Wyniki wyszukiwania - Biblioteka Nauki

1

Fault Diagnosis of Suspension System Based on Spectrogram Image and Vision Transformer

100%

Arun B. , Venkatesh N. S. , Sugumaran V.

Eksploatacja i Niezawodność

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2024

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tom Vol. 26, no. 1

art. no. 174860

EN

The suspension system in an automobile is essential for comfort and control. Implementing a monitoring system is crucial to ensure proper function, prevent accidents, maintain performance, and reduce both downtime and costs. Traditionally, diagnosing faults in suspension systems has relied on specialized setups and vibration analysis. The conventional approach typically involves either wavelet analysis or a machine learning approach. While these methods are effective, they often demand specialized expertise and time consumable. Alternatively, using deep learning for suspension system fault diagnosis enables faster and more precise real-time fault detection. This study explores the use of vision transformers as an innovative approach to fault diagnosis in suspension systems, utilizing spectrogram images. The process involves extracting spectrogram images from vibration signals, which serve as inputs for the vision transformer model. The test results demonstrate that the proposed fault diagnosis system achieves an impressive accuracy rate of 98.12% in identifying faults.

2

Exploring automated object detection methods for manholes using classical computer vision and deep learning for autonomous vehicles

86%

Rao S. , Mitnala N.

Machine Graphics and Vision

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2023

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tom Vol. 32, No. 1

25--53

EN

Open, broken, and improperly closed manholes can pose problems for autonomous vehicles and thus need to be included in obstacle avoidance and lane-changing algorithms. In this work, we propose and compare multiple approaches for manhole localization and classification like classical computer vision, convolutional neural networks like YOLOv3 and YOLOv3-Tiny, and vision transformers like YOLOS and ViT. These are analyzed for speed, computational complexity, and accuracy in order to determine the model that can be used with autonomous vehicles. In addition, we propose a size detection pipeline using classical computer vision to determine the size of the hole in an improperly closed manhole with respect to the manhole itself. The evaluation of the data showed that convolutional neural networks are currently better for this task, but vision transformers seem promising.

3

Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds

72%

Sobahi N. , Atila O. , Deniz E. , Sengur A. , Acharya U. R.

Biocybernetics and Biomedical Engineering

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2022

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tom Vol. 42, no. 3

1066--1080

EN

The polymerase chain reaction (PCR) test is not only time-intensive but also a contact method that puts healthcare personnel at risk. Thus, contactless and fast detection tests are more valuable. Cough sound is an important indicator of COVID-19, and in this paper, a novel explainable scheme is developed for cough sound-based COVID-19 detection. In the presented work, the cough sound is initially segmented into overlapping parts, and each segment is labeled as the input audio, which may contain other sounds. The deep Yet Another Mobile Network (YAMNet) model is considered in this work. After labeling, the segments labeled as cough are cropped and concatenated to reconstruct the pure cough sounds. Then, four fractal dimensions (FD) calculation methods are employed to acquire the FD coefficients on the cough sound with an overlapped sliding window that forms a matrix. The constructed matrixes are then used to form the fractal dimension images. Finally, a pretrained vision transformer (ViT) model is used to classify the constructed images into COVID-19, healthy and symptomatic classes. In this work, we demonstrate the performance of the ViT on cough sound-based COVID-19, and a visual explainability of the inner workings of the ViT model is shown. Three publically available cough sound datasets, namely COUGHVID, VIRUFY, and COSWARA, are used in this study. We have obtained 98.45%, 98.15%, and 97.59% accuracy for COUGHVID, VIRUFY, and COSWARA datasets, respectively. Our developed model obtained the highest performance compared to the state-of-the-art methods and is ready to be tested in real-world applications.

4

TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19

72%

Meng J. , Tan Z. , Yu Y. , Wang P. , Liu S.

Biocybernetics and Biomedical Engineering

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2022

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tom Vol. 42, no. 3

842--855

EN

The recognition of medical images with deep learning techniques can assist physicians in clinical diagnosis, but the effectiveness of recognition models relies on massive amounts of labeled data. With the rampant development of the novel coronavirus (COVID-19) worldwide, rapid COVID-19 diagnosis has become an effective measure to combat the outbreak. However, labeled COVID-19 data are scarce. Therefore, we propose a two-stage transfer learning recognition model for medical images of COVID-19 (TL-Med) based on the concept of ‘‘generic domain-target-related domain-target domain”. First, we use the Vision Transformer (ViT) pretraining model to obtain generic features from massive heterogeneous data and then learn medical features from large-scale homogeneous data. Two-stage transfer learning uses the learned primary features and the underlying information for COVID-19 image recognition to solve the problem by which data insufficiency leads to the inability of the model to learn underlying target dataset information. The experimental results obtained on a COVID-19 dataset using the TL-Med model produce a recognition accuracy of 93.24%, which shows that the proposed method is more effective in detecting COVID-19 images than other approaches and may greatly alleviate the problem of data scarcity in this field.

5

AI-based Maize and Weeds detection on the edge with CornWeed Dataset

58%

Iqbal N. , Manss C. , Scholz C. , Koenig D. , Igelbrink M. , Ruckelshausen A.

Annals of Computer Science and Information Systems

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2023

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tom Vol. 35

577--584

EN

Agricultural applications with AI methods are used more heavily and the lack of wifi connections on the fields make cloud services unavailable. Consequently, the AI models have to be processed directly on the edge. In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection. The current paper also presents the CornWeed data set, which has been recorded on land machines, showing labelled maize crops and weeds for plant detection. The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as FPS metrics for these networks on multiple edge devices. Moreover, for the FPS analysis, the detection algorithms are converted to ONNX and TensoRT engine files as they could be used as future standards for model exchange.