检索结果-内蒙古大学图书馆

Physical modeling of some micro-mechanical gyroscope

Journal of Computational information Systems 2012年第1期8卷 143-150页

作者： Yang, Jinxian Li, Bingfeng Key Laboratory of Mine Spatial Information Technologies of State Bureau of Surveying and Mapping Henan Polytechnic University Jiaozuo 454003 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454003 China

In order to obtain basic working law of the micro-gyroscope, the physical model-differential equation is derived based on giving the structure and working principle of some micro-gyroscope. For the single-axis micro-gyroscope, z-axis angular velocity can only been detected in the z-axis direction, ignoring some items, then equations are *** sense and drive equations are solved for analyzing the basic working *** solution of equation consists of two parts: the driving frequency is the particular solution-steady-state solution and the general solution-transient solution. And the solution of the drive equation is similar with that of the sense equation. Finally, general solution of the equations is analyzed, and further different damping ratios have some effect on the motion form. And it gives theory base for the improvement design. © 2012 Binary information Press January 2012.

关键词： Differential equations

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Enhanced oracle bone corrosion detection using attention-guided YOLO with ghost convolution

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Discover Artificial Intelligence 2024年第1期4卷 1-13页

作者： Gao, Feng Yang, Ziheng Liu, Qiyu Zhang, Zhan Li, Bang Zhang, Han Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Anyang455000 China School of Computer & Information Engineering Anyang Normal University Henan Anyang455000 China

Oracle bone inscriptions (OBIs), as the important records of early Chinese characters, possess profound cultural and historical significance. However, These fragments are susceptible to further damage due to corrosion. Consequently, the identification and localization of corroded regions on these fragments to prevent further deterioration has emerged as a critical task. Current object detection algorithms exhibit limitations in identifying corroded regions on oracle bone fragments, which is manifested by suboptimal detection accuracy, F1 scores, and average precision (AP) values. This deficiency hinders their capability to effectively manage the complexity and diversity of corrosion patterns present on oracle bone fragments. To tackle this challenge, this study incorporates advanced attention mechanisms, including Squeeze-and-Excitation Networks (SE), Coordinate attention, and Convolutional Block Attention Module (CBAM), into the YOLOv5 detection architecture. Additionally, we introduce Ghost convolution into the backbone network to effectively retain critical feature map information while optimizing computational efficiency. Our experimental results indicate that the integration of CBAM attention and Ghost convolution within the YOLOv5 backbone markedly improves the detection accuracy of corroded regions on oracle bone fragments. Specifically, compared to the baseline YOLOv5 model, the proposed models incorporating SE, CBAM, and Ghost convolution obtain the improvements of 2.2%, 6.3%, and 8.5% in AP, respectively. This improvement in detection performance not only facilitates the identification of corroded areas but also contributes to the preservation of oracle bone heritage and the continuity of cultural knowledge. © The Author(s) 2024.

关键词： Object detection

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Research on Object-oriented Exploration Algorithm

Research on Object-oriented Exploration Algorithm

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

作者： Shen, Xiajiong Zhang, Peng Yang, Jiyong Chen, Wan Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781450387828

In formal concept analysis, attribute exploration is a very important methodology, which can explore the logical laws between attributes in a continuous and interactive way. Object-oriented exploration is very meaningful while it is similar to attribute exploration. It can explore the implication relationship between objects. But for the existing work, almost all are attribute exploration, object-oriented exploration is rare. Research on object-oriented exploration has great practical value, such as object classification, retrieval and excavation. In order to address this problem, this article takes the object as the main research point in formal concept analysis based on related knowledge of traditional attribute exploration, and proposes theories of object-oriented exploration. According to these related theories and the framework of attribute exploration algorithm, an object-oriented exploration algorithm is proposed in this paper. The algorithm can be used to explore and understand the relationship between objects, which facilitates to understand better the relationship between objects and attributes. © 2020 ACM.

关键词： Formal concept analysis

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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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Few-Shot Node Classification on Attributed Networks Based on Prototypical Network 19

Few-Shot Node Classification on Attributed Networks Based on...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhao, Yaliang Zhang, Guangming Wang, Jinke Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9798350346558

In today's society, the interactions between various entities form complicated attributed networks. The node classification task as the core task of analyzing attribute networks has gained a considerable amount of attention from researchers. The existing graph neural network-based node classification approaches all demand lots of labeled data, but a high percentage of node classes in practical scenarios contain only restricted labeled data. So their classification effect is not good. In response to this problem, we put forward an improved graph prototypical network (IGPN) to enhance the few-shot node classification's accuracy. In particular, we start by obtaining the embedding representations of nodes on the attributed network through a graph attention network. Then combined with the node's degree information and neighbor information, the importance coefficient of all nodes in the support set is learned. After that, we can obtain the prototype representation of every class. Finally, the node classification can be completed by the distance of the query nodes' representation from all class prototypes. Our method is preferable to the baseline method on three datasets according to a large number of experiments. © 2022 IEEE.

关键词： Large dataset

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Secure and Efficient Authentication Key Agreement Protocol for Wireless Sensor Networks 6

Secure and Efficient Authentication Key Agreement Protocol f...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Ma, Qingyu Deng, Miaolei College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan International Joint Laboratory of Grain Information Processing Zhengzhou China

ISBN: (纸本)9798331541750

In order to solve the problem of communication security and privacy protection in wireless sensor networks (WSN), this paper proposes a secure and efficient authentication key agreement protocol for WSN, which is based on hash function, XOR operation and physical unclonable function(PUF), and can give attention to both efficiency and security. The protocol is suitable for multi-server scenario, and the sensor can authenticate and negotiate keys with all servers in the system after registering once. In addition, the mutual authentication stage between sensor and server does not require the participation of registry. © 2024 IEEE.

关键词： Authentication Protocol

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Location Rating Based on Graph Neural Network

Location Rating Based on Graph Neural Network

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

作者： Zheng, Fengbin Ding, Li Dang, Lanxue Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781450387828

With the rapid development of social networks, people are increasingly interested in sharing their location. Location recommendation has become an important personalization service for location-based social networks (LBSN). Location rating is one of the important tools. However, location-based social networks contain multidimensional network structures and node information. Existing approaches mostly focus on network structures that utilize one of these dimensions, which makes it difficult to efficiently aggregate information from multiple dimensions simultaneously. To overcome these difficulties, one of the recent approaches is the social recommendation based on graph neural networks (GNNs). Based on this, this paper proposes a graph neural network framework for location rating. Graph neural networks are highly inductive and can efficiently aggregate network structure and node information. In particular, the method not only aggregates homogeneous social networks composed of user and heterogeneous bipartite graphs composed of user-location, but also constructs location networks using location sequences to propose an aggregation model for location networks. Experiments are conducted on two datasets, and the results show that the method improves on the root mean square error (RMSE) and mean absolute error (MAE) metrics. © 2020 ACM.

关键词： Location

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Infusion Prior information Prismatic Generative Adversarial Learning Network for Multi-material Decomposition in Dual-bin Photon Counting Computed Tomography 3

Infusion Prior Information Prismatic Generative Adversarial ...

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3rd International Conference on Electronic information engineering and Computer, EIECT 2023

作者： Ren, Junru Wang, Shaoyu Wang, Yizhong Liang, Ningning Shen, Ye Li, Lei Cai, Ailong Yan, Bin Information Engineering University Department of Henan Key Laboratory of Imaging and Intelligent Processing Zhengzhou China

ISBN: (纸本)9798350357707

Spectral computed tomography based on photon-counting detector (PCCT) is a promising technique with high spatial and energy resolution. Multi-material decomposition is a typical application of PCCT, which can provide accurate material identification. However, due to the finite photons in each energy bin, the number of energy bins cannot grow without limit while the image quality suffers from serious noise. Despite the quantitative capability for multi-material, existing methods require adequate energy bins corresponding to the number of materials. In this paper, an infusion prior information prismatic generative adversarial learning network is proposed based on generative adversarial learning for multi-material decomposition in dual-bin PCCT. First, to alleviate the pathological nature, prior images corresponding to full spectrum is introduced into the solution. Second, generative adversarial leaning is utilized to characterization the material with its nonlinear calculation capability. Thirdly, the idea of interactive structure is draw for multi-material decomposition task. Numerical experiments based on structured fibro-glandular tissue data were carried out to verify the effectiveness of the proposed method. The results demonstrate its accuracy on material quantification over other methods in this field under the condition of finite energy bins. © 2023 IEEE.

关键词： Learning systems

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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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