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A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face

Journal of Rock Mechanics and Geotechnical engineering 2022年第4期14卷 1077-1088页

作者： Jiayao Chen Yifeng Chen Anthony G.Cohn Hongwei Huang Jianhong Man Lijun Wei Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Department of Geotechnical EngineeringTongji UniversityShanghai200092China School of Computing University of LeedsLeedsLS29JTUK Department of Computer Science and Technology Tongji UniversityShanghai211985China School of Civil Engineering Shandong UniversityJinan250061China Luzhong Institute of Safety Environmental Protection Engineering and Materialsand School of Mechanical and Electrical EngineeringQingdao University of Science and TechnologyQingdao255000China

This paper presents a novel integrated method for interactive characterization of fracture spacing in rock tunnel *** main procedure includes four steps:(1)Automatic extraction of fracture traces,(2)digitization of trace maps,(3)disconnection and grouping of traces,and(4)interactive measurement of fracture set spacing,total spacing,and surface rock quality designation(S-RQD)*** evaluate the performance of the proposed method,sample images were obtained by employing a photogrammetrybased scheme in tunnel *** were then conducted to determine the optimal parameter values(*** threshold,angle threshold,and number of fracture trace grouping)for characterizing rock fracture *** applying the identified optimal parameters involved in the model,the proposed method could lead to excellent qualitative results to a new tunnel *** perform a quantitative analysis,three methods(***,straightening,and the proposed method)were employed in the same study and comparisons were *** proposed method agrees well with the field measurement in terms of the maximum and average values of measured spacing ***,the proposed method has reasonably good accuracy and interactive advantage for estimating the ultimate fracture spacing and *** can be a possible extension of existing methods for fracture spacing characterization for two-dimensional(2D)rock tunnel faces.

关键词： Fracture spacing Rock tunnel Image processing Fracture set grouping

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Application of functional near-infrared spectroscopy(fNIRS) for studying the influence of capsaicin stimulation on brain perception

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Food Science and Human Wellness 2025年

作者： Ran Tao Hang Liang Huiting Qiao Xin Liu Na Xu Ling Wang Xinglian Xu Lingqi Li Yujia Yang Peng Wang State Key Laboratory of Meat Quality Control and Cultured Meat Development， Ministry of Education China， Jiangsu Collaborative Innovation Center of Meat Production and Processing， Quality and Safety Control， College of Food Science and Technology， Nanjing Agricultural University Danyang SuHuichuang Brain Scientific Research Center School of Biological Science and Medical Engineering， Beihang University College of Computer Science， Sichuan Normal University

Spicy taste have been utilized in the food industry as appetizers， stomachics， and beverages and in the health sector to alleviate high cholesterol and hypertension. However， an understanding of neural mechanisms and cognitive processes associated with spicy diets remains limited due to the lack of instruments that directly monitoring the brain’s reaction to spicy sensations. This study aimed to examine the neural responses of participants to oral capsaicin solutions through functional near-infrared spectroscopy and reveal distinct neural activation patterns associated with spiciness induction and relief phases. Results showed that the perception of spiciness is a dynamic process， and significant differences in sensory intensity and emotion were observed between the phases. The prefrontal regions of the brain were activated during spicy flavor perception and presented clear lateralization effects. Further spatial localization results indicated that the orbitofrontal， dorsolateral prefrontal， and ventral lateral prefrontal cortices were the main causes of difference in activation among the phases. In addition， correlational analyses indicated that spicy flavor perception activated cortical areas related to emotion and reward processing. Overall， this research enhances our understanding of the neural mechanisms underlying addiction to spicy food and may be useful in the development of food on the basis of consumers’ preferences and behavioral responses.

关键词： Taste Capsaicin fNIRS Prefrontal cortex Oral irritation

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CHDNet: Enhanced Arbitrary Style Transfer via Condition Harmony DiffusionNet

CHDNet: Enhanced Arbitrary Style Transfer via Condition Harm...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenkai He Jianhui Zhao Ying Fang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Arbitrary Style Transfer (AST) renders an image by adopting the style of any chosen artwork while preserving its content structure. Despite the widespread popularity of feedforward AST methods, they tend to merely optimize the statistical characteristics of images, leading to unnatural outputs and displeasing low-quality distortions. In contrast, diffusion models effectively address this issue by reconstructing the overall image. Unlike typical image generation tasks controlled by a single condition, style transfer demands the simultaneous consideration of multiple conditions. We propose Condition Harmony DiffusionNet (CHDNet), which distinguishes between style and content conditions, integrating them into harmonious conditions to collaboratively guide the generation process. We innovatively introduce a content aware attention, designed to extract semantic features of the content image across multiple dimensions, distinctly setting it apart from the style condition. Furthermore, we have improved the skip connections in the diffusion model, which introduces a slight increase in model complexity but results in a substantial improvement in the representation of fine details. Further refinements in the image sampling process empower us with great control over the stylization effect in the generated results. Our method successfully employs diffusion models via harmonious conditions to solve AST, achieving outstanding effects. Experiments demonstrate that our method achieves stateof-the-art arbitrary style transfer.

关键词： Measurement Image synthesis Computational modeling Semantics Neural networks Process control Image sampling

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CROSS-SCENE PERSON TRAJECTORY ANOMALY DETECTION BASED ON RE-IDENTIFICATION

CROSS-SCENE PERSON TRAJECTORY ANOMALY DETECTION BASED ON RE-...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Yuanxun Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

In this work, we consider the cross-scene person trajectory anomaly detection problem, which detects the anomalous trajectories across multiple nonoverlapping scenes. This problem is highly significant for public security, but it is still underexplored. Since the trajectory is not continuous across nonoverlapping camera views, we take use of person re-identification (re-ID) to associate the same pedestrian in different scenes while mitigating its inaccuracy by a directional probabilistic graph. To better distinguishing normal samples from anomalies, We formulate a maximized margin graph autoencoder (MMGAE) model, and the reconstruction error of the MMGAE is regarded as an anomaly indicator for the sample. To verify the effectiveness of our approach, we collected and labeled a new dataset. we also explore the impact of the re-ID performance on the anomaly detection problem and the effect of an inaccurately constructed graph on the MMGAE. © 2021 IEEE

关键词： Trajectories

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Robust Cross-modal Medical Image Translation via Diffusion Model and Knowledge Distillation

Robust Cross-modal Medical Image Translation via Diffusion M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuehan Xia Saifeng Feng Jianhui Zhao Zhiyong Yuan Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Medical image translation holds significant value, but its difficulty is amplified due to variations in noise patterns and the requisite anatomical invariance of image content. Various deep learning approaches, such as mainstream Generative Adversarial Networks (GANs), have been developed to learn multimodal mappings for obtaining translated images. However, the results produced by generators remain far from perfect for medical images, given the challenging requirements of style variations in noise patterns and anatomical invariance. In this paper, a medical image translation framework is proposed based on a diffusion model and knowledge distillation. To enhance the robustness of adversarial training and the accuracy of generated images, unlike traditional GANs, this framework incorporates an adaptive forward diffusion module for data augmentation following the generator. Additionally, the discriminator is designed as a timestep-dependent discriminator. Both real and generated images undergo the same forward diffusion process, and the discriminator learns to discriminate between real and generated images at each time step. Finally, an additional refinement network is composed of structurally similar but differently inputted teacher and student modules. Unlike existing knowledge distillation approaches, our teacher module is designed as a registration network with more inputs to better learn noise distribution and further refine translation results during training. Subsequently, knowledge is thoroughly distilled into the student module to ensure the generation of superior translation results. Extensive experiments on two public medical image datasets, along with comparisons with SOTA methods, demonstrate that the model produces higher quality and more robust images.

关键词： Knowledge engineering Training Noise Diffusion processes Diffusion models Generative adversarial networks Brain modeling

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MFN: Explainable DNA triple helixes Stabilized Design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoru Wen Lijun Sun Lei Xie Yanfen Zheng Ben Cao Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy.

关键词： Scalability Design methodology DNA Crosstalk Genomics Games Reliability engineering Regulation Gene therapy Bioinformatics

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Single-View Scene Point Cloud Human Grasp Generation

arXiv

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arXiv 2024年

作者： Wang, Yan-Kang Xing, Chengyi Wei, Yi-Lin Wu, Xiao-Ming Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Stanford University StanfordCA United States Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

In this work, we explore a novel task of generating human grasps based on single-view scene point clouds, which more accurately mirrors the typical real-world situation of observing objects from a single viewpoint. Due to the incompleteness of object point clouds and the presence of numerous scene points, the generated hand is prone to penetrating into the invisible parts of the object and the model is easily affected by scene points. Thus, we introduce S2HGrasp, a framework composed of two key modules: the Global Perception module that globally perceives partial object point clouds, and the DiffuGrasp module designed to generate high-quality human grasps based on complex inputs that include scene points. Additionally, we introduce S2HGD dataset, which comprises approximately 99,000 single-object single-view scene point clouds of 1,668 unique objects, each annotated with one human grasp. Our extensive experiments demonstrate that S2HGrasp can not only generate natural human grasps regardless of scene points, but also effectively prevent penetration between the hand and invisible parts of the object. Moreover, our model showcases strong generalization capability when applied to unseen objects. Our code and dataset are available at https://***/iSEE-laboratory/S2HGrasp. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Pctlfusion: A Progressive Fusion Network Via Contextual Texture Learning for Infrared and Visible Image

SSRN

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SSRN 2024年

作者： Lu, Yixiang Gong, Daiwei Zhao, Dawei Maksim, Davydau Gao, Qingwei The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Belarusian State University of Informatics and Radioelectronics Minsk Belarus

The objective of infrared and visible image fusion is to generate a fused image that contains rich texture details and salient targets. However, most of the existing fusion methods tend to focus on preserving texture details in visible image and salient targets in infrared image, ignoring the fact that infrared image have richer texture details and better visual effects than visible image under low-light conditions. In other words, differences in information sources under the condition of illumination imbalance is ignored in setting the task goal. In addition, existing network designs usually ignore multimodal information interactions during the coding phase, which may result in loss of information. In this work, we propose a progressive fusion network via contextual texture learning for infrared and visible image, termed as PCTLFusion. To enhance the limited texture information, we design a content and contextual texture learning module (CCTLM) to model the texture and content of both the source images, respectively. Then, an affine transform-based multimodal pre-fusion module (ATMPM) is designed to learn and pre-fuse the complementary and similar enhanced information, which achieves information interaction during encoding. To learn fully feature information using multilayers, we iterate CCTLM and ATMPM three times in the process of model forming. Finally, the fused image is reconstructed by decoding the output of the last ATMPM. Numerous experiments show that our proposed fusion algorithm is superior to the state-of-the-art existing methods in both subjective and objective evaluations. © 2024, The Authors. All rights reserved.

关键词： Textures

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EPI-RMDL: Prediction of Enhancer-Promoter Interactions Based on RoFormer Mechanism and Deep Learning

EPI-RMDL: Prediction of Enhancer-Promoter Interactions Based...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mengyun Song Jie Wen Yabin Kuang Minzhu Xie College of Information Science and Engineering Hunan Normal University Changsha China Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education) College of Mathematics and Statistics Hunan Normal University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Enhancer-Promoter Interactions (EPIs) play a crucial role in gene expression regulation. However, traditional experimental methods for detecting EPIs are often time-consuming and costly, prompting a growing demand for computational approaches. In this work, we propose a novel deep-learning model, termed EPI-RMDL, for the prediction of enhancer-promoter interactions based only on the DNA sequences. EPI-RMDL at first encodes the DNA sequences of a set of enhancers and promoters into an information matrix via the dna2vec method. Subsequently, local and global features are extracted from the matrix using a three-layer convolution neural network. Then, the features are processed through three RoFormers, which are enhanced transformers with Rotary Position Embedding (RoPE), in order to obtain relative positional information and interaction details between promoters and enhancers. Finally, a special matching mechanism is incorporated to analyze the interplay among the output vectors generated by the front-end RoFormers. We trained a general model by integrating data from six distinct cell lines and fine-tuned it with specific cell-line data to obtain an optimal model EPI-RMDL best for each cell line. Benchmarking against six state-of-the-art methods using datasets from six cell lines, our model demonstrates superior performance. Specifically, the EPI-RMDL_best model achieves a mean AUROC of 95.8% and an average AUPR of 80.8%.

关键词： Microprocessors Biological system modeling DNA Computer architecture Predictive models Feature extraction Transformers Data models Vectors Regulation

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Joint Optimization Algorithm for Adaptive Bit Allocation Based on Temporal-Spatial Information

Joint Optimization Algorithm for Adaptive Bit Allocation Bas...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Shaokang Wang Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) China Ministry of Education Engineering Research Center of Blockchain and Network Convergence Technology China

Versatile Video Coding (VVC) standard further improves the video compression ratio. Despite inter prediction and intra prediction effectively eliminating temporal and spatial residual information, there is still room for further improvement in rate control (RC) accuracy and rate distortion (R-D) performance. In view of the above issues, the joint optimization RC algorithm for adaptive bit allocation for VVC is proposed. In this paper, the proposed algorithm utilizes a specific pre-analysis mechanism so that the Temporal-Spatial Information (TSI) is obtained. And then the TSI is integrated into the RC model so as to optimize adaptive bit allocations for frame-level and coding tree unit level (CTU-level). Experimental results have shown that compared to the default RC under common test conditions (CTC), the proposed RC algorithm achieves obvious 2.26% Bjøntegaard Delta rate (BD-rate) saving, and the RC accuracy can be improved in the Random Access (RA) configuration.

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