的子模块去实现,利用CMOS电压比较器来实现了过零检测、系统时钟脉冲产生、触摸控制等操作,并通过仿真软件验证了设计模块功能的正确性,最后经流片测试芯片功能满足设计要求。     

Semantic content-based image retrieval: A comprehensive study

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.     

Depth video spatial and temporal correlation enhancement algorithm based on just noticeable rendering distortion model

Spatial and temporal inconsistency of depth video deteriorates encoding efficiency in three dimensional video systems. A depth video processing algorithm based on human perception is presented. Firstly, a just noticeable rendering distortion (JNRD) model is formulated by combining the analyses of the influence of depth distortion on virtual view rendering with human visual perception characteristics. Then, depth video is processed based on the JNRD model from two aspects, spatial and temporal correlation enhancement. During the process of spatial correlation enhancement, depth video is segmented into edge, foreground, and background regions, and smoothened by Gaussian and mean filters. The operations of the temporal correlation enhancement include temporal–spatial transpose (TST), temporal smoothing filter and inverse TST. Finally, encoding and virtual view rendering experiments are conducted to evaluate the proposed algorithm. Experimental results show that the proposed algorithm can greatly reduce the bit rate while it maintains the quality of virtual view.     

基于支持向量回归的立体图像客观质量评价模型

立体图像质量评价是评价立体视频系统性能的有效途径,而如何利用人类视觉特性对立体图像质量进行有效评价是目前的研究难点。该文根据图像奇异值有较强稳定性的特点,结合立体图像的主观视觉特性,提出了一种基于支持向量回归(Support Vector Regression, SVR)的立体图像客观质量评价模型。该模型通过分析立体图像的视觉特性,提取左右图像的奇异值作为立体图像的特征信息,然后根据立体图像的不同失真类型情况对其特征进行融合,通过SVR预测得到立体图像质量的客观评价值。实验结果表明,采用该文提出的客观评价模型对立体数据测试库进行评价,Pearson线性相关系数值在0.93以上,Spearman等级相关系数值在0.94以上,均方根误差值接近6,异常值比率值为0.00%,符合人眼视觉特性,能够很好地预测人眼对立体图像的主观感知。