catena-Poly[[bis­[1-phenyl-3-(1H-1,2,4-triazol-1-yl)propan-1-one-κN 4]cadmium(II)]-di-μ-azido-κ4 N 1:N 3]

In the crystal structure of the title complex, [Cd(N3)2(C11H11N31)2]n, there are two crystallographically independent CdII atoms. Both exist in an octa­hedral environment composed of four N atoms of the N3 − groups and two N atoms from two monodentate 1-phenyl-3-(1H-1,2,4-triazol-1-yl)propan-1-one ligands that are positioned trans to each other. Adjacent CdII centres in the crystal structure are bridged by a pair of N3 − anions in a μ-1,3-fashion, forming an infinite one-dimensional array

Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

The emerging 1mm resolution 3D data collection technology is capable of covering the entire pavement surface, and provides more data sets than traditional line-of-sight data collection systems. As a result, quantifying the impact of sample size including sample width and sample length on the calculation of pavement texture indicators is becoming possible. In this study, 1mm 3D texture data are collected and processed at seven test sites using the PaveVision3D Ultra system. Analysis of Variance (ANOVA) test and linear regression models are developed to investigate various sample length and width on the calculation of three widely used texture indicators: Mean Profile Depth (MPD), Mean Texture Depth (MTD) and Power Spectra Density (PSD). Since the current ASTM standards and other procedures cannot be directly applied to 3D surface for production due to a lack of definitions, the results from this research are beneficial in the process to standardize texture indicators’ computations with 1mm 3D surface data of pavements

Transfer Learning Based Traffic Sign Recognition Using Inception-v3 Model

Traffic sign recognition is critical for advanced driver assistant system and road infrastructure survey. Traditional traffic sign recognition algorithms can't efficiently recognize traffic signs due to its limitation, yet deep learning-based technique requires huge amount of training data before its use, which is time consuming and labor intensive. In this study, transfer learning-based method is introduced for traffic sign recognition and classification, which significantly reduces the amount of training data and alleviates computation expense using Inception-v3 model. In our experiment, Belgium Traffic Sign Database is chosen and augmented by data pre-processing technique. Subsequently the layer-wise features extracted using different convolution and pooling operations are compared and analyzed. Finally transfer learning-based model is repetitively retrained several times with fine-tuning parameters at different learning rate, and excellent reliability and repeatability are observed based on statistical analysis. The results show that transfer learning model can achieve a high-level recognition performance in traffic sign recognition, which is up to 99.18 % of recognition accuracy at 0.05 learning rate (average accuracy of 99.09 %). This study would be beneficial in other traffic infrastructure recognition such as road lane marking and roadside protection facilities, and so on