Mian Muhammad Danyal^1,2, Sarwar Shah Khan^3,4,*, Rahim Shah Khan⁵, Saifullah Jan², Naeem ur Rahman⁶

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340

Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >

Lili Bai^1,*, Wenhui Li¹, He Ren^1,2, Feng Li¹, Tao Yan¹, Lirong Chen³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4513-4531, 2024, DOI:10.32604/cmc.2024.051348

Abstract Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery, where weak fault characteristic signals hinder accurate fault state representation, we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform (FAWT) with Nonlinear Quantum Permutation Entropy. FAWT, leveraging fractional orders and arbitrary scaling and translation factors, exhibits superior translational invariance and adjustable fundamental oscillatory characteristics. This flexibility enables FAWT to provide well-suited wavelet shapes, effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults. In our approach,… More >

Tengfei Li¹, Wenhui Zhang¹, Ke Mi¹, Qingming Lin¹, Shuangwei Zhao^2,*, Jiayi Song²

Energy Engineering, Vol.121, No.7, pp. 1991-2007, 2024, DOI:10.32604/ee.2024.049460

Abstract Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers (LVCBs). A fault diagnosis algorithm based on an improved Sparrow Search Algorithm (ISSA) optimized Backpropagation Neural Network (BPNN) is proposed to improve the operational safety of LVCB. Taking the 1.5kV/4000A/75kA LVCB as an example. According to the current operating characteristics of the energy storage motor, fault characteristics are extracted based on Empirical Wavelet Transform (EWT). Traditional BPNN has problems such as difficulty adjusting network weights and thresholds, being sensitive to initial weights, and quickly falling into More >

Chengpeng Zhang, Junfeng Shi^*, Caiping Huang

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 465-483, 2024, DOI:10.32604/sdhm.2024.048705

Abstract In this paper, an intelligent damage detection approach is proposed for steel-concrete composite beams based on deep learning and wavelet analysis. To demonstrate the feasibility of this approach, first, following the guidelines provided by relevant standards, steel-concrete composite beams are designed, and six different damage incidents are established. Second, a steel ball is used for free-fall excitation on the surface of the steel-concrete composite beams and a low-temperature-sensitive quasi-distributed long-gauge fiber Bragg grating (FBG) strain sensor is used to obtain the strain signals of the steel-concrete composite beams with different damage types. To reduce the… More >

Haijian Shao^1,2,*, Suqin Lei¹, Chenxu Yan³, Xing Deng¹, Yunsong Qi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1507-1537, 2024, DOI:10.32604/cmes.2024.050140

Abstract This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy, specifically tailored for environments characterized by markedly low luminance levels. Conventional methodologies struggle with the challenges posed by luminosity fluctuations, especially in settings characterized by diminished radiance, further exacerbated by the utilization of suboptimal imaging instrumentation. The envisioned approach mandates a departure from the conventional YOLOX model, which exhibits inadequacies in mitigating these challenges. To enhance the efficacy of this approach in low-light conditions, the dehazing algorithm undergoes refinement, effecting a discerning regulation of the transmission rate at the pixel… More > Graphic Abstract

Shahid Naseem¹, Tariq Mahmood^2,3, Amjad Rehman Khan², Umer Farooq¹, Samra Nawazish⁴, Faten S. Alamri^5,*, Tanzila Saba²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 801-817, 2024, DOI:10.32604/cmc.2024.047623

Abstract Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that proves more informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring,… More >

Mahmood A. Mahmood^1,2,*, Khalaf Alsalem¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604

Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is… More >

Pu Zhang¹, Yingjun Li¹, Xinyu Zhu¹, Shizhan Xu¹, Pinwu Guan^1,*, Wei Liu², Yanwei Guo², Haibo Wang²

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 143-159, 2024, DOI:10.32604/sdhm.2024.046986

Abstract Grouting defects are an inherent challenge in construction practices, exerting a considerable impact on the operational structural integrity of connections. This investigation employed the impact-echo technique for the detection of grouting anomalies within connections, enhancing its precision through the integration of wavelet packet energy principles for damage identification purposes. A series of grouting completeness assessments were meticulously conducted, taking into account variables such as the divergent material properties of the sleeves and the configuration of adjacent reinforcement. The findings revealed that: (i) the energy distribution for the high-strength concrete cohort predominantly occupied the frequency bands More >

Jiaqun Wang^1,2, Guanxu Pan², Youhe Zhou², Xiaojing Liu^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 297-318, 2024, DOI:10.32604/cmes.2023.030622

Abstract In this study, a wavelet multi-resolution interpolation Galerkin method (WMIGM) is proposed to solve linear singularly perturbed boundary value problems. Unlike conventional wavelet schemes, the proposed algorithm can be readily extended to special node generation techniques, such as the Shishkin node. Such a wavelet method allows a high degree of local refinement of the nodal distribution to efficiently capture localized steep gradients. All the shape functions possess the Kronecker delta property, making the imposition of boundary conditions as easy as that in the finite element method. Four numerical examples are studied to demonstrate the validity More >

Zhicheng Liu¹, Long Zhao^1,*, Guanru Wen¹, Peng Yuan², Qiu Jin¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 541-555, 2023, DOI:10.32604/sdhm.2023.029760

Abstract The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element… More > Graphic Abstract