检索结果-内蒙古大学图书馆

2nd International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2023

作者： Wang, Futian Ding, Zhongfeng Shi, Tao Tang, Jin Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781510664968

Most problems in the power system are related to the temperature of electrical equipment. The abnormal high temperature of electrical equipment will not only cause damage to equipment itself, but also threaten the safety of people's life and property. Therefore, patrol inspectors of power system will carry out routine inspection on electric equipment to ensure safety. Semantic segmentation for electrical equipment carries a big weight in the inspection. With the result of semantic segmentation of power equipment, patrol inspectors can quickly judge whether the temperature of power equipment is normal, and then take the corresponding action. For this reason, we propose the EdgeFormer, which is a typical end-to-end network using thermal image to segment electrical equipment. In our method, we employ an edge information extraction network to attain rich edge features to promote the segmentation performance in electrical equipment's edges and interiors, and we also design the global enhancing module (GEM) to get rich semantic information. Besides, we also propose a feature inserting module (FIM) to fuse the feature maps from different stages together. Lots of experiments have been conducted on the LS-ETS dataset and the results show that our EdgeFormer has achieved the best performance. © 2023 SPIE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

An Early Detection Model of Prostate Cancer Based on MRI 9

An Early Detection Model of Prostate Cancer Based on MRI

引用

9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Liu, Yuankun Xing, Lumin Wang, Di Nie, Xin Zhang, Xueyan Wu, Di Liu, Wenjian The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Chongqing Jianzhu College Chongqing China Shandong Youth University of Political Science School of Information Work Office Jinan China University of Southern Queensland School of Mathematics Physics and Computing QLD Australia

ISBN: (纸本)9798350391367

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively. © 2024 IEEE.

关键词： Urology

来源：评论

学校读者我要写书评

暂无评论

MFCM-DTI model of multimodal feature fusion: Prediction of drug-target interaction

MFCM-DTI model of multimodal feature fusion: Prediction of d...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Wenjun Ma, Wanjun Yang, Mengyun Tang, Xiwei Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China Hunan First Normal University School of Computer Science Changsha China Hunan First Normal University School of Intelligent Manufacturing Changsha China

ISBN: (纸本)9798350386226

Drug repositioning is a vital area of biomedicine, where confirming interactions between drugs and specific targets is essential for establishing the efficacy of pharmaceutical agents. Traditional in vitro screening methods have limitations, prompting the use of computer simulations as an effective alternative for predicting drug-target interactions (DTI). This approach has gained significant attention in the scientific community. In this study, we introduce MFCM-DTI, a DTI prediction model that employs multimodal features to accurately capture the intricate interactions between drug molecular structures and key amino acids of target proteins. Our results demonstrate that MFCM-DTI outperforms existing models in prediction accuracy and robustness. Furthermore, MFCM-DTI has been successfully used to predict interactions between key SARS-CoV-2 proteins and existing drugs, providing a solid foundation for developing therapeutic agents against SARS-CoV-2 infection. This study underscores the broad applicability and strong predictive capabilities of MFCM-DTI in drug-protein interaction prediction, opening new avenues for research. The predicted drug targets and interaction data offer valuable insights for future experimental validation and clinical trials, potentially driving innovation in the biomedical field. © 2024 IEEE.

关键词： Prediction models

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

Speaker diarization based on multi-timescale feature fusion 2

Speaker diarization based on multi-timescale feature fusion

引用

2nd International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2023

作者： Wang, Futian Chen, Lailong Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781510664968

Deep and convolutional neural networks have performed well in capturing speaker characteristics, while the ECAPA-TDNN model has demonstrated outstanding performance in both the fields of speaker validation and speaker diarization. Within this essay, during the speech segmentation stage, we uniformly redivide the speech on multiple time scales based on the oracle voice activity detection. Meanwhile, we fine-tune the ECAPA-TDNN architecture by adding a RepVGG module to extract more abundant features, then aggregate all of the outputs. Finally, we use DOVER-Lap to integrate the results obtained after the clustering of multiple schemes in a way to obtain the final temporal labeling. The best results achieves 1.91% of the diarization error rate on the classical AMI conference corpus. © 2023 SPIE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Gland Segmentation via Feature-Enhanced Contrastive Learning and Dual-Consistency Strategy

引用

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4314-4324页

作者： Yu, Jiejiang Li, Bingbing Pan, Xipeng Shi, Zhenwei Wang, Huadeng Lan, Rushi Luo, Xiaonan Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Ganzhou Municipal Hospital Department of Pathology Ganzhou341000 China Southern Medical University Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China

In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gland with the background. To address this challenge, we propose a new semi-supervised method named DCCL-Seg for gland segmentation, which follows the teacher-student framework. Our approach can be divided into follows steps. First, we design a contrastive learning module to improve the ability of the student model's feature extractor to distinguish between gland and background features. Then, we introduce a Signed Distance Field (SDF) prediction task and employ dual-consistency strategy (across tasks and models) to better reinforce the learning of gland internal. Next, we proposed a pseudo label filtering and reweighting mechanism, which filters and reweights the pseudo labels generated by the teacher model based on confidence. However, even after reweighting, the pseudo labels may still be influenced by unreliable pixels. Finally, we further designed an assistant predictor to learn the reweighted pseudo labels, which do not interfere with the student model's predictor and ensure the reliability of the student model's predictions. Experimental results on the publicly available GlaS and CRAG datasets demonstrate that our method outperforms other semi-supervised medical image segmentation methods. © 2013 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A robust and automatic method for the recognition of speech category in online learning discourse

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3913-3924页

作者： Jiang, Dazhi He, Zhihui Chen, Yifei Xu, Linyan Lin, Jiali Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510399 China Department of Computer Science Shantou University Shantou515063 China Department of Management Economics and Industrial Engineering Politecnico di Milano MI Milan20156 Italy School of Business Shantou University Shantou515063 China

It is a rapid and irresistible developing trend that the traditional classroom learning mode is transformed into an online learning mode. In the online learning process, it is important to achieve effective voice separation and provide a good lecture experience for students. In this paper, a robust and automatic method for the recognition of speech categories (teacher speech, student speech, silence speech and overlapping speech) is presented. The system, which is part of artificial intelligence in education (AIED), is a hybrid speech processing system that includes GMM modeling, GMM clustering, silence processing and overlapping processing. The online learning environment we are based on is not a customized environment that defines the roles of teachers or students, but a more casual, broader and more general online environment. The method proposed and designed based on this situation has better adaptability and scalability. The experimental results show that our automatic detection of speech categories fares well when compared to the results from professional coders. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Neural Dynamics for Distributed Collaborative Control of Manipulators With Time Delays

引用

IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 854-863页

作者： Long Jin Xin Zheng Xin Luo School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 IEEE State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China

Time-delay phenomena extensively exist in practical systems,e.g.,multi-agent systems,bringing negative impacts on their *** work analyzes a collaborative control problem of redundant manipulators with time delays and proposes a time-delayed and distributed neural dynamics *** assumptions that the network topology is fixed and connected and the existing maximal time delay is no more than a threshold value,it is proved that all manipulators in the distributed network are able to reach a desired *** proposed distributed scheme with time delays considered is converted into a time-variant optimization problem,and a neural dynamics solver is designed to solve it ***,the proposed neural dynamics solver is proved to be stable,convergent and ***,the allowable threshold value of time delay that ensures the proposed scheme’s stability is *** examples and comparisons are provided to intuitively prove the validity of the proposed neural dynamics scheme and solver.

关键词： Distributed collaborative control neural dynamics redundancy resolution repetitive motion planning(RMP) time delay

来源：评论

学校读者我要写书评

暂无评论

Feature Mutual Reinforcement Learning and Resampling for RGB-T Tracking 2

Feature Mutual Reinforcement Learning and Resampling for RGB...

引用

2nd International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2023

作者： Wang, Futian Zhou, Xuan Wang, Wenqi Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781510664968

More and more researchers have paid attention to the tracking of visible-thermal infrared (RGB-T). How to fully exploit the complementary features of visible and thermal infrared images and fully integrate them is a key issue. After extracting image features, many researchers simply fuse the features by adding, connecting operations or designing fusion modules. However, these methods ignore the effects of different levels of fusion features on target modeling and specific feature extraction. In this work, we propose a RGB-T tracking network (MRLRNet) based on feature mutual reinforcement learning and resampling. Specifically, we design a feature mutual reinforcement learning module, which combines different layers of features to achieve progressive fusion. After each layer feature is extracted, the aggregation features are used to enhance specific modal features to achieve better specific feature representation and reduce noise and redundancy features. At the same time, we design a resampling module, which calculates the offset of two adjacent frames by phase correlation operation, and recalculates the Gaussian sample points to solve the problem of ground target loss caused by sudden camera movement. A large number of experiments on three RGB-T tracking datasets, GTOT, RGBT234 and LasHeR, demonstrate the effectiveness of this method. © 2023 SPIE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Sparrow Search Algorithm with Adaptive Collaborative Updating

Journal of Network Intelligence

引用

Journal of Network Intelligence 2024年第3期9卷 1706-1724页

作者： Hong, Chuanbo Liang, Quan Liang, Qiaoxin Yu, Wenjie Yu, Wenze Xiong, Neng School of Mechanical and Electrical Engineering Quanzhou University of Information Engineering Quanzhou362000 China College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Quanzhou Management Branch Fujian Expressway Group Cp. Ltd Quanzhou362000 China

The research of swarm intelligence shows a rapid development trend. People have studied a lot of swarm intelligence algorithms inspired by biology. Sparrow search algorithm is concerned by many scholars because of its powerful search performance. However, in the case of high-dimension optimization problems, if only a single sparrow search algorithm is used to solve optimization problems, the optimal solution often cannot be found, and the phenomenon of dimension disaster will occur or it is easy to fall into the local optimal solution. In order to make sparrow search algorithm have better performance in solving high-dimensional complex problems, In this paper, we propose a new Sparrow Search Algorithm with Adaptive Collaborative Updating (SSA-ACU). The SSA-ACU algorithm initializes the population through the chaotic search strategy to make the population distribution more diverse. Then, adaptive parameters are introduced to update the position of the leader to provide a more reliable direction for particle search. Finally, in order to enhance the development ability of the population, the position of the adjoint is updated through the pseudo-affine transformation mechanism to change the mode of its position update. Then CEC2013 was used to test and compare SSA-ACU algorithm with other sparrow search algorithm variants and classical algorithms, and it was verified that SSA-ACU algorithm has good performance in convergence speed and optimization ability. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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：