Zeyi Wang¹, Mingxi Jiao¹, Daliang Wang¹, Minxu Liu¹, Minglei Jiang², He Wang³, Shiqiang Li^3,*

Energy Engineering, Vol.121, No.8, pp. 2275-2291, 2024, DOI:10.32604/ee.2024.028223 - 19 July 2024

Abstract This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method. Three different electrical quantities are selected as observations in the compressed sensing algorithm. The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels. Subsequently, by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,… More >

Haitao Hu¹, Hongmei Ma², Shuli Mei^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3813-3832, 2023, DOI:10.32604/cmc.2023.041416 - 08 October 2023

Abstract Biological slices are an effective tool for studying the physiological structure and evolution mechanism of biological systems. However, due to the complexity of preparation technology and the presence of many uncontrollable factors during the preparation processing, leads to problems such as difficulty in preparing slice images and breakage of slice images. Therefore, we proposed a biological slice image small-scale corruption inpainting algorithm with interpretability based on multi-layer deep sparse representation, achieving the high-fidelity reconstruction of slice images. We further discussed the relationship between deep convolutional neural networks and sparse representation, ensuring the high-fidelity characteristic of… More >

Wangpeng He^1,*, Yue Zhou¹, Xiaoya Guo², Deshun Hu¹, Junjie Ye³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896 - 03 August 2023

Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process.… More >

N. A. Lawrance^*, T. S. Shiny Angel

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3439-3455, 2023, DOI:10.32604/csse.2023.030311 - 03 April 2023

Abstract The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: More >

Xin Wang, Zhilin Zhu, Zhen Hua^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 893-908, 2023, DOI:10.32604/cmes.2022.021287 - 31 August 2022

Abstract Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute More >

Israa Ismail^1,*, Mohamed Meselhy Eltoukhy^1,2, Ghada Eltaweel¹

Computer Systems Science and Engineering, Vol.45, No.1, pp. 167-181, 2023, DOI:10.32604/csse.2023.028906 - 16 August 2022

Abstract Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s). In this paper, we proposed a single image super-resolution algorithm. It uses the nonlocal mean filter as a prior step to produce a denoised image. The proposed algorithm is based on curvelet transform. It converts the denoised image into low and high frequencies (sub-bands). Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands. In parallel, we applied sparse representation with over complete dictionary for the denoised image. The proposed algorithm then combines the dictionary More >

Kandasamy Kittusamy^*, Latha Shanmuga Vadivu Sampath Kumar

Computer Systems Science and Engineering, Vol.44, No.3, pp. 1989-2005, 2023, DOI:10.32604/csse.2023.026501 - 01 August 2022

Abstract Medical Image Fusion is the synthesizing technology for fusing multimodal medical information using mathematical procedures to generate better visual on the image content and high-quality image output. Medical image fusion represents an indispensible role in fixing major solutions for the complicated medical predicaments, while the recent research results have an enhanced affinity towards the preservation of medical image details, leaving color distortion and halo artifacts to remain unaddressed. This paper proposes a novel method of fusing Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) using a hybrid model of Non Sub-sampled Contourlet Transform (NSCT) and… More >

Jingming Xia¹, Yi Lu¹, Ling Tan^2,*, Ping Jiang³

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 613-624, 2021, DOI:10.32604/cmc.2021.013457 - 12 January 2021

Abstract Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse More >

Wei Sun^{1, 2, *}, Xiaorui Zhang^{2, 3}, Xiaozheng He⁴, Yan Jin¹, Xu Zhang³

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2489-2510, 2020, DOI:10.32604/cmc.2020.012343 - 16 September 2020

Abstract Vehicle type recognition (VTR) is an important research topic due to its significance in intelligent transportation systems. However, recognizing vehicle type on the real-world images is challenging due to the illumination change, partial occlusion under real traffic environment. These difficulties limit the performance of current stateof-art methods, which are typically based on single-stage classification without considering feature availability. To address such difficulties, this paper proposes a twostage vehicle type recognition method combining the most effective Gabor features. The first stage leverages edge features to classify vehicles by size into big or small via a similarity… More >

Caifeng Cheng^1,2, Deshu Lin^3,*

Journal on Internet of Things, Vol.2, No.1, pp. 47-54, 2020, DOI:10.32604/jiot.2020.09117 - 06 August 2020

Abstract In this paper, the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied. The advantages and disadvantages of greedy reconstruction algorithm are analyzed. The disadvantages of signal sparsely are preset in this algorithm. The sparsely adaptive estimation algorithm is proposed. The compressed sampling matching tracking algorithm supports the set selection and culling atomic standards to improve. The sparse step size adaptive compressed sampling matching tracking algorithm is proposed. The improved algorithm selects the sparsely as the step size to select the support set atom, and the maximum correlation value. Half of the More >