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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Han, Daojun Xiang, Hongzhen Chen, Haolin Xie, Yi Institute of Data and Knowledge Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781450387828

Taking a military operation as an example, this paper makes an in-depth study of what tactics a single UAV should adopt when facing the interception of multiple enemy UAV groups. According to the analysis of the impact diagram, the aerial range of the UAV standoff defines a threat area, that is, when the UAV arrives at the threat area, it needs to adopt a strategy for penetration, or it will be intercepted. The strategy adopted by UAV in the threat area is analyzed, and the decision control method is made. © 2020 ACM.

关键词： Unmanned aerial vehicles (UAV)

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AMOD-Net: Atention-based Multi-Scale Object Detection Network for X-Ray Baggage Security Inspection 5

AMOD-Net: Atention-based Multi-Scale Object Detection Networ...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhu, Xiaoke Zhang, Jitong Chen, Xiaopan Li, Danyang Wang, Yufei Zheng, Minghao School of Computer and Information Engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

X-ray baggage security checking is an extremely important task, which can detect various dangerous objects in airports, stations and other public places to prevent crimes and protect personal safety. However, at present, most of the recognition is done manually, which is inefficient and error-prone. As a complementary, object detection algorithm is beneficial to avoiding errors caused by manual detection. Although the universal object detection is well developed and the performance of the universal detectors is very advanced, the performance of these detectors in X-ray image detection is mediocre. In this paper, we propose an Attention-based Multi-Scale Object Detection Network (called AMOD-Net) for X-ray baggage security inspection. To solve the problems of stacking and occlusion existed in the X-ray baggage image, we design a channel selection attention module for AMOD-Net. To make better use of the feature information, we construct a deep feature fusion structure for AMOD-Net. Experiments on the X-ray baggage dataset demonstrate that our approach achieves very competitive results. © 2021 ACM.

关键词： Object detection

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Identification of Plant Stomata Based on YOLO v5 Deep Learning Model 5

Identification of Plant Stomata Based on YOLO v5 Deep Learni...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Ren, Fangtao Zhang, Yawei Liu, Xi Zhang, Yingqi Liu, Ying Zhang, Fan School of Computer and Information Engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

Stomata is an important structure in all terrestrial plants and is very vital in controlling plant photosynthesis and transpiration flow. Precise detection of plant stomata is the basis for studying stomata characteristics. Traditional detection methods are mostly manual operations, which is a tedious and inefficient process. Manually extracting features requires high image quality. Choosing appropriate features depends on certain prior knowledge, especially for the object with large morphological changes such as plant stomata. With the widespread use of deep learning technology, efficient solutions to this task have become possible. This article combines the characteristics of the corn leaf stomatal data sets to improve the latest object detection model YOLO v5)You Only Look Once(. By introducing the attention mechanism, that is, adding the SE module to the backbone network, the precision and recall of stoma detection are improved. At the same time, The loss function has been improved from to for avoiding some problems that may occur when selecting the best prediction box. Experimental results show that the precision and recall rates of the improved model on the corn leaf stomata data sets have reached 94.8% and 98.7% respectively, lay the foundation for the measurement of stomatal parameters. In addition, this paper also can help agriculturists and botanists to build their own data sets for stomatal research by explaining the methods of acquiring, pre-processing, and annotating data sets. © 2021 ACM.

关键词： Object detection

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Prediction of Hepatocellular Carcinoma Diseases Based on Methylation data and Screening of Hub Genes 5

Prediction of Hepatocellular Carcinoma Diseases Based on Met...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhang, Yawei Ren, Fangtao Liu, Xi Zhang, Fan School of Computer and Information Engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

DNA methylation is of great significance to the diagnosis, treatment and disease prediction of hepatocellular carcinoma (HCC). The commonly used DNA methylation microarrays have high data dimensions, and different CpG sites detected may map to the same gene. To extract more effective features for HCC disease prediction, this study uses a linear regression model that integrates TCGA database methylation data and gene expression data, based on the DNA methylation microarray data of the GEO database (GSE113017), the corresponding gene expression data was predicted and the differentially expressed genes (3766) with significant differences were screened out, which was used as the feature of the data set. Constructing an Artificial Neural Network (ANN) to train a machine learning model for HCC disease prediction and perform 10-fold cross-validation. The resulting model has an accuracy of 95.1% for HCC disease prediction based on DNA methylation microarray data. Compared with other HCC prediction methods, this method has better performance. Then analyze the differentially expressed genes with protein-protein interaction network (PPI network), and use the top five connected genes in the network as hub genes, namely: GNGT2, GNB4, FPR2, CDC20, NMUR1, which can be used as biomarkers for the diagnosis, treatment and prognosis of HCC. © 2021 ACM.

关键词： Machine learning

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Fast parallel extraction method of normalized vegetation index 15

Fast parallel extraction method of normalized vegetation ind...

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15th International Conference on Computer Science and Education, ICCSE 2020

作者： Zuo, Xianyu Qi, Tongyuan Qiao, Baojun Deng, Zhitao Ge, Qiang Henan University Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781728172675

At present, accelerated processing of remote sensing big data has become an important research topic in the field of remote sensing. Remote sensing image processing based on large-scale clusters currently is the mainstream. However, how to fully tap the computing power of a single computing node in the cluster has become issues that cannot be ignored in the field of remote sensing image processing. Traditional GPU programming is difficult to develop, the development cycle is long, and the requirements for developers are very high. In order to improve the efficiency of GPU programming and shorten the development cycle of parallel programs, Nvidia, Grary, PGI and CAPS jointly launched a new programming standard-OpenAcc. In this paper, OpenAcc-NDVI, as a fast parallel extraction method is used to optimize NDVI algorithm. Based on different computing scenarios, two granularity acceleration models are proposed. It has been verified by multiple experiments that when the data size reaches 10000 ∗ 10000, OpenAcc-NDVI can achieve an acceleration of about 5.3 times. After error analysis, the error of algorithm experiment result is 0, there is no loss of precision. The NDVI algorithm based on OpenAcc has excellent acceleration performance, efficient development process, and high calculation accuracy. © 2020 IEEE.

关键词： Graphics processing unit

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A remote sensing work-flow recommendation algorithm based on logical structure

A remote sensing work-flow recommendation algorithm based on...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Wu, Yaohua Liu, Yang Lin, Yinghao Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9781510664616

With the development of remote sensing products towards the direction of civilianization and popularization, work-flow customization plays an important role in the production of remote sensing products. The traditional customized work-flow model has large time cost, complex operation and high requirements for professional knowledge. The work-flow recommendation system can improve the construction efficiency of remote sensing work-flow to some extent and assist users to design high-quality remote sensing work-flow models. However, most of the existing remote sensing work-flow modeling methods ignore the logical structure characteristics of the work-flow, leading to large errors in the calculation results of similarity. Difficult to make work-flow recommendations effectively. Therefore, this paper proposes a customized recommendation algorithm for remote sensing work-flow based on logical structure. By focusing on logical structure, the reliability of similarity calculation between work-flow is improved, so as to find similar work-flow to help users recommend the next modeling node. Firstly, the work-flow model needs to be preprocessed: the user converts the constructed work-flow model into a process structure tree by using Petri net workflow, and uses the path table generation algorithm based on logical structure to convert the model information into data information and store it in the database for subsequent data processing;Then the flow data in the flow tree set was converted into a path table according to certain rules, and then the longest common subsequence similarity of each data in the path table was calculated to obtain the similarity calculation results based on the logical structure characteristics, the most similar work-flow in the work-flow library is found and the recommendation is made for the user. The method proposed in this paper is evaluated experimentally on the real data set, in terms of recall, precision and F1-score, which shows that the method

关键词： Remote sensing

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A Novel Scheme for Crawling and Mining of Housing Transaction data 4

A Novel Scheme for Crawling and Mining of Housing Transactio...

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4th International Conference on Artificial Intelligence and big data, ICAIBD 2021

作者： Chen, Luyao Xu, Tao International Education College Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9780738131702

Housing transaction data from online housing platform contains many valuable information about development trends of real estate, but is also the first-hand reference for citizens to carry out housing transaction. However, how to find the key information hidden in housing transaction data from Internet is still a serious challenge. To solve it, based on web crawling technology and visual analysis technique, this paper proposes a novel scheme for gaining and analyzing housing transaction data. It includes data crawling, data prepossessing, data analysis and other steps. An empirical research is conducted with housing transaction data of Hangzhou, China. The results show the spatial distribution characters of housing price, and the most cost-effective type of real estate in Hangzhou. © 2021 IEEE.

关键词： Web crawler

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Research progress on an intelligent production and distribution of the remote sensing products

Research progress on an intelligent production and distribut...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Kong, Dezhen Zhao, Jinhuan Tian, Meng Liu, Yang Henan Province Space Information Processing Engineering Laboratory KaifengHenan475004 China Henan Key Laboratory of Big Data Analysis and Processing KaifengHenan475004 China School of Computer and Information Engineering Henan University KaifengHenan475004 China

ISBN: (纸本)9783030539795

Since the amount of remote sensing data that can be obtained every day has reached ten terabytes, the storage and sharing of massive remote sensing data and the construction of product production application architecture have become imminent research topics. The purpose of this study is to build an efficient system that can meet the needs of massive remote sensing data storage, calculation, and distribution. The article elaborates on the latest research progress of cloud service remote sensing data distribution and intelligent production from the four aspects of remote sensing cloud service development, data distribution technology, cloud service technology, and operation mechanism of remote sensing cloud computing processing model, and proposes cloud service remote sensing key issues facing data distribution, and summarize and prospect related research, with a view to promoting the further development of research on cloud service remote sensing data distribution and intelligent production. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Remote sensing

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Heterogeneous-attributes enhancement deep framework for network embedding

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Frontiers of Computer Science 2021年第6期15卷 121-131页

作者： Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230022China National Key Laboratory of Blind Signal Processing Chengdu 610041China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230022China

Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network ***,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute ***,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more *** that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining *** this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised *** extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.

关键词： network embedding heterogeneous-attributes deep framework inconsistent

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Winter wheat phenology classification using stacking ensemble learning algorithm based on Sentinel-1A SAR images 5

Winter wheat phenology classification using stacking ensembl...

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5th International Conference on Geoscience and Remote Sensing Mapping, ICGRSM 2023

作者： Duan, Haoxin Yang, Huijin Zhao, Jianhui Li, Ning School of Computer and Information Engineering Henan University Kaifeng China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781510672789

Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. The experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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