Classify and Localize Threat Items in X-Ray Imagery With Multiple Attention Mechanism and High-Resolution and High-Semantic Features

Xia, Ruiyang; Li, Guoquan; Huang, Zheng; Wen, Lingyun; Pang, Yu

doi:10.1109/tim.2021.3102743

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…[20] Yang et al optimized the prediction model by replacing the traditional activation function with GELU when monitoring the total power consumption of customer electricity meters using a time convolution network. [21] Although GELU can enhance the prediction model to a certain extent, effective fault prediction for batch processes remains challenging owing to its multi-phase and dynamic nature. Batch process dynamics refer to the behaviour and characteristics of a batch process that processes a fixed number of materials or products at a time, as opposed to a continuous process, in which material flows continuously through the system.…”

Section: Introductionmentioning

confidence: 99%

GELU‐LSTM‐encoder‐decoder fault prediction for batch processes based on the global–local percentile method

Gao,

Li,

et al. 2024

Can J Chem Eng

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Fault prediction is a crucial strategy for ensuring the safety and efficiency of batch processes, and it aims to predict the degradation trend and isolate the fault. Operating mistakes or external environment changes can lead to the degradation of batch processes, eventually resulting in faults. The point at which degradation begins is known as the time to start prediction (TSP). Determining the TSP provides more reliable degradation information for fault prediction and enhances prediction accuracy. Nevertheless, it is difficult to obtain TSP in batch processes because of some complex data features, such as multi‐phase and strong dynamics, which result in a significant loss of fault prediction. To address these challenges, a fault prediction method is proposed based on the global–local percentile method applied to Gaussian error linear unit‐long short‐term memory‐encoder‐decoder (GELU‐LSTM‐encoder‐ED). Firstly, squared prediction error (SPE) is normalized to eliminate differences in data features. Secondly, a global–local percentile method is proposed to determine the TSP. The period is divided based on the threshold fluctuation to derive the model input. Then the global percentile model is employed to identify the interval to which the TSP belongs. Subsequently, the local percentile model is employed to analyze this interval and identify the TSP. Thirdly, the LSTM based on the encoder‐decoder model, enhanced with the GELU, is utilized to improve the performance of fault prediction. Finally, two fault datasets on the penicillin fermentation process and Escherichia coli fermentation process are employed to demonstrate the performance of the presented method compared with other shared methods.

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Section: Introductionmentioning

confidence: 99%

GELU‐LSTM‐encoder‐decoder fault prediction for batch processes based on the global–local percentile method

Gao,

Li,

et al. 2024

Can J Chem Eng

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show abstract

“…A highresolution network (HRNet) applied to object detection and semantic segmentation by following the heatmap estimation framework, particularly the development of object detection, can help realize high spatial precision. [8,9] A novel bottom-up human pose estimation method (HigherHRNet) was used to localize key points more precisely for a small object. [10] The Hourglass network, popularized in the domain of human pose estimation, is similar to other encoder-decoder networks and has a denser use of residual blocks.…”

mentioning

confidence: 99%

Guidewire Endpoint Detection Based on Pixel‐Adjacent Relation during Robot‐Assisted Intravascular Catheterization: In Vivo Mammalian Models

Du,

Yi,

Omisore

et al. 2024

Advanced Intelligent Systems

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Existing surgical guidewire endpoint localization methods in X‐ray images face challenges owing to their small size, simple appearance, nonrigid nature of objects, low signal‐to‐noise ratio of X‐ray images, and imbalance between the number of guidewire and background pixels, which lead to errors in surgical navigation. An eight‐neighborhood‐based method for increasing the localization accuracy of guidewire endpoint to improve the safety of interventional procedures is proposed herein. The proposed method includes two stages: 1) An improved U‐Net network is employed for segmenting the data of the guidewire to extract regions of interest containing guidewire endpoints with higher precision and to reduce interference from other anatomical structures and imaging artifacts. 2) The proposed method detects guidewire endpoints using the adjacent relationship between pixels in the eight‐neighborhood regions. This stage covers skeletonization extraction, removal of bifurcation points, and repair of fracture points. This study achieves mean pixel errors of 2.02 and 2.13 pixels in an in vivo rabbit and porcine X‐ray fluoroscopy images, outperforming ten classic heatmap and regression methods, achieving state‐of‐the‐art detection results. The proposed method can also be applied to detect other tiny surgical instruments such as stents and balloons, while preserving the flexibility of the guidewire bending angle.

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Pig Face Recognition Based on Trapezoid Normalized Pixel Difference Feature and Trimmed Mean Attention Mechanism

Tao

et al. 2022

IEEE Trans. Instrum. Meas.

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Classify and Localize Threat Items in X-Ray Imagery With Multiple Attention Mechanism and High-Resolution and High-Semantic Features

Cited by 5 publications

References 22 publications

GELU‐LSTM‐encoder‐decoder fault prediction for batch processes based on the global–local percentile method

GELU‐LSTM‐encoder‐decoder fault prediction for batch processes based on the global–local percentile method

Guidewire Endpoint Detection Based on Pixel‐Adjacent Relation during Robot‐Assisted Intravascular Catheterization: In Vivo Mammalian Models

Pig Face Recognition Based on Trapezoid Normalized Pixel Difference Feature and Trimmed Mean Attention Mechanism

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