硝基NPK复合肥的生产技术与市场前景

论述了硝基NPK复合肥生产的基本原理、生产特点、工艺设计、消耗定额以及市场与效益分析。     

热作模具钢表面激光堆焊耐磨合金层的研究

本文主要通过在H13钢上用大功率CO_2激光束进行堆焊,阐述了激光堆焊工艺变化对堆焊组织和性能的影响,在本试验条件下,得到了高密度弥散金属化合物组织的激光堆焊层,平均硬度为HV800,与基体呈冶金结合,耐磨性比H13模具钢提高149%。     

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。     

国外加氢裂化技术的最新进展

介绍了国外加氢裂化技术在催化剂、生产工艺和反应器内构件设计方面的新进展，对我国加氢变化技术的发展提出了建议。     

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     

CuO/ZnO甲醇合成催化剂的正电子湮没谱学研究

发展了用正电子湮没寿命参数测定用共沉淀方法制备的金属氧化物系固溶度的新方法。测量了一系列用共沉淀方法制备的不同CuO at％含量的CuO/ZnO甲醇合成催化剂粉末(压制成片)还原前后的正电子寿命谱。分析该种催化剂的固溶特性及其与正电子寿命参数的关系,得出还原后样品的固溶度为12CuOat％.支持了CuO/ZnO甲醇合成催化剂以Cu~+离子为活性中心的观点。     

Adaptive motion control using neural network approximations

In this paper, we present a new adaptive technique for tracking control of mechanical systems in the presence of friction and periodic disturbances. Radial Basis Functions (RBFs) are used to compensate for the effects of nonlinearly occurring parameters in the friction and periodic disturbance model. Theoretical analysis, such as stability and transient performance, is provided. Furthermore, the performance of the adaptive RBF controller and its non-adaptive counterpart are compared.     

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.