Toward the adaptation of component‐based architectures by model transformation: behind smart user interfaces

Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component‐based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run‐time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component‐based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component‐based architectures by using model transformation. Our aim is to generate at run‐time a dynamic model transformation, because the rules describing their behavior are not pre‐set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run‐time a rule subset from a repository. Selected rules are used to generate, through a higher‐order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre‐set transformation, which enables the dynamic adaptation of component‐based architectures. Copyright © 2014 Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

熔融制样-能量色散X射线荧光光谱仪分析硅酸盐中32 种组分

为充分发挥小型实验室设备的功能,实验采用熔融制样,使用台式能量色散X射线荧光光谱仪(10W, Rh靶)定量分析了硅酸盐样品中的Na₂O、MgO、Al₂O₃、SiO₂、P₂O₅、SO₃、Cl、K₂O、CaO、TiO₂、V、Cr、MnO、Fe₂O₃、Co、Ni、Cu、Zn、Ga、Rb、Sr、Y、Zr、Nb、As、Ba、La、Ce、Th、U、Hf、Pb等32种主、次、痕量组分。对3种不同的解谱方法进行比较,确定Na、Mg、Al、Si、P、As、Ce、Cr、Cu、Ga、La、Nb、Ni、Pb、Th、U、V、Y选择感兴趣区解谱,其余元素用高斯或经改进的高斯函数的最小二乘法拟合解谱。选用有代表性的多个硅酸盐类样品,比较了理论检出限及10次重复测定计算3倍标准偏差得出的检出限,发现以3倍标准偏差作为检出限再取它们的平均值则更具代表性和使用意义。各组分的检出限为0.2~1740μg/g。精密度试验表明,各组分测定结果的相对标准偏差(RSD)为0.12%~10.5%(Na为轻元素,由于含量低,小能谱测定的精密度稍差);对两个土壤标准样品进行正确度验证,测定值与标准值一致。     

一种灰度化混合法在集装箱箱号识别中的运用

研究一种基于机器视觉的集装箱箱号识别方法。对于集装箱彩色图像预处理过程中的灰度化方法，传统的灰度化算法不能有效弥补图像中污损或其他信息缺失的部分，因此，本文提出使用主成分分析法（PCA）结合贝叶斯阈值估计灰度变化率的混合法对图像的灰度化进行优化，可以在判断图像中某一点灰度值与周围相邻像素点的灰度值的变化率后，弥补缺失信息，有效确定边缘特征，从而使后续的字符识别准确率大大提高。最后使用该算法模型设计实现一套用于港口集装箱的智能检测系统。经过Matlab实验验证，在对50幅港口集装箱箱号图像的识别中，通过使用本文提出的混合灰度化方法，与普通的均值法和加权平均法的灰度化方法相比，准确率更高，其中单一字符准确率可达96%，箱号准确率可达92%。     

基于卷积神经网络的构件分类策略的研究

为了提高软件复用过程中构件检索的效率，分析了软件构件分类技术的优缺点以及构件特征，从构件刻面信息的角度，采用卷积神经网络技术，提出一种基于卷积神经网络的构件分类策略；利用卷积神经网络对构件刻面特征进行提取，减少人为因素，提高刻面信息提取精确性，并训练出基于卷积神经网络的构件分类模型，通过具体的实验，来论证该模型的准确性，以达到提高构件检索效率的目的。     

组件树理论和方法研究综述

组件树方法是数学形态学理论及方法的进一步发展，它利用一系列阈值截集产生连通分量，构造其上的层次关系。利用组件树方法发展出的一系列高效的图像处理算法，非常适合图像底层特征的快速提取，在广大的领域，尤其是医学图像处理领域，得到了广泛的应用。对组件树方法的发展和现状进行了系统的回顾，概述了现存的组件树建树算法以及基于组件树的图像处理算法。对组件树在多值（彩色）图像相关领域、树的快速构建、并行化处理算法方面的研究进行了展望。     

果实喷施脱落酸对黑虎香葡萄酒品质的影响

黑虎香葡萄属于鲜食、酿酒兼用品种，香气独特、抗逆性强。对转色期黑虎香葡萄果实喷施不同浓度脱落酸（ABA），研究其对葡萄酒品质的影响。结果表明，喷施500 mg/L ABA处理效果最好，显著提高黑虎香葡萄酒酒精度、干浸出物、花色苷和总酚含量，显著降低酸含量（P＜0.05）；显著增加黑虎香葡萄酒萜烯类、醇类和醛酮类等香气物质含量（P＜0.05），含量影响较大的香气成分为乙酸乙酯、乙酸苯乙酯、苯乙醇、壬醇、戊二酮、香叶基丙酮、乙缩醛、香茅醇和香叶醇。     

Predicting pollutant removal in constructed wetlands using artificial neural networks(ANNs)

Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs) for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN) method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA) and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.     

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.     

Geographical discrimination of honeys through antioxidant capacity,mineral content and colour

The assessment of geographical origin of honey is economically important for producers and consumers as every region may present particular quality characteristics. In this study, honeys from the seven different regions of Buenos Aires province (Argentina) were characterised by their antioxidant capacity (DPPH, FRAP), total phenolic content (TPC), mineral composition, colour and ash. Honeys showed significant differences among their antioxidant capacity (DPPH), ash, colour and mineral content (P ≤ 0.05). Besides, a good antioxidant activity and low amounts of Cu and Zn (<1.0–1.5 and 0.7–1.8 mg kg^?1, respectively) were found in the samples. Significant Pearson's correlations (P ≤ 0.05) among the different parameters were found. Moreover, the linear discriminant analysis allowed the classification of honeys in their original groups with a prediction success of 98%. The present results suggest that honeys could be correctly classified by their geographical origin through their TPC, colour, ash and mineral concentrations.