Adaptive partitioning PCA model for improving fault detection and isolation

In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.

Fish-Eye Image Distortion Correction Based on Adaptive Partition Fitting

The acquisition of images with a fish-eye lens can cause serious image distortion because of the short focal length of the lens.As a result,it is difficult to use the obtained image information.To make use of the effective information in the image,these distorted imagesmust first be corrected into the perspective of projection images in accordance with the human eye’s observation abilities.To solve this problem,this study presents an adaptive classification fitting method for fish-eye image correction.The degree of distortion in the image is represented by the difference value of the distances fromthe distorted point and undistorted point to the center of the image.The target points selected in the image are classified by the difference value.In the areas classified by different distortion differences,different parameter curves were used for fitting and correction.The algorithm was verified through experiments.The results showed that this method has a substantial correction effect on fish-eye images taken by different fish-eye lenses.

Adaptive partition intuitionistic fuzzy time series forecasting model

To enhance the accuracy of intuitionistic fuzzy time series forecasting model, this paper analyses the influence of universe of discourse partition and compares with relevant literature. Traditional models usually partition the global universe of discourse, which is not appropriate for all objectives. For example, the universe of the secular trend model is continuously variational. In addition, most forecasting methods rely on prior information, i.e., fuzzy relationship groups (FRG). Numerous relationship groups lead to the explosive growth of relationship library in a linear model and increase the computational complexity. To overcome problems above and ascertain an appropriate order, an intuitionistic fuzzy time series forecasting model based on order decision and adaptive partition algorithm is proposed. By forecasting the vector operator matrix, the proposed model can adjust partitions and intervals adaptively. The proposed model is tested on student enrollments of Alabama dataset, typical seasonal dataset Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and a secular trend dataset of total retail sales for social consumer goods in China. Experimental results illustrate the validity and applicability of the proposed method for different patterns of dataset.

Adaptive partitioning-based discrete unified gas kinetic scheme for flows in all flow regimes

To improve the efficiency of the discrete unified gas kinetic scheme(DUGKS)in capturing cross-scale flow physics,an adaptive partitioning-based discrete unified gas kinetic scheme(ADUGKS)is developed in this work.The ADUGKS is designed from the discrete characteristic solution to the Boltzmann-BGK equation,which contains the initial distribution function and the local equilibrium state.The initial distribution function contributes to the calculation of free streaming fluxes and the local equilibrium state contributes to the calculation of equilibrium fluxes.When the contribution of the initial distribution function is negative,the local flow field can be regarded as the continuous flow and the Navier-Stokes(N-S)equations can be used to obtain the solution directly.Otherwise,the discrete distribution functions should be updated by the Boltzmann equation to capture the rarefaction effect.Given this,in the ADUGKS,the computational domain is divided into the DUGKS cell and the N-S cell based on the contribu-tion of the initial distribution function to the calculation of free streaming fluxes.In the N-S cell,the local flow field is evolved by solving the N-S equations,while in the DUGKS cell,both the discrete velocity Boltzmann equation and the correspond-ing macroscopic governing equations are solved by a modified DUGKS.Since more and more cells turn into the N-S cell with the decrease of the Knudsen number,a significant acceleration can be achieved for the ADUGKS in the continuum flow regime as compared with the DUGKS.

An efficient adaptive space partitioning algorithm for electromagnetic scattering calculation of complex 3D models

The space partitioning algorithm based on the rounding and addressing operations has been proved to be an efficient space partitioning algorithm with the potential for real-time calculation.An improvement on this kind of space partitioning algorithms for solving complex 3D models is presented.Numerical examples show that the efficiency of the improved algorithm is better than that of the original method.When the size of most target elements is smaller than the size of spatial grids,the efficiency of the improved method can be more than four times of that of the original method.An adaptive method of space partitioning based on the improved algorithm is developed by taking the surface element density or the curvature as the threshold for deep partitioning and conducting the deep partitioning using the octree method.A computer program implementation for applying the method in some typical applications is discussed,and the performance in terms of the efficiency,reliability,and resource use is evaluated.Application testing shows that the results of the adaptive spacing partitioning are more convenient for the follow-up use than that of the basic uniform space partitioning.Furthermore,when it is used to calculate the electromagnetic scattering of complex targets by the ray tracing(RT)method,the adaptive space partitioning algorithm can reduce the calculation time of the RT process by more than 40%compared with the uniform space segmentation algorithm.

Low Complexity Remote Radio Unit Selection and Adaptive Bit Partition Algorithm in Cooperative Distributed MIMO Systems

For multi-user cooperative Distributed MIMO (D-MIMO) systems, a low-complexity Remote Radio Unit (RRU) selection and adaptive bit partition algorithm is proposed to maximize the transmission Signal-to-Interference-Noise Ratio (SINR). Considering limited feedback, each user can adaptively select an RRU cluster to maintain the best communication quality. Under this condition, only one codebook is utilized for quantizing the Channel State Information (CSI) with variable dimensions, which effectively reduces the codebook storage amount. Furthermore, we propose an adaptive bit partition algorithm, which separately allocates bits to quantize the desired channels and interference channels. The optimal solution is achieved through an optimization theory to minimize the effect of inter-cell interference. Simulation results show that the proposed algorithm substantially improves the performance compared to other non-adaptive schemes.

High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction

To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)prediction.First,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images.In the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding space.To the best of our knowledge,quadtree partitioning is first applied to RDH-EI.Simulation results show that the proposed method is reversible and separable,and that its average embedding capacity is improved.For gray images with a size of 512×512,the average embedding capacity is increased by 25565 bits.For all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.

Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation

Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.

Structured-light Image Compression Based on Fractal Theory

The method of fractal image compression is introduced which is applied to compress the line structured light image. Based on the self similarity of the structured light image, we attain satisfactory compression ratio and higher peak signal to noise ratio (PSNR). The experimental results indicate that this method can achieve high performance.