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  • Open Access

    ARTICLE

    Optical Fibre Communication Feature Analysis and Small Sample Fault Diagnosis Based on VMD-FE and Fuzzy Clustering

    Xiangqun Li1,*, Jiawen Liang2, Jinyu Zhu2, Shengping Shi2, Fangyu Ding2, Jianpeng Sun2, Bo Liu2

    Energy Engineering, Vol.121, No.1, pp. 203-219, 2024, DOI:10.32604/ee.2023.029295

    Abstract To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis, this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition (VMD), fuzzy entropy (FE) and fuzzy clustering (FC). Firstly, based on the OTDR curve data collected in the field, VMD is used to extract the different modal components (IMF) of the original signal and calculate the fuzzy entropy (FE) values of different components to characterize the subtle differences between them. The fuzzy entropy of each curve is used as the feature vector, which… More >

  • Open Access

    ARTICLE

    Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

    Chunlin Xiong1,2, Zhengqiu Weng3,4,*, Jia Liu1, Liang Gu2, Fayez Alqahtani5, Amr Gafar6, Pradip Kumar Sharma7

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3013-3032, 2024, DOI:10.32604/cmes.2023.031088

    Abstract With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users’ privacy information. At present, mobile user authentication methods based on human-computer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly includes: (1) Construct a smart… More >

  • Open Access

    ARTICLE

    Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition

    Shoutao Li1,2,*, Ruyi Qu1, Yu Zhang1, Dingli Yu3

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2809-2823, 2023, DOI:10.32604/iasc.2023.036999

    Abstract Freezing of Gait (FOG) is the most common and disabling gait disorder in patients with Parkinson’s Disease (PD), which seriously affects the life quality and social function of patients. This paper proposes a FOG recognition method based on the Variational Mode Decomposition (VMD). Firstly, VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal. Secondly, to improve the accuracy and speed of the recognition algorithm, use the CART model as the base classifier and perform the feature dimension reduction. Then use the RUSBoost ensemble algorithm to solve the problem of unbalanced sample size and… More >

  • Open Access

    ARTICLE

    Fault Current Identification of DC Traction Feeder Based on Optimized VMD and Sample Entropy

    Zhixian Qi1,2,*, Shuohe Wang1,2, Qiang Xue1,2, Haiting Mi3, Jian Wang1,2

    Energy Engineering, Vol.120, No.9, pp. 2059-2077, 2023, DOI:10.32604/ee.2023.028595

    Abstract A current identification method based on optimized variational mode decomposition (VMD) and sample entropy (SampEn) is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder cannot distinguish between train charging current and remote short circuit current. This method uses the principle of energy difference to optimize the optimal mode decomposition number k of VMD; the optimal VMD for DC feeder current is decomposed into the intrinsic modal function (IMF) of different frequency bands. The sample entropy algorithm is used to perform feature extraction of each IMF, and then the eigenvalues of… More >

  • Open Access

    ARTICLE

    Research on Leak Location Method of Water Supply Pipeline Based on MVMD

    Qiansheng Fang, Haojie Wang, Chenlei Xie*, Jie Chen

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1237-1250, 2023, DOI:10.32604/cmes.2022.021131

    Abstract At present, the leakage rate of the water distribution network in China is still high, and the waste of water resources caused by water distribution network leakage is quite serious every year. Therefore, the location of pipeline leakage is of great significance for saving water resources and reducing economic losses. Acoustic emission technology is the most widely used pipeline leak location technology. The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters, ignoring periodic noise and components unrelated to pipeline leakage. Aiming at the above problems, this paper proposes a leak location method for water… More >

  • Open Access

    ARTICLE

    Study of Denoising in the Electricity Anti-Stealing Detection Based on VMD-WTD Combination

    Huakun Que1, Guolong Lin2, Wenchong Guo1, Xiaofeng Feng1, Zetao Jiang1, Yunfei Cao2,*, Jinmin Fan2, Zhixian Ni3

    Energy Engineering, Vol.119, No.4, pp. 1453-1466, 2022, DOI:10.32604/ee.2022.018448

    Abstract In order to solve the failure of electricity anti-stealing detection device triggered by the noise mixed in high-frequency electricity stealing signals, a denoising method based on variational mode decomposition (VMD) and wavelet threshold denoising (WTD) was applied to extract the effective high-frequency electricity stealing signals. First, the signal polluted by noise was pre-decomposed using the VMD algorithm, the instantaneous frequency means of each pre-decomposed components was analyzed, so as to determine the optimal K value. The optimal K value was used to decompose the polluted signal into K intrinsic mode components, and the sensitive mode components were determined through the… More > Graphic Abstract

    Study of Denoising in the Electricity Anti-Stealing Detection Based on VMD-WTD Combination

  • Open Access

    ARTICLE

    Spatio-temporal Model Combining VMD and AM for Wind Speed Prediction

    Yingnan Zhao1,*, Peiyuan Ji1, Fei Chen1, Guanlan Ji1, Sunil Kumar Jha2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710

    Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for… More >

  • Open Access

    ARTICLE

    Identification of Denatured Biological Tissues Based on Improved Variational Mode Decomposition and Autoregressive Model during HIFU Treatment

    Bei Liu1, Xian Zhang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1547-1563, 2022, DOI:10.32604/cmes.2022.018130

    Abstract During high-intensity focused ultrasound (HIFU) treatment, the accurate identification of denatured biological tissue is an important practical problem. In this paper, a novel method based on the improved variational mode decomposition (IVMD) and autoregressive (AR) model was proposed, which identified denatured biological tissue according to the characteristics of ultrasonic scattered echo signals during HIFU treatment. Firstly, the IVMD method was proposed to solve the problem that the VMD reconstruction signal still has noise due to the limited number of intrinsic mode functions (IMF). The ultrasonic scattered echo signals were reconstructed by the IVMD to achieve denoising. Then, the AR model… More >

  • Open Access

    ARTICLE

    Short-term Wind Speed Prediction with a Two-layer Attention-based LSTM

    Jingcheng Qian1, Mingfang Zhu1, Yingnan Zhao2,*, Xiangjian He3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 197-209, 2021, DOI:10.32604/csse.2021.016911

    Abstract Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power. Temporal-spatial wind speed features contain rich information; however, their use to predict wind speed remains one of the most challenging and less studied areas. This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attention-based long short-term memory (LSTM), termed 2Attn-LSTM, a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data. To eliminate the unevenness of the original wind speed,… More >

  • Open Access

    ARTICLE

    Vibration Based Fault Diagnosis of a Hydraulic Brake System using Variational Mode Decomposition (VMD)

    R. Jegadeeshwaran1, V. Sugumaran2, K.P. Soman3

    Structural Durability & Health Monitoring, Vol.10, No.1, pp. 81-97, 2014, DOI:10.3970/sdhm.2014.010.081

    Abstract In automobile, brake system is an essential part responsible for control of the vehicle. Vibration signals of a rotating machine contain the dynamic information about its health condition. Many research papers have reported the suitability of vibration signals for fault diagnosis applications. Many of them are based on (Fast Fourier Transform) FFT, which have their own drawback with nonstationary signals. Hence, there is a need for development of new methodologies to infer diagnostic information from such non stationary signals. This paper uses vibration signals acquired from a hydraulic brake system under good and simulated faulty conditions for the purpose of… More >

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