纳米金属氧化物的液相法制备技术

综述了液相法制备纳米金属氧化物的主要技术,讨论了各种液相法制备方法的反应机理、优缺点、改进措施及适用性,指出液相法制备技术存在的问题及展望方向.     

阵列信号单信道处理技术

阵列信号处理已广泛应用于许多电子系统中，如微波无源比幅测向系统等，但通道间的振幅和相位的不平衡常常引起较大的测向误差。为了匹配通道间的振幅和相位平衡，可利用卫星通信中的多址联接技术（如ＦＤＭＡ、ＴＤＭＡ或ＣＤＭＡ）构成单信道处理系统来代替多信道系统。文中详细地研究了ＣＤＭＡ单信道系统，建立了系统模型，分析了系统特性，模拟结果证明了这种单信道理论和技术的可行性。     

一种瞬变步长LMS自适应滤波算法

基于规一化起动、平滑递推和ＰＬＳ算法导出了一种新的变步长ＬＭＳ算法。文中分析了起动后的平稳连接和提高起始收敛速度的条件，证明了步长因子μ（ｋ）的瞬变特性及其对减小失调噪声的作用。计算机仿真效果较好，支持了理论分析。     

高纯度Cr26Mo钢焊缝的力学和耐碱蚀性能

本文用钨极氩弧焊附加氩气延续保护的焊接方法对高纯度 Cr26Mo 钢进行可焊性研究。检验焊缝质量和力学性能,用失重法和电化学方法测试焊缝在阿蒙古天然碱苛化烧碱的高温浓碱液条件下的腐蚀行为,确定了腐蚀速度,评定了耐蚀性等级。观察焊缝金相组织,分析焊缝区碱蚀表面的腐蚀形态,判别力学性能试样的断口形貌特征和断裂类型,并鉴定了腐蚀过程中所形成的保护膜和沉淀物的形貌和晶体结构。表明该钢焊缝有较好的力学性能和耐内蒙天然碱苛化烧碱液的性能。     

“夏利”汽车覆盖件板材机械性能指标许用值的确定

板材机械性能指标显示影响着汽车覆盖件冲压成形，本文在采取成形极限曲线与网格应变分析技术相结合进行理论和试验研究的基础上，提出了“夏利”汽车覆盖件各件板材机械性能指标许用值，用以指导科学选材，经一年来的生产使用表明，方法可行，经济效益显著。     

"冲压工艺学"网络教材的开发

介绍了"冲压工艺学"网络教材的开发过程,对其结构、内容、导航、反馈等做了分析.     

原子簇的合成方法、性质表征与结构

利用给电子有机和无机配位体的交换反应和金属卤化物与羰基金属阴离子的复分解反应,合成异核原子簇具有反应条件温和易于控制,收率较高的特点。本文描述Fe_3(CO)_9(C_2H_2)_3等11个原子簇的合成方法,配位状态与簇核的结构。     

优化TOPSIS模型在木材加工企业的评价应用——基于SCM视角

考虑到林产品经销商在参与木材加工企业构建供应链时需选取其作为上游合作伙伴,构建包括竞争力、绿色行为、质量、成本和交货期5个一级指标及其14个二级指标的评价指标体系,运用TOPSIS优化改进模型,结合模糊数学评价木材加工企业,最后通过案例证实该评价指标体系的可行性,为林产品经销商提供参考借鉴。     

二语作格动词语义加工中的身物交互效应

This article investigates the theoretical basis and feasibility in the processing of English ergative verbs from the perspective of body-object interaction effects (BOI). It analyzes the motor degree of different verbs, probing into the semantic features and syntactic behaviors of ergative verbs. It also explores the relationships between the BOI effects and different type of verbs in semantic processing, examining the motor prominence of English ergative verbs, as well as analyzing the factors that cause the misusage of English ergative verbs in the second language processing. Further empirical research is needed to verify the BOI effects’ restrictive elements in the processing of English ergative verbs.     

A Compressive Sensing Based Analysis of Anomalies in Generalized Linear Models

In this article, we present a compressive sensing based framework for generalized linear model regression that employs a two-component noise model and convex optimization techniques to simultaneously detect outliers and determine optimally sparse representations of noisy data from arbitrary sets of basis functions. We then extend our model to include model order reduction capabilities that can uncover inherent sparsity in regression coefficients and achieve simple, superior fits. Second, we use the mixed ?₂/?₁ norm to develop another model that can efficiently uncover block-sparsity in regression coefficients. By performing model order reduction over all independent variables and basis functions, our algorithms successfully deemphasize the effect of independent variables that become uncorrelated with dependent variables. This desirable property has various applications in real-time anomaly detection, such as faulty sensor detection and sensor jamming in wireless sensor networks. After developing our framework and inheriting a stable recovery theorem from compressive sensing theory, we present two simulation studies on sparse or block-sparse problems that demonstrate the superior performance of our algorithms with respect to (1) classic outlier-invariant regression techniques like least absolute value and iteratively reweighted least-squares and (2) classic sparse-regularized regression techniques like LASSO.