检索结果-内蒙古大学图书馆

Information and Communication Technology (ICTech), International Conference of

作者： Hongliang Wang Xingyu Liu Yang Li Guoyang Xing Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

In this paper, we propose a monocular image depth estimation method based on ResNet and bitmap estimation. Traditional monocular depth estimation methods usually utilize convolutional neural networks (CNNs) for end-to-end training, but often require large amounts of labeled data. To overcome the problem of difficult data labeling, we use a competitive cooperative learning approach to jointly train depth estimation networks, camera motion estimation, optical flow estimation, and motion segmentation networks. We use ResNet as the backbone network for depth estimation, which achieves efficient feature transfer through residual concatenation. In addition, we introduce a bit-position estimation module to assist the depth estimation task by estimating camera motion and bit-position information. Experimental results show that our proposed method achieves good performance in the monocular depth estimation task and still achieves satisfactory results in the absence of labeled data.

关键词： Training Depth measurement Motion segmentation Motion estimation Estimation Cameras Information and communication technology Labeling Convolutional neural networks Optical flow

来源：评论

学校读者我要写书评

暂无评论

Cooperative Inference with Interleaved Operator Partitioning for CNNs

arXiv

引用

arXiv 2024年

作者： Liu, Zhibang Xu, Chaonong Liu, Zhizhuo Huang, Lekai Wei, Jiachen Li, Chao Beijing Key Lab of Petroleum Data Mining China University of Petroleum Beijing102249 China Research Center for Space Computing System Zhejiang Lab Hangzhou311121 China

Deploying deep learning models on Internet of Things (IoT) devices often faces challenges due to limited memory resources and computing capabilities. Cooperative inference is an important method for addressing this issue, requiring the partitioning and distributive deployment of an intelligent model. To perform horizontal partitions, existing cooperative inference methods take either the output channel of operators or the height and width of feature maps as the partition dimensions. In this manner, since the activation of operators is distributed, they have to be concatenated together before being fed to the next operator, which incurs the delay for cooperative inference. In this paper, we propose the Interleaved Operator Partitioning (IOP) strategy for CNN models. By partitioning an operator based on the output channel dimension and its successive operator based on the input channel dimension, activation concatenation becomes unnecessary, thereby reducing the number of communication connections, which consequently reduces cooperative inference delay. based on IOP, we further present a model segmentation algorithm for minimizing cooperative inference time, which greedily selects operators for IOP pairing based on the inference delay benefit harvested. Experimental results demonstrate that compared with the state-of-the-art partition approaches used in CoEdge, the IOP strategy achieves 6.39% ~ 16.83% faster acceleration and reduces peak memory footprint by 21.22% ~ 49.98% for three classical image classification models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

research on a Clinical Data Classification Model based on BERT, Bi-LSTM, and Attention Mechanism

Research on a Clinical Data Classification Model Based on BE...

引用

International Conference on Computer and Communications (ICCC)

作者： Kui Zhao Han Wang Pengfei Xiu Xuerao Li Xingyu Zhou Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In the field of medical diagnosis, patients' clinical data encompass information such as their primary symptoms, current disease progression, past treatment experiences, and examination results, which are crucial for physicians to accurately grasp the condition and formulate treatment plans. Clinical data from patients are typically recorded in the form of long-text documents. Therefore, to better analyze clinical long-text data and enhance the accuracy and efficiency of clinical diagnosis, this paper proposes a clinical long-text classification model that integrates the BERT model, named L-A-BERT. The L-A-BERT model combines the deep bidirectional representation capabilities of BERT, the long and short-term memory characteristics of the Bidirectional Long Short-Term Memory network (LSTM), and attention mechanisms to better understand and analyze clinical long-text data. The study employed real patient data for experimental analysis, and the results demonstrated that the L-A-BERT model performs exceptionally well in clinical long-text classification tasks, with the effectiveness of each component within the model further validated through ablation experiments.

关键词： Analytical models Attention mechanisms Accuracy Computational modeling Text categorization Bidirectional control Data models Encoding Medical diagnosis Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Load Balancing Strategy based on Stochastic Theory 8

Load Balancing Strategy Based on Stochastic Theory

引用

8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Guo, Ruifeng Li, Youkang Wang, Hongliang Xiu, Pengfei Liu, Yunjiang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China Liaoning Provience Hum.-Comp. Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9781665450515

In order to further improve the load balancing strategy, this paper summarizes and designs a load balancing method based on random selection. By introducing the Balls into Bins problem into the load balancing strategy, random selection is used to reduce the time spent in the allocation process. And batch selection is used to reduce the possibility of overloading nodes. The result shows that these methods algorithm can get a better task allocation method in as short a time as possible. © 2022 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

A Vein Segmentation Method based on Improved U-Net 12

A Vein Segmentation Method Based on Improved U-Net

引用

12th International Conference of Information and Communication Technology, ICTech 2023

作者： Wang, Hongliang Zhu, Liang Deng, Changyi Xiu, Pengfei Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

ISBN: (纸本)9798350332919

Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for personalised therapy, quantitative diagnosis, and prognosis of acute ischemic stroke. Therefore, the accurate segmentation of veins in SWI images has important significance in supplementary diagnosis and medical research. However, vein segmentation in SWI is a challenging task because different veins show distinct variability and uneven intensity and are difficult to manually split. This paper proposes an improvement method based on U-net, by using dense connection and mixing loss function, the best Dice coefficient and the ASSD were obtained by other comparison methods. The method presented in this article can be used as a potential tool to accurately measure SWI cerebral veins and help physicians in decision making. © 2023 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

End-to-End Remote Sensing Image Scene Classification with Vision Transformers 23

End-to-End Remote Sensing Image Scene Classification with Vi...

引用

6th International Conference on Big Data Technologies, ICBDT 2023

作者： Yuan, Hao Liu, Kun Shi, Jiechuan Wang, Can Wang, Weiwei School of Information Science and Engineering University of Jinan Jinan250022 China China High-resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China QiLu Research Institute Jinan250200 China

ISBN: (纸本)9798400707667

In recent years, the development of deep learning technology has led to widespread attention on Vision Transformer (ViT) as an emerging image classification method. Remote sensing image classification is an important task in the field of remote sensing, with extensive application prospects. This paper aims to explore the remote sensing image classification method based on Vision Transformer, addressing the limitations of traditional convolutional neural networks in terms of global perception capability, context information retrieval, and positional encoding. The classification performance of the Vision Transformer model is evaluated and compared on remote sensing datasets. Vision Transformer is a deep neural network model based on self-attention mechanism that can capture the global context information in images and has achieved remarkable performance in various computer vision tasks. Furthermore, experimental results demonstrate that the remote sensing image classification method based on Vision Transformer exhibits outstanding accuracy and generalization ability. Compared to traditional convolutional neural networks, it can better capture the global features in remote sensing images and has better scalability when dealing with large-scale remote sensing image data. Experimental results on different remote sensing image datasets show that the model performs well compared to state-of-the-art methods. Specifically, Vision Transformer achieves average classification accuracies of 95.41%, 98.26%, 93.74% and 95.25% on the AID, UC-Merced, NWPU-RESISC45 and Optimal31 datasets, respectively. © 2023 ACM.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

research on a Clinical Data Classification Model based on BERT, Bi-LSTM, and Attention Mechanism 10

Research on a Clinical Data Classification Model Based on BE...

引用

10th International Conference on Computer and Communications, ICCC 2024

作者： Zhao, Kui Wang, Han Xiu, Pengfei Li, Xuerao Zhou, Xingyu Shenyang Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Revisiting Spatio-Temporal Forecasting: Feature Propagation Carry More Weights Than How They Do

Revisiting Spatio-Temporal Forecasting: Feature Propagation ...

引用

International Conference on Intelligent Transportation

作者： Shen Fang Hongyan Liu Chengcheng Yu Tian Xie Wei Hua Zhejiang Lab Research Center for Intelligent Manufacturing Computing Hangzhou China Urban Mobility Institute Tongji University Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education at Tongji University Tongji University Shanghai China Zhejiang Lab Research Center for Space Computing System Hangzhou China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Spatio-temporal forecasting, a prominent research focus for understanding the dynamics of data flow in various domains, has recently extended its significance to traffic prediction as a notable application. Considering topology of data flow, existing methods mainly utilize Graph Convolutional Networks (GCNs), where graph construction is the basic concern and generally determines how data features are propagated. However, through extensive investigations and theoretical evidence, it is revealed that graph construction strategy, which is usually regarded as the key step to success, has actually provided very little benefit, while the presence of feature propagation itself on spatiotemporal domain is more significant, i.e., feature propagation carry more weights than how they do. Thus, by making slight refinements of a feature normalization method, we propose a Spatio-Temporal Propagation (STP) module, which does not require intervention of a specific graph structure, yet simple and effective. Various experiments on public datasets verify that the proposed STP module is an on-the-shelf tool that can be accessed to the end of current models or even replace GCNs as an alternative for capturing spatiotemporal features, while achieving better predictions. All the source codes are open accessed on GitHub.

关键词： Network topology Graph convolutional networks Source coding Computational modeling Predictive models Spatiotemporal phenomena Topology Forecasting Intelligent transportation systems Software development management

来源：评论

学校读者我要写书评

暂无评论

All Muscle Activity Test and Evaluation (AMATE): An analytical tool to assess lower limb muscle activation for stroke patients

All Muscle Activity Test and Evaluation (AMATE): An analytic...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xiyan Xiong Nan Gao Qing Zhang Xiao Ma Pavel Smirnov Duchun Zeng Junxiao Xue Chun Yang Feng Lin Research Center for Frontier Fundamental Studies Zhejiang Lab Hangzhou China School of Aerospace Engineering Tsinghua University Beijing China Research Center for Astronomical Computing Zhejiang Lab Hangzhou China Department of Physical Medicine and Rehabilitation Zhejiang Provincial People’s Hospital Hangzhou China Research Center for Space Computing System Zhejiang Lab Hangzhou China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Assessing muscle activation and monitoring stroke recovery progress are vital aspects of evaluating motor impairment. Surface electromyography, a common tool for measuring muscle activity, has practical limitations for clinical implementation and fails to capture deep muscle activities, leading to challenges in accurately identifying muscle activation patterns in stroke patients. To address these limitations, we introduce an All Muscle Activity Test and Evaluation (AMATE) system, combining wearable sensor technology with a musculoskeletal model. AMATE computes activation levels of all lower limb muscles during various activities without requiring surface electrodes. It also generates an Activation Dissimilarity Index (ADI) to illustrate muscle activation patterns among individuals. We evaluated AMATE’s effectiveness through a pilot study involving 22 walking trials with healthy subjects and stroke patients. The results highlight AMATE’s capability in aiding clinicians to analyze and understand diverse lower limb muscle activation patterns in stroke patients, particularly identifying muscles with abnormal activation for targeted rehabilitative training.

关键词： Training Legged locomotion Muscles Stroke (medical condition) Motors Electromyography Indexes Engineering in medicine and biology Wearable sensors Monitoring

来源：评论

学校读者我要写书评

暂无评论

Biogas Fermentation Monitoring research based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning

Biogas Fermentation Monitoring Research Based on Borderline-...

引用

Information and Communication Technology (ICTech), International Conference of

作者： Pengfei Xiu Lijun Fu Yukun Sun Hongliang Wang Xuerao Li Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model.

关键词： Accuracy Biological system modeling Stacking Process control Production Predictive models Prediction algorithms Biogas Fermentation Monitoring

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：