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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaoke Zhu Zhiwei Dong Xiaopan Chen Yufeng Shi Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments.

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A Review of Detection-Related Multiple Object Tracking in Recent Times

A Review of Detection-Related Multiple Object Tracking in Re...

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International Conference on Advanced Communication Technology (ICACT)

作者： Suya Li Ying Cao Xin Xie Henan University School of Computer and Information Engineering Jinming Campus Kaifeng China School of Computer and Information Engineering Jinming Campus Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

Multi-object tracking (MOT) is garnering more attention due to its widespread application in the area of autonomous driving, human-computer interaction, and intelligent video surveillance. Especially in recent years, MOT has rapidly developed thanks to related technologies such as object detection, which has helped in handling interfering factors such as crowded scene occlusion, small objects, and similar appearances. Among these, Detection-based MOT is the mainstream for accurately forming objects' trajectories. Therefore, according to the analysis of the last three years' research, this paper particularly focuses on discussing the continuous optimization strategies of MOT around the development of object detection at each stage. In addition, this article also introduces the commonly used benchmark datasets and related applications of MOT.

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Weather radar echo prediction method based on recurrent convolutional neural network

Weather radar echo prediction method based on recurrent conv...

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IEEE International Conference on big data

作者： Xiajiong Shen Kunying Meng Daojun Han Kai Zhai Lei Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Air Traffic Management Branch Civil Aviation of China Zhengzhou China

ISBN: (纸本)9781665445993

The emergence of strong convective weather has caused serious threats to people’s lives and production activities, and also caused serious damage to the ecological environment. Because this kind of weather has a short duration and a small impact area, it is difficult to be accurate predicted. Doppler weather radar is one of the devices that can sample strong convective weather continuously for a long time, and plays an extremely important role in the detection and prediction of severe weather. The prediction of radar echo intensity by using deep learning methods has largely improved the prediction accuracy of strong convective weather, but the prediction of radar echo shape change is not yet ideal, so the trend of strong convective weather cannot be accurately judged. This paper proposes a weather radar echo prediction method based on cyclic convolutional neural network, which is called the EDD model, its design was inspired by the RU_Net families of deep learning models. The feature fusion layer was added to the neural network, thus ensuring that the feature matrix contains more feature parameters and improving the problem of inaccurate prediction of radar echo shape change due to insufficient information during upsampling. The EDD model is trained using Doppler weather radar echo maps, and the experimental results show that the EDD model used in this paper can improve the prediction accuracy of radar echo intensity and make good predictions of radar echo shape changes, thus helping meteorologists to make more accurate judgments on the occurrence, development and change trends of strong convective weather.

关键词： Deep learning Meteorological radar Shape Biological system modeling Neural networks Predictive models Radar imaging

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analysis of the Causes of Cyanobacteria Bloom:A Review

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Journal of Resources and Ecology 2020年第4期11卷 405-413页

作者： ZHOU Bing CAI Xiaoli WANG Sen YANG Xinxin Key Laboratory of Analysis and Processing of Big Data Kaifeng 475004HenanChina School of Computer and Information Engineering Henan UniversityKaifeng 475004HenanChina School of Information Management Wuhan UniversityWuhan 430072China

Among water blooms,cyanobacteria bloom occurs over the widest range and is much more harmful than other *** occurrence in inland water bodies is affected by many factors,such as meteorology,hydrology,and human ***,the study of the causes of cyanobacterial bloom has become a major focus of *** China Knowledge Network Journal database contains 143 papers from China and abroad from the years 2004 to 2019 that are relevant to the study of cyanobacteria *** begin by analyzing keywords in these studies and creating a keyword distribution map which indicates the factors related to the *** on parameters such as the frequency of words appearing in the text,the full text of each of the 143 papers is analyzed to form a word cloud created by a program written in Python *** irrelevant terms are eliminated,the word cloud map can reveal potential factors that were not identified by keywords *** completing this macro analysis,we examined approximately 100 related papers from the China Knowledge Network Journal database and Web of Science database published from 2014 to ***,we summarize the main reasons for the outbreak of water *** factors causing blooms can be divided into natural factors and human *** the natural factors are illumination,water temperature and nutrient salt *** human factors are generally related to large-scale water conservancy *** paper analyzes and summarizes these factors,and provides a reference to aid in the prevention and treatment of algal *** information in the paper has a certain practical significance for the protection of water environments.

关键词： algae bloom eutrophication literature measurement temperature

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An Adaptive Irregular Convolution U-Net for Reconstructing Ancient Channel of the Yellow River

An Adaptive Irregular Convolution U-Net for Reconstructing A...

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IEEE SENSORS

作者： Zhishun Guo Jianhui Zhao Ning Li Lin Wu College 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 Kaifeng China

The lower reaches of the ancient Yellow River (AYR) migrated very frequently, like a loong swinging its tail on the land of China. This paper, based on Gaofen-3 full-polarization synthetic aperture radar (SAR) data, analyzed scattering characteristics of archaeolandscapes, which provided reliable evidences for the existence of AYR between Lankao and Sui County. Then, an adaptive irregular convolution kernel U-Net (AICK-U-Net) was proposed to reconstruct the part of channel of AYR, based on the image obtained by polarization decomposition, and the precision reached 96.21%. In summary, polarization decomposition and neural network in spaceborne SAR field, provided new insights for detecting and reconstructing archaeolandscapes.

关键词： Convolution Spaceborne radar Neural networks Scattering Sensors Rivers Reliability

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3d-Usanet：A Spatio-Temporal Coupled Based Transformer for Video Anomaly Detection Method

SSRN

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SSRN 2024年

作者： Mu, Huiyu Wang, Luhui Yin, Hongjian Li, Yonggan Dang, Lanxue Liu, Yang Zuo, Xianyu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Fair Competition Review Transaction Center of Henan Province Zhengzhou450008 China

Video anomaly detection aims to detect incidental and sudden patterns of events in complex scenes, which plays an essential role for maintaining public security and averting potential risks. Unsupervised learning is particularly effective in solving the problem of insufficient annotation of video abnormal behaviour in low resource scenarios. However，most relate methods decompose it into two sub-tasks: spatio anomaly detection (S-VAD) and temporal anomaly detection (T-VAD), which barely consider the coupled correlations and the difficulty of coupling global spatio-temporal information. To address this, we provide a new perspective on constructing a novel dynamic spatio-temporal coupling relationship by designing a 3D U-shape Attention Network (3D-UsANet). On the one hand, we construct a Spatio-Temporal Feature Modelling Transformer (STFMT) module to handle different spatio positions at the same time step. On the other hand, a Channel-wise Scale-aware Fusion Modelling Transformer (CSFMT) module is integrated into 3D-UsANet to overcome the multi-scale heterogeneous divides and disregard irrelevant characteristics, which adaptively captures global semantic dependencies and bridges the spatio-temporal semantic gap. Essentially, we evaluate the performance of 3D-UsANet on UCSD Ped1、UCSD Ped2 and CUHK Avenue datasets with the AUC score 83.50%、96.30%、86.70%，respectively. The extensive experimental results demonstrate the effectiveness of 3D-UsANet in detecting anomaly behaviors. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Adaptive Deployment of UAV-Aided Networks Based on Hybrid Deep Reinforcement Learning

Adaptive Deployment of UAV-Aided Networks Based on Hybrid De...

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IEEE Conference on Vehicular Technology (VTC)

作者： Xiaoyong Ma Shuting Hu Danyang Zhou Yi Zhou Ning Lu School of Computer and Information Engineering Henan University Kaifeng China International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Henan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China Queen’s University Kingston Ontario Canada

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

Unmanned aerial vehicles (UAVs) can be used as air base stations to provide fast wireless connections for ground users. Due to their constraints on both mobility and energy consumption, a key problem is how to deploy UAVs adaptively in a geographic area with changing traffic demand of mobile users, while meeting the aforemetioned constraints. In this paper, we propose an adaptive deployment strategy for UAV-aided networks based on hybrid deep reinforcement learning, where a UAV can adjust its movement direction and distance to serve users who move randomly in the target area. Through hybrid deep reinforcement learning, UAVs can be trained offline to obtain the global state information and learn a completely distributed control strategy, with which each UAV only needs to take actions based on its observed state in the real deployment to be fully adaptive. Moreover, in order to improve the speed and effect of learning, we improve hybrid reinforcement learning, by adding genetic algorithms and TD-error-based resampling optimization mechanism. Simulation results show that the hybrid deep reinforcement learning algorithm has better efficiency and robustness in multi-UAV control, and has better performance in terms of coverage, energy consumption and average throughput, by which average throughput can be increased by 20% to 60%.

关键词： Energy consumption Adaptive systems Simulation Reinforcement learning Throughput Unmanned aerial vehicles Optimization

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Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification

arXiv

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arXiv 2023年

作者： Liu, Chun Yang, Longwei Li, Zheng Yang, Wei Han, Zhigang Guo, Jianzhong Yu, Junyong School of Computer and Information Engineering Henan University Henan Zhengzhou450046 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Zhengzhou450046 China Henan Engineering Laboratory of Spatial Information Processing Henan University Henan Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Henan Zhengzhou450046 China College of Geography and Environmental Science Henan University Henan Zhengzhou450046 China Henan DeFan High-tech Software Co. Ltd Henan Zhengzhou450046 China

Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and then transferring the knowledge to the tasks which contain few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations on different levels and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current method of"Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification" which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applies contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-level relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods. All the codes are available at github https://***/HENULWY/STBDIP. © 2023, CC BY.

关键词： Image enhancement

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Convolutional Long-Short Term Memory Network for convective weather prediction based on 3D Doppler radar data 2020

Convolutional Long-Short Term Memory Network for convective ...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Xiajiong Shen Kunying Meng Zhongli Guo Lei Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Southwest Regional Air Traffic Management Bureau CAAC Senior engineer of aviation meteorology

ISBN: (纸本)9781450387828

Convectional weather is one of the weather which often occurs during the warm season, the scope of this kind of weather is normally small, short duration, etc, so it is difficult to predicted, the effects of convectional weather are very large, from the national economy to military activities will be threatened by it, so how to achieve more accurate of convection weather forecast will be of great significance. Doppler radar is one of the main tools for monitoring and early warning severe convective weather. It can provide important strong convective information for prediction. through its observed real-time echo intensity (reflectivity factor Z), radial velocity (V) and velocity spectrum width (W). Echo intensity is an important basis for judging whether severe, convective weather occurs, and radial velocity can identify wind damage and is also an important basis for judging the occurrence of severe convective weather. From the radial velocity, we can see the convergence, divergence and rotation characteristics of airflow, which are closely related to the occurrence and development of severe convective weather. Therefore, Doppler radar data plays a very important role in the detection and prediction of severe weather. At present, there are still problems about how to effectively extract these information from Doppler radar *** paper proposes a method of near prediction based on deep learning. The neural network is used to automatically learn data features for prediction. Convolution neural network is combined with long-short-time memory network. Firstly, a three-dimensional convolution is proposed to extract the spatial features of three-dimensional original data, and then the long-short-time memory network is used to extract the features of data in time dimension, thus improving the prediction accuracy. Deep learning method avoids the process of manually extracting data features, and uses historical 3D Doppler radar image data to predict convective weather fo

关键词： Convective weather

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Spatiotemporal Evolution analysis of NO 2 Column Density before and after COVID-19 Pandemic in henan Province Based on SI-APSTE Model

Research Square

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Research Square 2021年

作者： Liu, Yang Zhao, Jinhuan Song, Kunlin Cheng, Cheng Li, Shenshen Cai, Kun Henan Engineering Laboratory of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng475004 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking henan Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction. © 2021, CC BY.

关键词： Air pollution

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