Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

等离子体刻蚀工艺控制模型分析

讨论了主因素分析法以及神经网络法在等离子体刻蚀工艺中的应用.结果表明主元素分析法可以实现对数据的压缩,而神经网络算法则显示出比传统的统计过程控制算法更好的准确性.     

一种变步长D-Rake多用户检测器

在时变多径衰落信道下,接收到的CDMA信号功率变化较大,此时D-Rake盲自适应多用户检测器性能显著下降,将变步长LMS算法与基于主分量的相干合并引入到D-Rake(DecorrelatingRake)检测器中,构成一种变步长D-Rake,称之为VD-Rake(Variablestep-sizeDecorrelatingRake)检测器。该检测器能克服原D-Rake检测器对信号功率变化较敏感等缺点,有效地改善了D-Rake检测器的性能。     

中国古典园林三维造型研究

论述了采用层次结构化及参数化三维造型方法，建立一个以造型函数库为核心，从而构造各类、各级园林景观构件表面模型的方法和过程。对于某些非建筑类的园林配景构件，采用了Ｆｒａｃｔａｌ方法进行造型。     

Synthesis, Characterization, and Formation Process of Na-X Zeolite from Coal Fly Ash

Using the SiO₂ and Al₂O₃ components of the amorphous phase in coal fly ash (Fa), Fa was converted to Na-X zeolites in NaOH-NaAlO₂ solutions by stirring at 35°C for 72 hr and then aging at 85°C for a given period. The molar ratio SiO₂/Al₂O₃ of the starting materials was controlled from 2.0 to 13.2. The resulting materials were characterized by various means. Increasing the SiO₂/Al₂O₃ ratio of the starting material increased the degree of crystallinity of faujasite, exhibiting a maximum at SiO₂/Al₂O₃ = 8.0. The faujasite formed was identified as Na-X zeolite with Si/Al = 1.20. The amorphous phase in Fa was dissolved during the stirring to form a precursor of zeolite, such as amorphous aluminosilicate. The Na-X zeolite was formed by aging for 24 hr, and the degree of crystallinity of this material was increased with the increasing aging period. The cation exchange capacity and specific surface area were increased with the increasing degree of crystallinity of the Na-X zeolites.     

Introduction of a nonlinearity measure for principal component models

Although principal component analysis (PCA) is an important tool in standard multivariate data analysis, little interest has been devoted to assessing whether the underlying relationship within a given variable set can be described by a linear PCA model or whether nonlinear PCA must be utilized. This paper addresses this deficiency by introducing a nonlinearity measure for principal component models. The measure is based on the following two principles: (i) the range of recorded process operation is divided into smaller regions; and (ii) accuracy bounds are determined for the sum of the discarded eigenvalues. If this sum is within the accuracy bounds for each region, the process is assumed to be linear and vice versa. This procedure is automated through the use of cross-validation. Finally, the paper shows the utility of the new nonlinearity measure using two simulation studies and with data from an industrial melter process.     

软件构件技术在管理信息系统中的应用

软件构件技术是一种前沿的软件设计思想，其方法与传统的软件制作方法有很大区别．通过介绍软件构件技术在开发管理信息系统的应用，与其它软件制作方法进行比较，得出这样的结论：在软件编制中应用这种思想，不仅能大幅度地加快软件的开发速度，而且对整个软件行业的发展有着至关重要的推动作用。