基于扇区动态复杂性因素的航空管制员工作负荷计算

为了更加准确地计算和预测航空管制员的工作负荷,利用雷达管制模拟试验获取的数据,分别采用线性回归、神经网络的非线性回归和基于神经网络的支持向量机方法,建立了基于扇区复杂性因素的管制员工作负荷实时计算模型。结果表明,这3种模型的绝对误差平均值分别为0.969、1.049、0.240;相对误差平均值分别为16.667%、17.979%、6.229%;均方根误差分别为0.186、0.206、0.114。另外,若采用5%作为基准精度,基于神经网络的支持向量机模型可以将相对误差控制在-0.5%~0.5%,表现出较强的误差控制能力。研究表明,可以采用扇区动态复杂性因素来计算管制员的工作负荷,相比线性回归、神经网络的非线性回归方法,基于神经网络的支持向量机方法对管制员工作负荷的计算有更高的精度。     

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R²) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

基于机器学习的浮标实时自动化赤潮预警研究

实现赤潮预警对于减轻海洋环境灾害、避免海洋产业特别是海洋渔业重大经济损失具有重要意义。针对当前水文监测数据海量却难以实现实时自动化监测与预警,特别是难以利用传统监测手段实现对危害更大的赤潮的精准实时预测这一显著问题,提出利用浮标数据作为依据,借助机器学习在大数据分析和智能决策方面的优势,建立一种新颖的双重递进式赤潮预警机制的方法。首先,通过相关算法分析历史数据,以确认赤潮初步预警阈值;其次,对叶绿素a、pH、溶解氧等重要监测指标的当前和阶段性变化进行初步分析,判断是否达到预警触发条件;然后,进一步联合分类、回归、聚类、神经网络等机器学习相关方法,对数据进行深度挖掘;最后,通过这种递进式的机制对短期内是否会发生赤潮作出判断,以实现赤潮自动化预警预报。在此基础上,利用宁波梅山湾实际监测数据,证实了该方法在赤潮实时自动化预警中的有效性。     

售票机的不同位置对地铁车站火灾烟气特性影响的CFD模拟

合理的空间布局对地铁车站消防安全具有十分重要的作用,而售票机的布置方位是影响地铁车站火灾烟气特性的一个主要因素。以某城市在建的地铁站站厅层为物理模型,采用FDS软件对售票机在不同位置情况下火灾的烟气流动情况进行模拟分析,通过比较邻近售票机处的疏散出口的温度、烟气浓度以及能见度,提出了售票机和疏散出口之间较为合理的距离,为实际工程提供可参考的依据。     

基于多信息检测的车辆智能防撞预警技术研究

运用多源信息检测技术对驾驶状态进行综合检测,利用架设在车辆外部的摄像机获取本车与前方车辆之间的相对距离、相对速度信息,利用架设在车辆内部的摄像机获取驾驶人状态信息,通过综合判断降低车辆防撞检测的漏警率、误警率以及冗余报警率;构建车辆行驶安全判定规则,对行车实时危险和潜在危险进行检测、分析与判断,获取可靠的防撞警示提醒;使反应时间、相对距离、相对速度3个方面都能得到优化控制,从而避免车辆碰撞事故;同时也为提高车速、增加道路通行能力、实现自动驾驶等奠定良好的基础。     

支持向量机法在煤与瓦斯突出分析中的应用研究

通过分析采煤工作面煤与瓦斯涌出量与地质构造指标的对应关系,应用支持向量机(SVM)方法对煤与瓦斯涌出类型及涌出量进行分析。建立两类突出识别的SVM模型、多类型突出识别的H-SVMs模型以及预测瓦斯涌出量的支持向量回归模型。研究结果表明:SVM方法能够很好地对煤与瓦斯突出模式进行识别,所建立的采煤工作面瓦斯涌出量预测模型的精度高于应用BP神经网络预测精度;SVM理论基础严谨,决策函数结构简单,泛化能力强,并且决策函数中的法向量W可以反映突出模式识别的地质结构指标的权重。     

基于BP神经网络在森林防火专家系统不确定推理中的应用研究

提出将改进的BP神经网络应用于森林防火专家系统的不确定性推理中,其良好的自学习和泛化能力,可以解决基于可信度规则的知识表示在实际应用中导致的规则激增,推理速度缓慢的问题。该方法将不确定的知识用可信度区间表示,通过知识编码,设计并训练BP网络,最后用MATLAB进行仿真。实验结果表明:BP神经网络可以自动学习专家的典型经验,并且能将之准确的推广,隐含层神经元个数的确定和典型样本的选取决定了准确精度。在实际的专家系统不确定推理应用中具有应用价值。     

基于支持向量回归机的煤层瓦斯含量预测研究

为了对煤层瓦斯含量进行准确预测,应用支持向量回归机(SVR)理论建立煤层瓦斯含量预测模型,结合现场实测数据利用支持向量机(SVM)工具箱进行模型的求解及预测,并从均方根误差、希尔不等系数和平均绝对百分误差3个不同误差指标与人工神经网络预测模型进行比较分析。研究结果表明:SVR模型其预测精度及可行性高于神经网络模型,而且运算快,实时性较好,用于煤层瓦斯含量的预测较理想,具有良好的应用前景,可以为煤矿瓦斯防治提供理论依据。     

手提式砂轮机操作事故的产生与预防

从人机系统的观点出发,对两起手提式砂轮机伤人事故进行了分析,找出了产生事故的原因,分析了人机系统与事故的关系,提出了预防措施,给出了操作要领。