以DDS为核心的数字模块在相控阵雷达中的应用

介绍数字收 /发模块在相控阵雷达中的应用。首先分析了数字收 /发模块的核心部分 :直接数字式频率合成器和数字接收机的基本工作原理。由于直接数字式频率合成 (DDS)具有分辨力高、转换速度快等优点 ,使系统的整体性能得到了显著提高 ;该数字模块的控制部分、数据输入、数据输出均是数字序列。最后给出了实验系统的实验结果     

基于质量评价技术的多信道语音选优决策系统

在分析远距离多信道同源语音信道特性的基础上 ,用若干特征参数来表征信道传输的语音质量并建立了语音质量客观评价模型 ,结合人的听觉特性等因素开发出一种基于语音质量客观评价技术的优质语音信道选优决策系统。该系统能够确保语音通信始终在最佳信道上 ,且在切换过程中无跳变感觉 ,不影响听觉效果 ,现已在实际工程中得到成功运用并取得了满意效果。     

冗余系统费用模型及优化

根据冗余设计的不同形式 ,在Tillman并联系统费用模型的基础上引入了系统功能指标 ,并将其推广至其它形式的冗余系统 ,从而得到了一个较为完整的冗余系统费用模型。在此基础上 ,建立了用于优化并串联冗余系统设计的非线性混合整数规划模型 ,分别使用了模式搜索与启发式算法的组合算法以及遗传算法对其进行求解 ,着重讨论了两种方法的实现关键及各自的优劣。最后 ,经计算比较 ,提出了遗传算法与模式搜索、启发式算法相结合的求解方法 ,并说明了该方法的有效性。     

纳米碳管与氢能

世界人口快速增长、化石能源渐趋匮乏、环境污染日益严重是人类在21世纪亟待解决的三大难题.氢能是一种洁净的可再生能源,氢燃料电池汽车将是解决城市大气污染的最重要途径之一.因此各国科学家均在致力于研究高储量的储氢材料与系统,最有潜力者被认为是纳米炭材料的吸附储氢.高纯度单壁纳米碳管的合成是应用的基础和关键,我们采用氢等离子电弧方法,大量制备出高纯度的单壁纳米碳管,并全面开展储氢研究.初步结果表明单壁纳米碳管是一种很有前途的储氢材料,可能在氢能源的应用特别是氢燃料电池电动汽车的应用中占据重要地位,促进人类经济与社会实现可持续发展.     

数理逻辑之研究对象、学科归属、定义及研究领域

鉴于对数理逻辑这一学科的定义、研究对象、研究领域与学科归属等问题,尚无一个一致的说法,有的已不符合历史发展的现状.例如,就学科归属而言,就有两种说法其一说数理逻辑是数学的一个分支;其二说数理逻辑是逻辑学的一个分支,如此等等.有的数理逻辑著作中完全不论及这类问题.为此,本文从数理逻辑历史发展的几个侧面加以分析,并综合各家之长对数理逻辑的定义、研究对象、研究领域与学科归属等问题进行了探讨.     

优序法在管理成果评价中的应用

在对管理成果进行评价时 ,首先要建立指标体系 ,而在这些指标当中有的可以定量描述 ,有的只能定性来描述 ,所以在评价时应当将定性和定量指标进行综合考虑。优序法就是通过对多目标决策问题进行两两相对比较 ,最后给出全部方案的优序排序。此方法应用简单 ,既能处理定量问题 ,又能处理定性问题。在建立管理成果指标体系的基础上 ,利用优序法 ,给出了管理成果评价方法。     

基于模糊规则的病虫害预防研究

基于Takagi-Sugeno模糊逻辑系统,运用遗传算法提取模糊规划,构建系统的模糊模型,并用于病虫害的预测工作。图2,表1,参6。     

A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase

Grain weight is one of the most important components of grain yield and is controlled by quantitative trait loci (QTLs) derived from natural variations in crops. However, the molecular roles of QTLs in the regulation of grain weight have not been fully elucidated. Here, we report the cloning and characterization of GW2, a new QTL that controls rice grain width and weight. Our data show that GW2 encodes a previously unknown RING-type protein with E3 ubiquitin ligase activity, which is known to function in the degradation by the ubiquitin-proteasome pathway. Loss of GW2 function increased cell numbers, resulting in a larger (wider) spikelet hull, and it accelerated the grain milk filling rate, resulting in enhanced grain width, weight and yield. Our results suggest that GW2 negatively regulates cell division by targeting its substrate(s) to proteasomes for regulated proteolysis. The functional characterization of GW2 provides insight into the mechanism of seed development and is a potential tool for improving grain yield in crops.     

The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer disease

The recycling of the amyloid precursor protein (APP) from the cell surface via the endocytic pathways plays a key role in the generation of amyloid beta peptide (Abeta) in Alzheimer disease. We report here that inherited variants in the SORL1 neuronal sorting receptor are associated with late-onset Alzheimer disease. These variants, which occur in at least two different clusters of intronic sequences within the SORL1 gene (also known as LR11 or SORLA) may regulate tissue-specific expression of SORL1. We also show that SORL1 directs trafficking of APP into recycling pathways and that when SORL1 is underexpressed, APP is sorted into Abeta-generating compartments. These data suggest that inherited or acquired changes in SORL1 expression or function are mechanistically involved in causing Alzheimer disease.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.