Composites prepared from polyurethanes modified with silicone-acrylic nanopowders

Linear polyurethanes were obtained the reaction of 1,6-hexamethylene diisocyanate with poly(ɛ-caprolactone)diol and butane-1,4-diol. Synthesis was carried out in the presence of 1, 3 and 5 wt.% of polydimethylsiloxane-poly(methyl methacrylate) core–shell nanopowder. Solutions of resulting polyurethanes were cast on PTFE plates and dried at 140 °C to form films. The presence of structures originating from modifier was confirmed by IR and XPS spectroscopy. DSC analysis revealed the presence of crystalline phase in all samples. Contact angles were determined using standard fluids and surface free energy parameters were calculated. The results of these investigations proved that modification with silicone-acrylic nanopowder resulted in significant increase in hydrophobicity of polyurethane surfaces Changes in surface characteristics were also reflected in surface images obtained in AFM studies. It is suggested that the polyurethane composites obtained in this study can be tested as coatings for biomedical applications.     

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature.     

基于摄影测量的飞机阻力伞运动参数测量技术

针对某型阻力伞性能鉴定试飞科目要求，需要精确测量阻力伞在稳定工作时的垂直、水平摆角，从而为该产品的技术鉴定提供依据。本文根据加装在飞机机背中段的高速摄像机，获取阻力伞的运动影像，通过图像分析处理识别特征点的亚象素坐标，采用摄影测量的解算算法，推导出数学解算模型，实现了阻力伞运动参数的最优解算。结合真实飞行试验对测量结果进行了分析，数据结论准确。     

Forest parameters retrieval with dual-baseline polarimetric SAR interferometry based on clustering analysis

Forest parameters inversion is an important application of Polarimetric Synthetic Aperture Radar Interferometry(PolInSAR). To resolve the volume decorrelation coefficient ambiguity of single-baseline PolInSAR, a new dual-baseline PolInSAR approach is proposed in this paper. The correlation of dual-baseline data is fully investigated to resolve the ambiguity problem effectively, and the performance of the proposed approach is proved to be of great robustness in a very small baseline ratio system. Finally, experimental results with ESA dual-baseline PolInSAR simulated data validate the effectiveness and robustness of the proposed approach.     

注采比对“二三结合”开发模式渗流特征的影响

大庆油田的开发逐渐进入高含水阶段,厚油层动用程度低,"二三结合"开发模式是有效的提高厚油层动用程度,改善水驱开发效果,提高油田采收率的有效手段。在"二三结合"开发模式注聚阶段中注采比对井间压力、含水饱和度等变化有重要影响。为了探究注采比对开发层位的井间压力及饱和度的影响,利用油藏数值模拟方法,模拟了相同的开发模式不同注采比的生产情况,绘制了不同注采比情况下,不同层位的井间压力及含水饱和度变化情况,为维持地层压力平衡,保持油田稳定生产提供有力的依据。     

Supramolecular Assembly on Coordination of Azopolymer Complexes: A Review

This review gives an account of the coordination chemistry of supramolecular azopolymer complexes. The syntheses and structures of azomonomers and their azopolymer complexes were described. Spectral techniques such as (IR, ¹H-NMR, ESR) and thermal analysis were investigated. Supramolecular architectures assembled were exhibited through weak interaction including hydrogen bonding and π–π stacking. The spectral data indicate geometry of azopolymer complexes and the orbital reduction factors. ESR spectral data provided information about their structures on the basis of Hamiltonian parameters and degree of covalency. All the azopolymer complexes are ESR active due to the presence of an unpaired electron. The force constant F_U–O(mdyn/Å) and the bond length R_U–O (Å) of the U–O bond were calculated from the IR data and related to the electronic properties of the substituents. Wilson's method, the matrix method, Badger's formula, and the Jones and El-Sonbati equations were used to calculate the U–O bond distances from the values of the stretching and interaction force constants. The most probable correlations between U–O force constant to U–O bond distance were satisfactorily discussed in terms of Badger's rule, and the Jones and El-Sonbati equations. The thermal stability was investigated using thermogravimetric analysis. The results showed that the azopolymer complexes are mostly more stable than the homopolymer. The stability of the proton ligand/metal ligand constants in the monomeric and polymeric forms was studied carefully using potentiometery. Based on the thermodynamic functions, the dissociation process is nonspontaneous, endothermic and entropically unfavorable. The metal complexes that were formed exhibited spontaneous, endothermic and entropically favorable behavior.     

Rotary drying process modeling and online compensation

Rotary drying process modeling is a complex procedure due to the difficulties in measurement and estimation of kinetic model parameters. To solve the problem, a hybrid modeling method with online compensation is proposed in the present study. A mathematical model is built to describe the axial characteristics of rotary drying process. The drying rate which is the key parameter in the model is estimated by using a SVR-based fuzzy modeling approach, which can automatically extract fuzzy IF-THEN rules from support vectors. Laboratory experiments are conducted to obtain the drying rate sample data for the modeling purpose. In order to reduce the modeling errors for an industrial rotary dryer and improve the hybrid model prediction accuracy, an online matching coefficient is introduced, and a method based on improved online SVR is then applied for modeling error compensation. The experiment dada based modeling results have verified the effectiveness and demonstrated the accuracy and adaptability of the proposed hybrid modeling method.     

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.