Characterization of barium titanate reinforced acrylonitrile butadiene rubber composites for flexible electronic applications: influences of barium titanate content

The aim of this study was to develop high dielectric constant flexible polymers with a highly efficient and cost‐effective approach using acrylonitrile butadiene rubber (NBR) as the polymer matrix and barium titanate (BT) as the high dielectric constant filler. The BT powder was synthesized with a solid‐state reaction and was characterized using a particle size analyzer, XRD, SEM and Fourier transform infrared spectroscopy. NBR/BT composites were fabricated using an internal mixer with various BT loadings up to 160 phr. The influence of BT loading on the cure characteristics and mechanical, dynamic mechanical, thermal, dielectric and morphological properties was determined. The incorporation of BT in the NBR matrix shortened scorch time and increased delta torque. The mechanical properties, thermal stability and dielectric constant were greatly improved and increased with BT loading. The results suggest that the reinforcement effect was achieved due to strong hydrogen bonding or polar–polar interactions between NBR matrix and BT filler. This is further corroborated by the good dispersion of BT filler in the NBR matrix observed with SEM imaging. These findings can be applied to produce high‐performance dielectric elastomers. © 2020 Society of Industrial Chemistry     

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.     

The formation of fibrous structure of polyamide 6 induced by the three-step forming process and its prominent reinforcement in nitrile rubber

Nitrile rubber (NBR) blends with excellent performance have always been a hot research topic in petroleum field. Due to the excellent performance and compatibility of polyamide 6 (PA6), it provides an opportunity for the preparation of high-performance NBR/PA6 blends. In this article, NBR/PA6 blends were prepared by the three-step molding process. Experimentally, it was found that PA6 has a prominent reinforcement effect in NBR matrix. The variation of this mechanical property was investigated from different aspects of the crystal structure, crystallinities, phase morphology, and so on. It can be cleared that the formation of fibrous structure of PA6 phase is the main factor for reinforcement of the polymer blends. Meanwhile, the formation mechanism of the special phase structure induced by the three-step process is deeply expounded and its structural evolution schematic is established. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47472.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

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.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

Matrix influence on the piezoelectric properties of piezoelectric ceramic/polymer composite exhibiting particle alignment

This study was addressed to the influence of an electric field strength applied at fabrication process and matrix properties, such as the dielectric constant and the Young's modulus, on “pseudo‐1‐3 piezoelectric ceramic/polymer composite” in order to further enhance the piezoelectricity of that. The pseudo‐1‐3 piezoelectric ceramic/polymer composite consists of linearly ordered piezoelectric ceramic particles in polymer material. Silicone gel, silicone rubber, urethane rubber, and poly‐methyl‐methacrylate, which exhibit different dielectric constants and Young's modulus, were used as matrices to evaluate the matrix influence. The piezoelectricity of the pseudo‐1‐3 piezoelectric ceramic/polymer composite was evaluated using the piezoelectric strain constant d₃₃. The d₃₃ is one of the indices of the piezoelectric properties for piezoelectric materials. As a result, it was confirmed that d₃₃ of the pseudo‐1‐3 piezoelectric ceramic/polymer composite increased with the increase of the electric filed strength applied at fabrication process, though, it reached a constant value at a certain strength value. Further it was confirmed that dielectric constant of the matrix had a small influence on d₃₃ of the pseudo‐1‐3 piezoelectric ceramic/polymer composite, however, in case of matrix of lower Young's modulus, d₃₃ was increase. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41817.     

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.