偶联剂对地砖用PVC填充材料相态结构和力学性能的影响

研究了钛酸酯类偶联剂和硅烷类偶联剂对钙矿、硅酸盐填充的PVC填允材料力学性能、相态结构、密度、热失重率及吸水伸长率和耐热阻燃性能的影响。结果表明,偶联剂可改善填充PVC的抗冲性能,降低其拉伸强度,扫描电镜分析表明,填料越细,分散越均匀,越易被PVC树脂所包覆,力学性能越好,研究得到了比重大、热失重率低、吸水伸长率小、阻燃耐烧灼性好的高填充PVC硬质塑料地板砖。     

电泵井振动监测及传输系统设计

潜油电泵机组是具有超细长转轴的复杂旋转机械,有复杂的生产及安装工艺,且采用了多级离心泵,在生产过程中会产生强大的振动源,测量并分析机组振动状况,是评估潜油电泵机组的运行状况、提高可靠性的重要手段。本文采用MMA3201振动加速度计监测振动状况,并采用4 mA²0 mA电流环将信号传输到井上MCU进行分析处理,根据监测和处理结果,适时提泵检修,采取合理的减振措施,延长潜油电泵的使用寿命。     

LC2294处理器片内A／D转换器测试

介绍了通过对 LC2294处理器片内 A /D 转换器测试,构建简单可靠,通用性好的自动测试系统,提出了一种软硬件结合的 A /D 测试方法,给出了测试系统原理图,并详细描述了软件实现过程。     

基于需求更新的救灾品配送公平与效率协调模型

鉴于需求准确度与救灾品配送效率之间的悖反关系,研究了基于需求更新的救灾品配送效率与公平问题.通过对某市民政局进行调研,了解到台风灾害发生时的需求变化规律,设计一种通过已知需求灾区对未知需求灾区的需求进行贝叶斯更新的需求更新方式,由此建立救灾品配送模型.将该模型应用于某台风救灾实例,通过实例分析表明了该模型的求解速率与精度,并找到了最优"决策点",实现了救灾品配送公平与效率的平衡.     

用于光刻系统仿真的多边形处理算法

为提高光刻仿真效率,通过对光刻原理进行研究,提出了2种多边形处理算法,将掩模上的多边形图案进行切分优化,将其划分成若干矩形或三角形。在Linux环境下应用C语言设计出一个完整的光刻仿真系统,设计的具体光学参数为:光源波长为193nm,数值孔径为0.3~0.8,部分相干系数可调范围为0.2~0.8,可一次性仿真1μm×1μm到10μm×10μm范围内的45 nm~0.18μm工艺的复杂版图,并通过多次实验进行验证。实验结果表明:原版图图像的边缘细节得到保留,且该算法有效地减少了光刻模拟的计算复杂度与计算时间,整体效率提升20%以上,为当前智能传感器系统芯片的制造节省了宝贵时间。     

近十年常温干法精脱硫专利技术发展动态

收集了近十年来国内的常温干法精脱硫专利，并分类列举出各种脱硫剂的物化性质和几种常温精脱硫工艺，重点阐述了该技术的优缺点及其未来发展方向。     

以废弃的耐火材料、高铝瓷、催化剂等生产耐磨陶瓷

从技术、经济、生态、政策等角度，对采用氧化铝和硅酸铝质工业废料制取耐磨陶瓷作了全面、系统的分析研究，论证了废渣制瓷技术上的可行性，阐明了经济上和生态上的优越性，提出了解决具体问题的办法。     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Optimal entropy-constrained non-uniform scalar quantizer design for low bit-rate pixel domain DVC

In this paper, an optimal entropy-constrained non-uniform scalar quantizer is proposed for the pixel domain DVC. The uniform quantizer is efficient for the hybrid video coding since the residual signals conforming to a single-variance Laplacian distribution. However, the uniform quantizer is not optimal for pixel domain distributed video coding (DVC). This is because the uniform quantizer is not adaptive to the joint distribution of the source and the SI, especially for low level quantization. The signal distribution of pixel domain DVC conforms to the mixture model with multi-variance. The optimal non-uniform quantizer is designed according to the joint distribution, the error between the source and the SI can be decreased. As a result, the bit rate can be saved and the video quality won’t sacrifice too much. Accordingly, a better R-D trade-off can be achieved. First, the quantization level is fixed and the optimal RD trade-off is achieved by using a Lagrangian function J(Q). The rate and distortion components is designed based on P(Y|Q). The conditional probability density function of SI Y depend on quantization partitions Q, P(Y|Q), is approximated by a Guassian mixture model at encocder. Since the SI can not be accessed at encoder, an estimation of P(Y|Q) based on the distribution of the source is proposed. Next, J(Q) is optimized by an iterative Lloyd-Max algorithm with a novel quantization partition updating algorithm. To guarantee the convergence of J(Q), the monotonicity of the interval in which the endpoints of the quantizer lie must be satisfied. Then, a quantizer partition updating algorithm which considers the extreme points of the histogram of the source is proposed. Consequently, the entropy-constrained optimal non-uniform quantization partitions are derived and a better RD trade-off is achieved by applying them. Experiment results show that the proposed scheme can improve the performance by 0.5 dB averagely compared to the uniform scalar quantization.     

Know by a handful the whole sack: efficient sampling for top-k influential user identification in large graphs

Influence Maximization aims to find the top-K influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Most existing greedy algorithms mainly focus on computing the exact influence spread, leading to low computational efficiency and limiting their application to real-world social networks. While in this paper we show that through supervised sampling, we can efficiently estimate the influence spread at only negligible cost of precision, thus significantly reducing the execution time. Motivated by this, we propose ESMCE, a power-law exponent supervised Monte Carlo estimation method. In particular, ESMCE exploits the power-law exponent of the social network to guide the sampling, and employs multiple iterative steps to guarantee the estimation accuracy. Moreover, ESMCE shows excellent scalability and well suits large-scale social networks. Extensive experiments on six real-world social networks demonstrate that, compared with state-of-the-art greedy algorithms, ESMCE is able to achieve almost two orders of magnitude speedup in execution time with only negligible error (2.21 % on average) in influence spread.