基于声学特性的鸡蛋蛋壳裂纹检测

通过自行研制的一套禽蛋裂纹检测装置,采集并分析敲击鸡蛋产生的响应信号,检测裂纹鸡蛋。采用基于归一化最小均方算法的自适应滤波器对信号进行去噪处理。结果表明:经自适应滤波后,敲击响应信号的分辨率和灵敏度均有显著提高。提取经滤波去噪后的鸡蛋敲击响应信号功率谱的5个特征参数,作为误差反传人工神经网络模型的输入向量进行判别。判别模型对实验鸡蛋的交互验证训练集和独立样本预测集的判别率均为97%。

Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network

The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.

化学计量学用于解析江西白酒的高效液相色谱指纹图谱

采用高效液相色谱法对江西五个不同生产厂家的36个样品进行了测定,构建了它们的指纹图谱,并运用基于主成分分析的投影判别法及聚类分析法对白酒的指纹图谱进行了模式识别研究,再运用主成分分析对指纹图谱的数据进行降维处理,构建反传人工神经网络,并对未知样品的属性进行了预报。结果表明,不同厂家生产的白酒其高效液相色谱指纹图谱存在一定差异,且主成分分析的投影判别法和聚类分析法均能对样品进行正确分类,经优化的反传人工神经网络具有稳定性好,预测结果准确度高的特点,可用于对未知样品的属性进行预报。本研究为白酒样品的鉴别提供了一种新的手段,为白酒的质量控制提供了一定的科学依据。

露天矿边坡监测中的小波滤噪与BPANN预测

针对边坡变形量预测难的问题,将小波分析与BP神经网络预测相结合,采用小波变换对边坡变形监测数据进行信噪分离,进而消除观测误差,通过BP神经网络预测模型BPANN对处理后数据进行再处理,对边坡变形量以及变形趋势进行预测。进而提出了一种基于小波变换和BPANN模型对露天矿边坡变形监测数据进行处理分析的方法,并在鞍山某露天矿进行了实际应用。实例结果表明:利用小波去噪与BPANN模型预测的监测点精度达到3 mm,满足二等变形监测的要求,数据处理简便,在露天矿边坡变形监测数据的消噪与预测中具有实际应用价值。

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and cross-validation.The stress−strain data extended by two models were implanted into finite element to simulate hot compression test.The results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing temperature.Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of SCA.Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy.Both models are effective and feasible in simulation,but BPANN model is superior in accuracy.

Improved BP Neural Network for Transformer Fault Diagnosis

The back propagation (BP)-based artificial neural nets (ANN) can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types, using the highly nonlinear mapping nature of the neural nets. An efficient BP-ALM (BP with Adaptive Learning Rate and Momentum coefficient) algorithm is proposed to reduce the training time and avoid being trapped into local minima, where the learning rate and the momentum coefficient are altered at iterations. We developed a system of transformer fault diagnosis based on Dissolved Gases Analysis (DGA) with a BP-ALM algorithm. Training patterns were selected from the results of a Refined Three-Ratio method (RTR). Test results show that the system has a better ability of quick learning and global convergence than other methods and a superior performance in fault diagnosis compared to convectional BP-based neural networks and RTR.

化学计量学用于解析黄连上清片的高效液相色谱指纹图谱(英文)

采用高效液相色谱获得黄连上清片的色谱指纹图谱,利用基于主成分分析的投影判别法分析实验结果,并用反传人工神经网络对未知样品进行预报。结果表明,不同厂家生产的黄连上清片存在显著差异,主成分分析投影判别法能对样品进行正确分类,从而建立了识别不同厂家黄连上清片的方法,能有效地控制中药黄连上清片的质量。此外,主成分分析还用于优化反传人工神经网络,统计多次预报的结果,表明经过优化的反传人工神经网络能对未知样品的来源进行准确预报。

酚类化合物抑制黑曲霉毒性与结构关系的数据挖掘

运用HF/6-31G~*、HF/6-311G^(**)、DFT-B3LYP/6-31G~*、DFT-B3LYP/6-311G^(**)方法全优化计算18种酚类化合物,获得相应的量子化学参数:最高占有轨道能(E_(HOMO))、最低空轨道能(E_(LUMO))、前沿轨道能级差(△E=E_(LUMO)-E_(HOMO))、分子偶极距(μ)、各原子净电荷(q_i)、苯环上净电荷增量△Q_R和分子体积(V)等。利用多元线性回归分析(MLR)和人工神经网络误差反传算法(BP)2种数据挖掘方法,研究酚类化合物对黑曲霉抑制毒性的定量构效关系(QSAR),采用去一法(LOO)交互检验的方法验证模型稳健性和预测能力,选出最佳模型。所建最佳MLR和BP模型的相关系数、LOO交互检验复相关系数分别为0.956、0.834和0.991、0.845,所建QSAR模型的稳健性和预测能力良好。结果表明酚类化合物对黑曲霉的抑制毒性与分子体积、最低空轨道能、苯环上净电荷增量的相关性较好;分子体积越大,化合物毒性越大;E_(LUMO)越低,毒性越大;△Q_R增大,苯环上的正电性增强,亲电性愈强,化合物毒性愈大。