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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhigang Liu Xin Luo Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Non-negative latent factor (NLF) models have been frequently applied to information extraction, pattern recognition, and community detection. An NLF model can well represent a high-dimensional and sparse (HiDS) matrix of non-negative data and efficiently acquire useful knowledge from it. A single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is highly efficient to build NLF model. However, its convergence ability on such a matrix is still unveiled in theory. This paper presents the convergence property of an SLF-NMU algorithm. We theoretically prove that it can guarantee its convergence to a Karush-Kuhn-Tucher (KKT) stationary point. Empirical studies on two HiDS matrices from practical applications indicate that an SLF-NMU algorithm make an NLF model to converge at a relatively steady state.

关键词： Sparse matrices Convergence Computational modeling Analytical models Linear programming Data models Technological innovation

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End-to-End Remote Sensing Image Scene Classification with Vision Transformers 23

End-to-End Remote Sensing Image Scene Classification with Vi...

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6th International Conference on Big Data Technologies, ICBDT 2023

作者： Yuan, Hao Liu, Kun Shi, Jiechuan Wang, Can Wang, Weiwei School of Information Science and Engineering University of Jinan Jinan250022 China China High-resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China QiLu Research Institute Jinan250200 China

ISBN: (纸本)9798400707667

In recent years, the development of deep learning technology has led to widespread attention on Vision Transformer (ViT) as an emerging image classification method. Remote sensing image classification is an important task in the field of remote sensing, with extensive application prospects. This paper aims to explore the remote sensing image classification method based on Vision Transformer, addressing the limitations of traditional convolutional neural networks in terms of global perception capability, context information retrieval, and positional encoding. The classification performance of the Vision Transformer model is evaluated and compared on remote sensing datasets. Vision Transformer is a deep neural network model based on self-attention mechanism that can capture the global context information in images and has achieved remarkable performance in various computer vision tasks. Furthermore, experimental results demonstrate that the remote sensing image classification method based on Vision Transformer exhibits outstanding accuracy and generalization ability. Compared to traditional convolutional neural networks, it can better capture the global features in remote sensing images and has better scalability when dealing with large-scale remote sensing image data. Experimental results on different remote sensing image datasets show that the model performs well compared to state-of-the-art methods. Specifically, Vision Transformer achieves average classification accuracies of 95.41%, 98.26%, 93.74% and 95.25% on the AID, UC-Merced, NWPU-RESISC45 and Optimal31 datasets, respectively. © 2023 ACM.

关键词： Neural network models

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Kinematic inventory of rock glaciers in the Nyainqentanglha Range using the MT-InSAR method

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International Journal of Digital Earth 2023年第1期16卷 3923-3948页

作者： Xuefei Zhang Min Feng Jinhao Xu Dezhao Yan Jing Wang Xiaoqing Zhou Tao Li Xiang Zhang Land Satellite Remote Sensing Application Center Ministry of Natural ResourcesBeijingPeople’s Republic of China Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and Resources(TPESER)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingPeople’s Republic of China Academy of Plateau Science and Sustainability Qinghai Normal UniversityXiningPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China Research Institute of Intelligent Computing Zhejiang LaboratoryHangzhouPeople’s Republic of China

Rock glaciers are typical periglacial landforms with tongue or lobate morphological shapes and characterized by the distinct front,lateral margins,and often by ridge-and-furrow surface topography textures as well as kinematic characteristics,widely distributed in alpine *** Synthetic aperture radar interferometry(MT-InSAR),is a remote sensing technique with demonstrated effectiveness for detecting landform ***,its application to rock glaciers is challenged by temporal decorrelation and atmospheric phase noises due to complex topography and snow *** designed a quadtree segmentation and parallel computing-based MT-InSAR method to improve the quality and efficiency of deformation measurement of rock *** applied the method to a rock glacier inventory of the Nyainqentanglha Range,China,derived from high-resolution Gaofen-2 images,to quantify the activity rate of each rock *** showed that 32.1%(6,389)of the identified rock glaciers exhibited slope-parallel deformation rates exceeding 100 mm/*** activities of the rock glaciers exhibited strong correlations with their distance to glaciers,precipitation,freeze-thaw magnitude,and permafrost occurrence *** results demonstrate the effectiveness of the developed segmentation-parallel MT-InSAR method for monitoring rock glacier deformation over a large region.

关键词： Rock glacier MT-InSAR quadtree segmentation Nyaingentanglha kinematics

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A Study on application of Artificial Intelligence in College English Teaching Management and Evaluation 3

A Study on Application of Artificial Intelligence in College...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Feng, Tao Deng, Jianjie Liu, Yiwen Gao, Yanxia Peng, Ling Fu, Jinrong School of Foreign Languages Huaihua University Hunan Huaihua418000 China School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China School of Computing Science University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9798400716775

With the rapid development of artificial intelligence, AI technology has been widely applied in various industries, including education. In English teaching, AI technology provides teachers with objective and instructive feedback, offers students a more personalized learning experience, helping them improve their English language skills, etc., and brings new development opportunities and challenges to English teaching as well. However, there are still some problems that need to be solved in college English teaching in our university. For this reason, based on the application of AI technology in college English teaching, this paper mainly explores the impact and application countermeasures of AI on college English teaching from the following four aspects: individuals with differences, teaching management, classroom teaching and teaching evaluation. In addition, the profound integration of college English education and AI will foster the advancement and transformation of college English instruction, ultimately serving as a catalyst for its reform. © 2023 ACM.

关键词： Artificial intelligence

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UJN-Traffic: A Benchmark Dataset for Performance Evaluation of Traffic Element Classification 7th

UJN-Traffic: A Benchmark Dataset for Performance Evaluation ...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Li, Yan Han, Shi-Yuan Shen, Yuan Liu, Zhen Dong, Ji-Wen Xu, Tao University of Jinan Jinan250022 China China High-Resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

The detection of traffic element in remote sensing imagesplays an important role in the construction of traffic infrastructure, and can provide dynamic monitoring and quality evaluation for traffic construction projects. The convolutional neural network (CNN)-based method has achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, the existing dataset can only be used for object detection, and the sample size and diversity need to be improved. These problems have brought challenges to the improvement and application of existing traffic element detection methods. This paper proposes a large-scale highresolution traffic element dataset based on multi-method detection analysis, and validates it based on the CNN-based object detection method. We screened more than 100 satellite images from high-resolution satellite images with a spatial resolution of 0.8 m. Manually labeled 5 categories and 100,000 traffic element samples. In the experiment, three CNN-based detection methods were used as the experimental baseline method. The experimental results proved effectiveness of the UJN-traffic traffic element dataset and provided a useful reference for traffic construction engineering and management. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

A Diverse Biases Non-negative Latent Factorization of Tensor...

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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Wu, Xuke Gou, Hang Wu, Hao Wang, Juan Chen, Minzhi Lai, Siyu Computer School China West Normal University Nanchong Sichuan637002 China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China University of Chinese Academy of Sciences Beijing100049 China Department of Medical Imaging North Sichuan Medical College Nanchong Sichuan637000 China

ISBN: (纸本)9781728168531

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy. © 2020 IEEE.

关键词： Factorization

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application of Multi-Scale Linear Attention in Road Segmentation of High-Resolution Remote Sensing Images

Application of Multi-Scale Linear Attention in Road Segmenta...

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Advances in Electrical Engineering and Computer applications( AEECA), 2020 IEEE International Conference on

作者： Shuangling Zhu Yunyun Deng Ailimujiang Gulinaz School of Cyber Security and Information Engineering Ili Normal University Yining China Key Laboratory of Intelligent Computing Research and Application in Ili River Valley Ili Normal University Yining China

ISBN: (纸本)9798350355260

With the rapid development of remote sensing technology, road segmentation of high-resolution remote sensing images remains a challenging task due to the complexity of road structures and the diversity of image resolutions. This study introduces a multi-scale linear attention mechanism and optimized context information extraction capabilities based on the EfficientViT model. By adopting finely tuned training and optimization strategies, including data augmentation and efficient hardware configuration, the model ensures rapid convergence and low loss in high-resolution image processing, significantly improving the efficiency and accuracy of road segmentation tasks in remote sensing images. This study demonstrates the advantages of EfficientViT in enhancing the accuracy and efficiency of road segmentation in remote sensing images. The innovative design and outstanding performance of EfficientViT provide valuable references and powerful tools for future remote sensing image analysis work.

关键词： Training Measurement Image segmentation Attention mechanisms Accuracy Roads Information retrieval Real-time systems Remote sensing Optimization

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Research and Exploration on Innovation and Entrepreneurship Practice Education System for Railway Transportation Specialists 9th

Research and Exploration on Innovation and Entrepreneurship ...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Yong, Jiu Dang, Jianwu Wang, Yangping Wei, Jianguo Lu, Yan The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Lanzhou730070 China College of Intelligence and Computing Tianjin University Tianjin300350 China The School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Four Electric BIM Engineering and Intelligent Application Railway Industry Lanzhou730070 China

ISBN: (纸本)9789819959709

With the background of concept of rail transit major engineering, guided by industry demand, and based on the cultivation of practical abilities for outstanding engineering and technology talents in rail transit characteristic majors, we are committed to creating a national strategic innovation and entrepreneurship practical education system for rail transit characteristic professionals, continuously improving the practical teaching mode. We will carry out the construction of a "matrix style" practical teaching system that integrates the four modernizations, the construction of a virtual simulation practical teaching platform that integrates expertise and innovation, and open sharing, as well as the reform and exploration of the entire chain of integrated practical education. The scheme focuses on improving students’ ability to solve complex engineering problems and their ability to apply composite knowledge and innovate and start businesses, cultivating a large number of outstanding rail transit engineering talents with solid professional foundations, outstanding practical abilities, and innovative and entrepreneurial qualities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Light rail transit

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A Lane Detection Method for Advancing Sustainable Energy Internet

A Lane Detection Method for Advancing Sustainable Energy Int...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Xiting Peng Yang Yang Xiaoyu Zhang Xiaoling Zhang School of Information Science and Engineering Shenyang University of Technology Liaoning Liaohe Laboratory Shenyang Key Laboratory of Advanced Computing and Application Innovation Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang Key Laboratory of Industrial Intelligent Chip and Network System Innovation Application Shenyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Artificial Intelligence (AI) technology plays a key role in driving the development of a green and smart Energy Internet. As one of the application areas of the Energy Internet, lane detection in intelligent transportation systems can provide accurate real-time traffic data, thus supporting intelligent scheduling and resource optimization, reducing congestion, energy consumption, and emissions, and improving overall energy efficiency. Therefore, deep learning-based lane detection methods can further enhance the intelligence level of the Energy Internet and promote its efficient operation. However, current methods often rely on Convolutional Neural Networks (CNNs), which excel at local feature extraction but struggle to capture global information in complex conditions. In varying light, shadows, and obstacles, CNNs struggle to integrate information, leading to inaccuracies, particularly in dense traffic or complex road structures, where diverse local features result in identification errors and hinder lane continuity. This reduces decision-making efficiency and increases energy consumption. Consequently, we present LFKNet, a lane detection framework designed for the fusion of the Lane Focus Mechanism (LFM) and Kolmogorov-Arnold Network (KAN) to enhance accuracy and robustness. It consists of two key components: the Lane Focus Mechanism , which utilizes the geometrical topology of lanes for improved global fea ture extraction in complex scenarios, and the Kolmogorov-Arnold Network, which employs a learnable nonlinear activation function to optimize performance on complex and nonlinear features. The experiments demonstrate that LFKNet achieves 97.00% accuracy with a 97.76% F1-score on the Tusimple dataset. It also achieves a 79.43% F1-score on the CULane dataset. Overall, LFKNet’s performance validates its effectiveness in lane detection, enabling accurate traffic monitoring and analysis while providing reliable data support for the Energy Internet.

关键词： Energy consumption Accuracy Lane detection Decision making Energy Internet System integration Feature extraction Robustness Topology Artificial intelligence

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