基于RBM-GASA-BPNN 的潜在高价值旅客预测

针对用BP神经网络(Back Propagation Neural Network, BPNN)进行潜在高价值旅客预测时出现的特征表达能力弱、稳定性差、易陷入局部极值的不足,提出一种新颖的基于 RBM-GASA-BPNN的潜在高价值旅客预测方法.该方法首先通过聚类算法划分旅客类别,设置类别标签;然后利用受限玻尔兹曼机(Restricted Boltzmann Machine, RBM)提取旅客行为特征并确定最优BPNN 初始权值和偏置的寻优范围,又利用遗传模拟退火算法(Genetic Algorithm-Simulate Anneal, GASA)对BPNN参数进行精调,确定了最优的BPNN初始权值和偏置;最后,利用优化后的BPNN对旅客进行分类预测.实验结果表明,本文提出的方法克服了基于BPNN的分类预测方法的缺陷,具有更高的分类预测准确率和潜在高价值旅客预测能力.     

基于RBM-GASA-BPNN的潜在 高价值旅客预测

针对用BP神经网络(Back Propagation Neural Network, BPNN)进行潜在高价值旅客预测时出现的特征表达能力弱、稳定性差、易陷入局部极值的不足,提出一种新颖的基于 RBM-GASA-BPNN的潜在高价值旅客预测方法.该方法首先通过聚类算法划分旅客类别,设置类别标签;然后利用受限玻尔兹曼机(Restricted Boltzmann Machine, RBM)提取旅客行为特征并确定最优BPNN 初始权值和偏置的寻优范围,又利用遗传模拟退火算法(Genetic Algorithm-Simulate Anneal, GASA)对BPNN参数进行精调,确定了最优的BPNN初始权值和偏置;最后,利用优化后的BPNN对旅客进行分类预测.实验结果表明,本文提出的方法克服了基于BPNN的分类预测方法的缺陷,具有更高的分类预测准确率和潜在高价值旅客预测能力.     

Pedestrian detection for traffic safety based on Accumulate Binary Haar features and improved deep belief network algorithm

ABSTRACT

In order to improve traffic safety and protect pedestrians, an improved and efficient pedestrian detection method for auto driver assistance systems is proposed. Firstly, an improved Accumulate Binary Haar (ABH) feature extraction algorithm is proposed. In this novel feature, Haar features keep only the ordinal relationship named by binary Haar features. Then, the feature brings in the idea of a Local Binary Pattern (LBP), assembling several neighboring binary Haar features to improve discriminating power and reduce the effect of illumination. Next, a pedestrian classification method based on an improved deep belief network (DBN) classification algorithm is proposed. An improved method of input is constructed using a Restricted Bolzmann Machine (RBM) with T distribution function visible layer nodes, which can convert information on pedestrian features to a Bernoulli distribution, and the Bernoulli distribution can then be used for recognition. In addition, a middle layer of the RBM structure is created, which achieves data transfer between the hidden layer structure and keeps the key information. Finally, the cost-sensitive Support Vector Machine (SVM) classifier is used for the output of the classifier, which could address the class-imbalance problem. Extensive experiments show that the improved DBN pedestrian detection method is better than other shallow classic algorithms, and the proposed method is effective and sufficiently feasible for pedestrian detection in complex urban environments.     

几种道路底基层材料力学性能的对比试验

印度对经常用作柔性路面底基层的材料--粉煤灰、粗砂、石屑和河床材料(RBM)进行了加州承载比(CBR)试验、静载试验和循环三轴试验.得出石屑的CBR值最大,但其在三轴试验的动载作用下性能要比其他材料差.粉煤灰的CBR值较低,但其应力-应变性能比石屑好.试验结果表明:对比本次研究的其他材料,RBM是一种较优良的底基层铺装材料,它有较大的CBR值、高E(模量)值和Mr(回弹模量)值,并且具有较低的永久变形值.