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arXiv 2024年

作者： Wang, Yuhe Wang, Yating National & Local Joint Engineering Laboratory for Big Data Analysis and Computing Technology Beijing100190 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States School of Mathematics and Statistics Xi’an Jiaotong University Shaanxi Xi’an710049 China

Fluid flow through bimodal porous media, characterized by a distinct separation in pore size distribution, is critical in various scientific and engineering applications, including groundwater management, oil and gas production, and carbon sequestration. This note delves into the physical similarity of fluid flow within such media, bridging the gap between microscale phenomena and macroscale observations. We present a representative mathematical model that conceptualizes bimodal porous media as a double-continuum system, distinguishing between macroporous and microporous regions. The model captures the complex interactions between these regions, particularly focusing on the challenges of modeling fluid flow when there is significant disparity in pore sizes. By employing a heuristic approach grounded in pore-scale tomography, we derive governing equations that describe fluid flow and analyze the solution characteristics. The results reveal unique features of the fluid flow in bimodal systems, such as the occurrence of boundary discontinuities and the delayed transient response, which are not observed in conventional porous media. This work provides ground for further studies in bimodal porous media, offering insights that could enhance predictive modeling and optimization in various applications concerning porous media with similar bimodal pore size distributions. © 2024, CC BY.

关键词： Transient analysis

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Medical image super-resolution algorithm based on multi-scale feature aggregation

Medical image super-resolution algorithm based on multi-scal...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xinyu Han Xun Gong School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Manufacturing Industry Chains Collaboration and InformationSupport Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

Ultrasound images are vital for medical diagnostics but often suffer from information loss and blurred details due to limitations in imaging systems and sensor technologies. Many researchers have proposed super-resolution algorithms to enhance medical images. But existing super-resolution methods struggle with the uneven distribution of noise and clarity. To address this, we propose a super-resolution algorithm for medical images based on multi-scale feature aggregation Leveraging the architecture of Unet as our primary framework, our method enhances output details through multi-scale hole convolutions. Taking into consideration the characteristics of ultrasonic images, we propose a frequency domain-based module to enhances edge information while effectively denoising the image. Furthermore, to improve the quality of the output images, we introduce an image processing module grounded in global information. The module ensures clarity while considering the overarching context, thereby preserving global consistency and enhancing output quality. We experiment on three datasets to demonstrate the effectiveness of our model. Additionally, significant improvements in medical image segmentation are observed, proving the practicability of our proposed approach.

关键词： Image segmentation Ultrasonic imaging Image edge detection Frequency-domain analysis Superresolution Noise reduction Noise Medical diagnosis Artificial intelligence Medical diagnostic imaging

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Causal Deep Learning for Real-Time Detection of Cardiac Surgery-Associated Acute Kidney Injury: Derivation and Validation in Seven Time-Series Cohorts

SSRN

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SSRN 2024年

作者： Zhong, Qin Cheng, Yuxiao Li, Zongren Wang, Dongjin Rao, Chongyou Jiang, Yi Li, Lianglong Wang, Ziqian Liu, Pan Che, Hebin Li, Pei Lv, Xin Suo, Jinli He, Kunlun Medical Big Data Research Center Chinese PLA General Hospital Beijing China National Engineering Research Center of Medical Big Data Application Technology Chinese PLA General Hospital Beijing China Department of Automation Tsinghua University Beijing China Department of Cardiovascular Surgery Nanjing Drum Tower Hospital Chinese Academy of Medical Science Peking Union Medical College Nanjing China College of Systems Engineering National University of Defense Technology Changsha China Beijing Key Laboratory of Chronic Heart Failure Precision Medicine China

Background: Cardiac surgery-associated Acute Kidney Injury (CSA-AKI) is a complex complication significantly contributing to an increased risk of mortality. Effective CSA-AKI management relies on timely diagnosis and interventions. However, many cases are detected too late. Despite the advancements in novel biomarkers and data-driven predictive models, existing practices are primarily constrained due to the limited discriminative and generalization capabilities and stringent application requirements, presenting significant challenges to the timely and effective diagnosis and interventions in CSA-AKI management. Methods: This retrospective model development and prospective validation study aimed to develop a causal deep learning architecture, named REACT, to achieve precise and dynamic predictions of CSA-AKI within the subsequent 48 hours. This study included adult'patients (aged ≥18 years) from seven distinct cohorts undergoing major open heart surgery for model training and validation. data for model development and internal validation were sourced from electronic health records (EHR) of two large centers in Beijing, China, from January 2000 to December 2022. External validation was conducted on the EHRs of three independent centers in China between January 2000 and December 2022, along with cross-national data from the public databases MIMIC-IV and eICU in the United States. To facilitate implementation, we also developed a publicly accessible web calculator and applet. The model's prospective application was validated from June to October 2023 at two centers in Beijing and Nanjing. Findings: The final derivation cohort included 14,513 eligible patients with no critical information missing, a median age of 56 years, 38·0% women, and 21·0% developed CSA-AKI. The external validation dataset included 20,813 patients from China and 28,023 from the United States. REACT reduced 1,325 input variables to six essential "causal" factors for CSA-AKI prediction. In internal v

关键词： Cardiovascular surgery

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Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Research of mask wearing detection method using Improved YOLO network

Research of mask wearing detection method using Improved YOL...

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Chinese Control and Decision Conference, CCDC

作者： Fei Xiao Zixi Liu Shengbin Zhao Shuai Yuan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China Daqing Oilfield Limited Company DaQing China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Mask wearing detection technology is a practical and research-oriented area within object detection. Due to the complexity of mask wearing detection scenarios, issues such as small target detection and low image clarity often result in missed detections and false alarms. The YOLO algorithm, known for its fast detection speed, is commonly used in mask wearing detection. However, the original YOLO algorithm struggles to achieve satisfactory results in dense crowd scenarios. Therefore, this paper proposes an improved YOLOv5 algorithm based on the BiFPN structure and incorporating attention mechanisms to enhance the accuracy of mask wearing detection while maintaining real-time performance. The proposed approach firstly introduces the Bidirectional Feature Pyramid (BiFPN) structure into the backbone network to enhance the feature extraction capability. Additionally, attention mechanisms are incorporated to filter out irrelevant information, conserve computational resources, and improve the detection performance for small targets. Experimental results on a custom dataset demonstrate that the YOLOv5_BiFPN algorithm achieves a 4% improvement in accuracy and an 11.7% increase in mAP50 compared to the original YOLOv5. Moreover, the inclusion of attention mechanisms further boosts the accuracy by an additional 1.6%. These findings confirm that the improved YOLOv5s model exhibits superior performance in the task of mask wearing detection.

关键词： YOLO Attention mechanisms Accuracy Filtering algorithms Feature extraction Information filters Real-time systems

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Stvanet: A Spatio-Temporal Visual Attention Framework with Large Kernel Attention Mechanism for Citywide Traffic Dynamics Prediction

SSRN

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SSRN 2023年

作者： Yang, Hongtai Jiang, Junbo Zhao, Zhan Pan, Renbin School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Institute of System Science and Engineering Southwest Jiaotong University Chengdu611756 China Department of Urban Planning and Design The University of Hong Kong Hong Kong Special Administrative Region Hong Kong School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China

Enhancing the efficiency and safety of the Intelligent Transportation System requires effective modeling and prediction of citywide traffic dynamics. Most studies employ convolutional neural networks (CNNs) with a 3D convolutional structure or spatio-temporal models with self-attention mechanisms to capture the spatio-temporal information of traffic distribution. Although 3D CNNs excel at capturing local contextual information, they are computationally complex due to the large number of parameters and cannot capture long-range dependence. By contrast, although self-attention mechanisms originally designed to address challenges in natural language processing can capture long-range dependence, their application to 2D image structures requires breaking down the inherent 2D context into a 1D sequence, increasing the computational complexity and neglecting the adaptability between local contextual information and channels. Accordingly, we propose a spatio-temporal visual attention neural network (STVANet), a novel spatio-temporal visual attention 2D CNN, which integrates a unique visual attention module with a large kernel attention (LKA) mechanism and a feedforward component to capture long-range dependence and channel information in urban traffic data while preserving the 2D image structure. LKA-based spatio-temporal attention networks extract spatial and temporal features from weekly, daily, and recent hourly periods, and aggregate them with weighted consideration of external features to make predictions. Evaluation of real-world datasets demonstrates STVANet’s superiority over baseline models, showcasing its potential in citywide traffic prediction. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Maize Crop Row Recognition Algorithm Based on Improved Unet Network

SSRN

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SSRN 2022年

作者： Diao, Zhihua Guo, Peiliang Zhang, Baohua Zhang, Dongyan Yan, Jiaonan He, Zhendong Zhao, Suna Zhao, Chunjiang School of Electrical Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China College of Artificial Intelligence Nanjing Agricultural University Nanjing211800 China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China

The accurate identification of the centerlines of crop rows represents the core principle of agricultural robot visual navigation technology. However, few existing deep-learning-based maize crop row centerline recognition algorithms generally have limitations such as poor robustness and weak adaptability. To mitigate these issues, this study proposes a centerline detection algorithm based on a convolutional neural network. The U-shaped Network (UNet) – a traditional semantic segmentation network – was enhanced to create the Atrous Spatial Pyramid Pooling UNet (ASPP-UNet) network for maize crop row and background segmentation, and the improved vertical projection method was subsequently employed to measure the crop rows’ feature points. Finally, the least squares method was used to fit the centerlines. Experimental results yielded the Mean Intersection Over Union (MIoU), Mean Pixel Accuracy (MPA), Mean Precision (MP), and Mean Recall (MR) metrics of ASPP-UNet network to be 83.23%, 90.18%, 91.79%, and 90.18% respectively. These figures represent respective increases of 10.03%, 11.86%, 9.43% and 11.24% compared to the Fully Convolutional Network (FCN), and 7.80%, 5.52%, 2.71%, and 5.52% compared to the UNet. Furthermore, the average fitting time and angle error of the improved vertical projection method were reduced to 65 ms and 4.57°, compared to 80 ms and 6.12°in the traditional vertical projection method, and 86 ms and 5.67°in the left and right edge centerline method. Likewise, the accuracy of proposed method increased to 91.79%, compared to 87.21% and 90.16% of the two aforementioned methods, respectively. Therefore, the proposed method successfully meets the accuracy and real-time demands of agricultural robot vision navigation, and can function effectively under varying environmental pressures. © 2022, The Authors. All rights reserved.

关键词： Agricultural robots

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Research of Improved TD3 Robotic Arm Path Planning using Evolutionary Algorithm

Research of Improved TD3 Robotic Arm Path Planning using Evo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Feng Zhang Jianqiang Xiong Shuai Yuan Ke Wen School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

Nowadays, application of automated intelligent robot arm devices to improve industrial production efficiency has become a popular research field in the world. The previous off-line path planning method of robotic arm has the inadequacies of low efficiency and slow speed. Although the deep reinforcement learning has accomplished many achievements in the path planning of control manipulator, there are still some problems such as long training time and low planning accuracy. To solve the abovementioned issues, we propose an improved Twin Delayed Deep Deterministic policy gradient (TD3) algorithm (Improved Cross-Entropy Method-TD3: ICEM-TD3) for the path planning of the robotic arm. First, this paper combines evolutionary strategies with TD3 to generate action networks. Then the exploration of TD3 algorithm in the action space is replaced by the exploration in the parameter space. In addition, this paper designs a new reward function to weaken the redundancy of planning and accelerate the convergence speed of training. Finally, the Gazebo simulator is adopted to verify the proposed algorithm, and the results illustrate that the proposed algorithm can greatly improve the accuracy of the path planning of the manipulator using deep reinforcement learning.

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Graph Condensation for Inductive Node Representation Learning

Graph Condensation for Inductive Node Representation Learnin...

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International Conference on data engineering

作者： Xinyi Gao Tong Chen Yilong Zang Wentao Zhang Quoc Viet Hung Nguyen Kai Zheng Hongzhi Yin The University of Queensland Brisbane Australia German Research Centre for Artificial Intelligence (DFKI) Kaiserslautern Germany Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Griffith University Gold Coast Australia University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. To address this limitation, graph condensation has emerged as a promising technique, which constructs a small synthetic graph for efficiently training GNNs while retaining performance. However, due to the topology structure among nodes, graph condensation is limited to condensing only the observed training nodes and their corresponding structure, thus lacking the ability to effectively handle the unseen data. Consequently, the original large graph is still required in the inference stage to perform message passing to inductive nodes, resulting in substantial computational demands. To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning. This enables direct information propagation on the synthetic graph, which is much more efficient than on the original large graph. Specifically, MCond employs an alternating optimization scheme with innovative loss terms from transductive and inductive perspectives, facilitating the mutual promotion between graph condensation and node mapping learning. Extensive experiments demonstrate the efficacy of our approach in inductive inference. On the Reddit dataset, MCond achieves up to 121.5× inference speedup and 55.9× reduction in storage requirements compared with counterparts based on the original graph.

关键词： Training Representation learning Social networking (online) Network topology Message passing data engineering Robustness

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Multi-view clustering guided by unconstrained non-negative matrix factorization

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Knowledge-Based Systems 2023年 266卷

作者： Deng, Ping Li, Tianrui Wang, Dexian Wang, Hongjun Peng, Hong Horng, Shi-Jinn School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu611756 China Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. To satisfy the non-negativity constraint of the matrix, NMFMvC is usually solved using the Karush–Kuhn–Tucker (KKT) conditions. However, this optimization method is poorly scalable. To this end, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model. First, the objective function was constructed by decoupling the elements of the matrix and combining the elements with a non-linear mapping function in a non-negative value domain. The objective function was then optimized using the stochastic gradient descent (SGD) algorithm. Subsequently, three uNMFMvC methods were constructed based on different mapping functions and detailed reasoning was provided. Finally, experiments were conducted on eight public datasets and compared with cutting-edge multi-view clustering methods. The experimental results demonstrate that the proposed model has significant advantages. © 2023 Elsevier B.V.

关键词： Non-negative matrix factorization

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