Cu基络合物催化甲醇液相氧化羰化反应

针对甲醇液相氧化羰基化法合成碳酸二甲酯(DMC)工艺,开发了新型Cu基络合催化剂(CuB rnLm)。对CuB rnLm催化剂的活性及其稳定性进行了研究。实验结果表明,采用该催化剂,甲醇转化率和DMC选择性较高;元素价态和物质结构分析表明,CuB rnLm催化剂循环使用5次后仍保持较好的稳定性。采用正交设计和中心响应曲面法设计实验,并使用S tatistica软件进行统计分析,寻求出反应的主要影响因素,并得到优化的工艺条件:反应温度100~110℃、反应压力3.0~3.5M Pa、反应时间4~6h、CuB rnLm催化剂质量浓度(以甲醇的体积计)0.15~0.20g/mL。在此工艺条件下,甲醇转化率可达23%以上,DMC的选择性为96%~98%。     

UC3875在超声电源功率控制系统中的应用

根据超声波发生器的功率控制系统原理 ,阐述了移相控制策略对超声波发生器输出电压波形的影响 ,介绍了移相控制专用芯片UC3875的电路结构和使用设计方法。     

石油沥青60℃动力粘度测定影响因素的探讨

粘度是表征沥青稠度的一个重要指标。通过论述石油沥青粘度测定法(真空毛细管法)中毛细管的选择、温度、真空度、取样量等对试验结果的影响及试验过程中的其它注意事项,表明了在测定石油沥青的60℃动力粘度时,必须严格按照试验方法的要求进行测定,以保证试验结果的准确性。     

Hf-doped and NH/sub 3/-nitrided high-K gate dielectric thin film with least drain current degradation and flatband voltage shift

A novel technique to form high-K dielectric of HfSiON by doping base oxide with Hf and nitridation with NH/sub 3/, sequentially, is proposed. The HfSiON gate dielectric demonstrates excellent device performances such as only 10% degradation of saturation drain current and almost 45 times of magnitude reduction in gate leakage compared with conventional SiO/sub 2/ gate at the approximately same equivalent oxide thickness. Additionally, negligible flatband voltage shift is achieved with this technique. Time-dependent dielectric breakdown tests indicate that the lifetime of HfSiON is longer than 10 years at V/sub dd/=2 V.     

Effect of Fog and Clouds on the Image Quality in Millimeter Communications

The effect of fog and clouds in millimeter communication is discussed, and the attenuation caused by fog and clouds is reviewed. Signal-to-noise ratio (SNR) of image is derived using relating models of fog and clouds attenuation. According to the relation of image quality and its signal-to-noise ratio, the system behavior is forecasted theoretically. It is shown that the signal-to-noise ratio of receiver at certain transmitter power is inverse with radio wave frequency, from about 70dB at 10GHz to 48dB for fog and 49dB for clouds. The image quality of received signal at certain transmitter power is inverse with radio wave frequency, from about 7 grade at 10GHz to 5.27 grade for fog and 5.37 grade for clouds. The above calculated results are consistent with experimental results.     

高氧化态（Ⅱ─Ⅳ）三核金属簇合物构型的人工神经网络判别方法

本文运用一典型的人工神经网络模型─“反向传播”模型，对高氧化态（Ⅱ─Ⅳ）三核金属簇合物的构型分布进行了分析，得到了较好的分类、预报结果为化合物结构分析提供了新的工具。     

Windows系统中两种动态交换数据的方法

本文介绍了Ｗｉｎｄｏｗｓ环境下基于消息的ＤＤＥ和基于动态链接库的ＤＤＥＭＬ的区别，并详细叙述了如何使用ＤＤＥＭＬ编制Ｗｉｎｄｏｗｓ应用程序。     

R&D activities on wireless systems in China

Various wireless systems and the research preceding their practical use in China are described. The topics discussed include research establishments, research and development projects underway, challenges inherent to digital radio, and expansion opportunities afforded by satellite communications     

Improving signal prediction performance of neural networks through multiresolution learning approach.

In this paper, we extend the original work on multiresolution learning for neural networks, and present new developments on the multiresolution learning paradigm. The contributions of this paper include: (1) proposing a new concept and method of adjustable neural activation functions in multiresolution learning to improve neural network learning efficacy and generalization performance for signal predictions; (2) providing new insightful explanations for the multiresolution learning paradigm from a multiresolution optimization perspective; (3) exploring underlying ideas why the multiresolution learning scheme associated with adjustable activation functions would be more appropriate for the multiresolution learning paradigm; and (4) providing rigorous validations to evaluate the multiresolution learning paradigm with adjustable activation functions and comparing it with the schemes of multiresolution learning with fixed activation functions and traditional learning. This paper presents systematically new analytical and experimental results on the multiresolution learning approach for training an individual neural network model, demonstrates our integral solution on neural network learning efficacy, and illustrates the significant improvements on neural networks' generalization performance and robustness for nonlinear signal predictions.