Development of a ship classification method based on Convolutional neural network and Cyclostationarity Analysis

Barros, Rodrigo Emanoel de B.A.; Ebecken, Nelson F. F.

doi:10.1016/j.ymssp.2021.108778

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…Chen et al proposed CNN to classify mechanical fault types in two datasets: gearbox and motor bearing [46]. Barros et al proposed CNN to classify ships based on acoustic signatures [47]. CNN was proposed by Ben-Cohen et al as a technique for classifying CT images of the liver [48].…”

Section: ) Deep Learning Literature Reviewmentioning

confidence: 99%

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Akshay,

Gopalakrishnan,

Sowmya

et al. 2024

IEEE Access

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Nonlinear fluid dynamical systems, such as thermoacoustic systems, aeroelastic systems are archetypical complex systems involving state transitions upon a change in bifurcation parameter. These state transitions in any certain direction are always undesirable and can radically alter the operational paradigms associated with these systems. Hence, predicting the impending dynamical state is paramount for avoiding such undesirable transitions. The hitherto research so far focused largely on metric-based and modelbased indicators to foretell an impending transition and is often fraught with difficulties when deployed in practicable scenarios. In this study, we assuage this end of concern by proposing a model-agnostic datadriven method for automated classification of the dynamical states of nonlinear fluid dynamical systems. By using recurrence plots we transform the time series pertaining to the dynamical states into images and subsequently employ a convolution neural network (CNN) to classify the generated images. This study also proceeds to present cross-domain classifications via a trained deep learning (DL) model and successfully classify the dynamical states of one fluid dynamical system (say, thermoacoustic) with the dynamical states of another fluid dynamical system (say, aeroelastic). The underlying methodology for the above is based on open set (OS) domain adaptation -inherent to transfer learning schemes. Towards enhancing the confidence levels of our proposed methodology, we carry out four cross-domain numerical experiments, wherein we consistently get about 94 -98% accuracy.

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Section: ) Deep Learning Literature Reviewmentioning

confidence: 99%

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Akshay,

Gopalakrishnan,

Sowmya

et al. 2024

IEEE Access

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“…The test strip image after edge detection is binarized and processed by opening operation. The Hough transform method [11] is used to detect lines in the image. The principle is to map the image to Hough space and extract lines.…”

Section: Hough Line Detection Algorithm Based On Canny Edge Detectionmentioning

confidence: 99%

“…The test strip image is obtained using an image sensor. After obtaining the test strip image signal, it is filtered and normalized, and the Resnet [11] is used to extract features from the test strip image. After extracting feature parameters, a trained extreme learning machine classification model is used to classify the test strip.…”

Section: Test Strip Recognition Algorithmmentioning

confidence: 99%

Qualitative identification of fluorescence immunochromatography strips based on residual networks

Zhong,

Jiang,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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The article proposes a qualitative identification scheme of fluorescent immunoassay strips based on residual networks to address problems such as poor strip positioning accuracy and inadequate strip size specifications in current photoelectric fluorescent immunoassay quantitative detection systems, which result in low accuracy and detection efficiency. The proposed method employs the Hough line detection algorithm, which is based on Canny edge detection, to extract the tilt angle of the test strip. It then combines this with contour extraction of the strip image to calculate its contour center. By utilizing the test strip tilt angle and contour center, the article accurately locates the test strip position. The residual network is utilized for extracting strip features, while the extreme learning machine is employed for discriminating the validity and positive/negative results of the fluorescent strips. Validation experiments on a novel coronavirus fluorescent immunoassay strip demonstrate that the residual network model based on extreme learning machine proposed in this article achieves 100% accuracy, precision, recall, and F1-score values for strip classification, effectively improving the recognition accuracy and detection efficiency of the fluorescent immunoassay quantitative detection system.

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“…The existing feature extraction methods for UATR can be roughly classified into the following categories. (1) Feature based on power or energy, such as Power Spectral Density (PSD) [1] and Cyclo‐stationarity [2]. (2) Feature based on time‐frequency analysis, including LOw Frequency Analysis and Recording [3], Wavelet Analysis [4], Wigner‐Ville Distribution [5] and any other features.…”

Section: Introductionmentioning

confidence: 99%

Underwater acoustic target recognition based on automatic feature and contrastive coding

Sun

Luo

2023

IET Radar Sonar & Navi

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Underwater acoustic target recognition (UATR) technology based on deep learning and automatic encoding has become an important research direction in the underwater acoustic field in recent years. However, the existing methods do not have favourable self‐adaptability for different data because of the complex and changeable underwater environment, which easily leads to an unsatisfactory recognition effect. The concept of contrastive learning is introduced into UATR and a model named Contrastive Coding for UATR (CCU) is proposed. Based on the unsupervised contrastive learning framework, the model has been modified for the underwater acoustic field. Thus, the CCU can generate adaptable automatic features according to different data. The experimental test shows that the model is superior to other automatic encoding models and has achieved excellent recognition performance on different underwater acoustic datasets.

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Development of a ship classification method based on Convolutional neural network and Cyclostationarity Analysis

Cited by 10 publications

References 27 publications

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Qualitative identification of fluorescence immunochromatography strips based on residual networks

Underwater acoustic target recognition based on automatic feature and contrastive coding

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