Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

基于启发式BP算法的软件缺陷预测模型

针对软件缺陷预测时缺陷数据集中存在的类别分布不平衡问题，结合上采样算法SMOTE与Edited Nearest Neighbor (ENN) 数据清洗策略，提出了一种基于启发式BP神经网络算法的软件缺陷预测模型。模型中采用上采样算法SMOTE增加少数类样本以改善项目中的数据不平衡状况，并针对采样后数据噪声问题进行ENN数据清洗，结合基于启发式学习的模拟退火算法改进四层BP神经网络后建立分类预测模型，在AEEEM数据库上使用交叉验证对提出的方案进行性能评估，结果表明所提出的算法能够有效提高模型在预测类不平衡数据时的分类准确度。     

A simulation study of reduction kinetics for sponge iron production in a rotary hearth furnace

ABSTRACT

A mathematical model has been developed by coupling genetic algorithm (GA) with heat and material balance equations to estimate rate parameters and solid-phase evolution related to the reduction of iron ore-coal composite pellets in a multi-layer bed Rotary hearth Furnace (RHF). The present process involves treating iron ore-coal composite pellets in a crucible over the hearth in RHF. The various solid phases evolved at the end of the process are estimated experimentally, and are used in conjunction with the model to estimate rate parameters. The predicted apparent activation energy for the wustite reduction step is found to be lower than those of the reduction of higher oxides. The thermal efficiency is found to decrease significantly with an increase in the carbon content of the pellet. Thermal efficiency was also found to increase mildly up to three layers. Multilayer bed remains as a potential design parameter to increase thermal efficiency.     

基于ABAQUS的显式动力学分析方法研究

显式动力学(The Explicit Dynamic)是针对隐式求解器的一个补充,其分析方法对于求解广泛、各类非线性结构力学问题是一个非常有效的工具,显式方法中的单个增量步取决于模型的最高固有频率,与持续时间、载荷类型无关。ABAQUS/Explicit主要用于碰撞、接触以及失效分析。探讨了ABAQUS显式动力学的基本理论、求解的问题,对显示动力学分析方法中的求解算法、稳定时间极限和能量平衡问题进行研究,给出了显示动力学分析的一般性分析方法。通过案例验证了该方法的可行性,应用该方法可有效解决实际工业生产中的碰撞问题、复杂的接触问题以及复杂的后屈曲问题,可为显式动力学分析方法应用于工程实践奠定理论与方法基础。     

A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow

PIV (Particle Image Velocimetry) technique for flow field measurement has achieved popular self-identify through over ten years development, and its application range is becoming wider and wider. PIV post-processing techniques have a great influence on the success of particle-fluid two-phase flow field measurement and thus become a hot and difficult topic. In the present study, a Phase Respective Identification Algorithm (PRIA) is introduced to separate low-density solid particles or bubbles and high-density tracer particles from the PIV image of particle-fluid two-phase flow. PTV (Particle Tracking Velocimetry) technique is employed to calculate the velocity fields of low-density solid particles or bubbles. For the velocity fields of high-density solid particles or bubble phase and continuous phase traced by high-density smaller particles, based on the thought of wavelet transform and multi-resolution analysis and the theory of cross-correlation of image, a delaminated processing algorithm (MCCWM) is presented to conquer the limitation of conventional Fourier transform. The algorithm is firstly testified on synthetic two-phase flows, such as uniform steady flow, shearing flow and rotating flow, and the computational results from the simulated particle images are in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual bubble-liquid two-phase flow and jet flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images of particle-fluid two-phase flow.     

Service composition for generic service graphs

Service composition is a promising approach to multimedia service provisioning, due to its ability to dynamically produce new multimedia content, and to customize the content for individual client devices. Previous research work has addressed various aspects of service composition such as composibility, QoS-awareness, and load balancing. However, most of the work has focused on applications where data flow from a single source is processed by intermediate services and then delivered to a single destination. In this paper, we address the service composition problem for multimedia services that can be modeled as directed acyclic graphs (DAGs). We formally define the problem and prove its NP hardness. We also design a heuristic algorithm to solve the problem. Our simulation results show that the algorithm is effective at finding low-cost composition solutions, and can trade off computation overhead for better results. When compared with a hop-by-hop approach for service composition, our algorithm can find composition solutions that aress 10% smaller in cost, even when the hop-by-hop approach uses exhaustive searches.     

基于交会图和多元统计法的神经网络技术在火山岩识别中的应用

针对火山岩储层的特殊性(复杂性、离散性和随机性),应用BP神经网络技术对火山岩测井解释中岩性识别问题进行了研究。该方法的技术关键是样本集和初始权重的建立,以及模型的优选。本文提出了一种基于交会图和多元统计法的学习样本生成方法,即根据取心岩样的地球化学和岩石学研究,用交会图技术建立样本集,采用聚类分析和距离判别法确定初始权重。将研究方法应用在松辽盆地杏山地区火山岩岩性识别问题中,取得了很好的效果,岩性解释符合率高于90%。文中通过四种岩性识别处理模式的对比研究,表明赋权重处理模式为最优处理模型。在神经网络模型预测过程中,需充分利用已有的地质经验和测井曲线信息建立典型可靠的样本文件,同时考虑神经网络方法中各种因素的影响,优选模型和计算参数才能使预测结果符合实际情况。     

基于模式理论光栅椭偏参数反演的数值模拟

将一种广泛用于求解系统优化问题的方法——正单纯形法，求解光栅的椭偏方程。首先，利用求解光栅的傅立叶模式理论对TE和TM波的复反射系数进行求解。然后计算出其相应的椭偏参数(△，Ψ)，并在该值的基础上加入不同偏差的随机高斯噪声，将加入噪声后的值(△m，Ψm)作为模拟测量值。最后使用优化算法进行反演。通过对几种常用面形光栅椭偏参数的数值模拟，一方面表明傅立叶模式理论计算光栅的椭偏参数不仅精度高。而且速度快；另一方面表明利用正单纯形法得到的光栅参数值很接近于正演时假设的参数值，从而从理论上证明了利用椭偏法测量光栅各种光学参数的可行性。     

应用改进BP网络对潼关高程的预测

作者在分析了影响汛前和汛后潼关高程变化值的因素的基础上，利用改进BP网络模型对非特征汛期和汛期潼关高程的变化值进行了计算。计算中不采用潼关的绝对高程，而用高程的变化量作为网络的较出物正量，从而减少计算误差，同时又能客观地考虑各影响因子的作用。改进后的BP模型学习时间短，精度满足要求。用该模型计算的潼关高程变化趋势与实际水库调度运行中的高程基本吻合。     

半合成发动机油基础油调配的仿真研究

采用矿物油、聚α-烯烃、多元醇酯和高压加氢BS光亮油调配半合成发动机油基础油,并测定不同配比下基础油的油膜强度、摩擦系数和粘度指数,通过双隐层BP神经网络对基础油调配进行仿真研究,建立了半合成发动机油基础油调配配方优化模型,经过优化训练后,最终得出综合性能优良的配方组合。按49.9957∶4.3693∶42.4578∶3.1772的配比调配基础油,可以使基础油的油膜强度、摩擦系数和粘度指数分别达到56 kg,0.065和112,经实验后仿真结果与实验结果(56 kg;0.065598;112)极其吻合。