Rolling element bearings are critical building blocks of any rotating machine. Achieving effective and precise fault diagnosis through various neuralnetworkmodels plays a pivotal role in ensuring the accuracy of rol...
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Rolling element bearings are critical building blocks of any rotating machine. Achieving effective and precise fault diagnosis through various neuralnetworkmodels plays a pivotal role in ensuring the accuracy of rolling element bearing fault diagnosis. This research paper represented comparative study of artificial neuralnetwork (ANN), 1-D CNN, multi-input 1-D CNN, and 2-D CNN in fault diagnosis of rolling element bearings. The experiment was conducted on a roller bearing test rig over 2000 hours at constant speed of 800 rpm along with radial load of 1.5 kN till the development of naturally occurring operational surface defects on the bearing components. The proposed neuralnetwork architecture utilized multiple parallel convolutional layers to effectively extract rich and complementary fault features. The model was configured by implementing the categorical crossentropy loss function and Adam optimizer. Evaluation of the neuralnetworkmodels was performed using a confusion matrix and t-SNE visualization to ensure accurate fault identification. Comparative analysis among the convolutionalneuralnetwork techniques was conducted to show their effectiveness toward fault diagnosis. The multi-input 1-D CNN achieved 97% prediction accuracy. The results demonstrate that multi-input 1-D CNN model provides better accuracy in fault diagnosis compared to the other models.
Identifying plant diseases plays a pivotal role in managing plant health. It poses significant challenges due to the diverse manifestations of diseases. This study investigates the impact of environmental conditions a...
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ISBN:
(纸本)9798350387919;9798350387902
Identifying plant diseases plays a pivotal role in managing plant health. It poses significant challenges due to the diverse manifestations of diseases. This study investigates the impact of environmental conditions and imaging factors on analysing multispectral images captured through six filters spanning various portions of the Near Infrared spectrum for tomato disease identification. Two distinct datasets were compiled for analysis. Dataset 1 comprised uniform images across all filters. Dataset 2 incorporated variations in image capture to explore the influence of environmental and imaging factors on filter performance. Classification between healthy and diseased states was conducted on both datasets utilising the popular convolutionalneuralnetwork, Vision Transformer, Hybrid Vision Transformer, and Swin Transformer models. Among all the filters tested, K590 demonstrated the highest average accuracy, reaching 88.69% for Dataset 1 and 93.31% for Dataset 2. Furthermore, this filter consistently outperformed others in terms of precision and recall across both datasets. ViT-B16 emerged as the most effective model across all evaluation metrics, with an average accuracy of 89.92%. Furthermore, comparisons were drawn with prior literature, encompassing balanced and unbalanced datasets for tomato disease classification tasks. The findings indicated that environmental and imaging factors do not significantly influence disease classification using multispectral imaging.
In this paper, a fast interactive instance segmentation (IIS) system is proposed and it is composed of an effective heatmap generator, a multi-resolution network (MRNet), and an adaptive threshold refiner to promptly ...
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In this paper, a fast interactive instance segmentation (IIS) system is proposed and it is composed of an effective heatmap generator, a multi-resolution network (MRNet), and an adaptive threshold refiner to promptly and precisely predict the masks of the objects. The proposed heatmap generator after interaction clicks can help the MRNet to successfully learn the sensitive features for better prediction. Based on convolutional neural network models, the proposed MRNet backbone produces multiple features across multiple resolutions and can intrinsically predict the sharp contour of the object. After the probabilistic prediction achieved by the MRNet, the Otsu's threshold refiner is proposed to further remove some uncertain pixels in the predicted mask. Experimental results demonstrate that the proposed IIS system can promptly predict sharp masks of the targeted objects with mIoU of 89.1% in PASCAL VOC 2012 [1] validation set. Compared to other existing interactive methods, the proposed system can effectively predict the segmentation mask with higher accuracy and less interaction efforts.
Sugar beet, a sugar crop, faces a persistent threat from foliar and root diseases, finally leading to substantial yield losses. Traditional methods of disease identification and severity assessment are often time-cons...
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This article introduces a diabetic retinopathy screening program based on artificial intelligence that will be implemented in three hospitals in Mexico. It details the steps for the clinical integration of the system ...
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This article introduces a diabetic retinopathy screening program based on artificial intelligence that will be implemented in three hospitals in Mexico. It details the steps for the clinical integration of the system and tests preliminary convolutional neural network models based on Mexican guidelines.
Owing to convolutionalneuralnetwork (CNN) models' success in various fields of computer vision, the authors proposed an advanced convolutionalnetwork (ACTNet) to enhance the accuracy of visual tracking. Differe...
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Owing to convolutionalneuralnetwork (CNN) models' success in various fields of computer vision, the authors proposed an advanced convolutionalnetwork (ACTNet) to enhance the accuracy of visual tracking. Different from prior methods, they regard a CNN as not only a semantic feature map extractor but also a position predictor. Rectified Linear Unit (RLU) and sigmoid are both used in ACTNet for feature extraction and position determination. To avoid overfitting in pre-training, they introduce adding Erlang noise to create more training samples and to improve the robustness of each base learner. Experiments on widely used evaluation datasets demonstrate that their proposed ACT method outperforms state-of-the-art methods.
Automatically recognising people by their biometric characteristics is a well-established research area. Biometric systems are vulnerable to many different types of presentation attacks made by persons showing photo, ...
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Automatically recognising people by their biometric characteristics is a well-established research area. Biometric systems are vulnerable to many different types of presentation attacks made by persons showing photo, video, or mask to spoof the real identity. This study introduces a novel approach to detect face-spoofing, by extracting the local features local binary pattern (LBP) and simplified weber local descriptor (SWLD) encoded convolutionalneuralnetwork (CNN) models, WLD and LBP features are combined together to ensure the preservation of the local intensity information and the orientations of the edges. These two components are complementary to each other. Specifically, differential excitation preserves the local intensity information but omits the orientations of edges. On the contrary, LBP describes the orientations of the edges but ignore the intensity information, the proposed approach presents a very low degree of complexity which makes it suitable for real-time applications, Finally, a non-linear support vector machine (SVM) classifier with kernel function was used for determining whether the input image corresponds to a live face or not. Authors' experimental analysis on two publicly available databases REPLAY-ATTACK and CASIA face anti-spoofing showed that their approach performs better than state-of-the-art techniques following the provided evaluation protocols of each database.
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