炉外精炼过程中炉渣的有效控制

对连铸机浇铸之有前钢水的炉外处理技术，尤其是对炉渣的控制进行了研究，从而使钢的纯净度和连铸可浇性大大提高。     

电晕放电注入双向拉伸聚丙烯薄膜驻极性能的探讨

以一种双向拉伸聚丙烯（ＢＯＰＰ）薄膜在不同温度下进行恒温处理，然后进行负电晕驻极。结果表明，ＢＯＰＰ薄膜随恒温处理的温度升高，热刺激电流（ＴＳＣ）峰值愈低。利用经验方法，估算了它们的活化能，并探讨了导致ＴＳＣ峰温变化的原因。     

Studies of the neutralizing activity and avidity of anti-human immunodeficiency virus type 1 Env antibody elicited by DNA priming and protein boosting

DNA vaccination is an effective means of eliciting strong antibody responses to a number of viral antigens. However, DNA immunization alone has not generated persistent, high-titer antibody and neutralizing antibody responses to human immunodeficiency virus type 1 (HIV-1) envelope glycoprotein (Env). We have previously reported that DNA-primed anti-Env antibody responses can be augmented by boosting with Env-expressing recombinant vaccinia viruses. We report here that recombinant Env protein provides a more effective boost of DNA-initiated antibody responses. In rabbits primed with Env-expressing plasmids, protein boosting increased titer, persistence, neutralizing activity, and avidity of anti-Env responses. While titers increased rapidly after boosting, avidity and neutralizing activity matured more slowly over a 6-month period following protein boosting. DNA priming and protein immunization with HIV-1 HXB-2 Env elicited neutralizing antibody for T cell line-adapted, but not primary isolate, viruses. The most effective neutralizing antibody responses were observed after priming with plasmids which expressed noninfectious virus-like particles. In contrast to immunizations with HIV-1 Env, DNA immunizations with the influenza virus hemagglutinin glycoprotein did not require a protein boost to achieve high-titer antibody with good avidity and persistence.     

烧结直接淬火对WC－TiC－Co合金WC相微观结构及力学性能的影响

本文以ＴＥＭ和ＸＲＤ为主要手段，研究ＷＣ－ＴｉＣ－Ｃｏ硬质合金（ＹＴ１４）在直接淬火后ＷＣ相的微观结构和微观应力，比较了常规烧结态与淬火态试样ＷＣ相之间的差异，探讨了直接淬火对合金力学性能的影响。结果表明，在保持合金其他性能不降低的同时，直接淬火的抗弯强度比常规烧结提高２８％左右。并首次发现，经直接淬火热处理的ＹＴＩ４合会中与Ｃｏ相相邻的ＷＣ晶粒边界出现滑移台阶，这是淬火过程中热应力使ＷＣ晶粒内位错在｛０００１｝密排面聚集，并可能沿｛０００１｝＜１１２０＞易滑移系统强滑移的结果。直接淬火试样的微观应力比重新加热淬火的低。     

Downloading trace study for BitTorrent P2P performance measurement and analysis

With the increasing client population and the explosive volume of Internet media content, the peer-to-peer networking technologies and systems provide a rapid and scalable content distribution mechanism in the global networks. The BitTorrent protocol and its derivatives are among the most popular peer-to-peer file sharing applications, which contribute a dominant fraction of today??s Internet traffic. In this paper, we conduct the performance measurement and analysis of BitTorrent systems with an extensive volume of real trace logs. We use several downloading-side metrics, including overall downloading time, maximum of downloading bandwidth, average bandwidth utilization, maximum of downloading connections, and average number of active connections, to derive various interesting results from the downloading-side aspect of network resource usage. Performance examination learns many new observations and characteristics into the virtue of BitTorrent protocols and systems, thereby providing beneficial information for bandwidth allocation and connection control in BitTorrent client applications. Therefore, this study is complementary to many previous research works that mainly focused on system-oriented and uploading-side performance measurements.     

基于区间树硬件加速索引的Marching Cubes算法

为了充分利用GPU的海量线程并行架构,提高等值面可视化效率,提出一种基于区间树硬件加速索引的Marching Cubes算法.该算法在预计算阶段利用GPU构造多区域的区间树作为体数据体素的值域索引;在实时运行阶段根据用户给定的阈值,通过该索引并行地搜索活跃体素,并生成活跃体素的多级索引,然后分配线程处理活跃体素,抽取并绘制等值面.将文中算法应用到不同体数据上的实验结果表明,其能够显著地提高现有Marching Cubes算法的效率;与现有的GPU基准算法相比,最高能达到4～10倍的加速比.     

Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification

Knowledge and Information Systems - Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn...     

An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.

     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.