检索结果-内蒙古大学图书馆

International Conference on Condition Monitoring and Diagnosis, CMD

作者： Cattareeya Suwanasri Ittiphong Yongyee Thanapong Suwanasri Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Electrical and Software Systems Engineering The Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9788986510225

ISBN: (纸本)9798350353877

The deterioration of transmission lines has a profound effect on the reliability and safety of the power grid. Accurate estimation of their age is critical for effective maintenance and investment planning. This paper proposes a method that combines the percentage statistical health index (%SHI) with failure probability curve-fitting (FPCF) to estimate the ages of overhead transmission lines. %SHI assesses equipment condition through scoring and weighting, while FPCF correlates SHI with the probability of failure. Evaluation of 924 towers situated in various terrains across Thailand reveals discrepancies between estimated and actual ages, influenced by environmental factors. To enhance reliability, proactive maintenance strategies such as regular inspections and advanced monitoring are recommended.

关键词： Power transmission lines Poles and towers Probability Inspection Maintenance Safety Planning Curve fitting Monitoring Investment

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-Based Learning for Deep Clustering

SSRN

引用

SSRN 2024年

作者： Chang, Luyao Niu, Xinzheng Li, Zheng Hua Zhang, Zhiheng Li, Shenshen Fournier-Viger, Philippe School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Biliary Surger West China Hospital Sichuan University Sichuan Chengdu610041 China School of lnformation and Engincering Sichuan Tourism University Sichuan Chengdu610100 China College of Computer Science and Software Engineering Shenzhen University Shenzhen Shenzhen518060 China

Deep clustering has gained prominence due to its impressive ability to handle high-dimensional real-world data, such as images. However, due to the lack of supervisory information, current clustering methods struggle to effectively distinguish false positives with visual-feature similarity from false negatives with ambiguous features, leading to unreliable clustering results. To address this challenge, we propose a method named Uncertainty-based Learning for Deep Clustering (ULDC) to discover more optimal cluster structures within data, thus enhancing clustering performance. ULDC first models uncertainty in the projection of data features to obtain accurate estimates of feature reliability. Then, based on similarity and uncertainty, ULDC constructs a reliable space for feature learning. This space shows resilience to interference from unreliable data, thereby reducing the negative impact of that data. Additionally, semantic distribution calibration is performed to further obtain semantically meaningful clusters by aligning the semantic distributions among samples that are semantically similar. Extensive experimental results demonstrate that ULDC achieves either state-of-the-art or highly competitive clustering performance across five diverse and challenging benchmark datasets. The source code is available at: https://***/ANONYanonymous/ULDC. © 2024, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Overlap Bias Matching is Necessary for Point Cloud Registration

arXiv

引用

arXiv 2023年

作者： Shi, Pengcheng Zhang, Jie Cheng, Haozhe Wang, Junyang Zhou, Yiyang Zhao, Chenlin Zhu, Jihua School of Software Engineering Xi’an Jiaotong University Xi’an China School of Computer and Information Technology Beijing Jiaotong University Beijing China Beijing China

Point cloud registration is a fundamental problem in many domains. Practically, the overlap between point clouds to be registered may be relatively small. Most unsupervised methods lack effective initial evaluation of overlap, leading to suboptimal registration accuracy. To address this issue, we propose an unsupervised network Overlap Bias Matching Network (OBMNet) for partial point cloud registration. Specifically, we propose a plug-and-play Overlap Bias Matching Module (OBMM) comprising two integral components, overlap sampling module and bias prediction module. These two components are utilized to capture the distribution of overlapping regions and predict bias coefficients of point cloud common structures, respectively. Then, we integrate OBMM with the neighbor map matching module to robustly identify correspondences by precisely merging matching scores of points within the neighborhood, which addresses the ambiguities in single-point features. OBMNet can maintain efficacy even in pair-wise registration scenarios with low overlap ratios. Experimental results on extensive datasets demonstrate that our approach’s performance achieves a significant improvement compared to the state-of-the-art registration approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface measurement

来源：评论

学校读者我要写书评

暂无评论

Continual Knowledge Graph Embedding by Structural Feature Distillation 1st

Continual Knowledge Graph Embedding by Structural Feature Di...

引用

1st International Conference on Applied Computational Intelligence, Informatics and Big Data, ACIIBD 2024

作者： Mu, Chong Kang, Zhao Yuan, Qianghua Liu, Sicong Zhang, Lizong School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu610000 China KASH Institute of Electronics and Information Industry Xinjiang Kashi844000 China

ISBN: (纸本)9783031918520

This paper presents a novel Continual Knowledge Graph Embedding (CKGE) approach that integrates local structural feature distillation to address the limitations of traditional static KGE methods. Our model dynamically updates embeddings without full retraining, reducing computational costs while retaining previously learned information. By employing a weighted sampling strategy based on betweenness centrality and a knowledge distillation mechanism that emphasizes entity-local structure similarity, our CKGE method effectively mitigates catastrophic forgetting. Experiments on four benchmark datasets reveal that our model surpasses existing baselines, showcasing its efficiency and effectiveness in continually learning knowledge graphs. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Malathi K Vunnava Dinesh Babu M. Ramkumar M. Ayyadurai Vivek K Hamzeh J Aljawawdeh Department of CSE Saveetha School of Engineering Saveetha Institute of Science and Technology (SIMATS) Saveetha University Chennai Department of CSE RV Institute of Technology Guntur Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones.

关键词： Training Support vector machines Accuracy Shape Computational modeling Estimation Predictive models Feature extraction Reliability Testing

来源：评论

学校读者我要写书评

暂无评论

Designing Data Permissions in the Enterprise Application Environment

Designing Data Permissions in the Enterprise Application Env...

引用

Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Ying Yuan Xin Yi Junbin ShangGuan School of Computer & Information Engineering Xiamen University of Technology Institute of Space Information Technology Xiamen University of Technology Xiamen China Software department Bozhongtuxin (Xiamen) Information Technology Co. Ltd Xiamen China

With the continuous improvement of social informationization, the digitalization of corporate construction is also developing comprehensively. Data, as a crucial source for creating wealth and competitive advantage, is undoubtedly the most significant asset for all enterprises. However, many companies face the dilemma of insufficient data granularity and configuration flexibility, which undermines the security and availability of their data assets. Therefore, this paper proposes an Api-based data permission control model (ADPC) based on the theoretical foundation of RBAC in the background of data application in the enterprise environment. Firstly, the model maps data objects to different levels of environmental variables in the corporate department structure, then flexibly authorizes and controls permissions through API configuration, and finally verifies its security with SQL AST. The ADPC model was implemented using the Spring Boot, Spring Security and MyBatis framework and was applied in a real enterprise environment. Through this approach, the ADPC model effectively addresses the limitations of traditional data permission control mechanisms in the enterprise application environment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Simple and Interactive Transformer for Fine-Grained Emotion Detection

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 347-358页

作者： Dianbo Sui Bo Li Hang Yang Yubo Chen Kang Liu Dianhui Chu Jun Zhao School of Computer Science and Software Engineering Harbin Institute of Technology Weihai China School of Economics Peking University Beijing China Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Fine-grained emotion detection is a long-standing task in sentiment analysis. One of the crucial challenges in this task is to capture emotion correlations. Previous studies either ignore this challenge or resort to a chain-link structure (e.g. seq2seq), both of which encounter some thorny issues in reality. Unlike previous studies, we approach this challenge by proposing a masked language modeling like mechanism, named masked emotion modeling (MEM), which can capture emotion correlations in a simple but effective manner. Specifically, we augment the transformer model with emotion states and require the transformer model to recover masked emotion states. Furthermore, to fully enhance the effect of the proposed MEM, we also introduce a taxonomy-guided sampling strategy at the training phase and an iterative decoding strategy at the test phase. On two widely used datasets, we demonstrate that our method excels at capturing emotion correlations and achieves state-of-the-art results.

关键词： Correlation Emotion recognition Transformers Training Speech processing Predictive models Micromechanical devices Iterative decoding Adaptation models Electronic mail

来源：评论

学校读者我要写书评

暂无评论

Epileptic Seizure Detection Using the EEG Signal Empirical Mode Decomposition and Machine Learning

Epileptic Seizure Detection Using the EEG Signal Empirical M...

引用

Smart Computing and Application (ICSCA), International Conference on

作者： Jana Nassir Majeda Alasabi Saeed Mian Qaisar Muzammil Khan Computer Science Department Effat University Jeddah Saudi Arabia Electrical and Computer Engineering Department Effat University Jeddah Saudi Arabia LINEACT Laboratory CESI Engineering School Villeurbanne France Department of Computer and Software technology University of Swat Swat Pakistan

Epileptic seizures affect millions of people worldwide. Medical treatments exist to help lessen the severity of the damage caused by these seizures. However, people with epilepsy still struggle with unexpected seizures. People who experience epileptic seizures have Electroencephalogram (EEG) signals that show different features in comparison to a healthy brain. In this study, EEG signals are studied to detect the seizures. The incoming signals are denoised by using linear phase filters. In next step these are divided in fix-length segments. Then, each segment is broken down using the Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs). For an automatic identification of EEG signals, features are extracted from the collected IMFs and then processed using machine learning techniques. A dataset on epilepsy that is available to the public is used for evaluation. To determine which is the best predictor for the under-consideration dataset, four different classification methods are performed and the results are examined. The system achieves a classification accuracy of 96.70%.

关键词： Support vector machines Empirical mode decomposition Epilepsy Medical treatment Pattern classification Nonlinear filters Sensitivity and specificity

来源：评论

学校读者我要写书评

暂无评论

Tri-PANet: Triplanar Ensemble Network with Parallelly Aggregating Global and Local Information for Brain Tumor Segmentation

Tri-PANet: Triplanar Ensemble Network with Parallelly Aggreg...

引用

Machine Vision, Image Processing and Imaging technology (MVIPIT), International Conference on

作者： Li Fang Zhou Jun Li School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

Accurate segmentation of brain tumor images holds great importance in the clinical diagnosis and effective treatment of brain tumors diseases. Nonetheless, tumors exhibit extensive variations in their characteristics, both in terms of their visual appearance and their location within brain MR images. This poses a significant challenge in achieving robust tumor segmentation. Furthermore, sample labeling bias problem also limits the segmentation accuracy of current methods. In this work, we propose a innovation approach named Tri-PANet for brain tumor segmentation. On the one hand, Tri-PANet learns both local features and global dependencies of images to better obtain shape and location information from a global perspective. On the other hand, Tri-PANet constrains tumor regions that are prone to mislabeling by a triplanar ensemble framework, with an independent tumour core prediction module. We evaluate our method using publicly accessible brain tumor segmentation datasets BraTS2019 and BraTS2020. The experimental results demonstrate that our propose model effective segmentation the tumor region and consistently outperform state-of-the-art methods.

关键词： Image segmentation Visualization Technological innovation Accuracy Shape Imaging Object segmentation

来源：评论

学校读者我要写书评

暂无评论

A Swin transformer encoder-based StyleGAN for unbalanced endoscopic image enhancement

引用

computers in Biology and Medicine 2024年 175卷 108472-108472页

作者： Deng, Bo Zheng, Xiangwei Chen, Xuanchi Zhang, Mingzhe School of Information Science and Engineering Shandong Normal University Jinan250352 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250352 China State Key Laboratory of High–end Server & Storage Technology Jinan250101 China

With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic systems have become an effective tool for reducing the diagnostic costs and shortening the treatment cycle of patients. Typically, the performance of these systems depends on deep learning models which are pre-trained with large-scale labeled data, for example, early gastric cancer based on endoscopic images. However, the expensive annotation and the subjectivity of the annotators lead to an insufficient and class-imbalanced endoscopic image dataset, and these datasets are detrimental to the training of deep learning models. Therefore, we proposed a Swin Transformer encoder-based StyleGAN (STE-StyleGAN) for unbalanced endoscopic image enhancement, which is composed of an adversarial learning encoder and generator. Firstly, a pre-trained Swin Transformer is introduced into the encoder to extract multi-scale features layer by layer from endoscopic images. The features are subsequently fed into a mapping block for aggregation and recombination. Secondly, a self-attention mechanism is applied to the generator, which adds detailed information of the image layer by layer through recoded features, enabling the generator to autonomously learn the coupling between different image regions. Finally, we conducted extensive experiments on a private intestinal metaplasia grading dataset from a Grade-A tertiary hospital. The experimental results show that the images generated by STE-StyleGAN are closer to the initial image distribution, achieving a Fréchet Inception Distance (FID) value of 100.4. Then, these generated images are used to enhance the initial dataset to improve the robustness of the classification model, and achieved a top accuracy of 86 %. © 2024 Elsevier Ltd

关键词： Image enhancement

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：