电网设备状态检修的时变决策模型

为实现将成熟的设备状态评估技术有效地融入到传统的电网检修计划中,建立了电网设备状态检修时变决策模型,使决策根据设备状态变化进行调整.首先给出时变决策机制的定义和相应的表达,在此基础上推导设备运行风险和系统运行风险,进一步建立了考虑设备多次检修的电网状态检修决策模型,在计及相关检修约束前提下优化电网总运行风险.最后,通过多场景下的算例分析,表明该模型是可行且有效的,有工程应用的潜力.     

0412-1961

利用前文的实验方法,本文研究铁液中Si,Nb活度的相互作用.为了使脱氧产物稳定,本文和前文不同,采用下列组装的定氧电池: Mo     

冲压件CAPP系统中几何信息提取实现

基于对AutoCAD的DXF文件格式的详细分析，充分利用多义线的表达简洁性优点，并结合冲压件自身特点，提出了冲压件几何信息获取的方法及程序框架，详细说明了几何信息库的创建方法，并进一步采用Delphi6．0语言编程，实现了冲压几何信息库的建立和几何信息自动提取。     

非调质钢的合金设计与热加工工艺及性能

论述了非调质钢的性能特点，分析了V、Nb、Ti、Mn、Cr、Si等常用合金元素对非调质钢组织和力学性能的影响，并对热加工工艺、热处理等因素个性能的影响作了分析和讨论。     

Zn-Al系二元相图的研究

利用高温X射线衍射法（XRD）、示差扫描量热分析法（DSC）、热膨胀法（PRD）等方法重新测定Zn-Al二元体系相图。在XRD曲线图中β（ZnAl）相的存在，表明存在β（ZnAl）固溶单相区；DSC曲线图表明在274～279℃范围内存在共析相变反应；PRD曲线图表明在340℃不存在相变反应。综合XRD、DSC和PRD结果，绘制了部分Zn-Al二元体系相图，为高铝锌基合金的实际相变应用提供参考。     

TiO2悬浮液光电流响应的影响因素

利用电化学分析仪对TiO2悬浮液在紫外光照射下产生的光电流进行了收集测试.具体研究了悬浮液浓度、辅助添加剂加入与否等测试条件对TiO2悬浮液光电流的影响.试验表明,随着TiO2悬浮液浓度的增加光电流达到一定的峰值,而分散剂的加入是提高光电流响应值的关键因素.光电流测试是用来表征TiO2悬浮液分散性能及光催化反应能力的有效手段之一.     

2—甲基—1，4—萘醌对DNA光敏损伤的诱导作用

选择波长337nm的激光作为激励光源，借助凝胶电泳研究了2－甲基－1，4－萘醌诱导的DNA光敏损伤。结果表明：在氧气饱和、脱氧条件下光敏损伤显著，DNA损伤主要与光子剂量、核酸与萘醌浓度比及DNA存在形式有关。     

适应不同温度的几种钛熔块的研究

用TiO2作乳浊剂通过调节各种熔剂的用量制得了适用于各种不同温度的钛熔块釉。     

新颖除草剂研究进展

综述了新型除草剂研制的方向、重点和特点。涉及磺酰脲类除草剂 ,环状亚胺类除草剂 ,苯氧羧酸类除草剂 ,有机磷类除草剂。主要介绍了具有除草活性好的丙酮酸脱氢酶抑制剂研究新进展。     

Parallel matrix factorization for recommender systems

Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems. To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. Alternating least squares (ALS) and stochastic gradient descent (SGD) are two popular approaches to compute matrix factorization, and there has been a recent flurry of activity to parallelize these algorithms. However, due to the cubic time complexity in the target rank, ALS is not scalable to large-scale datasets. On the other hand, SGD conducts efficient updates but usually suffers from slow convergence that is sensitive to the parameters. Coordinate descent, a classical optimization approach, has been used for many other large-scale problems, but its application to matrix factorization for recommender systems has not been thoroughly explored. In this paper, we show that coordinate descent-based methods have a more efficient update rule compared to ALS and have faster and more stable convergence than SGD. We study different update sequences and propose the CCD++ algorithm, which updates rank-one factors one by one. In addition, CCD++ can be easily parallelized on both multi-core and distributed systems. We empirically show that CCD++ is much faster than ALS and SGD in both settings. As an example, with a synthetic dataset containing 14.6 billion ratings, on a distributed memory cluster with 64 processors, to deliver the desired test RMSE, CCD++ is 49 times faster than SGD and 20 times faster than ALS. When the number of processors is increased to 256, CCD++ takes only 16 s and is still 40 times faster than SGD and 20 times faster than ALS.