隧洞围岩稳定性评价的Bayes判别分析法及应用

提出了隧洞围岩稳定性评价的Bayes判别分析法。通过介绍Bayes判别分析法的理论,并考虑影响隧洞围岩稳定性的因素,建立具体的Bayes判别分析模型进行判别。以隧道工程中的大量实测资料作为学习样本进行训练,并利用回代确认估计法对训练后的模型进行检验,最后将该模型对隧洞的围岩情况进行预测。研究结果表明,经过训练后的Bayes判别分析模型误判率很低,判别准确率很高,可以在实际工程中应用。

基于侧边抛磨光纤传感器的便携式折射率检测仪

基于单模和多模侧边抛磨光纤对不同折射率液体的光敏性不同的原理,以MSP430F149微处理器为主控芯片,配合光源、光探测器、折射率检测模块和控制模块,设计开发了便携式折射率检测仪。实验数据表明,基于侧边抛磨光纤的折射率检测仪可以用于折射率的定量分析和定性判别分析;仪器的光功率标准差<5%,折射率标准差<0.02%。实现了基于侧边抛磨光纤的折射率检测仪的一体化、便携化、商品化。

Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods

The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.

Stability classification model of mine-lane surrounding rock based on distance discriminant analysis method

Based on the principle of Mahalanobis distance discriminant analysis (DDA) theory, a stability classification model for mine-lane surrounding rock was established, including six indexes of discriminant factors that reflect the engineering quality of surrounding rock: lane depth below surface, span of lane, ratio of directly top layer thickness to coal thickness, uniaxial comprehensive strength of surrounding rock, development degree coefficient of surrounding rock joint and range of broken surrounding rock zone. A DDA model was obtained through training 15 practical measuring samples. The re-substitution method was introduced to verify the stability of DDA model and the ratio of mis-discrimination is zero. The DDA model was used to discriminate 3 new samples and the results are identical with actual rock kind. Compared with the artificial neural network method and support vector mechanic method, the results show that this model has high prediction accuracy and can be used in practical engineering.

Emotion recognition of Uyghur speech using uncertain linear discriminant analysis

To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.

近红外光谱检测技术在精细化工生产过程中的应用

针对精细化学品D3生产过程中酸碱原料浓度检测和D2生产过程中粗品/精品快速检测鉴定需求,论述了近红外光谱检测的基本原理,给出了偏最小二乘定量建模和马氏距离分类判别等化学计量学算法,构建了Antaris II傅里叶变换近红外光谱检测系统。通过配制样品,采集光谱数据,并经求导、滤波和标准化数据处理,应用偏最小二乘法建立了D3生产过程HCl水溶液摩尔浓度、NaOH水溶液质量百分数和摩尔浓度的近红外光谱检测模型,应用马氏距离结合主元分析法建立了D2生产过程中粗品/精品判别分析模型,给出了详细的检测实验数据。生产现场样品检测结果表明,D3酸碱浓度的定量检测模型具有很高的检测精度,D2粗品/精品判别分析模型判别结果准确,可在相关精细化工生产过程中推广应用,提高生产过程检测鉴定效率。