The invention proposes a dangerous driving behavior real-time detection method based on deep learning, and the method comprises the steps: building a front car image collection system, collecting a training sample, carrying out the manual marking, and building a dangerous driving behavior data set; proposing a spatial pyramid pooling convolution depth confidence normalized classification network (SPP-CDBRNet) model according to the characteristics of the dangerous driving behavior data set based on a deep learning method; carrying out the preprocessing of the built data set, carrying out the training of the SPP-CDBRNet model through a random gradient descent method with a momentum and the data set after preprocessing, and obtaining an SPP-CDBRNet which precisely recognize whether there is a dangerous driving behavior (behaviors of using a cellphone and smoking during driving); carrying out the detection of a front car image through the determined SPP-CDBRNet model, and achieving the real-time detection of the dangerous driving behavior. The method can effectively improve the detection precision of dangerous driving behaviors, is good in instantaneity and mobility, and is good in application prospect.

Dangerous driving behavior real-time detection method based on deep learning

The invention provides a high-dimension text characteristic selection method based on graph clustering. The method comprises the steps of eliminating irrelevant characteristics, and constructing a weighted directionless graph; quickly clustering the characteristics by means of a community discovery algorithm; searching a class cluster space according to a principle of maximal correlation and least redundancy, and eliminating redundant characteristics in the class cluster; and selecting an optimal characteristic subset according to a relation between the characteristic and the kind. According to the method of the invention, characteristic clustering is performed according to high-efficiency community discovery through using a characteristic that the graph can represent characteristic spatial distribution; the representative characteristic is selected; and problems of data distribution condition neglecting and different importance degrees between each characteristic and the kind are settled. Furthermore the method settles blindness in clustering so that a text classification result has higher accuracy and high stability.

High-dimension text data characteristic selection method based on graph clustering

In this paper, we propose a high-level spatiotemporal feature extraction based on deep convolutional bidirectional encoder-decoder representation network with gated recurrent unit (GRU) cell for dynamic fault diagnosis. Although traditional recurrent neural networks (RNNs) have a major issue with extracting the spatial dependencies, temporal convolutional operation is designed to extract the local features which provide insight into different types of faults. The system dynamics are further extracted with the context by the bidirectional encoder-decoder network, which distils the representations from future time steps. Moreover, GRU cell in both encoder and decoder deploys a more compact structure than a usual RNNs network due to the reduction of gates. The resulting deep network is not only generalizing the importance of temporal feature, but it also allows the interpretable feature representation and classification simultaneously. Detailed comparative case study on the benchmark Tennessee Eastman process demonstrates that the proposed method is competitive with the classical long-short term memory (LSTM) network for the fault detection and diagnosis.

Sequential Fault Detection and Classification in Chemical Process Using a Deep Convolutional Encoder-Decoder Architecture Based on System Dynamics

The invention discloses a moving human body attitude similarity measuring method According to the method, a hierarchical human body attitude measuring mode is adopted; the main similarity of attitudes is preferred to detail similarity, and therefore, measurement results can better accord with human subjective judgment results, and therefore, calculation efficiency and calculation accuracy are higher The moving human body attitude similarity measuring method includes the following steps that: (1) a distance measuring model of Mahalanobis distance is represented by a formula (2); human body feature vectors are constructed, and as for each human body attitude, direction vectors of adjacent joint points are adopted as data for composing the feature vectors; (3) a sparse-representation attitude distance measuring model integrating main attitude features and detail features is constructed; and (4) a final expression (9) is obtained, and optimization solution is carried out through adopting a seqential quadratic programming algorithm

Moving human body attitude similarity measuring method

The invention aims at solving the problem that the irrelevant characteristics and the redundant characteristics in gathering software defect data reduce the performance of the model for forecasting the number of defects in software, and therefore, proposes a characteristics selection method oriented to the forecasting of the number of defects in software. The method comprises: first, using the correlation among the characteristics for spectral clustering to the characteristics set; letting the characteristics clustering with high redundancy fall into the same cluster; in the clustering result, utilizing the correlation between the characteristics and the number of defects in software, selecting a few characteristics with the highest correlation from each cluster wherein the redundancy among the characteristics is reduced yet the irrelevant characteristics are not eliminated; and obtaining a characteristics sub-set that benefits the performance of the model for forecasting the number of defects. The technical scheme of the invention has the characteristics of simplicity and rapidness, and the obtained characteristic subset helps to improve the performance of the model for forecasting the number of defects in software.

Characteristics selection method oriented to the forecasting of the number of defects in software

The invention discloses a sorting method based on non-supervision feature selection By means of the method, high dimensional data are expressed in similar diagrams, distances between sample points are obtained through the ITML, and a similar matrix of the original high dimensional data is set up; then the SM algorithm is executed on the similar matrix and a diagonal matrix corresponding to the similar matrix to achieve mapping of original sample sets to feather vector space; then through learning of sparse coefficient vectors and MCFS scores, weight coefficients of all attributes in the original sample set are obtained, and the attribute which can best express the original sample information is selected out; finally a support vector machine is used for setting up a sorting model of the selected data to predict fatigue states of a driver The method selects features of the high dimensional data under the condition of maintaining data aggregate structures before the sorting model is set up, and the negative effect of curse of dimensionality on data sorting is avoided

Sorting method based on non-supervision feature selection

In many applications of kernel method, such as support vector machine (SVM), the performance greatly depends on the choice of kernel. It is not always clear what is the most suitable kernel for one specified task. Multiple kernel learning (MKL) allows to optimize over linear combinations of kernels. A two-step approach, which alternately optimizes the standard SVM and kernel weights, is proposed to solve the non-convex MKL problem. The generalized convexity of MKL is studied which guarantees the strong duality of the corresponding optimization problem. A further contribution is the utilization of a computationally efficient inexact-projection-based method to optimize the standard SVM. In addition, a nonmonotone gradient method is proposed to optimize the kernel weights and the Hessian matrix is approximated with a variation of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach. The global convergence of the proposed two-step approach is analyzed. The utility of the proposed scheme is demonstrated by empirical study with several datasets, and its performance is compared with state-of-the-art methods in terms of accuracy and scalability. The results show that the proposed method performs as well as existing methods in accuracy, but takes much less time to converge to the stationary point.

Generalized convexity-based inexact projection method for multiple kernel learning

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Sorting method based on non-supervision feature selection

Generalized convexity-based inexact projection method for multiple kernel learning