Combining of intelligent models through committee machine for estimation of wax deposition

Deposition of the wax is one of the thorny issues in the petroleum industry, invoking costly problems during the transportation and production of crude oil. Owing to its devastating impacts on oil companies' economy, it is essential to develop a simple and robust strategy for the quantitative estimation of wax deposition. In this paper, support vector regression (SVR) is first proposed to estimate the amount of wax deposition. Subsequently, an artificial neural network (ANN) is developed for wax deposition prediction. Eventually, a sophisticated committee machine (CM) is constructed for combining the results of the SVR and ANN models. Optimal contribution of each model in final prediction of the wax deposit is determined through genetic algorithm in CM. Statistical error analysis shows that the CM model performs better than the individual models performing alone.     

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使用人工神经网络(ANN)对HL-2A装置破裂放电进行了离线预测研究。采用了两种方法训练网络,一种方法是采用原始实验数据作为网络输入训练网络,另一种是把训练样本中的Mirnov原始实验信号进行预处理,目的是突出Mirnov原始信号隐含的破裂信息。比较这两种方法,结果表明第二种方法获得的网络对破裂放电能够做出更加准确的预测。     

Back‐propagation artificial neural network and attenuated total reflectance‐Fourier transform infrared spectroscopy for diagnosis of basal cell carcinoma by blood sample analysis

A diagnostic method for the cancer, based on investigation of infrared spectra of blood samples, has been developed. The two‐layer modified principal component feed forward back‐propagation artificial neural network (BP‐ANN) was used to classify the attenuated total reflectance‐Fourier transform infrared (ATR‐FTIR) spectra of blood samples obtained from healthy people and those with basal cell carcinoma (BCC). Results showed 98.33% of accuracy, in comparison with the current clinical methods. In the first step, 20 blood samples (10 normal and 10 cancer cases) were applied to construct the calibration model. Spectroscopic studies were performed in 900–1800 cm⁻¹ spectral region with 3.85 cm⁻¹ data space. In order to modify the capability of ANN in prediction of test samples, two different algorithms were applied. The obtained results confirmed the compatibility of the proposed network with the architecture of 20‐8‐2 (input‐hidden‐output) with the pattern model. It was concluded that analysis of blood samples by ATR‐FTIR spectroscopy and ANN chemometric technique would be a reliable approach for detection of BCC. Copyright © 2009 John Wiley & Sons, Ltd.     

基于傅立叶变换的人工神经网络近红外光谱定量分析法

将原始光谱进行一定的预处理后,以其快速傅立叶变换FFT的前N个系数作为人工神经网络(ANN)的输入量,不仅确保了大量有用信息参与模型的建立,同时实现了优越的滤波功能。以汽油的辛烷值和煤粉干燥基高位发热量(Qgr.d)的近红外光谱建模,当采用前20个FFT系数的傅立叶变换-径向基网络(FFT-RBF)时,辛烷值模型的预测误差均方根(RMSEP)可达0.152,相关系数为0.976,当采用前30个FFT系数时,快速FFT-RBF煤粉干燥基高位发热量模型的RMSEP为0.256,相关系数为0.923,说明FFT-RBF模型有着很好的预测能力。研究表明基于傅立叶变换的人工神经网络近红外光谱定量分析法,特别是FFT-RBF具有良好的预测能力。     

Prediction of characteristic properties of crude oil blending with ANN

Mineral oil is one of the most important materials on earth and it is used widely for its several features. Mineral oils derived from petroleum products are commonly used to decrease the friction effects in machine parts and, thus, they both prevent wear/overheating and facilitate power transmission. In this study, various binary mixtures of various base oils (SN-80, SN-100, SN-150, SN-50, SN-500) were prepared at different volumetric ratios. Kinematic viscosity (at 40°C and 100°C), viscosity index, flash point, pour point, and density (at 20°C) measurements were performed for characterization of the prepared mixtures. These values were modeled by an artificial neural network (ANN) and the model was tested with root mean squared error (RMSE), mean absolute percentage error (MAPE, %), and regression coefficient (R) values. A higher value of correlation coefficient and smaller values of MAPE and RMSE indicate that the model performs better. For predicting kinematic viscosity at 40°C, correlation coefficients were calculated for training and testing the network as 0.9999 and 0.9995, respectively. Respective MAPE values were determined as 1.011% and 1.8771%.     

Investigation of sub‐micron size cenosphere fillers and filler loading on the mechanical and tribological peculiarity of polyester composites

This paper investigates the effect of sub‐micron size cenosphere filler and filler loading on mechanical and dry sliding wear property of polyester composites. Composites are fabricated by filling with 10 and 20 wt% of 800 and 200‐nm size of cenosphere filler particles. Neat polyester composite is also prepared for comparison analysis. Dry sliding wear test is conducted for these composites over a range of sliding distance with different sliding velocities and applied loads on a pin‐on‐disc wear test machine. Taguchi methodology and analysis of variance (ANOVA) is used to analyze the friction and wear characteristics of the composites. The artificial neural network (ANN) approach is implemented to the friction and wear data for corroboration. In this work, mechanical properties of composites such as hardness, tensile strength, tensile modulus, flexural strength, and compressive strength revealed that mechanical properties and wear resistance of the composites increase with a decrease in the particle size. The measured Young's moduli are comparable to standard theoretical prediction models. The morphology of worn composite specimens has been examined by scanning electron microscopy to understand the dominant wear mechanisms. Finally, optimal factor settings are determined using a genetic algorithm (GA). Copyright © 2017 John Wiley & Sons, Ltd.     

BP混沌混合神经网络在光纤陀螺温度漂移预测中的应用

提出使用BP混沌混合神经网络建立FOG温度漂移模型的方法.该方法在BP算法中采用了改进型Logistic-Map映射生成的混沌变量,能够避免陷入局部最小,可迅速达到全局最优.应用该方法分析某型FOG温度漂移实测数据,结果表明其具有良好的预测效果.     

航天光学遥感器在轨MTF评价的一元方法

通过对航天光学遥感器MTF模型和遥感图片的分析,从图像中提取出与MTF有关的特征信息,采用人工神经网络(ANN)作为工具,将这些特征信息作为ANN的输入向量。在对大量MTF已知的遥感图片进行训练后,ANN可以对未知的遥感图片进行MTF测试。这种方法被称为MTF的一元评价方法,即通过对遥感器传输下来的任意一幅地面景物图像进行MTF的在轨评价,无需在地面铺设特定形状靶标或已知的参考图片。实验结果表明,平均评价误差约为5%,具有很强的抗噪声能力。