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2021 China Automation Congress, CAC 2021

作者： Ma, Xiao Liu, Yu-Hu School of Mathematics Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

ISBN: (纸本)9781665426473

This paper presents an improved automata model for traffic flow based upon models for cellular automata and traffic flow. The proposed model is concerned with the headway and multiple velocities. The proposed model enforces the stochastic rules for moving a car on the basis of traffic in front of it. Rules for updating the velocity of car relies upon the location of the car as well as front traffic and their velocities. In particular, by using numerical simulations, the model proposed in this study can reach the steady-state more quickly for random perturbations. © 2021 IEEE

关键词： Cellular automata

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MSLKCNN: A Simple and Powerful Multi-scale Large Kernel CNN for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Xun Ng, Alex Hay-Man Lei, Fangyuan Ren, Jinchang Guo, Li Du, Zheyuan Guangdong University of Technology School of Information Engineering Guangzhou510006 China Guangdong University of Technology School of Civil and Transportation Engineering Guangzhou510006 China Guangdong Polytechnic Normal University Guangdong Provincial Key Laboratory of Intellectual Property Big Data Guangzhou510665 China Robert Gordon University National Subsea Centre AberdeenAB21 0BH United Kingdom Ministry of Natural Resources Land Satellite Remote Sensing Application Center Beijing100048 China Geoscience Australia Canberra2609 Australia University of New South Wales School of Civil and Environmental Engineering SydneyNSW2052 Australia

Deep learning-based hyperspectral image (HSI) classification models typically utilize multiple feature extraction layers to learn the features of land covers. Nevertheless, they encounter challenges, e.g., 1) Transformers require substantial computational resources, and 2) these layers are carefully assembled and designed. Recently, large kernel convolutional neural networks (LKCNNs) show excellent performance in natural visual tasks. To tackle these limitations and explore the capability of LKCNNs for HSI classification, we present a novel simple and powerful multi-scale large kernel convolutional neural network architecture (MSLKCNN) with the largest kernel size as large as 15 × 15, in contrast to commonly used 3 × 3, for HSI classification. MSLKCNN avoids these specialized designs, comprising a noise suppression module (NSM) and a multi-scale large kernel convolution (MSLKC). Specifically, NSM is first used to suppress the noise and reduce the number of the bands before extracting the features. Then, MSLKC, as the only feature extraction layer of MSLKCNN, joints three parallel convolutions to capture the features of various types (i.e. spectral, spectral-spatial) and ranges (i.e., small local, larger local, and global) from the dimension of scale: (C1) convolution with a kernel size of 1 × 1 is used to extract spectral features;(C2) multi-scale large kernel depthwise separable convolution (MLKDC) is proposed to learn the spectral-spatial features of different ranges including short-range, middle-range, and long-range;and (C3) multi-scale dilated depthwise separable convolution (MDDC) is designed to aggregate the spectral-spatial features between land covers at various distances. Extensive experimental results on three public HSI datasets demonstrate the competitiveness of the proposed MSLKCNN compared with several state-of-the-art methods. © 1980-2012 IEEE.

关键词： Convolutional neural networks

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Efect of Pre‑stretch Strain at High Temperatures on the Formability of AZ31 Magnesium Alloy Sheets

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Acta Metallurgica Sinica(English Letters) 2023年第1期36卷 48-60页

作者： Yongqiao Li Lifei Wang Xiaohuan Pan Qiang Zhang Guangsheng Huang Bin Xing Weili Cheng Hongxia Wang Kwang Seon Shin College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China Chongqing Innovation Center of Industrial Big-Data Co.Ltd. National Engineering Laboratory for Industrial Big-Data Application TechnologyChongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center for Magnesium Alloys Chongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National UniversitySeoul 881416Republic of Korea

High-temperature pre-stretching experiments were carried out on the AZ31 Mg alloy at 723 K with strain levels of 2.54%,6.48%,10.92%,and 19.2%to alter the microstructure and texture for improving room-temperature *** results showed that the strain-hardening coefcient increased,while the Lankford value *** addition,the Erichsen values of all pre-stretch sheets were enhanced compared with that of the as-received *** maximum Erichsen value increased from 2.38 mm for the as-received sample to 4.03 mm for the 10.92%-stretched sample,corresponding to an improvement of 69.32%.This improvement was mainly attributed to the gradual increase in grain size,and the(0001)basal texture was weakened due to the activated non-basal slip as the high-temperature pre-stretching strain levels *** visco-plastic self-consistent analysis was performed on the as-received and high-temperature pre-stretched *** confrmed the higher activity of the prismatic slip in 10.92%-stretched sample,leading to divergence and weakening of basal texture *** results in an augmentation of the Schmid factor under diferent slip ***,it can be concluded that high-temperature pre-stretching technology provided an efective method to enhance the formability of Mg alloy sheets.

关键词： High-temperature deformation Grain growth Magnesium alloys Texture Visco-plastic self-consistent modeling Formability

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TJOSConf: Automatic and Safe System Environment Operations Platform 2022

TJOSConf: Automatic and Safe System Environment Operations P...

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11th International Conference on Software and Computer applications, ICSCA 2022

作者： Wang, Yida Jiang, Shuangshuang Cui, Bin School of Cs & National Engineering Laboratory for Big Data Analysis and Applications Peking University China Alibaba Group China

ISBN: (纸本)9781450385770

With exploding number of servers in large IT corporations, system environment management of servers at scale is a big challenge. System environment needs to be regularly updated to meet the system demand of services or fix emerging bugs. It is of vital importance to ensure stability of services during these system updates. However, the heterogeneity of workload and diversity of update scripts make it difficult to estimate how a system update affects the servers and services running on them. Unexpected failures caused by various reasons can also arise during or after update execution, making the situation even worse. This paper aims to solve the system management challenge with an operations platform called TJOSConf. During system updates, TJOSConf is able to interact with services to make sure that expected impacts and unexpected failures can be recognized and handled in time, making system update highly automatic and safe. Various system update scripts can be easily incorporated into TJOSConf as plugins, making the platform scalable. Use cases of TJOSConf in Alibaba prove its effectiveness. © 2022 ACM.

关键词： System environment management operations platform rolling upgrade

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The Existence of a Class of Balanced Multi-output Rotation Symmetric Boolean Functions

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Chinese Journal of Electronics 2018年第5期27卷 995-1001页

作者： DU Jiao FU Shaojing QU Longjiang LI Chao PANG Shanqi College of Mathematics and Information Science Henan Normal University Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control Henan Normal University College of Computer Science National University of Defense Technology College of Liberal Arts and Science National University of Defense Technology

A new characterization of balanced rotation symmetric（n, m）-functions is presented. Based on the characterization, the nonexistence of balanced rotation symmetric（pr, m）-functions is determined, where p is an odd prime and m ≥ 2. And there exist balanced rotation symmetric（2r, m）-functions for 2 ≤ m ≤ 2r-r. With the help of these results, we also prove that there exist rotation symmetric resilient（2r, m）-functions for 2 ≤ m ≤ 2r-r-1.

关键词： Cryptography Rotation symmetric Orbit matrix Correlation immune Resilient functions

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Prediction of high-speed train delay propagation based on causal text information

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Railway engineering Science 2023年第1期31卷 89-106页

作者： Qianyi Liu Shengjie Wang Zhongcan Li Li Li Jun Zhang Chao Wen National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong UniversityChengdu 610031China China Railway Chengdu Group Co. LtdChengdu 610031China

The delay-causing text data contain valuable information such as the specific reasons for the delay,location and time of the disturbance,which can provide an efficient support for the prediction of train delays and improve the guidance of train control *** on the train operation data and delay-causing data of the Wuhan-Guangzhou high-speed railway,the relevant algorithms in the natural language processing field are used to process the delay-causing text *** also integrates the train operatingenvironment information and delay-causing text information so as to develop a cause-based train delay propagation prediction *** Word2vec model is first used to vectorize the delay-causing text description after word *** mean model or the term frequency-inverse document frequency-weighted model is then used to generate the delay-causing sentence vector based on the original word ***,the train operating-environment features and delay-causing sentence vector are input into the extreme gradient boosting(XGBoost)regression algorithm to develop a delay propagation prediction *** this work,4 text feature processing methods and 8 regression algorithms are *** results demonstrate that the XGBoost regression algorithm has the highest prediction accuracy using the test features processed by the continuous bag of words and the mean *** with the prediction model that only considers the train-operating-environment features,the results show that the prediction accuracy of the model is significantly improved with multi-ple regression algorithms after integrating the delay-causing feature.

关键词： High-speed rail Delay propagation Cause of delay Word2vec Natural language processing

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analysis and modeling for illuminance and signal-to-noise of smart traffic information system

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Journal of engineering and Applied Science 2022年第1期69卷 1-17页

作者： Chen, Yaoting Chen, Huanting School of Business Minnan Normal University Zhangzhou363000 China Analysis and Application for Business Big Data of Fujian Provincial Key Laboratory Zhangzhou363000 China College of Physics and Information Engineering Minnan Normal University Zhangzhou363000 China

The traffic lights using by light emitting diode (LED) are able to indicate and provide the information about the ambient. The signal transimission performance of traffic system is related to a variety of factors such as the signal-to-noise ratio (SNR), illuminance. Several factors are influence on illumiance and SNR, such as eatsink temperature and driver parameters. It has not been any substantial investigate on the operated effects of traffic light with sinusoidal waveform driver on illuminance and SNR. With the use of heatsink temperature and driver parameters of the LED source with sinusoidal waveform driver, prediction modeling for the illuminance and SNR of the mixed white LED sources with heatsink temperature, frequency, amplitude voltage, bias voltage and amplification factors are proposed in this paper to enhance the illuminance and SNR model of LED source inside smart traffic information system. The proposed model should carry out a series of calibration step for illuminance, heatsink temperature and driver parameters, which are easy for traffic designer and source system designers to follow in the traffic information system. The proposed model has been measured for the mixed white LED sources, with reasonably good agreements between theoretical and practical results. © 2022, The Author(s).

关键词： Signal to noise ratio

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Multi-criteria consensus sorting model with flexible linguistic preferences based on fuzzy information granulation from the perspective of preference disaggregation

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engineering applications of Artificial Intelligence 2025年 154卷

作者： Zhang, Shiji Zhang, Shitao Tian, Hao Deveci, Muhammet Liu, Xiaodi School of Microelectronics and Data Science Anhui University of Technology Ma'anshan243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Ma'anshan243032 China Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul34942 Turkey The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Information Technologies Western Caspian University Baku1001 Azerbaijan

Multi-criteria group sorting (MCGS) that considers linguistic preferences involves multiple individuals evaluating alternatives and assigning them to pre-determined ordered categories based on specific criteria. Nevertheless, due to the limited availability of class information and the constrained cognitive capacity of decision-makers (DMs), it becomes challenging for DMs to furnish explicit preference information to reach consensus. Besides, decision parameters such as consensus threshold and class thresholds are also too hard to be assumed in advance. Therefore, this paper studies the preference disaggregation problem in MCGS, in which DMs are allowed to provide their pairwise comparisons in flexible linguistic expressions (FLEs) as preference information. To more fully utilize the preferences, semantic granulation is introduced. First, an individual consistency recognition model is proposed to identify inconsistent preferences and provide modification directions to the corresponding individuals. Next, semantic granulation and maximum entropy are combined into a granulation-driven information transformation model to convert the preference information based on FLEs into triangular fuzzy numbers (TFNs). Subsequently, in the consensus-driven preference disaggregation model, decision parameters and sorting results can be obtained at the premise of consensus by adjusting weights. Ultimately, to substantiate the effectiveness of the proposal, two numerical applications concerning the sorting of government venture capitals and information system suppliers are presented, along with comparative analysis, sensitivity analysis, and simulation analysis. © 2025 The Authors

关键词： Information granules

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A data-driven approach for recommending UI element layout

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Science China(Information Sciences) 2020年第9期63卷 61-63页

作者： Yonghao LONG Xiangping CHEN Yuting XIE School of Information Science and Technology National Engineering Research Center of Digital LifeSun Yat-sen University Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and Design Sun Yat-sen University

Dear editor,Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. GUI design plays an important role in offering a smooth user experience. The complexity of the apps often depends on the user interface (UI)[1]with minor data processing or data processing delegates to the backend component.

关键词： UI development Software mining Layout recommendation data-driven development Intelligent Software Development

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Detection of Densely Aggregated Wheat Rust Spores in Complex Background Microscopic Images 5

Detection of Densely Aggregated Wheat Rust Spores in Complex...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Lu, Feiran Lei, Yu Liang, Dong Chen, Xu Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Hefei China Anhui University School of Internet Hefei China

ISBN: (纸本)9798350373820

To solve the issue of inaccurate detection of summer spores of wheat stripe rust due to complex backgrounds, small targets, and dense adherence among individual spores, an improved model based on YOLOv7 is proposed. This work involves creating a summer spore sample dataset to establish a complex environmental data set for simulated collection. The addition of the CBAM attention mechanism helps elevate the model's precision in detecting spores. Moreover, the ASFF adaptive feature fusion mechanism has been improved to preserve more information of small target features, thus reducing omissions. The introduction of the Wise-IoU loss function enhances the quality of detection anchor boxes and improves the model's localization performance. Trials indicate that the revised model has achieved an mean average precision (mAP) of 98.1% at the IOU threshold of 0.5. Compared to other models, both the average accuracy of detection and the speed of detection have been significantly boosted. The outcomes demonstrate that the enhanced model can accurately and precisely detect wheat stripe rust summer spores. © 2024 IEEE.

关键词： Adaptive feature fusion Attention mechanism Fungal spore recognition Small target detection YOLOv7

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