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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Hongyu Tang Dengli Bu Guanhao Mo Panlin Lu Zhenhai Li Long Huang Key Laboratory of Intelligent Computing and Distributed Information Processing School of Computer Science and Technology Guangxi University of Science and Technology Guangxi Colleges and Universities Liuzhou China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

To address the issues of complex background interference and micro-defect detection in printed circuit board (PCB) surface inspection, while accommodating the deployment constraints of edge devices in industrial scenarios, this paper proposes a lightweight real-time detection Transformer (LRTDETR), an improved architecture based on RT-DETR. First, the original ResNet18 backbone is replaced with RepNCSPELAN, a lightweight network leveraging structural re-parameterization to reduce model complexity. Second, a global-local bidirectional feature pyramid network is introduced in the neck, which enhances the multi-scale feature representation through an adaptive weighted feature fusion mechanism, significantly improving small defect detection. Experimental results demonstrate that the improved model achieves a 96.9% mean average precision (mAP) with only a 0.2% performance drop compared to the baseline. Notably, the model size, the parameters, and the FLOPs are reduced by $58.3 \%, 53.5 \%$ , and 57.3%, respectively. The inference speed reaches 39 FPS, balancing accuracy and efficiency for real-time industrial inspection and edge deployment. This work provides a practical solution for highprecision, resource-efficient defect detection in industrial edge computing environments.

关键词： Adaptation models Computational modeling Image edge detection Printed circuits Inspection Feature extraction Transformers Real-time systems Neck Defect detection

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An empirical study on the structure evolution of deep learning models: taking SAR image processing a case study

An empirical study on the structure evolution of deep learni...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Huanxi Liu Xiang He Dawei Feng Han Bao National Laboratory for Parallel and Distributed Processing (PDL) National University of Defense Technology China School of Information and Communication National University of Defense Technology China

With the continuous improvement on model performance, deep learning models have been widely deployed and achieved promising outcomes in various fields in recent years. However, due to the escalating volumes of training data and the complexity of application problems, it becomes more and more challenging to design a neural network with better performance by hand. Analysing the evolution of typical neural network structures has important reference significance for designing a network structure. In this paper, we select the open source models in SAR image processing for an empirical analysis on the evolution of neural network structures. We analyse the evolution of 239 open source deep learning models from the aspects of framework, computing unit, model computation amount and the combined use of various computing units. Results reveal that preference and co-occurrence exist in computing units, while the average number of convolution, activation and normalization layer increases significantly over time. Model complexity shows an overall upward trend, and the characteristics of SAR image are more and more taken into consideration during the model structure design.

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mingyu Liu Yijie Wang Xiaohui Zhou Yongjun Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors.

关键词： Learning systems Time-frequency analysis Fast Fourier transforms Time series analysis Signal processing Feature extraction Entropy Iterative methods Speech processing Anomaly detection

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F3A: Fairness-Aware AI-Workloads Allocation Considering Multidimensional User Demands in JointCloud

F3A: Fairness-Aware AI-Workloads Allocation Considering Mult...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jiacheng Yang Guodong Yi Fei Gao Peichang Shi Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users’ demands while ensuring CSPs’ fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users’ multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

关键词： Industries Data centers Cloud computing Job shop scheduling Costs Scheduling algorithms Large language models

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DCSA: The Deployment Mechanism of Chained Serverless Applications in JointCloud Environment

DCSA: The Deployment Mechanism of Chained Serverless Applica...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

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Proposing an intelligent mesh smoothing method with graph neural networks

arXiv

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arXiv 2023年

作者： Wang, Zhichao Chen, Xinhai Yan, Junjun Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology National University of Defense Technology Changsha410073 China

In CFD, mesh smoothing methods are commonly utilized to refine the mesh quality to achieve high-precision numerical simulations. Specifically, optimization-based smoothing is used for high-quality mesh smoothing, but it incurs significant computational overhead. Pioneer works improve its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality meshes. However, they pose difficulty in smoothing the mesh nodes with varying degrees and also need data augmentation to address the node input sequence problem. Additionally, the required labeled high-quality meshes further limit the applicability of the proposed method. In this paper, we present GMSNet, a lightweight neural network model for intelligent mesh smoothing. GMSNet adopts graph neural networks to extract features of the node's neighbors and output the optimal node position. During smoothing, we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume elements. With a lightweight model, GMSNet can effectively smoothing mesh nodes with varying degrees and remain unaffected by the order of input data. A novel loss function, MetricLoss, is also developed to eliminate the need for high-quality meshes, which provides a stable and rapid convergence during training. We compare GMSNet with commonly used mesh smoothing methods on two-dimensional triangle meshes. The experimental results show that GMSNet achieves outstanding mesh smoothing performances with 5% model parameters of the previous model, and attains 13.56 times faster than optimization-based smoothing. © 2023, CC BY.

关键词： Graph neural networks

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CNA: A Dataset for Parsing Discourse Structure on Chinese News Articles

CNA: A Dataset for Parsing Discourse Structure on Chinese Ne...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Zhenliang Guo Zhen Huang Yong Dou Xiubin Yu Sijie Wang Zhongwu Chen Xinxin Su Xiaohang Liu National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

Discourse structure analysis has shown to be useful for many artificial intelligence (AI) tasks such as text sum-marization and text categorization. However, for the Chinese news domain, the discourse structure analysis system is still immature due to the limitation of the lack of expert-annotated datasets. In this paper, we present CNA, a Chinese news corpus containing 1155 news articles annotated by human experts, which covers four domains and four news media sources. Next, we implement several text classification methods as baselines. Experimental results demonstrate that document-level method can achieve a better performance, and we further propose a document-level neural network model with multiple sentence features which achieves the state-of-the-art performance. In the end, we analyze the content type distribution of each sentence in CNA and the prediction errors of our model that occurred on the test set. The codes and dataset will be open-sourced at https://***/gzl98/Chinese_Discourse_Profiling.

关键词： Analytical models Codes Text categorization Neural networks Predictive models Media Artificial intelligence

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Self-supervised Bidirectional Synchronization Estimation for Multimodal Deepfake Detection with Short-term Dependency 25

Self-supervised Bidirectional Synchronization Estimation for...

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Proceedings of the 2025 International Conference on Multimedia Retrieval

作者： Man Xiao Jianbin Ye Bo Liu Zijian Gao Kele Xu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Strategic Assessments and Consultation Institute Academy of Military Science Beijing China

ISBN: (纸本)9798400718779

Deepfake technology induces substantial societal challenges, establishing deepfake detection as an important area of research. However, existing research mainly relies on target deepfake datasets, which limits its generalizability across out-of-distribution tasks to some extent. Also, it often emphasizes visual modalities while neglecting the complementary information of the auditory data. Their autoregressive-based strategies also introduce long-term information interference, further constraining the detection performance. Consequently, the potential to exploit complementary relations between visual and auditory modalities and to leverage strongly correlated short-range information remains underexplored for the detection task. To address these challenges, this paper introduces Self-BiSterm, a novel self-supervised learning framework for deepfake detection. First, we propose a bidirectional synchronization distribution modeling mechanism, which calculates inconsistent distributions for video-to-audio and audio-to-video scenarios. This mechanism effectively measures audio-visual inconsistencies, improving the model's generalization performance in practical applications. Second, to mitigate the issue of long-term information distortion, we develop a short-term temporal dependency module to estimate the adjacent local receptive fields. This module facilitates the estimation of subsequent distributions by capturing short-term temporal dependencies with high precision. The effectiveness of the proposed Self-BiSterm framework is validated on various benchmarks, demonstrating superior performance compared to existing methods.

关键词： bidirectional synchronization estimation

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YOLOv5_CSL_F: YOLOv5's Loss Improvement and Attention Mechanism Application for Remote Sensing Image Object Detection 3

YOLOv5_CSL_F: YOLOv5's Loss Improvement and Attention Mechan...

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3rd International Conference on Wireless Communications and Smart Grid, ICWCSG 2021

作者： Wang, Junhua Xiao, Tao Gu, Qinyi Chen, Qian National University of Defense Technology Laboratory of Parallel and Distributed Processing Changsha China No.61646 Troop Department of Information Beijing China

ISBN: (纸本)9781665425988

With the continuous improvement of the resolution of satellite remote sensing images and aerial remote sensing images, more and more useful data and information are obtained from remote sensing images. At the same time, compared with ordinary images, remote sensing images have the characteristics of variable directions, unbalanced categories, complex backgrounds, and difficult detection of small objects. All of these make remote sensing image object detection very challenging. In this paper, based on the deep learning framework and the YOLOv5 object detection algorithm, according to the characteristics of remote sensing images, adopting Circular Smooth Label (CSL) [1] to calculate the loss of the rotating object detection bounding box and introducing the FcaNet [2] attention mechanism to design new feature fusion modules, we propose the remote sensing image object detection algorithm YOLOv5_CSL_F. We tested the algorithm model on the DOTA dataset. Compared with the detection performance of the original YOLOv5 algorithm, our algorithm improves the detection accuracy by 0.6%. © 2021 IEEE.

关键词： Object detection

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning 4

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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4th International Conference on Electronic Information Engineering and Computer science, EIECS 2024

作者： Yu, Yang Xie, Yufei Wang, Wenlin Wu, Guohua Lan, Haitao Lin, Enbo An, Ping Sun, Zibin Zhang, Haichuan Wu, Yixian National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9798331531409

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R2: 0.735). © 2024 IEEE.

关键词： Coolants

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