检索结果-内蒙古大学图书馆

An image segmentation fusion algorithm based on density peak clustering and Markov random field

Multimedia Tools and Applications 2024年第37期83卷 85331-85355页

作者： Feng, Yuncong Liu, Wanru Zhang, Xiaoli Zhu, Xiaoyan College of Computer Science and Engineering Changchun University of Technology Jilin Changchun130012 China Artificial Intelligence Research Institute Changchun University of Technology Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Image segmentation is a crucial task in the field of computer vision. Markov random fields (MRF) based image segmentation method can effectively capture intricate relationships among pixels. However, MRF typically requires an initial labeling field, and the number of classifications needs to be manually selected. To tackle these issues, we propose a novel medical image segmentation algorithm based on density peak clustering (DPC) and Markov random fields. Firstly, we improve DPC to make it applicable to grayscale images, named GIDPC. In the GIDPC method, local gray density and gray bias are defined to enable the automatic determination of the number of classifications. Then, GIDPC and MRF are combined to achieve image segmentation. Furthermore, a segmentation fusion method is employed to enhance the accuracy of image segmentation. We conduct comparison experiments on the whole brain atlas image library. Our proposed algorithm achieves high average values in uniformity measure, accuracy, precision and sensitivity, respectively. Experimental results demonstrate that the proposed algorithm outperforms other image segmentation methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A Method for Extracting Electronic Medical Record Entities by Fusing Multichannel Self-Attention Mechanism with Location Relationship Features 9th

A Method for Extracting Electronic Medical Record Entities b...

引用

9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Xu, Hongyan Wang, Hong Feng, Yong Wang, Rongbing Zhang, Yonggang College of Information Liaoning University Shenyang110036 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819959709

With the implementation of the "Internet+" strategy, electronic medical records are generally applied in the medical field. Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients’ states, but the existing methods for extracting entities from electronic medical records have problems of redundant information, overlapping entities, and low accuracy rates. Therefore, this paper proposes an entity extraction method for electronic medical records based on the network framework of BERT-BiLSTM, which incorporates a multichannel self-attention mechanism and location relationship features. First, the text input sequence was encoded using the BERT-BiLSTM network framework, and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mechanism. Then, the position relation characteristic was used to extract the local semantic message of the text, and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained. Next, the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix. Finally, the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus, and the experimental results indicate that the proposed method surpasses existing methods in terms of precision, recall, F1 value and training time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

A hybrid model of CNN+ViT of the recognition for Spodoptera frugiperda larval instar stages

A hybrid model of CNN+ViT of the recognition for Spodoptera ...

引用

2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Wang, Mingyang Lu, Ying Xu, Quanyuan Xiang, Tianzi Chang, Jumei Zhang, Hanrui College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Kunming650024 China Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Southwest Forestry University Yunnan Kunming650024 China

ISBN: (纸本)9781510675032

Spodoptera frugiperda (Fall armyworm, FAW) is one of the major agricultural pests in the world. It is of great significance for ensuring crop yield to accurately recognize FAW larval instars. This research proposes a hybrid model of Convolutional Neural Network (CNN) and Vision Transformer (ViT) for the recognition of FAW larval instars. The model uses CNN to extract local features of larval images and ViT to capture global image dependencies, achieving effective recognition of larval instars. To verify the model proposed in this paper, an image dataset of 5,006 images balancing the larval instars which are from the 1st to the 6th of FAWs is established. The proposed model achieved recognition accuracy of 99.60% and 92.11% respectively in the two cases of using and not using pre-training. The results show that the proposed model achieves higher recognition accuracy than AlexNet, VGG16, ResNet50, ResNet101, and ViT-B. Moreover, it does not rely on pre-trained weights and performs well on relatively small datasets. The research in this paper provides a new technique of the recognition for FAW instars and also provides a reference for other agricultural pest image recognition tasks. © 2024 SPIE.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Condition Monitoring and Root Cause Diagnosis for Industrial key Equipment based on an Adaptive Causal Index 41

Condition Monitoring and Root Cause Diagnosis for Industrial...

引用

第41届中国控制会议

作者： Jiarui Cui Chenlu Hu Xu Yang Yinghao Zhao Qun Yan Qing Li Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Driven by the increasing degree of automation in the process industry,the production process is increasingly dependent on the normal operation of critical ***,it is important to achieve root cause diagnosis and propagation path identification of faults for critical equipment in the ***,in practical applications,the complex structure of critical equipment leads to the fact that the causal relationship between components cannot be directly obtained through the *** makes it difficult to evaluate abnormal propagation ***,in view of the above problems,this paper uses the Granger causality test to analyze the relationship between equipment *** is followed by an adaptive index for condition monitoring which is designed by the value of *** this basis,the anomaly propagation network of the equipment is established and the abnormal states is traced through a dynamic Bayesian *** effectiveness of the proposed method is demonstrated by a dataset collected from the actual industrial *** show that the adaptive index is effective and it can accurately monitor the condition of equipment.

关键词： Adaptive causal index Condition monitoring Root cause diagnosis

来源：评论

学校读者我要写书评

暂无评论

Research on Electricity Consumption Model of Library Building Based on data Mining

引用

Energy engineering 2022年第6期119卷 2407-2429页

作者： Jiaming Dou Hongyan Ma Rong Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing102616China Institute of Distributed Energy Storage Safety Big Data Beijing University of Civil Engineering and ArchitectureBeijing102616China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing102616China

With the exponential development of Chinese population,the massive energy consumption of buildings has recently become an interest *** much research has been conducted on residential buildings,heating ventilation and air conditioning(HVAC),little research has been conducted on the relationship between student’s behavior,campus buildings,and their *** classical seasonal decomposition,hierarchical clustering,and apriori algorithm,this paper aims to provide an empirical model for consumption data in campus *** meter data from a library in Beijing,China,is adopted in this *** electricity consumption patterns are investigated on an hourly/daily/monthly *** to the monthly analysis,electricity consumption peaks each year around June and December due to teaching programs,social exams,and outdoor *** data analysis revealed a relatively stable consumption *** shows three different types of daily load *** data analysis demonstrated a high relationship between HVAC consumption and building total consumption,with a lift value of ***,links between temperature and subsystems were also *** a case study of library,this study provides a unique insight into campus electricity *** results could help to develop operational strategies for campus facilities.

关键词： Electricity consumption data mining load profile campus building

来源：评论

学校读者我要写书评

暂无评论

Winter wheat phenology classification using stacking ensemble learning algorithm based on Sentinel-1A SAR images 5

Winter wheat phenology classification using stacking ensembl...

引用

5th International Conference on Geoscience and Remote Sensing Mapping, ICGRSM 2023

作者： Duan, Haoxin Yang, Huijin Zhao, Jianhui Li, Ning School of Computer and Information Engineering Henan University Kaifeng China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781510672789

Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. The experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM

引用

Chinese Journal of Electronics 2021年第1期30卷 109-118页

作者： WANG Xuesong ZHAO Jijuan CHENG Yuhu YU Qiang School of Information and Control Engineering China University of Mining and Technology Xuzhou Key Laboratory of Artificial Intelligence and Big Data

In the problem of unsupervised domain adaption Extreme learning machine(ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully utilized. In addition, traditional matching method based on data probability distribution cannot find the common subspace of source and target domains under large difference between domains. In order to alleviate the pressure of double functions of classifier parameters, the entire ELM learning process is mainly divided into two stages: feature representation and adaptive classifier learning, thus a joint feature representation and classifier learning based unsupervised domain adaption ELM model is proposed. In the feature representation stage, the source and target domain data are projected to their respective subspace while minimizing the difference in probability distribution between the two domains. In the adaptive classifier learning stage, the smooth manifold regularization term of target domain is used to improve the parameter adaptive *** on six different types of datasets show that the proposed model has higher cross-domain classification accuracy.

关键词： entire ELM learning process unsupervised domain adaption ELM model output layer parameters image representation double functions parameter adaptive ability common subspace classifier parameters joint feature representation unsupervised domain adaption Extreme learning machine data probability distribution learning (artificial intelligence) higher cross-domain classification accuracy target domain probability pattern classification adaptive classifier learning stage traditional matching method target domains unsupervised learning feature representation stage domain adaptation functions

来源：评论

学校读者我要写书评

暂无评论

knowledge Enhanced Coreference Resolution via Gated Attention

Knowledge Enhanced Coreference Resolution via Gated Attentio...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： He, Kai Mao, Bing Zhou, Xiangyu Li, Yufei Gong, Tieliang Li, Chen Wu, Jialun Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge. © 2022 IEEE.

关键词： Model structures

来源：评论

学校读者我要写书评

暂无评论

TABLELLM: Enabling Tabular data Manipulation by LLMs in Real Office Usage Scenarios

arXiv

引用

arXiv 2024年

作者： Zhang, Xiaokang Luo, Sijia Zhang, Bohan Ma, Zeyao Zhang, Jing Li, Yang Li, Guanlin Yao, Zijun Xu, Kangli Zhou, Jinchang Zhang-Li, Daniel Yu, Jifan Zhao, Shu Li, Juanzi Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China Computer Science Anhui University China

We introduce TABLELLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension strategy, aiding in training LLMs to understand reasoning patterns more effectively as well as a cross-way validation strategy, ensuring the quality of the automatically generated data. To evaluate the performance of TABLELLM, we have crafted benchmarks tailored to address both document and spreadsheet formats as well as constructed a well-organized evaluation pipeline capable of handling both scenarios. Thorough evaluations underscore the advantages of TABLELLM when compared to various existing general-purpose and tabular data-focused LLMs. We have publicly released the model checkpoint, source code, benchmarks, and a web application for user interaction. Copyright © 2024, The Authors. All rights reserved.

关键词： Spreadsheets

来源：评论

学校读者我要写书评

暂无评论

Evolutionary genetics of wheat mitochondrial genomes

引用

The Crop Journal 2023年第6期11卷 1774-1781页

作者： Hui-Lin Hu Fan Zhang Pei Wang Fu-Hao Lu State Key Laboratory of Crop Stress Adaptation and Improvement School of Life SciencesHenan UniversityKaifeng 475004HenanChina Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng 475004HenanChina School of Mathematics and Statistics Henan UniversityKaifeng 475004HenanChina

The Triticum-Aegilops complex provides ideal models for the study of polyploidization,and mitochondrial genomes(mtDNA)can be used to trace cytoplasmic inheritance and energy production following *** this study,gapless mitochondrial genomes for 19 accessions of five Triticum or Aegilops species were *** genomics confirmed that the BB-genome progenitor donated mtDNA to tetraploid ***(genome formula AABB),and that this mtDNA was then passed on to the hexaploid ***(AABBDD).T urartu(AA)was the paternal parent of ***(AAGG),and an earlier ***(DD)was the maternal parent of ***(CCDD).Genic sequences were highly conserved within species,but frequent rearrangements and nuclear or chloroplast DNA insertions occurred during *** highly variable mitochondrial genes(atp6,cob,nad6,and nad9)were established as marker genes for Triticum and Aegilops species *** BB/GG-specific atp6 and cob genes,which were imported from the nuclear genome,could facilitate identification of their diploid *** haplotypes and repeat-sequence patterns indicated that BB was much closer to GG than to ***(SS).These findings provide novel insights into the polyploid evolution of the Triticum/Aegilops complex from the perspective of mtDNA,advancing understanding of energy supply and adaptation in wheat species。

关键词： Wheat Mitochondrion mtDNA Comparative genomics Polyploidization

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：