Ti-6Al-4V is a benchmark Ti alloy. Laser wire additive manufacturing (LWAM) offers advanced manufacturing capability to the alloy for applications possibly including exploration of outer space. As a typical multiple-variable process, LWAM is complex, which, however, can be analyzed, predicated or even optimized by artificial intelligence (AI) methods such as machine learning (ML). In this study, printing parameters of the Ti-6Al-4V is firstly optimized using single-track-single-layer experiments, and then single-track-multiple-layer samples are printed, whose properties in terms of hardness and compressive strength are analyzed subsequently by both experiments and ML. The two ML approaches, artificial neural network (ANN) and support vector machine (SVM), are employed to predict the experimental results, whose coefficients of determination R² show good values. Further optimized properties are realized by adopting genetic algorithm (GA) and simulated annealing (SA) approaches, which contribute to high mechanical properties achieved, for instance, an engineering compressive strength of about 1694 MPa. The results here indicate that important mechanical properties of the LWAM-prepared Ti alloys can be well predicted and enhanced using suitable ML approaches.

为研究煤矸石充填复垦土壤重金属含量快速有效的监测方法, 以淮南创大生态园煤矸石充填复垦田间试验小区为研究区域, 首先采用化学方法监测土壤(0～20 cm)重金属(Cu, Cr, As)含量, 然后采用ASD(analytical spectral devices) FiSpec4型高光谱仪测量土壤样品的反射光谱, 提取光谱特征, 并对光谱进行一阶微分变换、 二阶微分变换及倒数对数变换; 将变换后的各光谱特征参数与监测的土壤重金属含量进行相关性分析, 并依据相关性分析结果选择显著相关的波段作为相关因子供建模使用。 采用多元逐步回归(stepwise multiple liner regression, SMLR)分析、 偏最小二乘回归(partial least squares regression, PLSR)及人工神经网络(artificial neural network, ANN)三种方法分别建立基于光谱反射率估算土壤重金属含量的预测模型, 并采用回归模型进行精度评定, 然后确定各重金属含量的最佳预测模型。 实验结果表明, 经过微分变换的光谱波段与土壤重金属含量达到了显著相关; 重金属Cu和Cr的一阶微分光谱的人工神经网络模型为最佳预测模型, 重金属元素As的二阶微分光谱的偏最小二乘回归模型为最佳预测模型。

随着餐饮业的发展, 餐饮烟气已经成为某些城市三大空气污染源之一。 由于餐饮烟气对人体健康威胁很大, 近年来对餐饮烟气的研究愈来愈热。 餐饮烟气中包含有大量食用油加热过程中裂解而产生的不饱和烃类, 危害着人类健康。 不同食用油裂解出来的成分以及含量有所不同, 通过构建一定的分类识别数学模型, 从而实现对食用油分类识别。 采用自主研发的傅里叶变换红外光谱仪, 采集了不同食用油油烟烟气红外光谱数据。 同时构建了主成分分析(PCA)分别结合概率神经网络(PNN)以及误差反向传播人工神经网络(BPANN)的分类识别算法。 将两种分类识别算法对不同食用油油烟烟气的傅里叶变换红外光谱数据进行分析。 通过样本数据对数学模型进行训练, 将训练好的数学模型对未知光谱数据进行分析, 来确定产生油烟烟气的食用油种类。 实验结果表明, 两种算法都能对不同的油烟种类进行较好地分类识别。 在全波段识别时, 识别率分别达到90.25%和97.0%。 通过对烟气光谱数据的吸收波段进行分析, 提取大气窗口并且具有较强可挥发性有机物(VOCs)吸收特征的波段（1 300～700 cm-1以及3 000～2 600 cm-1）; 将吸光度数据分成两个分离的吸收波段, 两种算法在3 000～2 600 cm-1波段都有较好的识别效果, PCA-PNN算法识别率为90.25%, PCA-BPANN算法识别率为92.25%。 可见, 两种人工神经网络算法都能有效对食用油烟种类进行识别。

The objective of the work is to investigate the classification of different movements based on the surface electromyogram (SEMG) pattern recognition method. The testing was conducted for four arm movements using several experiments with artificial neural network classification scheme. Six time domain features were extracted and consequently classification was implemented using back propagation neural classifier (BPNC). Further, the realization of projected network was verified using cross validation (CV) process; hence ANOVA algorithm was carried out. Performance of the network is analyzed by considering mean square error (MSE) value. A comparison was performed between the extracted features and back propagation network results reported in the literature. The concurrent result indicates the significance of proposed network with classification accuracy (CA) of 100% recorded from two channels, while analysis of variance technique helps in investigating the effectiveness of classified signal for recognition tasks.

应用电感耦合等离子体质谱(ICP-MS)测定了北京顺义、河北阜平和河北平山三个地区65个荆条蜜样品中38种元素含量, 其中B, Na, Mg, P, K, Ca, Fe和Zn等8种元素的含量处于较高水平, 其浓度均高于1 mg·kg-1。比较发现, 不同地区荆条蜜中元素含量存在一定差异。以具有显著性差异(p<0.05)的29种元素(Na, Mg, Al, K, Ti, V, Mn, Fe, Ni, Cu, Zn, Ga, As, Sr, Y, Mo, Cd, Ba, La, Ce, Pr, Nd, Sm, Gd, Dy, Ho, Tl, Pb和U)为变量, 应用主成分分析(PCA)和反向传输人工神经网络(BP-ANN)两种化学计量学方法按照不同产地对荆条蜜进行了分析。PCA可将所有变量降为4个主成分, 并能解释81.6%的变量, 结果表明, PCA基本可以将荆条蜜按照不同产地分开。应用BP-ANN建立不同产地荆条蜜溯源模型, 用全部样本建立模型时, 三个产地荆条蜜分类的正确率均为100%, 采用“留一法”进行交叉检验, 整体准确率为95.4%;以75%的样本建立模型, 25%的样本用于预测, 对BP-ANN模型的可靠性进行验证, 三个产地整体分类和预测的准确率均为100%。ICP-MS测定蜂蜜中多元素含量结合多变量模型可以实现不同产地荆条蜜溯源。