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81results about How to "Improve classification accuracy" patented technology

Large-size parcel sorting device used for logistics sorting

The invention relates to the technical field of logistics sorting, in particular to a large-size parcel sorting device used for logistics sorting. A sorting box and a detecting mechanism are included;the detecting mechanism is arranged on the front side of the sorting box; two side plates are fixedly connected to the lower side of the sorting box, and are both fixedly connected to the upper sideof a bottom plate; two driving rollers are arranged between the two side plates, and a plurality of driven rollers are arranged between the two driving rollers; the driving rollers and the driven rollers are rotationally connected with the side plates through bearings; a number one speed reducing motor is fixed to one side plate through a screw; an output shaft of the number one speed reducing motor penetrates through the corresponding side plate and is fixedly connected with one driving roller; and a wide belt is connected between the two driving rollers in a transmission manner. The aerial insulating cable fixing device for electric power supply and distribution has the characteristics of being compact and reasonable in structure and capable of achieving automatic sorting, a reciprocating swinging plate is used for pushing a small-size parcel to enter a slope, a large-volume parcel continues to be transported on a conveying belt, and a vertical plate, a transverse plate and a longitudinal plate form a detecting frame.
Owner:长沙佐迩信息科技有限公司

SVM classifier parameter optimization method based on improved particle swarm algorithm

The invention discloses a SVM classifier parameter optimization method based on an improved particle swarm algorithm. The method comprises the following steps: (1) performing 10-fold cross verification on collected sample data, selecting the parameter influencing the classifier performance as the to-be-optimized parameter; (2) initializing related parameters of the classifier and the particle swarm algorithm, and updating particle speed and location according to related parameters; (3) setting the to-be-optimized parameter of the classifier as the corresponding dimension value at the current location of the particle, and computing to obtain the fitness value corresponding to the current location of the particle; and (4) obtaining the fitness value evaluation particle according to the fitness value corresponding to the current location of the particle, and updating the individual optimal location and the population optimal location. Through the method disclosed by the invention, the mixed kernel function based on the polynomial kernel function and the Gaussian kernel function is constructed, the traditional particle swarm algorithm is improved, the kernel function parameter is optimized by utilizing the improved PSO-SVM algorithm, and then the comprehensive performance of the classifier is improved, the generalization capacity of the classifier is improved when the high classification precision is guaranteed.
Owner:SOUTHEAST UNIV

Polarimetric SAR image classification method based on feature attention and feature improvement network

The invention provides a polarimetric SAR image classification method based on feature attention and a feature improvement network. The polarimetric SAR image classification method mainly solves the problems that an existing polarimetric SAR image classification method based on deep learning is poor in intra-area consistency and inconvenient for end-to-end classification. The implementation schemecomprises the following steps of: 1) inputting a to-be-classified polarized SAR image and filtering the to-be-classified polarized SAR image; 2) synthesizing a pseudo color image and a classificationlabel of the polarized SAR image; 3) extracting initial features of the polarimetric SAR image and preprocessing the features; 4) respectively constructing an input representation layer, a feature attention sub-network, an encoder and a decoder, and sequentially connecting the input representation layer, the feature attention sub-network, the encoder and the decoder to form a feature attention and feature improvement network; 5) training fa eature attention and feature improvement network; 6) Inputting the polarization SAR image into the trained network to obtain the classification result. The polarimetric SAR image classification method is high in intra-area consistency, low in noise and high in classification precision, end-to-end learning and classification are realized, and the polarimetric SAR image classification method can be used for polarimetric SAR image classification.
Owner:XIDIAN UNIV

Tensor model-based multi-source data classification optimizing method and system

InactiveCN105913085AGuaranteed high efficiencyImprove classification accuracy
The invention relates to a tensor model-based multi-source data classification optimizing method and system. The tensor model-based multi-source data classification optimizing method comprises the following steps: in step a, under a Map-reduce distribution framework, multi-view data is subjected to tensor product operation, high order tensor data is obtained, and an initial support tensor machine classification model is built according to the high order tensor data; in step b, the data of all views is subjected to feature elimination operation via a support vector recursive feature elimination algorithm in an original space, and subscript data where the data of all views retains features is output; in step c, according to the subscript data where the data of all views retains features, parameters of the initial support tensor machine classification model are optimized, and a final support tensor machine classification model is determined; in step d, test samples are input to the support tensor machine classification model and are classified. Via use of the tensor model-based multi-source data classification optimizing method and system, classification accuracy of the classification model can be effectively improved, calculation complexity can be lowered, that redundant information in tensor data can be identified by the classification model is ensured, and classifying speed of the classification model can be further improved.
Owner:SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI

Optimal classification of land use and cover based on ELM for hyperspectral remote sensing images

InactiveCN109344777AImprove generalization performanceImprove classification accuracy
The invention discloses an ELM-based optimized classification method for land use and cover of hyperspectral remote sensing images, which comprises the following steps: firstly, a plurality of ELM-based classifiers are constructed, and a training data set is constructed for each ELM-based classifier; Then, T ELM-based classifiers are trained based on the training data sets of each ELM-based classifier, and the classification and prediction results of training samples in each training data set are obtained. Then, the ELM-based classifier set is pruned based on the classification prediction results. Finally, the hyperspectral remote sensing images to be classified, The spectral features of each pixel point are extracted, and the feature data of the object to be classified are obtained and inputted into the ELM-based classifiers in the set of classifiers retained after pruning, and the hyperspectral remote sensing images to be classified are classified and judged by ensemble, and the classification results of the hyperspectral remote sensing images to be classified are outputted. The invention realizes an optimized classification method for land use and cover of hyperspectral remote sensing images, which improves classification accuracy and classification processing efficiency.
Owner:UNIV OF ELECTRONIC SCI & TECH OF CHINA
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