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Abnormal Traffic Detection Method Based on DCNN-GRU Architecture in SDN

International Journal of Intelligent Systems 2025年第1期2025卷

作者： Xueyuan Duan Kun Wang Yu Fu Taotao Liu Yihan Yu Jianqiao Xu Lu Wang College of Computer and Information Technology Xinyang Normal University Xinyang 464000 Henan China *** Department of Information Security Naval University of Engineering Wuhan 430033 Hubei China *** Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang 464000 China *** School of Information and Communication Engineering Xinyang Vocational and Technical College Xinyang 464000 Henan China *** Department of Operational Research and Programming Naval University of Engineering Wuhan 430033 China *** Department of Economic Crime Investigation Henan Police College Zhengzhou 450046 Henan China

In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of service quality of SDN network, this paper proposes a large-scale abnormal traffic detection method of SDN network based on Distributed Convolutional Neural Networks and Gate Recurrent Unit (DCNN-GRU) architecture. This method utilizes lightweight detection agents based on CNN deployed on each controller to extract traffic features preliminarily. Then it inputs the feature data into the GRU-based deep detection model hosted in the cloud for collaborative training and completes the final abnormal detection task. Since the feature extraction tasks are distributed across multiple controllers, the cloud server only needs to relearn and classify the extracted feature data, which is less costly than directly extracting feature information from the original traffic data and occupies less bandwidth resources than transmitting complete data packets. The experiment shows that the method achieves an abnormal detection accuracy of 0.9939, a recall rate of 0.9831, and a false alarm rate of only 0.0244, obtaining a higher precision and lower false alarm rate than traditional detection methods.

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Research on 3D skeletal model extraction algorithm of branch based on SR4000 4

Research on 3D skeletal model extraction algorithm of branch...

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2019 4th International Conference on Intelligent Computing and Signal Processing, ICSP 2019

作者： Liu, Shiyang Yao, Jiaojiao Li, Hui Qiu, Changpeng Liu, Ruijun College of Information and Electrical Engineering China Agricultural University Beijing100083 China College of Horticulture China Agricultural University Beijing100083 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China

A branch 3D Skeleton extraction method based on SR4000 is proposed, which can be used for pruning robot visual recognition. In this method a 3D Skeleton model of the branches of apple trees in the initial fruit stage was successfully constructed. According to the experimental results, the average computational efficiency of the 2AHC(2-level Aggregation Hierarchy Clustering) is increased by 369%, the detection rate is 84.69%, and the error rate is 5.03%. The average detection rate of depth analytic hierarchy process was 64%;The overall 3D Skeleton restoration effect is better. Therefore, the algorithm can better reflect the qualitative relationship between branches, improve the computational efficiency, and provide algorithm support for automatic pruning robot visual recognition. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Computational efficiency

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An extragradient algorithm for fixed point problems and pseudomonotone equilibrium problems

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第4期82卷 89-96页

作者： Yao, Zhangsong Liou, Yeong-Cheng Zhu, Li-Jun School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China Department of Healthcare Administration and Medical Informatics Research Center of Nonlinear Analysis and Optimization Kaohsiung Medical University Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province and Health Big Data Research Institute Advanced Intelligent Perception and Control Technology Innovative Team of NingXia North Minzu University Yinchuan750021 China

Iterative methods for solving fixed point problems and pseudomonotone equilibrium problems have been considered. An extragradient algorithm has been presnted for finding a common element of the fixed points of pseudocontractive operators and the solutions of pseudomonotone equilibrium problems in Hilbert spaces. Weak convergence result of the suggested algorithm is proved. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

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Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection

arXiv

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arXiv 2021年

作者： Li, Kai Yang, Jie Ma, Siwei Wang, Bo Wang, Shanshe Tian, Yingjie Qi, Zhiquan School of Computer Science Institute of Digital Media Peking University Beijing100871 China School of Mathematics Sciences University of Chinese Academy of Sciences Beijing100049 China Advanced Institute of Information Technology Peking University Hangzhou310005 China School of Information Technology and Management University of International Business and Economics Beijing100029 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation University of Chinese Academy of Sciences No. 3 South Yitiao Zhongguancun Haidian District Beijing100190 China

Pixel-level road crack detection has always been a challenging task in intelligent transportation systems. Due to the external environments, such as weather, light, and other factors, pavement cracks often present low contrast, poor continuity, and different sizes in length and width. However, most of the existing studies pay less attention to crack data under different situations. Meanwhile, recent algorithms based on deep convolutional neural networks (DCNNs) have promoted the development of cutting-edge models for crack detection. Nevertheless, they usually focus on complex models for good performance, but ignore detection efficiency in practical applications. In this article, to address the first issue, we collected two new databases (i.e. Rain365 and Sun520) captured in rainy and sunny days respectively, which enrich the data of the open source community. For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet. Specifically, we introduce a novel olive-shaped structure for the encoder network, a light-weight multi-scale block and a new up-sampling method in the decoder network. Numerous experiments show that our model can better balance detection performance and efficiency compared with previous models. Especially, on the Sun520 dataset, our CarNet significantly advances the state-of-the-art performance with ODS F-score from 0.488 to 0.514. Meanwhile, it does so with an improved detection speed (104 frame per second) which is orders of magnitude faster than some recent DCNNs-based algorithms specially designed for crack detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Research on a method of fruit tree pruning based on BP neural network 4

Research on a method of fruit tree pruning based on BP neura...

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2019 4th International Conference on Intelligent Computing and Signal Processing, ICSP 2019

The mainstream pruning robots do not have the ability to make decisions independently. The pruning schemes are all artificially generated by experts according to the collected images. In order to improve the intelligence of pruning robot and reduce the labor cost of pruning work, it is necessary to study the robot pruning decision algorithm corresponding to different fruit tree varieties. In this paper, taking apples in the early fruit period as an example, referring to the technical principle of traditional fruit tree pruning and aiming at two types of interference in the pruning process, the back branches and interfering branches, a pruning decision algorithm based on BP neural network was proposed. The algorithm formed the training set by artificially collecting the accurate data of the spatial characteristics of the fruit tree branches and performed calibration for pruning type, and the neural network model was trained according to the calibrated data set. The model trained in the first stage showed the situation that the competition branches cannot be identified. Based on this, an improved algorithm was proposed to improve the classification performance of the competition branches. The experimental results verified that the F1 score of the method for the back branches was 0913;the F1 score for the centripetal branches was 0.867;the improved algorithm has an F1score of 0.755 for the competition branches;the overall conformed to the expectation, which could provide algorithm support for the pruning robot to make artificial intelligence decision. © Published under licence by IOP Publishing Ltd.

关键词： Fruits

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Training and inference Time Efficiency Assessment Framework for machine learning algorithms: A case study for hyperspectral image classification

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International Journal of Applied Earth Observation and Geoinformation 2025年 141卷

作者： Zheng, Xiaorou Jia, Jianxin Dong, Shoubin Wang, Yawei Lu, Runuo Chen, Yuwei Wang, Yueming Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute Espoo FI-02150 Finland Guangzhou Institute of Geography Guangdong Academy of Sciences Guangdong Guangzhou 311100 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Zhejiang Hangzhou 310024 China Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China The Advanced Laser Technology Laboratory of Anhui Province Hefei 230037 China

The increasing complexity and scale of remote sensing datasets, coupled with the challenges of accurately estimating algorithmic time efficiency, often lead to significant resource waste or even failure when using machine learning algorithms in urgent or resource-constrained scenarios. Accurate time efficiency estimation is critical for deploying effective algorithms, yet it remains challenging due to the many factors influencing computational performance. Traditional methods of evaluating time efficiency often neglect the effects of core model parameters and complex data scales in spectral and temporal dimensions. In addition, inference time, an essential factor in real-world applications, is often overlooked. To address these limitations, we propose the Time Efficiency Assessment Framework (TEAF), a novel method for evaluating the time efficiency of machine learning algorithms. Through mathematical reasoning, TEAF models the training and inference time as functions (ψ) of complex data scales and core model parameters. The strong linear correlation between ψ and the actual runtime allows TEAF to accurately predict the time and cost of machine learning tasks with a low computational overhead before algorithm execution. To validate this framework, we derived TEAF formulations for five classical machine learning algorithms and tested them on state-of-the-art hyperspectral image datasets and Sentinel-2 multispectral datasets. The results demonstrated that TEAF could accurately predict both training and inference time for various algorithms, with a strong linear correlation between ψ and actual runtime (R2>0.942). This study offers a practical solution to the challenges posed by the increasing volume and complexity of data in remote sensing image processing. The code is available at https://***/SCUT-CCNL/TEAF. © 2025 The Authors

关键词： Algorithm derivation Complexity data scale Hyperspectral image Inference time Time efficiency Time prediction

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The roles of urban buildings and vegetation in adjusting seasonal and daily air temperature 4

The roles of urban buildings and vegetation in adjusting sea...

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4th ISPRS Geospatial Week 2019

作者： Lan, Y. Huang, Z. Guo, R. Zhan, Q. Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services Shenzhen University China Guangdong Key Laboratory of Urban Informatics Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Urban Design Wuhan University Wuhan China

Exploring the spatiotemporal patterns of the relationships between urban indicators and urban temperature is essential to improve the mitigation effectiveness when we intend to adjust built environment for moderating urban thermal environment. In this study, RS, GIS technology and statistical methods were involved to investigate the spatiotemporal patterns of the impacts of urban buildings and vegetation on Air Temperature (AT). Building Density (BD) and Normalized Difference Vegetation Index (NDVI) are the indicators for urban buildings and vegetation respectively. The objectives of this study are: 1) to determine an appropriate scale for examining the building-AT relationships and vegetation-AT relationships;2) to explore the seasonal and daily characteristics of these relationships;and 3) to compare the effects of urban buildings and vegetation. The results show that, for both summer and winter, a scale of 200–250 m is optimal for examining building-AT relationships, and 960–1020 m is the desirable scale for studying vegetation-AT relationships. Based on the optimal scales, we find that for both buildings and vegetation, they only significantly impact night-time temperature in both summer and winter. For seasonal comparison, the building-AT relationships and vegetation-AT relationships are relatively stronger in summer than in winter, which are indicated by R-square of the regression results. When comparing the effects of urban building and vegetation, we find that increasing vegetation is more effective than reduce buildings to achieve the same air temperature reduction. Our findings are conducive to generating space-time targeted Urban Heat Island (UHI) mitigation strategies. © Authors 2019.

关键词： Vegetation

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Dense residual network: Enhancing global dense feature flow for character recognition

arXiv

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arXiv 2020年

作者： Zhang, Zhao Tang, Zemin Wang, Yang Zhang, Zheng Zhan, Choujun Zha, Zhengjun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China School of Computer Science and Technology Soochow University Suzhou215006 China Shenzhen China School of Computer South China Normal University Guangzhou510631 China Deparmtment of Computer Science and Technology University of Science and Technology of China Hefei China

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply stacking the convolutional layers fail to enable them to fully discover local and global feature information between layers. In this paper, we mainly investigate how to enhance the local and global feature learning abilities of DenseNet by fully exploiting the hierarchical features from all convolutional layers. Technically, we propose an effective convolutional deep model termed Dense Residual Network (DRN) for the task of optical character recognition. To define DRN, we propose a refined residual dense block (r-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts of inner layers at the same time. After fully capturing local residual dense features, we utilize the sum operation and several r-RDBs to construct a new block termed global dense block (GDB) by imitating the construction of dense blocks to adaptively learn global dense residual features in a holistic way. Finally, we use two convolutional layers to design a down-sampling block to reduce the global feature size and extract more informative deeper features. Extensive results show that our DRN can deliver enhanced results, compared with other related deep models. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Spatial optimization of rice systems with ratoon rice increases production and reduces methane emissions

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European Journal of Agronomy 2025年 170卷

作者： Ding, Qian Li, Yuechen Cao, Ruyin Chen, Jin Yao, Xiong Zhang, Wujun Chongqing Engineering Research Center for Remote Sensing Big Data Application Chongqing Jinfo Mountain National Field Scientific Observation and Research Station for Karst Ecosystem School of Geographical Sciences Southwest University Chongqing 400715 China Key Laboratory of Remote Sensing Application and Innovation Chongqing 401147 China Key Laboratory of Monitoring Evaluation and Early Warning of Territorial Spatial Planning Implementation Ministry of Natural Resources Chongqing 401147 China School of Resources and Environment University of Electronic Science and Technology of China Sichuan Chengdu 611731 China State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing 100875 China Chongqing Rice Ratooning Research Center Chongqing Academy of Agricultural Sciences Chongqing 401329 China

The ratoon rice system (RR) has potential for increasing rice production and reducing environmental impacts. However, research on the spatial optimization of rice systems with RR to balance rice production and environmental protection is limited. We explored the trade-offs in production and methane emissions between RR, the single-season rice system (SR), and the double-season rice system (DR) by employing spatial optimization as a research paradigm. On the basis of 70 field sites, we analyzed the yield and methane emission differences among RR, SR and DR. A random forest model was employed to assess the suitability of RR in China's rice fields. The potential of RR to increase production and reduce methane emissions in medium- and high-suitability areas under different spatial cultivation strategies was quantified through scenario simulations. We found that the annual yield of RR (12.40 ± 1.98 t ha⁻¹) was significantly higher than that of SR (73.53 %) but less than that of DR (1.35 %). However, its methane emissions (0.42 ± 0.19 t ha⁻¹) were lower than those of DR (38.25 %) but higher than those of SR (27.43 %). Suitability analysis revealed that 73.82 % of rice fields nationwide are suitable for RR, with 99.21 % suitability in DR areas. Scenario simulations indicated that, with the optimal spatial layout of rice systems, RR could increase rice production by 14.55 % without increasing methane emissions or reduce methane emissions by 5.31 % while maintaining rice production. This study demonstrated the significant potential of RR to increase rice production while reducing environmental impacts through spatial optimization and offered an innovative approach for sustainable rice production planning in China. © 2025 Elsevier B.V.

关键词： China Methane emission Production Ratoon rice Suitability

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Weakly supervised learning for textbook question answering

TechRxiv

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TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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