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3rd International Conference on Advanced Algorithms and Signal Image processing, AASIP 2023

作者： Zao, Feifei Lv, Lingling Ding, Feng Jin, Shixian Guo, Xiaoding Henan Province Engineering Research Center of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Zhengzhou450046 China School of Electrical Engineering North China University of Water Resources and Electric Power Zhengzhou450046 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Henan Provincial Educational Resources Guarantee Center Zhengzhou450003 China

ISBN: (纸本)9781510668522

The task of crime prediction is to predict the charges of a case based on the given description of the case, which has become a research hot spot. Most of the existing methods use neural networks and machine learning algorithms to predict. The prediction results have high accuracy, but the prediction results are often not well interpretable. In addition, some early traditional methods can explain the prediction results, but their accuracy is often very low. Formal concept analysis is an effective data extraction tool. Therefore, this paper combines formal concept analysis with neural network to improve the accuracy of the algorithm. The experimental results show that our method has higher prediction results and can better explain the prediction results than the original neural network model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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Dynamic Responses on Spatio-Temporal Variation and Climate Change Derived from GIMMS NDVI 3g+ (1982-2015) in Shendong Coalfield 10

Dynamic Responses on Spatio-Temporal Variation and Climate C...

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10th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2019

作者： Shun, Yang Chao, Ma Zhenzhen, Cui Shanshan, Huang School of Surveying and Mapping Land Information Engineering Henan Polytechnic University Jiaozuo China Key Laboratory of Mine Spatial Information Technologies of State Henan Polytechinc University Jiaozuo China

ISBN: (纸本)9781728146157

This study is to investigate the variation of Normalized Difference Vegetation Index (NDVI) in Shendong coalfield and its surrounding ecological environment, understanding the succession law of vegetation. It can be a guide for decision making on natural restoration and artificial rehabilitation. Based on the latest dataset of Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer derived NDVI (GIMMS NDVI3g+) (January 1982-December 2015, 34 years). Using the method of correlation analysis, partial correlation analysis, coefficient of variation, and the trend analysis, to investigate the vegetation variation on temporal, spatial and the response to climate change. Time series analysis shows that the growing season was extended again in the mining area of Shendong coalfield. The results of correlation and Partial correlation analysis, depicts the Shendong coalfield had a high correlation with air temperature and a low correlation with precipitation. 84.21% area of mining area have a medium fluctuation, 40% area of CK have a relatively high fluctuation. The linear trend analysis indicates that the change trend rate of the mining area is 29.32% smaller than the CK is 38.70%. It is urgent to estimate the consistency of the three version datesets and establish the transformation model of GIMMS NDVI. © 2019 IEEE.

关键词： Vegetation

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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Computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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Numerical and experimental studies on the effectiveness of time-varying electromagnetic fields in reducing electron density

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Plasma Science and Technology 2023年第6期25卷 30-40页

作者：郭韶帅谢楷徐晗付麦霞牛营营 Key Laboratory of Grain Information Processing and Control(Henan University of Technology) Ministry of EducationZhengzhou 450001People's Republic of China College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001People's Republic of China School of Aerospace Science and Technology Xidian UniversityXi'an 710071People's Republic of China

When an aircraft or a hypersonic vehicle re-enters the atmosphere,the plasma sheath generated can severely attenuate electromagnetic wave signals,causing the problem of communication blackout.A new method based on time-varying E×B fields is proposed to improve on the existing static E×B fields and mitigate the radio blackout *** use of the existing method is limited by the invalid electron density reduction resulting from current density j=0 A m^(-2)in plasma beyond the Debye *** most remarkable feature is the introduction of a time-varying electric field to increase the current density in the plasma to overcome the Debye shielding effect on static electric ***,a magnetic field with the same frequency and phase as the electric field is applied to ensure that the electromagnetic force is always acting on the plasma in one *** order to investigate the effect of time-varying E×B fields on the plasma electron density distribution,two directions of voltage application are considered in numerical *** simulation results indicate that different voltage application methods generate electromagnetic forces in different directions in the plasma,resulting in repulsion and vortex effects in the plasma.A comparison of the vortex effect and repulsion effect reveals that the vortex effect is better at reducing the electron *** local plasma electron density can be reduced by more than 80%through the vortex effect,and the dimensions of the area of reduced electron density reach approximately 6 cm×4 cm,meeting the requirements of electromagnetic wave ***,the vortex effect of reducing the electron density in RAM-C(radio attenuation measurements for the study of communication blackout)reentry at an altitude of 40 km is *** the basis of the simulation results,an experiment based on a rectangular-window discharge device is proposed to demonstrate the effectiveness of the vortex *** results show t

关键词： blackout magnetic window method time-varying E×B fields

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Narrow-Band Interference Suppression Method for SAR Based on CEEMD

Narrow-Band Interference Suppression Method for SAR Based on...

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2021 CIE International Conference on Radar, Radar 2021

作者： Zhang, Hengrui Lv, Zongsen Min, Lin Li, Ning College of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Network Information Center Office Henan University Kaifeng China

ISBN: (纸本)9781665498142

Narrow-band interference (NBI) is a common form of interference in SAR systems, severely reducing the quality of SAR images. The NBI suppression method based on empirical mode decomposition (EMD) can adaptively split the raw signal into multiple independent intrinsic modal functions (IMFs), and reconstruct the useful signal after removing the IMFs of the NBI. However, the mode mixing problem it brings will cause part of the useful signal to be removed simultaneously when removing the NBI. A novel NBI suppression method based on complementary ensemble EMD (CEEMD) is proposed in this paper to solve this problem. First, the moment kurtosis coefficient is used to detect the presence of NBI in the raw echo, and CEEMD is used to decompose the NBI-contained raw echo into a series of IMFs. Then, the second-order difference of permutation entropy (PE) of all IMFs is calculated to obtain the global threshold for distinguishing IMFs with NBI and useful signal. Finally, the target IMF is used to reconstruct useful signals to remove the NBI. Experimental results based on real C-band spaceborne SAR data with simulated NBI verify the performance of the proposed method. © 2021 IEEE.

关键词： complementary ensemble empirical mode decomposition (CEEMD) empirical mode decomposition (EMD) Narrow-band interference (NBI) permutation entropy (PE) synthetic aperture radar (SAR)

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Modeling spatial and temporal change of soil erosion based on multi-temporal remotely sensed data

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Research in Cold and Arid Regions 2015年第6期7卷 702-708页

作者： Pei Liu PeiJun Du RuiMei Han Chao Ma YouFeng Zou Key Laboratory of Mine Spatial Information Technologies of SBSM Henan Polytechnic University School of Surveying and Mapping Land Information Engineering Henan Polytechnic University Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing University

In order to monitor the pattern, distribution, and trend of land use/cover change （LUCC） and its impacts on soil erosion, it is highly appropriate to adopt Remote Sensing （RS） data and Geographic information System （GIS） to analyze, assess, simulate, and predict the spatial and temporal evolution dynamics. In this paper, multi-temporal Landsat TM/ETM＋ re- motely sensed data are used to generate land cover maps by image classification, and the Cellular Automata Markov （CA_Markov） model is employed to simulate the evolution and trend of landscape pattern change. Furthermore, the Re- vised Universal Soil Loss Equation （RUSLE） is used to evaluate the situation of soil erosion in the case study mining area. The trend of soil erosion is analyzed according to total/average amount of soil erosion, and the rainfall （R）, cover man- agement （C）, and support practice （P） factors in RUSLE relevant to soil erosion are determined. The change trends of soil erosion and the relationship between land cover types and soil erosion amount are analyzed. The results demonstrate that the CA_Markov model is suitable to simulate and predict LUCC trends with good efficiency and accuracy, and RUSLE can calculate the total soil erosion effectively. In the study area, there was minimal erosion grade and this is expected to con- tinue to decline in the next few years, according to our prediction results.

关键词： land use/cover change (LUCC) soil erosion CA_Markov model revised universal soil loss equation (RUSLE)

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FF-LSTM: Phenotype prediction based on feature fusion 3

FF-LSTM: Phenotype prediction based on feature fusion

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3rd International Conference on Electronic information engineering and Data processing, EIEDP 2024

作者： Zhang, Ge Zhang, Zhou Kou, Xuye Chen, Yitong School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510680531

Phenotypic prediction before crop planting and harvest facilitates plant phenotyping analysis and the implementation of precision agriculture, which is crucial for food security policy formulation, crop management, and food security. Environmental data collected in the field, as important factors affecting phenotypes, can be used together with genotypes to train phenotypic prediction models to improve their prediction capabilities. Although the rise of deep learning has provided a powerful tool for predicting phenotypes using genotype and environmental data, current research only performs simple splicing operations when processing genotype and environmental data and fails to effectively model the impact of genotype and environment on phenotype. Therefore, it is still challenging to accurately predict phenotype using genotype and environment. To solve the problems mentioned above, we design a phenotype prediction model named FF-LSTM. In this method, LSTM is used to extract features from SNP and environmental, and the feature fusion method weights the extracted features to simulate the influence of genotype and environment on phenotype. We conducted experiments on the dataset and verified the effectiveness of this method by analyzing and evaluating the experimental results. © 2024 SPIE.

关键词： Forecasting

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Hyperbolic lorentz attention for synthetic lethality prediction 3

Hyperbolic lorentz attention for synthetic lethality predict...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Ge Chen, Yitong Kou, Xuye Zhang, Zhou School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510681279

Synthetic lethal interactions are critical genetic interactions that have been discovered for identifying new drug targets and potential cancer drug combination strategies. As a targeted approach to selectively kill cancer cells, it has attracted great attention in the field of cancer treatment. However, with the rapid growth of high-throughput data, effective identification of synthetic lethal interactions remains challenging. Although many graph topology methods focus on predicting SL interactions, heterogeneous graphs that exist in the real world tend to be scale-free, where the number of high-order neighbor nodes of high-degree nodes grows exponentially. The graph embedding method based on Euclidean space may not be able to effectively capture the internal hierarchical structure of the scale-free network, and there is not enough space to accommodate the nodes, making the node embedding highly distorted, which greatly limits the modeling ability. In this paper, we propose a model named HLASL based on the hyperbolic Lorentz attention mechanism, it ensures that the learned node features follow a Lorentz manifold. Specifically, in hyperbolic space, we use Lorentzian manifolds to encode heterogeneous information from KGs and information about synthetic lethality. In addition, we introduce the Lorenz knowledge graph attention mechanism to distinguish the weights of different information from the hyperbolic space to optimize gene embedding. Experiments on two datasets show that HLASL outperforms several state-of-the-art baseline methods in synthetic lethality prediction. © 2024 SPIE.

关键词： Forecasting

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Single cell clustering using ZINB Model and variational graph attention autoencoder 3

Single cell clustering using ZINB Model and variational grap...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Ge Kou, Xuye Chen, Yitong Zhang, Zhou School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510681279

Single-cell RNA sequencing allows to discovery of new cell subtypes based on transcriptomic information. Clustering analysis is an effective approach for exploring single-cell heterogeneity. Nevertheless, current single-cell clustering methodologies are challenged by the high-dimensional, sparse, and dropouts on the one hand, and on the other hand, they neglect the thorough exploration of potential relationships between cells and noise removal. To tackle these challenges, this study introduces a novel self-optimized single-cell clustering algorithm named scZVEA, which combines Zero- Inflated Negative Binomial (ZINB) model and variational graph attention autoencoder. The scZVEA framework comprises three key modules. Firstly, a Deep Count Autoencoder (DCA) is designed to model data distribution and eliminate noise. Then, a variational graph attention autoencoder to extract latent features from the data. Lastly, the self-optimized clustering module enables the two previously independent clustering and feature modules to mutually benefit each other by iteratively updating cluster centers to further enhance clustering performance. Experimental findings based on six authentic scRNAseq datasets illustrate that the proposed clustering algorithm significantly enhances clustering accuracy when compared to alternative methods. © 2024 SPIE.

关键词： Clustering algorithms

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Blockchain Security Research Progress and Hotspots

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第2期49卷 433页

作者： Yuan, Ke Yan, Yingjie Shen, Lin Tang, Qian Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China College of Cybersecurity Nankai University Tianjin300350 China

Blockchain is poised to advance the cause of product innovation and technology evolution as a novel decentralized infrastructure and distributed computing model. However, there are serious security risks in the blockchain itself, which will restrict the development of blockchain and related applications. To better grasp the current status of blockchain security research and explore the research hotspots and frontier, this study used Citespace as a bibliometric analysis tool to collect 3993 research papers between 2014 and 2020 from the WoS Core Collection as a data sample. We conduct a comprehensive overview of blockchain security research in terms of authors, research institutions, publications, countries, citations and keywords in a visual graph. This paper shows that the literature record continues to be refreshed, and it peaked in 2019. China and the USA are the main regions for collaborative research. They have established partnerships with many countries whose research institutions and authors play an important role in the collaborative network. Blockchain security research hotspots are mainly related to "cryptocurrency", "supply chain", "smart contract", "healthcare", "consensus mechanism", "fog computing", "cyber attack" and "Internet of Thing (IoT)". The lineage of blockchain development has evolved from an early focus on cryptocurrency and blockchain security risk analysis to securing other areas, such as financial systems, the IoT and healthcare through blockchain. In the future, blockchain security research will be combined with much newer technologies to form diverse developments © 2022. IAENG International Journal of Computer *** Rights Reserved.

关键词： Blockchain

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