Road Extraction from High Resolution Remote Sensing Image based on Shape Analysis with Gestalt Theory

Simulating the psychological experience of human vision，a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly，based on the spectral and texture information，the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally，three types of identified road information are verified and merged，and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye，and the overall classification accuracy is higher，indicating that the algorithm is effective and feasible and has good use value.     

煤矿工业场地道路照明接地系统问题探讨

文章简要介绍了煤矿工业场地的特点和低压配电系统接地方式分类,从安全性、经济性的角度分析了TN接地系统和TT接地系统在道路照明回路中的优缺点。通过对比TN-S系统与TT系统的接地故障电流差异、TN-S接地系统用于道路照明的等电位联结困难程度,总结出煤矿工业场地的道路照明配电系统适宜采用TT接地方式。并且详细阐述了TT接地系统剩余电流动作保护器保护整定方法和灵敏度校验方法,列举了煤矿工业场地道路照明选用TT系统时需要注意的中性线不可重复接地的问题。     

Performance of LiNi1/3Co1/3Mn1/3O2 prepared from spent lithium-ion batteries by a carbonate co-precipitation method

Spherical LiNi_1/3Co_1/3Mn_1/3O₂ cathode particles were resynthesized by a carbonate co-precipitation method using spent lithium-ion batteries (LIBs) as a raw material. The physical characteristics of the Ni_1/3Co_1/3Mn_1/3CO₃ precursor, the (Ni_1/3Co_1/3Mn_1/3)₃O₄ intermediate, and the regenerated LiNi_1/3Co_1/3Mn_1/3O₂ cathode material were investigated by laser particle-size analysis, scanning electron microscopy–energy-dispersive spectroscopy (SEM-EDS), thermogravimetry–differential scanning calorimetry (TG-DSC), X-ray diffraction (XRD), inductively coupled plasma–atomic emission spectroscopy (ICP-AES), and X-ray photoelectron spectroscopy (XPS). The electrochemical performance of the regenerated LiNi_1/3Co_1/3Mn_1/3O₂ was studied by continuous charge–discharge cycling and cyclic voltammetry. The results indicate that the regenerated Ni_1/3Co_1/3Mn_1/3CO₃ precursor comprises uniform spherical particles with a narrow particle-size distribution. The regenerated LiNi_1/3Co_1/3Mn_1/3O₂ comprises spherical particles similar to those of the Ni_1/3Co_1/3Mn_1/3CO₃ precursor, but with a narrower particle-size distribution. Moreover, it has a well-ordered layered structure and a low degree of cation mixing. The regenerated LiNi_1/3Co_1/3Mn_1/3O₂ shows an initial discharge capacity of 163.5 mA h g^?1 at 0.1 C, between 2.7 and 4.3 V; the discharge capacity at 1 C is 135.1 mA h g^?1, and the capacity retention ratio is 94.1% after 50 cycles. Even at the high rate of 5 C, LiNi_1/3Co_1/3Mn_1/3O₂ delivers the high capacity of 112.6 mA h g^?1. These results demonstrate that the electrochemical performance of the regenerated LiNi_1/3Co_1/3Mn_1/3O₂ is comparable to that of a cathode synthesized from fresh materials by carbonate co-precipitation.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.