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7th International Conference on Computer and Information processing Technology, ISCIPT 2022

作者： Xu, Tao Ma, Xiaqing Wang, Fengsi Liu, Chun Zhang, Zixiang Lu, Peiming Han, Daojun Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

In view of the problem that the distribution of knowledge points in the current knowledge system in the form of chapters can no longer meet the needs of students and teachers. This paper proposes to use SVM to identify knowledge points of tests, build a subject Knowledge Graph based on the identified knowledge points, and design and implement a subject Knowledge Graph system. This paper takes the mechanical movement of the first chapter of the eighth grade (upper) physics course as an example to conduct empirical research. By 10-fold cross-validation, the average F1 value of the algorithm used in this paper is 89.66%. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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Research on the Construction of Curriculum Knowledge Graph Based on GMM 3

Research on the Construction of Curriculum Knowledge Graph B...

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2022 3rd International Conference on Computer Information and big data Applications, CIBDA 2022

作者： Ma, Xiaqing Xu, Tao Wang, Fengsi Xu, Aopeng Zhang, Zixiang College of Computer and Information Engineering Henan University Henan Province Kaifeng City China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Province Kaifeng City China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Henan Province Zhengzhou China

ISBN: (纸本)9783800758760

The knowledge hierarchy provided by the curriculum syllabus is the basic principle for the implementation of course teaching and assessment, which usually summarizes knowledge units into a tree-like hierarchical structure in the form of chapters. But it cannot supply the complete picture of knowledge relationships because of the lack of horizontal connections between knowledge units in it. In order to present the characteristics of knowledge units and the many-tomany relationships between them comprehensively and accurately. This paper proposes a method for the construction of course knowledge structures from the massive test question bank with the Gaussian Mixture Clustering model. This method employs the Gaussian Mixture Clustering model to find the knowledge units and their associations from a large number of test questions, then employs the knowledge graph technology to make the course Knowledge Graph, and construct the curriculum knowledge hierarchy. Extensive empirical research focuses on the physics course in eighth grade (first) course in junior high school. Experimental results demonstrate that the constructed curriculum knowledge hierarchy of the physics course is complete and effective. Furthermore, the average coverage of some random final test papers is 89.2%. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Curricula

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High-resolution rock abundance derivation of lunar cold spot craters based on RA-SAR model

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Planetary and Space Science 2025年 263卷

作者： Sun, Jiacheng Lu, Xin Shu, Gaofeng Guo, Zhengwei Li, Ning School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Henan University Kaifeng475004 China

The distribution of rock abundance (RA) on the lunar surface plays a pivotal role in understanding its geological evolution. This study focuses on the derivation of high-resolution RA data for lunar cold spot craters using Mini-RF (Miniature Radio Frequency) synthetic aperture radar (SAR) data. Firstly, terrain correction was applied to the SAR data. Secondly, the correlation between the Stokes parameters (S1, S2, S3, and S4) and RA was examined using optically-derived RA data, which aligns with the resolution of the Mini-RF SAR images. By plotting scatter diagrams showing the relationships between the Stokes parameters and the optically-derived RA, strong statistical associations were established. Finally, based on these findings, we formulated a regression-based RA-SAR model. This model was applied to other lunar cold spot craters in order to derive their high-resolution RA distributions. The experimental results show that the model yields highly precise outcomes when validated against both Diviner RA and optical data. The study provides a new approach for inferring rock distribution across the lunar surface using SAR data, and offers valuable insights for advancing lunar geological research. © 2025

关键词： Cold spot crater Diviner Mini-RF Moon Rock abundance SAR

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Phase spectrum based automatic ship detection in synthetic aperture radar images

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Journal of Ocean Engineering and Science 2021年第2期6卷 185-195页

作者： Miaohui Zhang Baojun Qiao Ming Xin Bo Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina Institute of Data and Knowledge Engineering Henan UniversityKaifengChina School of Computer Science and Engineering Beihang UniversityBeijingChina

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea *** order to eliminate the influence of land regions in SAR images,a novel land removing method is *** removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these ***,an appropriate land segmentation threshold is accordingly ***,an automatic ship detector based on phase spectrum is *** proposed detector is free from various idealized assumptions and can accurately detect ships in SAR *** results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.

关键词： Ship detection saliency detection phase spectrum sea-land segmentation

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Estimation and spatiotemporal variations of black carbon aerosol over the North China Plain in 2018

Estimation and spatiotemporal variations of black carbon aer...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Cai, Kun Qi, Yongkai Li, Shenshen Zhang, Shuo College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668898

With the rapid economic development in the North China Plain, the significant emissions of black carbon (BC) aerosol have exerted notable impacts on climate, environment, and health, making it a hot topic in environmental studies in recent years. To compensate for the insufficient ground monitoring data, the PM2.5 satellite remote sensing inversion data and chemical model simulation data were utilized, applying the proportionality factor method to estimate the satellite concentration of BC. The estimation results were validated using BC monitoring data from seven stations in 2018, and based on this, the spatiotemporal variations of BC satellite concentration was obtained. The results showed that the estimated BC concentration using the proportionality factor method had good accuracy, with a correlation coefficient (R2) of 0.72. The spatial distribution of BC over the North China Plain in 2018 exhibited a decreasing trend from the inner region to the outer region, with high concentration areas mainly located in provinces with high straw burning and steel production. The seasonal variation of BC in the North China Plain in 2018 was pronounced, showing higher concentrations in autumn and winter and lower concentrations in spring and summer. © 2023 SPIE.

关键词： Satellites

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A Task Scheduling Approach based on Particle Swarm Optimization for the Production of Remote Sensing Products

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2023年第1期50卷 23页

作者： Hou, Yan-e He, Wenwen Zuo, Xianyu Dang, Lanxue Han, Hongyu Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China Computer and Information Engineering Henan University Henan Kaifeng475004 China

The production of remote sensing products is very time-consuming, because it contains a large amount of remote sensing data to be processed. To increase the production efficiency of remote sensing products, a remote sensing processing job is usually divided into multiple tasks that are executed in parallel in the cluster system. So efficient task scheduling is the core problem of remote sensing products production system. This paper issued this problem and proposed a hybrid particle swarm optimization algorithm to improve the production efficiency of the remote sensing products. First, the task scheduling model of remote sensing products was established to minimize the maximum task execution time. Then, in the framework of particle swarm optimization, an inertia weight parameters adjustment strategy based on linear decreasing was adapted. For the solution obtained in each iteration, a local search procedure of task movement between the production nodes was employed to improve the quality of the solution. In addition, a probability acceptance rule was also introduced to allow the acceptance of poor solutions avoid trapping into local optimum prematurely. Finally, we carried out several experiments in the CloudSim simulation platform. The experimental results demonstrate that the proposed algorithm is effective and more competition when compared with the existing scheduling algorithms, such as ant colony optimization, tabu search and other classical scheduling algorithms © 2023, IAENG International Journal of Computer *** Rights Reserved.

关键词： Remote sensing

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification 8

Lightweight Transformer Network and Self-supervised Task for...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification 8

Point-Level and Set-Level Deep Representation Learning for C...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hu, Jihui Ye, Pengfei Li, Danyang Dong, Lingyun Chen, Xiaopan Zhu, Xiaoke School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China

ISBN: (纸本)9781665450515

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Infrared imaging

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TMFNet: Two-Stream Multi-Channels Fusion Networks for Color Image Operation Chain Detection

arXiv

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arXiv 2024年

作者： Niu, Yakun Tan, Lei Zhang, Lei Zuo, Xianyu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475000 China School of Computer and Information Engineering Henan University Kaifeng475000 China

Image operation chain detection techniques have gained increasing attention recently in the field of multimedia forensics. However, existing detection methods suffer from the generalization problem. Moreover, the channel correlation of color images that provides additional forensic evidence is often ignored. To solve these issues, in this article, we propose a novel two-stream multi-channels fusion networks for color image operation chain detection in which the spatial artifact stream and the noise residual stream are explored in a complementary manner. Specifically, we first propose a novel deep residual architecture without pooling in the spatial artifact stream for learning the global features representation of multi-channel correlation. Then, a set of filters is designed to aggregate the correlation information of multi-channels while capturing the low-level features in the noise residual stream. Subsequently, the high-level features are extracted by the deep residual model. Finally, features from the two streams are fed into a fusion module, to effectively learn richer discriminative representations of the operation chain. Extensive experiments show that the proposed method achieves state-of-the-art generalization ability while maintaining robustness to JPEG compression. The source code used in these experiments will be released at https://***/LeiTan98/TMFNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image fusion

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