Shen-Haw Ju^*, Yi-Chen Huang

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1631-1654, 2025, DOI:10.32604/cmes.2025.067679 - 31 August 2025

Abstract This study focuses on determining the second-order irregular wave loads in the time domain without using the Inverse Fast Fourier Transform (IFFT). Considering the substantial displacement effects that Floating Offshore Wind Turbine (FOWT) support structures undergo when subjected to wave loads, the time-domain wave method is more suitable, while the frequency-domain method requiring IFFT cannot be used for moving bodies. Nonetheless, the computational challenges posed by the considerable computer time requirements of the time-domain wave method remain a significant obstacle. Thus, the paper incorporates various numerical schemes, including parallel computing and extrapolation of wave forces… More >

Ronghua Wang¹, Shibao Sun^1,*, Pengcheng Zhao^1,*, Xianglan Yang², Xingjia Wei¹, Changyang Hu¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3519-3539, 2025, DOI:10.32604/cmc.2025.066212 - 03 July 2025

Abstract The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. However, existing methods often suffer from insufficient frequency-domain representation in practical applications, which greatly affects diagnostic performance. Therefore, this paper proposes a rolling bearing fault diagnosis method based on a Multi-Scale Fusion Network (MSFN) using the Time-Division Fourier Transform (TDFT). The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel. A multi-level, multi-scale filter-based approach is designed to More >

Chaozhi Cai, Yuqi Ren, Yingfang Xue^*, Jianhua Ren

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 665-682, 2025, DOI:10.32604/sdhm.2025.059044 - 03 April 2025

Abstract Rolling bearings are important parts of industrial equipment, and their fault diagnosis is crucial to maintaining these equipment’s regular operations. With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise, this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform (FFT) and variational mode decomposition (VMD), as well as the Senet (SE)-TCNnet (TCN) model. FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal, while VMD is used to decompose the… More >

Nouf Abdullah Almujally¹, Bisma Riaz Chughtai², Naif Al Mudawi³, Abdulwahab Alazeb³, Asaad Algarni⁴, Hamdan A. Alzahrani⁵, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1563-1580, 2024, DOI:10.32604/cmc.2024.049333 - 18 July 2024

Abstract The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes. Various technologies, such as augmented reality-driven scene integration, robotic navigation, autonomous driving, and guided tour systems, heavily rely on this type of scene comprehension. This paper presents a novel segmentation approach based on the UNet network model, aimed at recognizing multiple objects within an image. The methodology begins with the acquisition and preprocessing of the image, followed by segmentation using the fine-tuned UNet architecture. Afterward, we use an annotation tool to accurately label… More >

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 - 08 July 2024

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 >

Yin Ni¹, Wu Zeng^2,*, Peng Xia¹, Guang Stanley Yang³, Ruochen Tan⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5295-5312, 2024, DOI:10.32604/cmc.2024.049911 - 20 June 2024

Abstract Deepfake-generated fake faces, commonly utilized in identity-related activities such as political propaganda, celebrity impersonations, evidence forgery, and familiar fraud, pose new societal threats. Although current deepfake generators strive for high realism in visual effects, they do not replicate biometric signals indicative of cardiac activity. Addressing this gap, many researchers have developed detection methods focusing on biometric characteristics. These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography (rPPG) signal, resulting in high detection accuracy. However, in the spectral analysis, existing approaches often only consider the power spectral density… More >

Bin Liu¹, Shuai Liang¹, Renjie Ding¹, Shuguang Li^2,*

Energy Engineering, Vol.121, No.3, pp. 729-746, 2024, DOI:10.32604/ee.2023.044224 - 27 February 2024

Abstract The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks. Therefore, it is critical to find solutions to the issues of multifrequency parameter estimation and synchronous measurement estimation accuracy in the complex environment of distribution networks. By utilizing the multifrequency sensing capabilities of discrete Fourier transform signals and Taylor series for dynamic signal processing, a multifrequency signal estimation approach based on HT-IpDFT-STWLS (HIpST) for distribution networks is provided. First, by introducing the Hilbert transform (HT), the influence of noise on the estimation algorithm… More >

Arslan Akram^1,2, Imran Khan¹, Javed Rashid^2,3, Mubbashar Saddique^4,*, Muhammad Idrees⁴, Yazeed Yasin Ghadi⁵, Abdulmohsen Algarni⁶

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1311-1328, 2024, DOI:10.32604/cmc.2023.040512 - 30 January 2024

Abstract Algorithms for steganography are methods of hiding data transfers in media files. Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information, and these methods have made it feasible to handle a wide range of problems associated with image analysis. Images with little information or low payload are used by information embedding methods, but the goal of all contemporary research is to employ high-payload images for classification. To address the need for both low- and high-payload images, this work provides a machine-learning approach to steganography image classification… More >

Artyom M. Grigoryan^1,*, Sos S. Agaian²

Journal of Quantum Computing, Vol.6, pp. 1-14, 2024, DOI:10.32604/jqc.2023.026981 - 30 January 2024

Abstract In this work, we describe a method of calculation of the 1-D circular quantum convolution of signals represented by 3-qubit superpositions in the computational basis states. The examples of the ideal low pass and high pass filters are described and quantum schemes for the 3-qubit circular convolution are presented. In the proposed method, the 3-qubit Fourier transform is used and one addition qubit, to prepare the quantum superposition for the inverse quantum Fourier transform. It is considered that the discrete Fourier transform of one of the signals is known and calculated in advance and only More >

Youming Guo, Qinmu Wu^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1489-1514, 2023, DOI:10.32604/cmc.2023.042313 - 29 November 2023

Abstract Permanent magnet synchronous motor (PMSM) is widely used in various production processes because of its high efficiency, fast reaction time, and high power density. With the continuous promotion of new energy vehicles, timely detection of PMSM faults can significantly reduce the accident rate of new energy vehicles, further enhance consumers’ trust in their safety, and thus promote their popularity. Existing fault diagnosis methods based on deep learning can only distinguish different PMSM faults and cannot interpret and analyze them. Convolutional neural networks (CNN) show remarkable accuracy in image data analysis. However, due to the “black… More >