Optimization of VGG16 Algorithm Pattern Recognition for Signals of Michelson–Sagnac Interference Vibration Sensing System

Cheng, Jing; Song, Qiuheng; Peng, Hekuo; Huang, Jingwei; Wu, Haiyuan; Jia, Bo

doi:10.3390/photonics9080535

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Consequently, we modified the architecture of the transfer deep learning CNN models to convert the two-dimensional layers into onedimensional layers to match the PCG signal. This adaptation method has been previously applied in several studies, as described by Cheng et al [40] and Gao et al [41]. They adjusted the VGG-16 transfer deep learning model to process one-dimensional data using one-dimensional convolutional layers.…”

Section: Cnn-based Transfer Deep Learningmentioning

confidence: 99%

Enhanced Myocardial Infarction Identification in Phonocardiogram Signals Using Segmented Feature Extraction and Transfer Learning-Based Classification

Mandala,

Amini,

Adiwijaya

et al. 2023

IEEE Access

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Myocardial Infarction (MI), commonly known as a heart attack, is a type of cardiovascular disease characterized by the death of heart muscle cells. This condition occurs due to the blockage of blood vessels around the heart, inhibiting blood flow and causing an insufficient oxygen supply to the body. Typically, cardiovascular disease tests involve electrocardiogram (ECG) and photoplethysmogram (PPG) signals. In recent years, researchers have explored the application of Phonocardiogram (PCG) signals for cardiovascular detection due to their non-invasive, efficient, accessible, and cost-effective nature. While deep learning has been successful in object detection in digital images, its application to PCG signals for heart attack detection is rare. This study bridges this gap by introducing an enhanced technique called the Myocardial Infarction Detection System (MIDs). In contrast to previous deep learning research, this study employs a transfer learning algorithm as a classifier for MI feature datasets. Feature extraction is performed in segments to obtain more accurate MI features. Six feature extraction methods and transfer learning models based on Convolutional Neural Networks (CNN) using the VGG-16 architecture were selected as the primary components for MI identification. Additionally, this study compares these models with other CNN transfer learning models, such as VGG-19 and Xception, to assess their performance. Two experimental scenarios were conducted to evaluate MIDs performance in MI detection: experiments without hyperparameter tuning and with hyperparameter tuning. The results indicate that MIDs with CNN (VGG-16) after tuning exhibited the highest detection performance compared to other transfer learning CNN models, both with and without tuning. The accuracy, specificity, and sensitivity of MIDS detection with this configuration were 96.7%, 96.0%, and 97.4%, respectively. This research contributes to the development of an enhanced MI detection technique based on PCG signals using a transfer learning CNN.

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Section: Cnn-based Transfer Deep Learningmentioning

confidence: 99%

Enhanced Myocardial Infarction Identification in Phonocardiogram Signals Using Segmented Feature Extraction and Transfer Learning-Based Classification

Mandala,

Amini,

Adiwijaya

et al. 2023

IEEE Access

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“…Some research highlights the remarkable and unique properties of FBGs, and the interrogation technique involves both wavelength-shifting and intensity-based methods to create adaptable sensors [9,10]. This detection technique works for real-time fault detection at an applied place for finding the faulty site point or faulty machine with the association of artificial intelligence such as object detection or machine learning [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Vibration Monitoring System for Smart Industry Utilizing Optical Fiber Sensor Combined with Machine Learning

Kumar,

Shih,

Yao

et al. 2023

Electronics

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In this paper, we proposed and experimentally demonstrated the association of a fiber Bragg Grating (FBG) sensing system with You Only Look Once V7 (YOLO V7) to identify the vibration signal of a faulty machine. In the experiment, the YOLO V7 network architecture consists of a backbone, three detection heads (Headx3), a path aggregation network (PAN), and a feature pyramid network (FPN). The proposed architecture has an FBG sensor and the FBG interrogator employed for collecting sensing vibration signals or vibration data when degradation or fault occurs. An FBG interrogator collects vibration data independently, and then the YOLO V7 object detection algorithm is the recognition architecture of the vibration pattern of the signal. Thus, the proposed vibration recognition or detection is an assurance for detecting vibration signals that can support monitoring the machine’s health. Moreover, this research is promising for ensuring a high accuracy detection of faulty signals rate in industrial equipment monitoring and offers a robust system, resulting in remarkable accuracy with an overall model accuracy of 99.7%. The result shows that the model can identify the faulty signal more accurately and effectively detect the faulty vibration signal using the detection algorithm.

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“…In addition, the Sagnac interferometer can also be applied to the research of vibration based underwater gas pipeline leakage source location [21] . Further combined with deep learning and other methods, the Sagnac interferometer can also obtain a vibration measurement system with higher identification accuracy [22] . However, the Sagnac interferometers used in the above studies are all based on SMFs or polarization-maintaining fibers, and the measurement range of vibration frequencies is limited.…”

Section: Introductionmentioning

confidence: 99%

Research on the application of sagnac interference combined with multimode interference in vibration measurement

Tian,

Li,

Yang

et al. 2023

3rd International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2023)

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In order to further improve the frequency discrimination ability of fiber optic vibration sensor, this paper uses the multimode fiber (MMF) coupler to construct the Sagnac interferometer, which combines the multimode interference with Sagnac interferometer. Multimode fiber couplers with splitting ratio of 1:1 were fabricated by using MMFs with core diameters of 105 and 62.5 μm respectively, and the Sagnac loops were constructed with the couplers. For comparison and analysis, single-mode-multimode-single-mode interference structures and Mach-Zehnder interferometer were also fabricated by MMFs respectively. Furthermore, single mode fiber based Sagnac loop was also fabricated. Through a large number of experiments, these structures were used to measure vibration frequency, and their performance were compared in detail in the frequency range and the frequency accuracy of vibration detection. The results showed that the intensity difference between the primary peak and the secondary peak of the frequency spectrum obtained by the Sagnac interferometer with the core diameter of 105 μm step-index MMF is greater than 20 dB in the range of 0.3 to 20 kHz with frequency deviation in the order of Hz. With the large detectable frequency range and frequency recognition capability and higher sensitivity to the low frequency vibration, the Sagnac interferometer combined with multi-mode interference provided by the 105 μm step-index MMF is a kind of structure with the best performance among these studied structures in vibration measurement, which provides a new scheme for the research of vibration detection.

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Optimization of VGG16 Algorithm Pattern Recognition for Signals of Michelson–Sagnac Interference Vibration Sensing System

Cited by 6 publications

References 14 publications

Enhanced Myocardial Infarction Identification in Phonocardiogram Signals Using Segmented Feature Extraction and Transfer Learning-Based Classification

Enhanced Myocardial Infarction Identification in Phonocardiogram Signals Using Segmented Feature Extraction and Transfer Learning-Based Classification

Intelligent Vibration Monitoring System for Smart Industry Utilizing Optical Fiber Sensor Combined with Machine Learning

Research on the application of sagnac interference combined with multimode interference in vibration measurement

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