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First Principle Calculation of Electromagnetic Mechanism for Fe_2Si Bulk Material

Journal of Wuhan University of Technology(Materials Science) 2019年第1期34卷 64-68页

作者： LI Ruijie YANG Yinye College of Big Data and Information Engineering Guizhou University Special and Key Laboratory of Guizhou Provincial Higher Education for Photoelectric Information Analysis and Processing Guizhou Minzu University School of Materials Science and Engineering Guizhou Minzu University

The electronic structure and the magnetic properties of Fe_2Si bulk have been calculated by the first-principle density function theory method. The band structure shows that the hexagonal Fe^2 Si bulk is ferromagnetic which is a metal structure under spin-up, and a semiconductor with the band gap of 0.518 eV under spin-down. The density of states shows the Fe^1 3d–spin and Fe^3 3d–spin in the electronic system are the main factors that is the source of the ferromagnetic properties of Fe_2Si bulk. The electronic structure Si-ions is 3s23p6 and that of Fe-ions is e_g^2↑e_g^(*1)↓t_(2g)~3↑. The molecular magnetic moment of Fe_2Si is 2.00 μB. The potential diagram of Fe_2Si bulk shows the formation of covalent and ionic bonds between the Fe atom and the Si atom, it leads to the center charge of Fe is polarized and off center position. These special properties of Fe_2Si bulk are mainly caused by d-d exchange and p-d hybridization. The results offer a certain reference for the magnetic semiconductor Fe_2Si material.

关键词： Fe2Si electronic structure magnetic property first-principle

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Application of remote sensing based on algorithm process technology inspection processing method

Application of remote sensing based on algorithm process tec...

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

作者： Ge, Qiang Jiang, Kejian Han, Hongyu Liu, Huaizhou Zhang, Jing Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475001 China School of Computer and Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9781510664616

Aiming at a series of problems such as low evaluation efficiency of traditional algorithms, cumbersome process operation, high use cost and no complete algorithm inspection process system, this paper proposes an algorithm flow technology inspection processing system and method based on remote sensing applications, mainly including satellite product processing, algorithm processing, sample feature selection, sample extraction, and algorithm processing results comparison, It mainly realizes the functions of input, output and parameter adjustment required by different function functions, and designs an interface interaction system that adapts to the input and output elements required by different function modules. It realizes the modular, plug-in and componentized design of interface interaction of function modules, and obtains the evaluation system of algorithm credibility. © 2023 SPIE.

关键词： Inspection

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A MAB-based Hyper-heuristic for Vehicle Routing Problem with Time Window

A MAB-based Hyper-heuristic for Vehicle Routing Problem with...

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

作者： Hou, Yan-E Liu, Bingbing Fan, Gaojuan Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

ISBN: (纸本)9781510664616

In order to efficiently solve the vehicle routing problem with time window (VRPTW), a hyper-heuristic algorithm based on reinforcement learning was proposed. Firstly, the performance of the underlying heuristic algorithm was evaluated, and then a multi-armed bandit (MAB) algorithm was used to select the low-level heuristic algorithm. At the same time, a simulated annealing-based acceptance rule was used to determine whether to accept the solution obtained by each low-level heuristic to ensure the diversity of solutions. Experiments was carried out on some VRPTW benchmark instances, and the results show that the proposed algorithm is effective and stable. © 2023 SPIE.

关键词： Simulated annealing

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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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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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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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Differentially Private Discrete Multi-Attributed Network Releasing

Differentially Private Discrete Multi-Attributed Network Rel...

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

作者： Lin, Yinghao Wang, Yuye Zhao, Shihao Qiao, Baojun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University ShenZhen China

ISBN: (纸本)9781510664616

In the area of social network, different attributes have different effects on the structure of network. Most of the existing privacy protection methods for attributed networks ignore the situation which different attributes have different effects on the network structure. They protect the privacy of the attributes indiscriminately. In respect of the issues above, a differentially private discrete multi-attributed network releasing method is proposed. Firstly, a probability model of discrete multi-attributed network is structured and the correlation parameter between multiple attributes and network structure is defined. The factor with different effects of different attributes on network structure is added into the model. Then, the algorithm uses the correlation parameter to establish the partition model of metadata and divides the metadata into different groups. As the group has different network model and attribute between each other, the groups are independence. The differential privacy of discrete multi-attributed network is realized through sanitizing parameters of the model and allocating metadata using exponential mechanism. Finally, experiment on real datasets verifies that the algorithm can satisfy the characteristics of the discrete multi-attributed network. It can also improve the efficiency and data availability. © 2023 SPIE.

关键词： Metadata

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Semantic Segmentation of Remote Sensing Images Based on Swin Transformer 3

Semantic Segmentation of Remote Sensing Images Based on Swin...

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3rd International Conference on Artificial Intelligence, big data and Algorithms, CAIBDA 2023

作者： Lin, Yinghao Zhao, Shihao Wang, Yuye Xie, Yi Qiao, Baojun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University Shenzhen China

ISBN: (纸本)9781643684444

With the evolution of Earth observation technology and remote sensing technologies, the amount of data available for high-resolution remote sensing images has exploded, and high-precision image segmentation has become a current research hotspot. Semantic segmentation technology is becoming increasingly important in fields such as urban planning, land use management, and autonomous driving. Large disparities within intraclass and modest differences intraclass are hallmarks of high resolution remote sensing pictures. Traditional image semantic segmentation methods rely on human-computer interaction and have poor generalization ability. When facing remote sensing images with rich types of ground objects and significant differences in target scales, the segmentation accuracy is not high. In this paper, we suggest an UperSwin decoder structure. The decoder includes several Swin transformer blocks and a fusion upsampling module, where the multi head contextual attention module in the Swin transformer block simultaneously uses multi-scale features and upsampling output features from the backbone network. In addition, the fusion upsampling module concatenates the backbone network features with the output features of the Swin transformer block, and then performs upsampling operations, preserving more detailed information. This article evaluates the accuracy and intersection ratio indicators on the Potsdam and Vaihingen datasets, verifying the feasibility and effectiveness of the model. © 2023 The Authors.

关键词： Land use

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Fusion Attention-ASPP and Context Semantic for Complex Scene Semantic Segmentation

Fusion Attention-ASPP and Context Semantic for Complex Scene...

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

作者： Lin, YingHao Zhao, ShiHao Cai, Kun Qiao, BaoJun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University ShenZhen China

ISBN: (纸本)9781510664616

Semantic segmentation is widely used in remote sensing data extraction and classification. Existing semantic segmentation networks focus on capturing contextual information in many different ways, simply fusing features at different levels, and ultimately improving the accuracy of semantic segmentation. However, low-level semantic features lack spatial context guidance, and high-level semantic features tend to encode large objects with coarse spatial details, making segmentation results prone to losing fine details. In this paper, we analyze the advantages and disadvantages of different levels of feature maps, and enhance the feature representation from two aspects to solve this problem. On the one hand, inspired by the architectural idea of atrous spatial pyramic pooling (ASPP), we adjust the structure of ASPP module and add the attention module to ASPP, and a new Attention-ASPP(AASPP) module is constructed in this paper. On the other hand, feature information such as boundary contours is enhanced by channel attention modeling, thereby improving local detail representation. Comprehensive experimental results show that our model framework achieves excellent segmentation performance on two public datasets, WHU building dataset and ISPRS Potsdam dataset. © 2023 SPIE.

关键词： Semantic Segmentation

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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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