International Journal of Pattern Recognition and Artificial Intelligence

To solve the problem that existing driving data cannot correlate to the large number of vehicles in terms of driving risks, is the functionality of intelligent driving algorithm should be improved. This paper deeply explores driving data to build a link between massive driving data and a large number of sample vehicles for driving risk analysis. It sorted out certain driving behavior parameters in the driving data, and extracted some parameters closely related to the driving risk; it further utilized the principal component analysis and factor analysis in spatio-temporal data to integrate certain extracted parameters into factors that are clearly related to the specific driving risks; then, it selected factor scores of driving behaviors as indexes for hierarchical clustering, and obtained multi-level clustering results of the driving risks of corresponding vehicles; in the end, it interpreted the clustering results of the vehicle driving risks. According to the results, it is found that cluster for different risks proposed in this paper for driving behaviors is effective in the hierarchical cluster for typical driving behaviors and it also offers a solution for risk analyses between driving data and large sample vehicles. The results provide the basis for training on safe driving for the key vehicles, and the improvement of advanced driver assistance system, which shows a wide application prospect in the field of intelligent drive.

In the rolling bearing fault detection, the Hilbert–Huang transform (HHT) has made remarkable achievements, but at present, the HHT still has the end effect problem, which will cause a lot of data distortion, spectrum confusion that will affect fault diagnosis result and error in the detection of rolling bearing faults in a serious manner. In response to this problem, this paper proposes a method of multi-point continuation at both ends of the signal to suppress the endpoint effectExtend at both ends of the signal, then perform empirical mode decomposition (EMD). The experimental comparison shows that the method has an effect on the endpoint effect.

Good trimap is essential for high-quality alpha matte. However, making high-quality trimap is hardwork, especially for complex images. In this paper, an active learning framework is proposed to make high quality trimap. There are two active learning methods which are employed: minimization of uncertainty sampling (MUS) and maximization of expected model output change (EMOC). MUS model finds the informative area in image which can decrease the uncertain sampling of alpha matte. EMOC model finds the important areas in image which can give the maximum expected output change of alpha matte. Two methods are combined to define the active map. Active map shows important areas which are informative in image. It can help users to make high quality trimap. The analysis and evaluation of benchmark datasets show that proposed method is effective.

In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this paper also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available data sets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.

The star images obtained through the CCD camera can visually display the star structure. In order to get the wide starry image, we need to extract the characteristics of star images to achieve the star image stitching. In the star images, star points, whose characteristics are limited, are easily influenced by noise and are also difficult to extract. The number of stars is too large to stitch accurately. Thus, we propose a stitching algorithm based on blocking star images. First, we establish the maximum intensity projection model based on time sequence to locate the star points accurately. Then, according to the relative positions of star points, the block model is introduced to realize the establishment of the characteristics. Finally, the star image stitching is achieved from the perspective of the characteristic similarity. The experiments illustrate that CM (combination measure) reaches 0.87, and the proposed algorithm has better anti-noise performance and robustness.

A vision-based method for detecting the cracks in the concrete sleepers of the railway tracks will be introduced in this paper. The method is able to detect and partially classify the cracks of the concrete sleepers in two successive steps based on the image processing and pattern recognition techniques. The method has been implemented on the acquired image data frames followed by the analysis, experimental, comparison results and evaluation. The presented results are reasonable which indicates the goodness of the introduced method. The preliminary results of this work have been presented in [A. Delforouzi, A. H. Tabatabaei, M. H. Khan and M. Grzegorzek, A vision-based method for automatic crack detection in railway sleepers, in Kurzynski, M., Wozniak, M., Burduk, R. (eds.), Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, Polanica Zdroj, Poland. CORES 2017. Advances in Intelligent Systems and Computing, Vol. 578 (Springer, Cham, 2018), pp. 130–139, doi: 10.1007/978-3-319-59162-9_14].

The increased demand for virtual reality brings more challenges to 360^∘ video coding. 360^∘ video needs to be projected as a planar video before encoding. This process will produce projection distortion. The degree of projection distortion depends on the location of the pixel. Traditional coding algorithms cannot respond to this feature efficiently enough. In this paper, a fast Sample Adaptive Offset (SAO) algorithm for 360^∘ video is proposed. The proposed algorithm improves the SAO process. On the basis of retaining the whole SAO process, a simplified SAO process is added. First, the coding tree unit (CTU) of performing the simplified SAO process is filtered according to the rate distortion cost (RD-cost) of the intra- or inter-prediction and the location of the CTU. Subsequently, the CTU is sampled at intervals according to the equirectangular projection (ERP) characteristics, and the CTU of performing the simplified SAO process is determined. Experimental results show that the proposed algorithm achieves 60% time of SAO process reduction, with only 0.29% luma Bjontegaard delta rate (BD-rate) increases on average.

Image misalignment during image stitching is a common issue for the mechanically stitched CCDs in the aerial cameras due to the relative image motion between CCDs. In this paper, we analyze the error in imaging stitching for the mechanically stitched CCDs and propose a compensation method based on position and orientation system (POS). First, the imaging relationship of overlapping pixels in mechanical stitching is analyzed. The imaging model of adjacent CCD is constructed according to the collinear equation. The effects of stitching staggered distance, carrier attitude change and flight speed on the image mosaic of overlapping areas are given. Monte Carlo algorithm is used to analyze the statistical value of image mosaic error in overlapping area under typical working conditions. Then, a geometric correction method based on POS recording of external orientation elements of adjacent CCD imaging is proposed. The overlapping area stitching error is reduced from 14.9 pixels to 0.4 pixels which meets the engineering requirements. The mechanical stitching mathematical model, analysis and correction method established in this paper have strong engineering and application significance for mechanical stitching of aerial cameras.

In this paper, the performance of a two-dimensional (2D) Massive MIMO angle parameter system with a uniform circular array (UCA) topology at base station is studied based on the field measurements in indoor line-of-sight (LOS) scenario at a frequency of 4.45GHz. Many spatial parameters are extracted, including angle information and correlation characteristic. It is shown that correlation lies between antenna elements and users. It is illustrated in the envelope correlation coefficient which goes on a downward trend while the antenna spacing is increasing in the measurements.

The fault diagnosis intelligent algorithm makes full use of the associative memory and pattern recognition function of the neural network to compare the abnormal value of various parameters of the engine fault with the reference value of the known fault mode, which can shorten the fault diagnosis time and improve the diagnosis efficiency. BP neural network model as one of the most widely used neural network models in the world is of significance to solve nonlinear complex problems. Of course, there are also some deficiencies in it, such as long training time and ease to trap into local minimum. This paper utilized the global search advantage of genetic algorithm to optimize the optimal weight and threshold value of BP neural network. Furthermore, an improved BP neural network was put forward, which is greatly improved in stability, generalization and convergence rate. Taking fault diagnosis of automobile engine as an example, a simulation experiment was carried out on the established model. The research results indicate that improved neural network model owns a higher accuracy than pure GA model or BP neural network model (with an average accuracy improved by 19.04% than traditional model), and its effect is satisfactory.

When the input signals for acoustic echo cancellation (AEC) are related signals, the convergence speed of the traditional normalized least mean square (NLMS) algorithms is significantly reduced. In this paper, a joint optimization robust AEC algorithm is proposed to solve this problem. Based on the analysis of the convergence of the normalized subband adaptive filtering (NSAF) algorithm, the algorithm is optimized by minimizing the mean square error (MSE) of the NSAF algorithm, combining sub-band time-varying step factor and time-varying regularization parameter to update the filter weight vectors. And when the impulse noise occurs, the sub-band cut-off parameter is updated in a recursive manner, which makes the algorithm achieve fast convergence speed and low steady-state error, and has strong robustness to impulse noise. In a series of experiments on AEC, simulation results show that the performance of the algorithm is better than the existing algorithms.

The high accuracy of electroencephalogram (EEG) signal classification is the premise for the wide application of brain computer interface (BCI). In this paper, a hybrid method consisting of multivariate empirical mode decomposition (MEMD) and common space pattern (CSP) is proposed to recognize left-hand and right-hand hypothetical motion from EEG signals. Experiments were carried out using the BCI competition II imagery database. EEG signals were decomposed into multiple intrinsic mode functions (IMFs) by MEMD. The IMF functions with high correlation were processed by CSP, and AR coefficients and entropy values were extracted as features. After genetic algorithm optimization, classification is carried out. Our research results show that the K nearest neighbor (KNN) as an optimal classification model produces 85.36% accuracy. We also compare the proposed algorithm with the existing algorithms. The experimental results show that the performance of the proposed algorithm is comparable to or better than that of many existing algorithms.

A knowledge model for knowledge base system in the Web environment is proposed, which includes problem description layer and knowledge layer. A knowledge base is made up of a set of knowledge models. Knowledge models are used to support knowledge representation and reasoning. Visual knowledge modeling tool and visual knowledge service tool based on Web environment realize the construction of knowledge base system in Web environment. The knowledge base system supports the construction of agricultural expert system. By comparison with CommonKADS, it is proved that this method can improve the efficiency of knowledge acquisition, management and maintenance in Web environment. This method also has the reference significance to the construction of the knowledge base system in the Web environment in other domains.

Incremental multi-hop localization algorithm applies to networks with broad range and low density of anchor nodes. However, during the localization process, it tends to be affected by accumulative errors and collinear problem between anchor nodes. We have proposed an incremental multi-hop localization algorithm based on regularized weighted least squares method, and the algorithm uses weighted least squares method to reduce the influence of accumulative errors and uses regularized method to weaken the collinear problem between anchor nodes. The results of both real experiment and simulative experiment show that compared to previous incremental multi-hop localization algorithms, the algorithm proposed in this paper can not only well solve the accumulated errors problem and obtain high localization accuracy, but it has also considered the influence of collinear problem on localization computation during the localization process. We evaluate our method based on various network scenes, and analyze its performance. We also compare our method with several existing methods, and demonstrate the high efficiency of our proposed method.