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deep learning-based method for solving seepage equation under unsteady boundary

INTERNATIONAL JOURNAL FOR NUMERICAL methodS IN FLUIDS 2024年第1期96卷 87-101页

作者： Li, Daolun Shen, Luhang Zha, Wenshu Lv, Shuaijun Hefei Univ Technol Dept Math Hefei Peoples R China

deep learning-based methods for solving partial differential equations have become a research hotspot. The approach builds on the previous work of applying deep learning methods to partial differential equations, which avoid the need for meshing and linearization. However, deep learning-based methods face difficulties in effectively solving complex turbulent systems without using labeled data. Moreover, issues such as failure to converge and unstable solution are frequently encountered. In light of this objective, this paper presents an approximation-correction model designed for solving the seepage equation featuring unsteady boundaries. The model consists of two neural networks. The first network acts as an asymptotic block, estimating the progression of the solution based on its asymptotic form. The second network serves to fine-tune any errors identified in the asymptotic block. The solution to the unsteady boundary problem is achieved by superimposing these progressive blocks. In numerical experiments, both a constant flow scenario and a three-stage flow scenario in reservoir exploitation are considered. The obtained results show the method's effectiveness when compared to numerical solutions. Furthermore, the error analysis reveals that this method exhibits superior solution accuracy compared to other baseline methods.

关键词： asymptotic solution deep learning-based method solving PDE unsteady boundary without any labeled data

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A deep learning-based method for pixel-level crack detection on concrete bridges

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IET IMAGE PROCESSING 2022年第10期16卷 2609-2622页

作者： Ji Kun Zhang Zhenhai Yu Jiale Dang Jianwu Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou Gansu Peoples R China Gansu Artificial Intelligence & Graph Image Proc Lanzhou Gansu Peoples R China

Crack detection of the concrete bridge is an essential index for the safety assessment of bridge structure. It is more important to check the whole structure than to check the accuracy in the damage assessment. However, the traditional deep learning model method cannot completely detect the crack structure, which challenges image-based crack detection. For this reason, we propose deep bridge crack classification (DBCC)-Net as a classification-based deep learning network. By pruning the Yolox, the regression problem of the target detection is converted to the binary classification problem to avoid the network performance degradation caused by the translation invariance of the convolutional neural network (CNN). In addition, the network post-processing and a two-stage crack detection strategy are proposed to enable the network to detect cracks and extract crack morphology in high-resolution images quickly. In the first stage, DBCC-Net realizes the coarse extraction of crack position based on image slice classification. In the second stage, the complete crack morphology is extracted from the location suggested by the semantic segmentation network. Experimental results show that the proposed two-stage method has 19 frames per second (FPS) and 0.79 Miou (mean intersection over union) at the actual bridge images with 2560x2560 pixels. Although FPS is reduced, the Miou value is 7.8% higher than other methods, proving this paper's practical value.

关键词： high-resolution images convolutional neural network semantic segmentation network traditional deep learning model method regression analysis crack position network post-processing actual bridge images two-stage crack detection strategy deep learning-based method two-stage method convolutional neural nets image slice classification target detection bridges (structures) classification-based deep learning network regression problem damage assessment complete crack morphology essential index DBCC-Net feature extraction crack structure deep learning (artificial intelligence) image segmentation safety assessment image-based crack detection bridge structure binary classification problem cracks pixel-level crack detection deep bridge crack classification-Net concrete bridge object detection inference mechanisms network performance degradation crack detection image classification structural engineering computing image resolution

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Investigating the robustness of a deep learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions

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PHYSICS IN MEDICINE AND BIOLOGY 2023年第8期68卷 085005-085005页

作者： Deshpande, Rucha Avachat, Ashish Brooks, Frank J. Anastasio, Mark A. Washington Univ St Louis Dept Biomed Engn St Louis MO USA Univ Pittsburgh Dept Mech Engn & Mat Sci Pittsburgh PA USA Univ Illinois Dept Bioengn Urbana IL 61801 USA

Objective. Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challenging under laboratory conditions due to partial spatial coherence and polychromaticity. A deep learning-based method (DLBM) provides a nonlinear approach to this problem while not being constrained by restrictive assumptions about object properties and beam coherence. The objective of this work is to assess a DLBM for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations. Approach. Towards this end, an end-to-end DLBM was employed for QPR under laboratory conditions and its robustness was investigated across various system and object conditions. The robustness of the method was tested via varying propagation distances and its generalizability with respect to object structure and experimental data was also tested. Main results. Although the end-to-end DLBM was stable under the studied variations, its successful deployment was found to be affected by choices pertaining to data pre-processing, network training considerations and system modeling. Significance. To our knowledge, we demonstrated for the first time, the potential applicability of an end-to-end learning-based QPR method, trained on simulated data, to experimental propagation-based x-ray phase contrast measurements acquired under laboratory conditions with a commercial x-ray source and a conventional detector. We considered conditions of polychromaticity, partial spatial coherence, and high noise levels, typical to laboratory conditions. This work further explored the robustness of this method to practical variations in propagation distances and object structure with the goal of assessing its potential for experimental use. Such an exploration of any DLBM (irrespective of its network architecture) before practical deployment provides an understanding of its potential behavio

关键词： quantitative phase retrieval propagation-based x-ray phase contrast imaging laboratory conditions deep learning-based method

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Style Synthesis: AI-Powered Dress Try-On Experience 4

Style Synthesis: AI-Powered Dress Try-On Experience

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4th International Conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Sani, Siva RamaKrishna Mallireddy, Sai Manvitha Reddy Renati, Naveen Kumar Reddy Surya, Peela Leela Sai Lakireddy Bali Reddy Coll Engn Artificial Intelligence & Data Sci Dept Mylavaram Andhra Pradesh India

ISBN: (纸本)9798350386356;9798350386349

In the world of internet retail and fashion, virtual try-on systems have become a game changing innovation. This research introduces a revolutionary deep learning-based method for virtual try-on. The method first segments the clothing from the source image using segmentation techniques, then creates an interactive image to provide a virtual fitting experience, and finally combines accurate posture recognition using pretrained models with the imposition of the garment texture on the dense pose it predicted. Realtime experimenting with different clothing items, sizes, and positions is possible because to the interactive user interface. The technology is more user friendly because it can accommodate a variety of body types. This virtual try-on system, powered by deep learning, has the potential to significantly change customer involvement across a variety of industries, especially in fashion and ecommerce, by transforming the fashion retail scene.

关键词： Virtual try-on systems deep learning-based method Virtual fitting Posture Recognition Garment texture Dense pose Segmentation

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Automatic image co-segmentation: a survey

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MACHINE VISION AND APPLICATIONS 2021年第3期32卷 1-14页

作者： Liu, Xiabi Duan, Xin Beijing Inst Technol Sch Comp Sci Beijing Lab Intelligent Informat Technol Beijing Peoples R China

Image co-segmentation is important for its advantage of alleviating the ill-pose nature of image segmentation through exploring the correlation between related images. Many automatic image co-segmentation algorithms have been developed in the last decade, which are investigated comprehensively in this paper. We firstly analyze visual/semantic cues for guiding image co-segmentation, including object cues and correlation cues. Then, we describe the traditional methods in three categories of object elements based, object regions/contours based, common object model based. In the next part, deep learning-based methods are reviewed. Furthermore, widely used test datasets and evaluation criteria are introduced and the reported performances of the surveyed algorithms are compared with each other. Finally, we discuss the current challenges and possible future directions and conclude the paper. Hopefully, this comprehensive investigation will be helpful for the development of image co-segmentation technique.

关键词： Image co-segmentation Image segmentation Traditional methods deep learning-based method Evaluation

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Promising crack segmentation method based on gated skip connection

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ELECTRONICS LETTERS 2020年第10期56卷 493-494页

作者： Jabreel, M. Abdel-Nasser, M. Univ Rovira & Virgili Dept Comp Engn & Math Tarragona Spain Hodaideh Univ Dept Comp Sci Al Hudaydah Yemen Aswan Univ Dept Elect Engn Aswan Egypt

This Letter proposes a promising deep learning-based method for crack segmentation based on gated skip connection. The proposed gated skip connection enables the decoder layers to promote crack-aware feature representations from the encoder layers by applying high weights on the crack-relevant features that come from the encoder layers and lower weights for irrelevant features. Unlike the related methods, the authors do not apply any pre-processing or refinement steps to improve the crack segmentation results. The proposed method beats the state-of-the-art methods with an open benchmark database (IoU of 87.5).

关键词： image segmentation learning (artificial intelligence) image representation open benchmark database crack segmentation method crack-relevant features encoder layers crack-aware feature representations decoder layers deep learning-based method gated skip connection

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Siamese convolutional neural network-based approach towards universal image forensics

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IET IMAGE PROCESSING 2020年第13期14卷 3105-3116页

作者： Mazumdar, Aniruddha Bora, Prabin Kumar Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 Assam India

This study proposes a novel deep learning-based method which can detect different types of image editing operations carried out on images. Unlike most of the existing methods, which can only detect the editing operations considered in the training stage, the proposed method can generalise to manipulations not seen in the training stage. The method is based on the classification of image pairs as either similarly or differently processed using a deep siamese neural network. Once the network learns features that can discriminate different editing operations, it can check whether an image is processed with an editing operation, not present in the training stage, using the one-shot classification strategy. An image forgery detection and localisation technique is also proposed using the trained siamese network. The experimental results show the efficacy of the proposed method in detecting different editing operations and also show the ability in detecting and localising image forgeries.

关键词： learning (artificial intelligence) neural nets image representation image classification siamese convolutional neural network-based approach universal image forensics deep learning-based method image editing operations editing operation training stage image pairs deep siamese neural network different editing operations one-shot classification strategy image forgery detection localisation technique trained siamese network detecting localising image forgeries

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