International Trade

Values of net oil imports(-)/exports for Austria, Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, Japan, Canada, the United States and Australia. Updated on a monthly basis. Number of imports for Belgium, France, Germany, Greece, Republic of Ireland, Italy, Netherlands, Portugal, Spain, Sweden, the United Kingdom, and Other EU-15 (EU-15), Norway, Poland, Switzerland, Turkey, and Other Europe (OECD Europe), Canada, Chile, Mexico, and the United States (OECD Western Hemisphere), and Australia, Japan, Republic of Korea, New Zealand, (OECD Asia-Pacific), and Total OECED. Current data for Austria, Belgium, Finland, France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden, the United Kingdom, Other EU-15 (EU-15), Czech Republic, Hungary, Poland, Slovakia, Turkey, and Other Europe (OECD Europe), Canada, Mexico, and the United States (OECD Western Hemisphere), Australia, Japan, Republic of Korea, and New Zealand (OECD Asia-Pacific). Updated on a monthly basis. Current data for principal importers of natural gas and the amount in which they import from United States, Republic of Korea, United Kingdom, France, Japan, Italy, Germany and Spain (Table 19.1). Current data for principal exporters of natural gas and the amount they export to Canada, Trinidad and Tobago, the United States, Netherlands, Norway, Russia, Turkmenistan, Qatar, Algeria, Nigeria, Indonesia, Malaysia and Australia (Table 19.2). Updated on a monthly basis.     

Governance choices and policy outcomes in the Latin American and caribbean electricity sector

The paper first provides, for Latin America and the Caribbean, country-specific synthetic quantitative measures of the degree of adoption of governance reforms in the electricity sector, focusing on four key dimensions (market structure, private sector participation (PSP), regulatory autonomy and operational organization). These measures are then correlated with standard policy performance outcomes measures. The findings suggest that, the reforms, considered jointly, were statistically significantly associated with higher technical and process quality but not with social or service improvements. Considered individually, increased regulatory autonomy enjoyed the highest positive correlation with desirable outcomes, but PSP has no significant correlation with any of the outcomes. In sum, as of 2018, governance reforms, considered jointly, had still not delivered on some promised payoffs, notably in addressing energy poverty, although the sensitivity analysis suggests that, for some indicators, the relationships are stronger for larger than for smaller countries.     

A datamining approach to classify,select and predict the formation enthalpy for intermetallic compound hydrides

In this paper, two techniques of datamining tools were adopted, a principal component analysis (PCA) and artificial neural network (ANN). A PCA to classify, select and identify several combinations between transition element A and B (B = Ti, Zr, Hf, Sc, Y, La and Th) and ANN to predict ΔH for ternary hydrides. Based on the datasets selected from different works, a principal component analysis (PCA) has been applied to select, classify and identify around 76 possible combinations between transition metal elements A and B. The results showed that the clustering of combinations A-B are significantly influenced by the atomic parameters of element A, such atomic radius (R_A), Pauling's electronegativity (χ_A) and atomic electron density (Z_A/R_A³). From 76 combinations, 55 systems which have χ_A ≥ 1.5, Z_A/R_A³>1.28 and R_A < 1.46 Å are categorized as group 1. On the other hand, 21 systems which have χ_A < 1.5, Z_A/R_A³ < 1.28, and R_A > 1.46 Å are categorized as group 2. From the first group, 46 different combinations are identified and have a negative ΔH, within 18 well-known promising binary alloys of hydrogen storage.An (6-15-1) architecture of artificial neural network (ANN) has been developed to estimate the ΔH for the other ternary hydrides selected from different published works. The performance indices such as relative error, coefficient of determination (R²) and mean square error (MSE) were used to control the performance of obtained results. In addition to this, the ΔH obtained from ANN model were compared with those experimental data and theoretical results available in the literature.     

Classification of monovarietal Argentinean white wines by their elemental profile

The possibility of acquire a chemometric model to classify Argentinean white wines according to their provenance through elemental profile was assessed. A simple method for multielement determination in wines by inductively coupled plasma mass spectrometry along with chemometric pattern-recognition techniques is proposed. A total of 57 white wine samples of the main varieties from four winegrowing regions of Argentina: Mendoza, Rio Negro, San Juan, and Salta, were evaluated. The results of principal component analysis explained 95.95% of the variance data total. Linear discriminant analysis allowed correct discrimination according to the four geographical regions evaluated, using only five ultratrace elements (Ba, As, Pb, Mo, and Co). Discrimination rates higher than 96% for prediction and validation data sets were reached. The outcomes emphasize the skillfulness of ICPMS elemental determination in combination with chemometrics, for classification of white wine and show that could be a trustworthy technique to validate the geographical origin, authenticity and quality control of wines.     

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.     

Parallel quality-related dynamic principal component regression method for chemical process monitoring

Traditional quality-related process monitoring mainly focuses on the magnitude change of the quality variables caused by additive faults. However, the abnormal fluctuations in the quality variables caused by multiplicative faults are often overlooked. In this paper, a novel parallel dynamic principal component regression (P-DPCR) algorithm is proposed to monitor the changes in the magnitude and fluctuation of the quality variables simultaneously. Firstly, in order to eliminate the interference of quality-unrelated variables, the quality-related process variables are selected on the basis of correlation analysis. Secondly, the dynamic extension and moving window are carried out for process variables and quality variables, in which the dynamic variables space (called X-space/Y-space) and the variance space (called VX-space/VY-space) are constructed. Afterwards, double quality-related statistics based on the regression model of these four spaces are given, and the comprehensive monitoring decision can be obtained. Finally, two numerical cases and the Tennessee Eastman process are used to show the effectiveness of the proposed method.     

International Trade

Values of net oil imports(-)/exports for Austria, Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, Japan, Canada, the United States and Australia. Updated on a monthly basis. Number of imports for Belgium, France, Germany, Greece, Republic of Ireland, Italy, Netherlands, Portugal, Spain, Sweden, the United Kingdom, and Other EU-15 (EU-15), Norway, Poland, Switzerland, Turkey, and Other Europe (OECD Europe), Canada, Chile, Mexico, and the United States (OECD Western Hemisphere), and Australia, Japan, Republic of Korea, New Zealand, (OECD Asia-Pacific), and Total OECED. Current data for Austria, Belgium, Finland, France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden, the United Kingdom, Other EU-15 (EU-15), Czech Republic, Hungary, Poland, Slovakia, Turkey, and Other Europe (OECD Europe), Canada, Mexico, and the United States (OECD Western Hemisphere), Australia, Japan, Republic of Korea, and New Zealand (OECD Asia-Pacific). Updated on a monthly basis. Current data for principal importers of natural gas and the amount in which they import from United States, Republic of Korea, United Kingdom, France, Japan, Italy, Germany and Spain (Table 19.1). Current data for principal exporters of natural gas and the amount they export to Canada, Trinidad and Tobago, the United States, Netherlands, Norway, Russia, Turkmenistan, Qatar, Algeria, Nigeria, Indonesia, Malaysia and Australia (Table 19.2). Updated on a monthly basis.     

基于主曲线的书法字动态重现算法

如何高效逼真地动态重现书法作品的创作过程日益成为亟需解决的问题,因此文中提出基于主曲线的书法字动态重现方法,解决当前细化算法在提取骨架时存在的无效分支较多、不能保证笔画的连续性和准确性的问题.结合书法字自身结构对主曲线算法进行改进和优化.对书法图像进行骨架提取、骨架追踪、笔顺获取等处理,结合骨架信息和二值图像实现笔画宽度复原,针对笔画宽度存在的“肿瘤”问题,提出“角点剔除法”进行修正,结合HTML5的Canvas绘图技术进行书法字动态书写过程.在模拟数据集上的实验表明,文中算法的书法动态重现取得较好效果.     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。