Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.     

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.     

Stereo matching cost computation based on nonsubsampled contourlet transform

A new matching cost computation method based on nonsubsampled contourlet transform (NSCT) for stereo image matching is proposed in this paper. Firstly, stereo image is decomposed into high frequency sub-band images at different scales and along different directions by NSCT. Secondly, by utilizing coefficients in high frequency domain and grayscales in RGB color space, the computation model of weighted matching cost between two pixels is designed based on the gestalt laws. Lastly, two types of experiments are carried out with standard stereopairs in the Middlebury benchmark. One of the experiments is to confirm optimum values of NSCT scale and direction parameters, and the other is to compare proposed matching cost with nine known matching costs. Experimental results show that the optimum values of scale and direction parameters are respectively 2 and 3, and the matching accuracy of the proposed matching cost is twice higher than that of traditional NCC cost.     

Physical Stability of Octenyl Succinate–Modified Polysaccharides and Whey Proteins for Potential Use as Bioactive Carriers in Food Systems

The high cost and potential toxicity of biodegradable polymers like poly(lactic‐co‐glycolic)acid (PLGA) has increased the interest in natural and modified biopolymers as bioactive carriers. This study characterized the physical stability (water sorption and state transition behavior) of selected starch and proteins: octenyl succinate–modified depolymerized waxy corn starch (DWxCn), waxy rice starch (DWxRc), phytoglycogen, whey protein concentrate (80%, WPC), whey protein isolate (WPI), and α‐lactalbumin (α‐L) to determine their potential as carriers of bioactive compounds under different environmental conditions. After enzyme modification and particle size characterization, glass transition temperature and moisture isotherms were used to characterize the systems. DWxCn and DWxRc had increased water sorption compared to native starch. The level of octenyl succinate anhydrate (OSA) modification (3% and 7%) did not reduce the water sorption of the DWxCn and phytoglycogen samples. The Guggenheim–Andersen–de Boer model indicated that native waxy corn had significantly (P < 0.05) higher water monolayer capacity followed by 3%‐OSA‐modified DWxCn, WPI, 3%‐OSA‐modified DWxRc, α‐L, and native phytoglycogen. WPC had significantly lower water monolayer capacity. All Tg values matched with the solid‐like appearance of the biopolymers. Native polysaccharides and whey proteins had higher glass transition temperature (Tg) values. On the other hand, depolymerized waxy starches at 7%‐OSA modification had a “melted” appearance when exposed to environments with high relative humidity (above 70%) after 10 days at 23 °C. The use of depolymerized and OSA‐modified polysaccharides blended with proteins created more stable blends of biopolymers. Hence, this biopolymer would be suitable for materials exposed to high humidity environments in food applications.     

Prediction of brittle-to-ductile transitions in polystyrene

In this study it is attempted to predict brittle-to-ductile transitions (BDTs) in polystyrene blends, induced either by an increase in temperature or by a decrease in inter-particle distance. A representative, two-dimensional volume element (RVE) of a polystyrene matrix with 20% circular voids, is deformed in tension. During deformation a hydrostatic-stress based craze-nucleation criterion [1] is evaluated. The simulations demonstrate that crazes initiate at low temperatures while a transition from crazing to shear yielding (BDT) is found around 75 °C. The numerical results correlate well with tensile tests on similar heterogeneous polystyrene. The presence of an absolute length, as experimentally found, is more difficult to explain. Near a free surface a T_g-depression is measured for polystyrene and also the resistance to indentation in polystyrene is lower than expected from bulk properties. Both observations are rationalised by an enhanced segmental mobility of chains near a free surface. As a consequence of these findings, an absolute length-scale could be incorporated in the numerical simulations. For simplicity, the length-scale is modelled by taking a temperature gradient over a thin layer near the internal free surfaces of the RVE. Deformation of the RVE with different absolute length-scales shows that indeed also the experimentally found brittle-to-ductile transition can be predicted if the ligament thickness between the inclusions (‘voids’) in polystyrene is below a critical value of ca. 15 nm.     

基于矢量平均的信道估计算法

提出一种适用于DS-CDMA无线通信系统的低计算量而性能良好的信道估计算法。在DS-CDMA系统中，当接收信号经过解扩或多用户检测等时域预处理后，可以认为干扰大大减弱、期望信号在处理后的信号中占主要地位。这样，用预处理后信号相关阵的最大特征值对应的特征矢量可以很好地近似期望信号的信道矢量。但是，直接特征分解需要很大的计算量，特征跟踪计算量较低，但瞬态性能较差。本文提出一种基于相关矩阵列矢量平均的信道估计算法，该算法不需要特征分解或跟踪。仿真结果表明：新算法在降低计算量的情况下可以获得同直接特征分解方法几乎相同的性能。     

井下乳化/水热催化裂解复合降黏开采稠油技术研究

实验考察了胜利孤东稠油井下催化水热裂解和乳化/催化水热裂解降黏效果。所用催化剂为水溶性铁镍钒体系,Fe3+∶Ni2+∶VO4+=5∶1∶1,100 g稠油与30 g 0.5%催化剂水溶液在240℃反应24小时。原始黏度(50℃)11.0和8.36 Pa.s的两种稠油裂解并静置除水后,黏度降低76.2%和75.6%,室温放置60天后降黏率下降小于3个百分点,气相色谱显示裂解后轻组分明显增加,红外光谱显示稠油组分发生脱羧反应且芳环数减少。讨论了稠油催化水热裂解反应机理。所用化学助剂JN-A在油水中均可溶,耐温达250℃,耐矿化度达50 g/L,其水溶液以30∶100的质量比与稠油混合时形成低黏度的O/W乳状液。当水相含1.0%JN-A和0.5%催化剂时,两种稠油水热裂解后的反应混合物为O/W乳状液,黏度仅为319和309 mPa.s,静置除水后的稠油降黏率增加到86.5%和87.3%,其中的轻组分含量进一步增加。该井下乳化/催化水热裂解复合降黏法成功地用于孤东两口蒸汽吞吐井,稠油井作业后初期采出的原油黏度由~9 Pa.s降低到1 Pa.s左右,随采油时间延长而逐渐升高,约50天后超过4Pa.s。图2表6参5。     

Effect of oxygen deficiency (δ) on transition temperature of yttrium cuprate superconductors

The nature of pairing mechanism as well as transition temperature of yttrium cuprates is discussed using the strong coupling theory. An interaction potential has been developed for the layered structure with two conducting CuO₂(a–b) layers in a unit cell. The interaction potential properly takes care of electron-electron, electron-phonon and electron-plasmon interactions. Furthermore, the electron-phonon coupling parameter (λ), the modified Coulomb repulsive parameter (μ*) and the 2D acoustic phonon (plasmon) energy as a function of oxygen deficiency is worked out. Finally, the superconducting transition temperature (T _c) is then evaluated by using these coupling parameters and obtainedT _c = 95(92)K for Y(Yb)Ba₂Cu₃O_7−δ superconductors withδ = 0·0. The model parameters estimated from the layered structure approach are consistent with the strong coupling theory. The result deduced on the variation ofT _c withδ are in fair agreement with the earlier reported data on yttrium cuprates. The analysis of the above results are discussed.     

流媒体技术及应用

主要介绍了流媒体技术、原理及其应用，详述了作为流媒体实现的关键技术-流式传输和传输的过程，以及目前支持流媒体传输的协议和使用较多的流式视频格式。