检索结果-内蒙古大学图书馆

作者： Na ZHANG Yuzhi XIAO Haixing ZHAO Cunyang TANG Baoyang CUI College of Computer Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application

The multi-layer directed network model focuses on portraying the directionality,diversity and difference of the edges in the *** is also one of the powerful tools for analyzing the complexity of the network system and the heterogeneous interaction characteristics between network *** paper constructs three three-layer directed network models combined with traditional complex network theory and analyzes their degree distribution *** controlling the out-degree and in-degree of the middle-level nodes,a network structure with multi-peak characteristics is *** with numerical simulation,it is concluded that the number of links between the layers of the directed network makes the network characterize the characteristics of unimodal,bimodal,and trimodal;the combination of the optimal mechanism and the changes in the number of inter-layer links,the network characteristics show the coexistence of power law and *** research results of this paper have practical value for the analysis of network generation mechanism and correlation law by using multilayer directed network theory in the era of big data.

关键词： Complex network multi-layer directed network degree distribution multi-peak characteristics hierarchical connection

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TD-H:A Method for High-Quality Computer-Generated Holograms

TD-H:A Method for High-Quality Computer-Generated Holograms

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International Conference on Systems and Informatics (ICSAI)

作者： Zhiguo Wang Xiuyu Dong Jing Zuo Shengling Geng The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Xining China School of Computer Science Qinghai Normal University Xining China Academy of Plateau Science and Sustainability People's Government Of Qinghai Province Beijing Normal University Qinghai Normal University Xining China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Traditional computer-generated holography (CGH) algorithms rely on complex diffraction calculations, resulting in high computational demands and low efficiency. To generate high-quality holograms more rapidly, a deep learning approach simplifies the hologram generation process. The innovative method, Generate Holograms through Two Deep Neural Networks (TD-H), efficiently produces high-quality holograms by integrating two deep neural networks: the Attention Dense Prediction Transformer (ADPT) and the U-Net Model with Attention Mechanism (UAN). ADPT introduces an attention mechanism to predict depth information from the RGB values of images, while UAN combines the U-Net architecture with Multi-Head Attention (MHA) to efficiently generate high-quality holograms using both RGB values and the predicted depth map. On the DML/CVL RGB-D dataset, the average Peak Signal-to-Noise Ratio (PSNR) achieved by the ADPT and UAN models is 39.01 dB and 31.54 dB, respectively, with the ADPT model outperforming the second-ranking Dense Prediction Transformer (DPT) model by 3% in PSNR. The TD-H method not only simplifies the hologram generation process but also accurately converts and produces high-quality holograms, effectively addressing the issues of low efficiency and poor quality in hologram generation while representing natural reconstructed images in the depth direction.

关键词： Deep learning Solid modeling PSNR Attention mechanisms Computational modeling Predictive models Transformers Computational efficiency Standards Image reconstruction

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Invulnerability optimization analysis of interdependent networks

Invulnerability optimization analysis of interdependent netw...

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2021 IEEE International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2021

作者： Tang, Chunyang Xiao, Yuzhi Zhao, Haixing Ye, Zhonglin College of Computer Qinghai Normal University The Prov. and Ministerial Jt. Constr. of the State Key Lab. of Tibetan Intelligent Info. Proc. and Applic. Key Laboratory of Tibetan Information Processing Min. of Educ. Tibetan Info. Processing and Machine Translation Key Laboratory of Qinghai Province Xining China

ISBN: (纸本)9781665412629

The nodes in interdependent network have both information exchange and information dissemination, and the node degree correlation of network is easier to divide into different community structures. In order to study invulnerability of such networks, the global topology and the local community properties of network are considered in this paper, and attack strategies are proposed based on node overlapping degree and community structure. The relative size s of the most connected sub-graph and global efficiency E of the network are taken as invulnerability measures. Firstly, two kinds of interdependent network models are constructed by using correlation of nodes in different levels (random correlation and preferential correlation), and their static topological indexes are analyzed. Secondly, invulnerability experiments are carried with two kinds of synthetic networks in different attack strategies. Finally, as the application of model, the 'aviation-information' empirical network is constructed, and the influence of information network topology on the aviation network is explored. Combined with numerical simulation, attack strategy based on community structure is the most effective. The network generated by random correlation mechanism between layers has better invulnerability. Small-world information network has little influence on the invulnerability of aviation information network. The results of this paper have a certain guiding significance for model construction mechanism of the invulnerability of interdependent networks. © 2021 IEEE.

关键词： information dissemination

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The Research on Green Screen Video Segmentation Based on Multi-scale Deep Learning Techniques 24

The Research on Green Screen Video Segmentation Based on Mul...

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Proceedings of the 2024 2nd Asia Symposium on Image and Graphics

作者： Xiuyu Dong Zhiguo Wang Shengling Geng Mingquan Zhou School of Computer Science Qinghai Normal University Xining Qinghai China Academy of Plateau Science and Sustainability People's Government Of Qinghai Province Qinghai Normal University Xining Qinghai China Beijing Normal University Beijing China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Xining Qinghai China

ISBN: (纸本)9798400709906

As a key branch of artificial intelligence and machine learning, deep learning technology has continuously developed and improved, leading to significant progress and widespread applications in the field of video segmentation. However, within this technological wave, traditional green screen video segmentation methods seem to have been marginalized, and related research has received relatively little attention. To address the shortcomings of existing research, this approach proposes a new green screen video segmentation method that combines traditional chroma keying techniques with the lightweight deep learning network MobileNetV4, aiming to achieve efficient and accurate segmentation results. The chroma key module generates a rough mask, providing auxiliary information for subsequent feature extraction, while feature fusion and a recurrent decoder module enhance the consistency and detail retention between video frames. Experimental results demonstrate that this method performs excellently on both the VideoMatte240K dataset and a self-constructed dataset, effectively improving the precision and efficiency of video segmentation. The region similarity J reached 98%, and the contour accuracy F reached 94%, surpassing the average value Avg(J&F) of the second-ranked MODNet algorithm by 9.6%.

关键词： Chroma key

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TMFIF:Transformer-based multi-focus image fusion

TMFIF:Transformer-based multi-focus image fusion

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Rui Li Shengling Geng Dan Zhang Mingquan Zhou School of Computer Science Qinghai Normal University Xining China School of Computer and Software Nanyang Institute of Technology Nanyang China Academy of Plateau Science and Sustainability People’s Government of Qinghai Province & Beijing Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Multi-focus image fusion is a hot topic in the field of image processing, and it is a fundamental problem in the fields of image editing, image synthesis, and target retrieval. In previous fusion methods, although feature-rich datasets, models, and algorithms have been provided, there are still many problems with the effective fusion of distant and near views in complex backgrounds. To solve the challenging multi-focus image fusion problem more accurately, we introduce the Transformer Network based on Encoder Decoder (TMFIF), which can extract more generalized features based on the features of multi-focused images to achieve image fusion in complex backgrounds for better visual effects. In this work, we achieve fusion by inputting two images, near and far view. We compare the performance with other multifocal image fusion algorithms by conducting experiments on publicly available datasets and illustrated using existing evaluation methods and evaluation metrics; the results of the experiments show that our method visualizes the fusion effect better through the encoder and the decoder and the evaluation metrics are also relatively good.

关键词： Measurement Deep learning Computer vision Image synthesis Feature extraction Visual effects Transformers

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The Laplacian spectral ratio of connected graphs

arXiv

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arXiv 2023年

作者： Lin, Zhen Wang, Jiajia Cai, Min School of Mathematics and Statistics The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Qinghai Xining810008 China Academy of Plateau Science and Sustainability People's Government of Qinghai Province and Beijing Normal University Qinghai Xining810008 China

Let G be a simple connected undirected graph. The Laplacian spectral ratio of G, denoted by RL(G), is defined as the quotient between the largest and second smallest Laplacian eigenvalues of G, which is closely related to the structural parameters of a graph (or network), such as diameter, t-tough, perfect matching, average density of cuts, and synchronizability, etc. In this paper, we obtain some bounds of the Laplacian spectral ratio, which improves the known results. In addition, we give counter-examples on the upper bound of the Laplacian spectral ratio conjecture of trees, and propose a new *** Codes 05C05, 05C50 Copyright © 2023, The Authors. All rights reserved.

关键词： Undirected graphs

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Cascade Temporal Convolutional Network for Multitask Learning

Cascade Temporal Convolutional Network for Multitask Learnin...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Huiyun Zhang Heming Huang Henry Han Department of Computer Science School of Engineering and Computer Science Baylor University USA School of Computer Science Qinghai Normal University Xining China School of Computer Science Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

Multi-task learning (MTL) is a powerful technique in machine learning that enables the simultaneous training of multiple tasks. In this paper, we propose a novel MTL approach for the recognition of emotions, speakers, and gender in speech signals. Our approach presents a novel deep learning model called the Cascade Temporal Convolutional Network (CTCN to handle MTL efficiently. CTCN is designed to effectively improve the parallel processing ability of the model in multitasking learning. It provides an effective solution for improving the performance of deep learning in multitasking scenarios. CTCN is particularly adept at processing sequences of data with varying lengths and extracting features that are relevant to multiple tasks. Our experimental results demonstrate that the proposed CTCN approach for MTL achieves significantly superior performance on all tasks on benchmark datasets compared to other deep learning models. This study will inspire more future work in the MTL field.

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Face occlusion detection algorithm based on yolov5 2

Face occlusion detection algorithm based on yolov5

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2021 2nd International Conference on Signal processing and Computer Science, SPCS 2021

作者： Zhao, Yuanzhang Geng, Shengling School of Computer Science Qinghai Normal University Qinghai Xining810016 China Academy of Plateau Science and Sustainability Qinghai Normal University Qinghai Xining810016 China A State Key Laboratory of Intelligent Information Processing and Application of Tibetan Language Jointly Established by the Ministry and the Province Qinghai Normal University Qinghai Xining810016 China

The current face-mask recognition detection algorithm during the epidemic only distinguishes between wearing or not wearing a mask. Such detection often has certain loopholes, such as using other objects to cover their mouths and noses instead of masks to cheat the detection. To address such problems, this paper proposes a YOLOv5 based face occlusion detection algorithm, which is modified based on the YOLOv5 algorithm by improving the loss function as DIoU and increasing the experimental samples by introducing multiple data sets to improve the object detection effect. The experimental results show that the improved YOLOv5 algorithm has improved the object detection effect for different kinds of face occlusions, which verifies the method's effectiveness. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Object detection

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Error Correction of tibetan Verbs Based on Deep Learning

Error Correction of Tibetan Verbs Based on Deep Learning

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： HUA Guo‒ cai– rang Secha Jia BAN Ma– bao CAI Rang– jia School of computer science Qinghai Normal University China Qinghai Key Laboratory of Tibetan information processing and machine translation China Qinghai Tibetan information processing engineering technology research center China State Key Laboratory of Tibetan intelligent information processing and application China

Verbs are the core of the semantic structure represented by tibetan sentences, and automatic error correction of tibetan verbs is one of the important research topics in tibetan language processing. In this paper, we propose a Bi-LSTM neural network model for tibetan verb error correction by analyzing the usage rules of verbs in tibetan, summarizing the grammatical, semantic and spelling features of verbs, and proposes a Bi-LSTM neural network model for tibetan verb error correction based on these features, which not only extracts various features of verbs, but also capture the contextual information of verbs through Bi-LSTM neural network. The method proposed in this work solves the drawbacks of the traditional methods of low generalization and inability to obtain long-distance contextual implicit information. The experimental results show that the accuracy, recall and F1 values of the proposed method on the test set reach 97.3%,95.7% and 96.9%, respectively, indicating the effectiveness of the proposed method on the task of automatic error correction for tibetan verbs.

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Hotkey-Cache: A Hotkey-Separate Cache to Improve Performance for key-Value Store

HotKey-Cache: A HotKey-Separate Cache to Improve Performance...

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Cloud Computing, Big Data applications and Software Engineering (CBASE), International Conference on

作者： Jinkang Lu Ping Xie College of Computer Qinghai Normal University Xining P.R.China The State Key Laboratory Tibetan Intelligent Information Processing and Application Xining P.R.China Key Laboratory of Internet of Things Qinghai Province Xining P.R.China Academy of Plateau Science and Sustainability Xining P.R.China

In most key-value stores, data access patterns are typically non-uniform and display skewness, resulting in the presence of hotkeys and coldkeys. The caching mechanism of key-value stores, which relies on the Least Recently Used (LRU) policy like LRUCache, only accounts for the temporal relevance of the data. In addition, the caching mechanism based on the Least Frequently Used (LFU) policy like LFUCache, only considers the frequency of data access. The limitations of these caching mechanisms lead to a reduction in cache efficiency. This paper presents a novel caching mechanism called Hotkey-Cache, which focuses on separating hotkeys. Building upon LRUCache, Hotkey-Cache separate hotkeys and assign them to a specialized queue to prevent their direct eviction. This design simultaneously considers both the frequency of data access and the temporal relevance of the data. Finally, we deploy these caches in LevelDB. Experimental results indicated a 12.67% enhancement compared to LRUCache and a 28.52% improvement compared to LFUCache.

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