A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.

Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network

Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality.In this paper,an attention-based independently recurrent neural network(IndRNN)for sag source location and sag type recognition in sparsely monitored power system is proposed.Specially,the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system,and the desired outputs simultaneously contain the following information:the located lines where sag occurs;the corresponding sag types,including motor starting,transformer energizing and short circuit;and the fault phase for short circuit.In essence,the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible.A favorable feature of the proposed method is that it can be realized without system parameters or models.The proposed method is validated by IEEE 30-bus system and a real 134-bus system.Experimental results demonstrate that the accuracy of sag source location is higher than 99%for all lines,and the accuracy of sag type recognition is also higher than 99%for various sag sources including motor starting,transformer energizing and 7 different types of short circuits.Furthermore,a comparison among different monitor placements for the proposed method is conducted,which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.

The classification and significance of fine-grained deposits of micro-laminae rich in unconventional oil and gas resources

In this study,an in-depth analysis of the types,characteristics,and formation mechanisms of microlaminae and microscopic laminae was conducted in order to precisely examine the link or intersection of stratigraphy and petrology.This study was essentially a sedimentary examination of the minuteness-macro and micro-tiny layers between laminae and pore structure,as well as the types of structures and sedimentation.The results of this study bear important basic subject attributes and significance,as well as practical value for the basic theories and exploration applications of unconventional oil and gas geology.The quantitative data were obtained using the following:field macroscopic observations;measurements;intensive sampling processes;XRD mineral content analysis;scanning electron microscopy;high-power polarizing microscope observations;and micro-scale measurements.The quantitative parameters,such as laminae thicknesses,laminae properties,organic matter laminae,and laminae spatial distributions were unified within a framework,and the correlations among them were established for the purpose of forming a fine-grained deposition micro-laminae evaluation system.The results obtained in this research investigation established a basis for the classification of micro-laminae,and divided the micro-laminae into four categories and 20 sub-categories according to the development thicknesses,material compositions,organic matter content levels,and the spatial distributions of the micro-laminae.The classification scheme of the micro-laminae was divided into two categories and 12 sub-categories.Then,in accordance with the comprehensive characteristics of spatial morphology,the micro-laminae was further divided into the following categories:continuous horizontal laminae;near horizontal laminae;slow wavy laminae;wavy laminae;discontinuous laminae;and lenticular laminae.According to the structural properties of the laminae development,the micro-laminae was divided into the following categories:single laminae structures;laminated laminae structures;interlaminar structures;multiple mixed laminae structures;cyclic laminae structures;and progressive laminae structures.The research results were considered to be applicable for the scientific evaluations of reservoir spaces related to unconventional oil and gas resources.