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DiffECN: Differential ECN Marking for datacenter Networks

IEEE/ACM Transactions on Networking 2024年

作者： Huang, Hanlin Xu, Ke Li, Tong Liu, Zhuotao Du, Xinle Gao, Xiangyu Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing100872 China Tsinghua University Institute for Network Sciences and Cyberspace Beijing100084 China Huawei Shenzhen518129 China

ECN marking has been integrated into datacenter switches to enable high-throughput and low-latency transport. We observe that current marking schemes are coarse-grained: they blindly mark all flows when congestion occurs, causing large flows to occupy undeserved bandwidth and preventing newly arriving small flows from finishing quickly. In this paper, we propose, a differential marking strategy that marks only the flows that are the culprits of congestion and protects the remaining flows from being limited. We have implemented it in the Barefoot Tofino switch and performed extensive evaluations via both physical testbed and large-scale simulations. The results show that can restrain flows responsible for congestion successfully while providing desirable network performance. For instance, compared to the legacy way of ECN marking, achieves up to 32.5% (40.1%) lower average (99th percentile) flow completion time (FCT) for small flows while delivering similar FCT for large flows under production workloads. © 1993-2012 IEEE.

关键词： Congestion control (communication)

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No-Reference Stereoscopic Image Quality Assessment Based on Binocular Collaboration

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SSRN 2024年

作者： Guo, Wenzhong Wang, Hanling Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou University Fuzhou350116 China

Stereoscopic images usually consist of a left view, a right view, and depth information. Stereoscopic/3D image quality assessment (SIQA) is often more challenging compared to 2D-IQA due to the usual scene differences between left and right views and the complexity of the binocular vision fusion process. To address the problem of quality prediction bias of multi-distortion images, we investigated the visual physiology and the processing of visual information by the primary visual cortex of the human brain and proposed a no-reference stereoscopic image quality evaluation method. The method mainly includes an innovative end-to-end NR-SIQA neural network with a picture patch generation algorithm. The algorithm generates a saliency map by fusing the left and right views and then guides the image cropping in the database based on the saliency map. The proposed models are validated and compared based on publicly available databases. The results show that the model and algorithm together outperform the state-of-the-art NR-SIQA metric in the LIVE 3D database and the WIVC 3D database, and have excellent results in the specific noise metric. The model generalization experiments demonstrate a certain degree of generality of our proposed model. © 2024, The Authors. All rights reserved.

关键词： database systems

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Integrated Building Information Modelling and 3D Printing for Prefabricated Building

Integrated Building Information Modelling and 3D Printing fo...

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Chinese Control and Decision Conference, CCDC

作者： Yukang Wang Yu Zhou Xiaoping Zhou Junjun Han Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China Beijing Pi-Innovation Technology Ltd Beijing

The prefabricated components can be connected to form buildings in prefabricated building based on the design drawings. The production of components in the factory is not standardized and accurate, which leads to difficulty in connecting prefabricated components and low efficiency in construction. Excessive construction materials are piled up on the construction site and these wastes have caused a lot of pollution to the environment. Study proposes to integrating Building Information Modelling (BIM) and 3D printing for prefabricated buildings. Triangulation of BIM models is discussed. Prefabricated components information is extracted by Industry Foundation Class (IFC) files so that BIM can provide prefabricated component models. Additionally, the conversion relationship between BIM and 3D printing is established. Thirdly, the conversion rules and methods for converting BIM data into STL files are developed so that the 3D printer can print the prefabricated components according to the corresponding path. Finally, testing the data format conversion on BIM models shows that our presented approach enables us to convert a BIM model into a data format recognizable for 3D printing. Applying BIM and 3D print in prefabricated buildings will boost the fineness of components and the efficiency of construction and have good performance in decreasing engineering waste.

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An Improved Weighted Differential Evolution Algorithm Based on the Chaotic Mapping and Dynamic Reverse Learning Strategy 4

An Improved Weighted Differential Evolution Algorithm Based ...

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2022 4th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Wu, Fengbin Li, Shaobo Zhang, Junxing Lv, Dongchao Wu, Xuefang Li, Menghan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China College of Mechanical Engineering Guizhou University Guiyang550025 China College of Mathematics and Statistics Guizhou University Guiyang550025 China

The weighted differential evolution algorithm has the disadvantage of slipping into local optima and low convergence. To address these problems, an extended weighted differential evolution algorithm based on the chaotic mapping and optimal-worst dynamic opposite learning strategy is proposed. Firstly, the chaotic sequence generated by improved one-dimensional Logistic-Chebyshev mapping is employed to modify the starting population generated uniformly within the original algorithm, which aids in broadening the richness of the population. Secondly, to augment the probability of obtaining global optimum, an optimal-worst dynamic reverse learning strategy is used. Finally, a new evolutionary step size generation method is incorporated into the algorithm, which helps to acquire better solutions. The experimental outcomes obtained by testing 10 benchmark functions in CEC 2020 indicate that the proposed method has been greatly improved in accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Optimization

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Rtr-Cnn: Rotated Tray Region Selection and Seedling Health Information Detection by Cnn

SSRN

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SSRN 2024年

作者： Li, Yatao Lin, Hao Tong, Junhua Wang, Xiaoqin Jiang, Jiahang School of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou310018 China Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province Hangzhou310018 China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Wuyishan354300 China

Transplanting seedlings is the primary technique employed in fruit and vegetable production facilities. Utilizing mobile inspection transplanting machines in seedbed technology could effectively reduce manual labor. The detection of plug seedlings and the acquisition of health information are crucial for achieving automated seedling transplantation. Herein, a two-stage detection algorithm called RTR-CNN was developed to locate and identify multiple rotating trays with varying angles on seedbeds within greenhouses while differentiating the growth status of individual seedlings. This study focused on a seedling tray containing 200 cells. Corresponding matching templates were designed on the basis of the binary morphological characteristics of the seedling trays to identify trays at various angles. The recognition results were then segmented and distorted for each cell before being inputted into the backbone network for classification. Masked generative distillation was utilized to optimize the performance of the deployed network for edge computing hardware. The accuracy rate of the deployed network using the WideResNet101_2 teacher network reached 88.80%, and the two-classification accuracy rate reached 98.95%. The MobileNetV2 lightweight network was used for deployment, reaching an accuracy rate of 87.45% through distillation learning, and the two-classification accuracy rate reached 99.15% at a test speed of 159.30 fps with a batch size of 200. Compared with the training accuracy rate without distillation method, that with using distillation learning had a 0.95% increase, and the two-classification accuracy rate exhibited a 0.5% increase. © 2024, The Authors. All rights reserved.

关键词： Distillation

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Iterative Privileged Learning for Multi-view Classification 10

Iterative Privileged Learning for Multi-view Classification

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10th International Conference on Information Technology and Quantitative Management, ITQM 2023

作者： Fu, Saiji Tian, Yingjie Tang, Jingjing School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 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 UCAS Beijing100190 China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Sichuan 611130 China

Multi-view data is closely associated with privileged information, presenting opportunities for advancements in current studies. Firstly, they often overlook the dynamic nature of privileged information;Secondly, the current state-of-the-art privileged support vector machine, dSVM+, is primarily designed for single-view learning;Thirdly, some methods only address two-view classification problems;Finally, the performance enhancements achieved by these approaches remain unsatisfactory. To this end, this paper proposes a multi-view privileged support vector machine called MVdSVM+. The iterative learning strategy is put forward to update the privileged information dynamically. To our best knowledge, this is the first attempt of dSVM+ in multi-view learning. Meanwhile, it is designed for multiple views. Experiments show the effectiveness of MVdSVM+ over the benchmark methods. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative Management.

关键词： Classification (of information)

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HANE-SHC: Heterogeneous attributed network embedding with structural homophily contents

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Applied Intelligence 2025年第10期55卷 1-40页

作者： Fu, Yue Zhao, Shuliang Wu, Yongliang College of Information Engineering Hebei GEO University Hebei China College of Computer and Cyber Security Hebei Normal University Hebei China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei China Hebei Provincial Key Laboratory of Network and Information Security Hebei China School of Information Science and Technology Shijiazhuang Tiedao University Hebei China

Heterogeneous attributed networks (HANs) are ubiquitous in real-world, spanning domains from the academic network to the movie network. Heterogeneous attributed network embedding (HANE), a promising analytical technique in recent years, aims to generate low-dimensional dense vectors containing network structure and attribute content. Compared to the embedding approaches that only consider topology structure, researchers embed the network by leveraging both network topology and content to improve the quality of embedding vectors. However, when two nodes with a connected edge may have a small content similarity value, while two nodes without a connected edge may have a large content similarity value, so, in this situation, it is not appropriate to utilize the network topology and nodes’ content to generate embedding vectors. To overcome this challenge, we propose a novel approach of Heterogeneous Attributed Networks Embedding based on Structural Homophily Contents, abbreviated as HANE-SHC, which is based on convolutional neural networks to leverage both the network structure and the content of nodes connected with the embedded node to generate the representation vectors of the embedded node. To our knowledge, this is the first study that proposes the Structural Homophily Contents to enhance the heterogeneous attributed network embedding. Through comprehensive evaluations on downstream tasks including clustering, classification, link prediction, and visualization, the experimental results demonstrate that the HANE-SHC outperforms the baselines. On the artificial synthetic networks with various network structure-content dissimilarity scores, the HANE-SHC model consistently produces high-quality embedding vectors regardless of the network dissimilarity scores between structure and content, significantly outperforming existing approaches.

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Image Fusion Method Based on NSCT and Adaptive Sparse Representation 4

Image Fusion Method Based on NSCT and Adaptive Sparse Repres...

引用

4th International Conference on Video and Image Processing, ICVIP 2020

作者： Dong, Zhangyu Wei, Xin Wang, Mengyang School of Computer and Information Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230009 China

ISBN: (纸本)9781450389075

ABSTRACT: Aiming at the imaging characteristics of multispectral and panchromatic image and the problem that the traditional image fusion methods cannot take into account the spectral quality and spatial resolution of the image at the same time, a novel fusion method based on the Non-subsampled Contourlet Transform (NSCT) domain and adaptive sparse representation (ASR) algorithm is presented in this paper. It combines the advantages of principal component substitution and multi-scale analysis, and uses the ASR model to encode the low-frequency components, which improves the shortcomings of the traditional SR-based approach applications. Firstly, NSCT transform is performed on thefirst principal component of multi-spectral image obtained by PCA transform and panchromatic image,and the low-pass and high-pass coefficients of the source image are obtained. Secondly, the Max-absolute' rule and the ASR model are used to fuse the separated high-pass and low-pass bands respectively. Finally, we obtained the final fused image by the corresponding inverse transformation reconstruction. The results show that the fusion algorithm proposed in this paper performs well in both spatial resolution and spectral performance;it not only improves the subjective visual effect, but also improves the objective evaluation index. © 2020 ACM.

关键词： Image fusion

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Real-time Dense 3D Semantic Mapping Using RGB-D Camera

Real-time Dense 3D Semantic Mapping Using RGB-D Camera

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Chinese Control Conference (CCC)

作者： Guanci Yang Xiaoyuan Wang Dongying Zhu Yang Li Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang China School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

Focusing on the poor real-time performance of 3D semantic mapping using RGB-D cameras in indoor environments, this paper investigates the Real-time Dense 3D Semantic Mapping (RD3DSM) using RGB-D camera. Firstly, a lightweight semantic point cloud acquisition algorithm based on keyframe is designed, which is used to segment scene images and obtain the semantic point cloud real-timely. Secondly, to address the loss of inter-frame tracking in the semantic mapping process, ORB-SLAM3 system is employed as the underlying architecture of the visual 3D semantic mapping system to provide stable camera pose. Thirdly, an information fusion mechanism of the visual 3D semantic mapping system is designed, and the OctoMap 3D scene reconstruction module is adopted to implement dense 3D map representation with semantic information, and then a visual 3D semantic mapping algorithm based on ORB-SLAM3 is proposed. Finally, the designed RD3DSM system is integrated into a robot platform. The tests were conducted on the TUM dataset and natural indoor scenes. The results show the effectiveness and real-time performance of the RD3DSM system.

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Adrc & Mpc for Gas Pressure Energy Recovery Systems

SSRN

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SSRN 2024年

作者： Zhao, RuoChen Wei, Dong Xiong, YaXuan Zuo, MingXin School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China The University of Sheffield United Kingdom School of Environmental and Energy Engineering Beijing University of Civil Engineering and Architecture China Beijing Key Laboratory of Intelligent Processing for Building Big Data China

The gas pressure needs to be regulated from high to low in transmitting certain industrial gases or natural gas according to the user's demand. To reduce the effects of disturbances caused by load fluctuations on the gas user side, this paper establishes a gas pressure energy recovery control system to generate electricity using an expander-driven generator. An Active Disturbance Rejection Controller (ADRC) is designed for the generator rotational speed process, and a model predictive controller (MPC) is developed for the torque/flux linkage process;they operate in a cascaded manner. The MPC in the secondary loop is used to overcome the system time delay characteristics, thus avoiding the generator torque and flux linkage deviation from their set-points caused by gas load variations. ADRC in the primary loop observes the system disturbances and compensates for them to determine the set-point of the torque, thus ensuring a stable generator rotational speed. The proposed control strategy is verified on a gas pressure energy recovery experimental platform. A comparison of experimental results with the five currently common generator control methods shows that the proposed strategy reduces the generator speed fluctuations and the harmonic components in the generator current. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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