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As a possible countermeasure to the lack in algorithms for solving maximum regularization satisfiability problems as well as inadequate accuracy of intelligent optimization algorithms for solving maximum satisfiability problems, a discrete chaotic quantum based bat algorithm based on the bat algorithm (BA) was proposed herein. This algorithm was discretized by conversion of continuous values to discrete binary codes. By applying quantum theory, introducing quantum bit encoding and heuristic quantum mutation, the mutation is achieved by changing the probability amplitude of non optimal individuals through quantum rotation gates, solving the problems of precocity and slow convergence speed. In position update, chaotic mapping is used to replace fixed parameters, which enhances flexibility and diversity, and improves global optimization ability and solving efficiency. The experimental results show that the proposed algorithm has much higher accuracy than traditional heuristic algorithms in generating different scale examples in the stochastic regularization satisfiability problem instance generation model. At the same time, compared with the award-winning solver, it also has a certain level of competitiveness, verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The seriously ill-posed sideways heat equations were considered in the quarter plane. The classical quasi-reversibility method was applied to acquire an approximate but non-regularized solution to the problem. Interestingly, a regularization solution to the sideways heat equation was obtained through modification of the denominator of the solution. Then, a new regularization method was proposed, and the Hölder-type error estimates under a priori and a posteriori parameter choice rules were proved, respectively. Numerical experiments show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a super-resolution(SR) method through an internal and external dictionary-based regularization was proposed for improving the resolution of single low-resolution (LR) image. Firstly, the input was divided in to several similar groups, and then the principal components analysis (PCA) dictionary learning method was used to construct the internal dictionary of each corresponding group. Secondly, the high-frequency details of external high-resolution(HR) images was divided in to groups with similar structures, and the PCA method was adopted to construct the external dictionary corresponding to each group. Next, the nonlocal regression model was used to design two complementary regularities to solve the problem of SR uncertainty. Finally, the SR reconstruction was achieved through an iterative optimization algorithm with gradient descent. The experimental results showed that the proposed method could improve the peak signal to noise ratio(PSNR) by 0.2d Bon average and the structural similarity(SSIM) by 0.01 on average, showing better visual quality than the other compared methods. [ABSTRACT FROM AUTHOR]

In the field of image super-resolution, how to balance the sparsity and cooperation of the representation coefficients in the dictionary learning is of great significance for the reconstruction result. For this problem, this paper proposed an adaptive semi-coupled dictionary learning super-resolution method based on semi-coupled dictionary-learning method. The method us ed the kernel norm to construct a new regularization term to consider the sparsity and cooperation together, and adopted the alternating direction multiplier method (ADMM) to solve the optimal model. The experimental results show that the proposed method is more effective than some other existing dictionary-learning based reconstruction methods. It can balance the sparsity and correlation according to the variation of dictionary to produce an appropriate coefficient adaptively. Hence, it has anti-interferenee ability to the noisy environment. [ABSTRACT FROM AUTHOR]

The truncated singular value method (TSVD) improves the morbidity of model by truncating small singular values of the ill⁃posed observational equation matrix and increases the stability and accuracy of the parameter estimation. However, the structure of observation equation has been changed after truncat⁃ ing small singular value, which makes parameter valuation and residual biased, the unit weight variance cannot be calculated by using traditional estimation formula. This paper derives the unbiased formula of unit weight variance for TSVD regularization, and uses the first Fredholm integral equation and ill⁃posed trilat⁃ eration network as examples to verify the correctness of the formula. [ABSTRACT FROM AUTHOR]

Social recommendation based on matrix factorization enhances the learning accuracy by adding user trust relationship, but ignored the influence of related information between items on user interest in the process of model decomposition. This paper first provided an improved item similarity function to measure correlations between items by considering the user frequency factor, and then established a matrix factorization model with two regularization terms, which combined item relations regularization and social regularization into matrix factorization objective function to present the constraints on low rank approximation. It explored a major impact of the item association information on recommendation when optimizing the implicit feature matrix. The experiments on two real-world datasets demonstrate that this method outperforms other state-of-the-art matrix factorization approaches especially dealing with the large sparse data, and effectively alleviates the cold-start user problem. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the problem of signal denoising, this paper proposed a time-fractional nonlinear diffusion smoothing method by combining the classical nonlinear diffusion filtering and the fractional derivative. The proposed model used the fractional order time derivative to replace the time derivative in the classical nonlinear diffusion model. Firstly, it developed the numerical algorithm for the time-fractional nonlinear diffusion filtering, and then compared the proposed method with the classical smoothing methods ( nonlinear diffusion filtering, regularization method, Savitzky-Golay method and wavelet method) . The result shows that the time-fractional diffusion filtering method has a greater improvement in signal-to-noise ratio and root mean square error. Compared with the classical smoothing method, the proposed model obviously has a more excellent performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to make better use of low-resolution face images, this paper proposed a new face super-resolution reconstruction based on L1/2 regularization and local texture constraints. In the process of face reconstruction, this paper used nonnegative matrix factorization to amplify the face image to an appropriate medium resolution. In this process, it used local texture constraints to enhance texture feature extraction. Then, face image reconstruction used local sparse prior, and added reconstruction constraints and local texture constraints again. In order to make the obtained face image sparse coefficient more sparse, it used L1/2regularization to solve the problem. The experimental results show that the reconstructed face image retains the original image structure, and it can achieve good reconstruction results, and has better robustness. [ABSTRACT FROM AUTHOR]

Due to that most high-dimensional data not only has the similarities, but also has nonlinear relationships. This paper proposed a nonlinear feature selection algorithm based on local structure learning. Firstly, the algorithm mapped the data to highdimensional space through kernel functions, and expressed the nonlinear relationship between data features in high-dimensional space. Then, it exploited the similarity between the features in the low-dimensional space through local structural learning. At the same time, it eliminated the interference of noise by the low-rank constraint. Finally, it selected features by sparse regularization factors. It found the non-linear relationships between data features by the kernel function, and found the similarities between the data attributes as the local structure learning. The algorithm was a nonlinear feature selection algorithm embedded with local structure learning. Experimental results show that the algorithm has better results than other comparison algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at nonlinear and dynamic characteristics of batch process, this paper proposed a global-local regularization Gaussian mixture model ( GLRGMM) algorithm. At first, this algorithm introduced neighborhood preserving embedding algorithm to extract the local manifold structure. It preserved global structure of nonlinear process by seeking a low-dimensional projection while preserved local manifold features to the maximum extent at the same time. Then it introduced the regularization term into GMM to online monitor and updated Gaussian model. It obtained the manifold structure of nonlinear data and solved the data dynamic characteristic problem simultaneously. Finally, it used the integration of global-local monitoring indicators to effectively achieve online monitoring. The results verified by the penicillin fermentation process show that the proposed algorithm has better online monitoring effect than DPCA and GLNPE algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper discusses the ill-posed nonlinear least squares problem, and proposes an adaptive relaxation algorithm based on the regularization method for stabilizing the nonlinear parameter estimation. The improved algorithm achieves the adaptive selection on the regularization parameter and iterative step by using an incremental geometric regularization parameter and the minimal residual criterion. The numerical convergence experiments of the method are performed. The results show that the numerical precision of our proposed method is better than that of the linearized adjustment estimation, and the convergence property is more efficient than the iterative Tikhonov regularization method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As picture fuzzy clustering algorithm is not suitable for segmentation of image with noise or inhomogeneous intensity, an adaptive robust picture fuzzy clustering segmentation algorithm based on total Bregman divergence is proposed. An improved total Bregman divergence is constructed by combination of existing total Bregman divergence and neighborhood information of image pixel, which is suitable for image segmentation. It was introduced into the picture fuzzy c-means clustering optimization model, and a robust total Bregman divergence-based picture fuzzy clustering algorithm, in which the pixel spatial neighborhood information was embedded, was obtained. The difference between the gray values of current clustering pixel and its neighborhood pixel is used as the regularization factor of the robust picture fuzzy clustering model based total Bregman divergence, and thus the robust clustering segmentation method would be capable of suppressing the noise adaptively. The results show that the segmentation quality and anti-noise robustness of the proposed segmentation algorithm are improved more significantly than those of the existing picture fuzzy clustering and other robust fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Imaging Science & Photochemistry is the property of Imaging Science & Photochemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hyperspectral image has some typical characteristics such as too many of bands, hard to obtain labeling samples, easy of being interfered of spectral information. These characteristics lead to the dimensionality disaster and the spatial variability of spectral information. To solve these problems, it is proposed to introduce dissimilarity in Laplacian support vector machine (Diss-LapSVM) by the adding dissimilarity information to machine' manifold regularization term, which restrains the influence of the spatial variability effectively. Meanwhile, in order to introduce appropriately distribution of unlabeled samples, This paper provides linear neighborhood propagation (LNP) to construct graph Laplacian matrix. The results illustrated that the proposed method can improve the classification accuracy, especially for samples which have similar spectral features. [ABSTRACT FROM AUTHOR]

To improve the rate-distortion performance of distributed video compressive sensing ( DVCS), The sparse priors has only been exploited to not preserve the edges and textures of video frames well, the nonlocal similarity regularization term has been introduced to joint reconstruction model in order to effectively remove the blurs and blocking artifacts in the edge and texture regions. The simulation experiments show that the proposed joint reconstruction algorithm can effectively improve the objective and subjective quality of video, and enhance the rate-distortion performance of DVCS system at the cost of a certain computational complexity. [ABSTRACT FROM AUTHOR]

Aiming at the inaccuracy of estimating the complex blur kernel, which was based on the regularization method of the image normalized sparse prior, this paper introduced the image preprocessing and proposed a novel method of blind image deblurring. The method divided blind image deblurring into three procedures. Firstly, it adopted the bilateral filter and impact filter to preprocess the image. It could strengthen image edge and reduce noise, which would be good for estimating blur kernel. Then, estimated blur kernel based on the regularization method of the image normalized sparse prior. Finally, it adopted TV regularization method in non-blind deconvolution processing. Fast iterative shrinkage-thresholding algorithm solved the model of image blur kernel estimation, simultaneously fast total variation image restoration algorithm solved non-blind deconvolution mode. Experimental results show that the proposed method is fast and very robust, and can accurately estimate the blur kernel. Moreover, it can improve the restoration effect of the blur image. [ABSTRACT FROM AUTHOR]

There are many fog and dust images with much random noise in coalmine intelligent video surveillance. Therefore, the image degradation has seriously affected the subsequent video image processing. In this paper, an algorithm based on dark channel prior and bilateral filter was proposed which can realize fog and noise simultaneously removing. By combining the atmospheric scattering model, the degradation model of fog and dust images was established. Considering the characteristics of fog images,the methods and procedures for estimating the air light and rough transmittance were designed using the dark channel prior. By analyzing the optimization requirement of the rough transmission map and the bilateral filter characteristic, a joint bilateral filter was introduced for quickly obtaining the fine transmission map. The regularization objective function was constructed on the image degradation model. By solving a converted image and Gaussian bilateral filtering the image, fog and dust removal and simultaneously denoising were realized. Experimental results verify that the proposed algorithm is effective which computational efficiency is greatly improved compared with various restoration algorithms. Because of good restoring quality the algorithm is suitable for the environment of coalmine intelligent video surveillance. [ABSTRACT FROM AUTHOR]

To deal with the shortcoming of the preconditioning conjugate gradient (PCG) method with Tikhonov regularization in which the blurred image is extended with a zeros extension matrix. With the careful analysis of the PCG method with Tikhonov regularization under zero boundary condition, a new extension matrix for the blurred image was proposed. It could decrease the matrix vector multiplication computational error and modify the initial gradient. The improved algorithm is in accord with the real image blurring process and increases the quality of recovered image. Experiments show that, compared with the state-of-the-art algorithms of IST, TwIST and SALSA which solve the total-variation (TV) regularization, the proposed algorithm performs favorably. [ABSTRACT FROM AUTHOR]