检索结果-内蒙古大学图书馆

12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Xu, Haihong Lu, Shejie Hu, Siping Gao, Jinfeng Liu, Fang Wu, Haitao Hubei University of Science and Technology Information Center Institute of Engineering and Technology Xianning437100 China Huanghuai University International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Zhumadian463000 China

ISBN: (纸本)9798350310153

With the wide application of wireless LAN, the real-time and differentiated services of IEEE 802.11 MAC are becoming more and more important. IEEE 802.11 uses CSMA/CA mechanism in MAC layer to coordinate access, but it does not meet the different requirements of different users for QoS. Considering the different requirements of different priority users for QoS, a solution is proposed for this problem. The core idea of this strategy is: in a multi rate network, according to the priority of nodes in the network, through adaptive optimization of relevant parameters, the probability of high priority nodes accessing the channel can be improved, and its throughput can be improved accordingly;The channel access probability of nodes with lower priority is reduced accordingly, and the throughput is reduced accordingly. Therefore, this policy can expand the throughput of each priority and realize the service differentiation function. © 2022 IEEE.

关键词： IEEE Standards

来源：评论

学校读者我要写书评

暂无评论

Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

arXiv

引用

arXiv 2023年

作者： Li, Zihao Gao, Pan Yuan, Hui Wei, Ran Paul, Manoranjan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Control Science and Engineering Shandong University Jinan China Science and Technology on Electro-optic Control Laboratory Luoyang China Charles Sturt University Bathurst Australia

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations, which considers both local and global attentions. Specifically, considering local spatial coherence, local feature learning is performed through Relative Position Encoding and Attentive Feature Pooling. We incorporate the learned locality into the Transformer module. The local feature affects value component in Transformer to modulate the relationship between channels of each point, which can enhance self-attention mechanism with locality based channel interaction. We demonstrate its superiority experimentally on classification and segmentation tasks. The code is available at: https://***/jiamang/IBT © 2023, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fault Classification Method of Information System Based on Cascaded Convolutional Neural Network Model 5

Fault Classification Method of Information System Based on C...

引用

5th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2022

作者： Ou, Ruiting Chen, Shuo Qiao, Lin Liu, Ziang Cheng, Xu Qiao, Yongfeng State Grid Liaoning Information and Communication Company Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China College of Economics and Management Shenyang Agricultural University Shenyang China Dandong Dongfang Measurement & Control Technology Co. Ltd Dandong China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

Artificial intelligence plays an important role in data anomaly detection, which can significantly improve the speed and accuracy of fault diagnosis and detection in power information system. With its powerful fitting ability, deep learning begins to emerge in various fields, especially in the field of da-ta anomaly detection, which can also use deep learning methods. Due to various types and similar features of power data sets, fault classification cannot be carried out accurately. At present, frequent detection of text information needs to be performed manually. To solve this problem, this paper proposes a fault classification method for information system based on cascaded convolutional neural network model. BERT deep learning network is used for model training, and a cascaded classification network is constructed. Multiple BERT models are called hierarchically to achieve the fault detection effect of hierarchical classification. The method presented in this paper improves the accuracy of fault prediction and classification, which also provides fast, accurate and reliable decision information basis for improving fault diagnosis. © 2022 IEEE.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

A Surrogate-Assisted Evolutionary Algorithm Based on K-Means Clustering and Lévy Flight

A Surrogate-Assisted Evolutionary Algorithm Based on K-Means...

引用

Chinese Automation Congress (CAC)

作者： Yaxin Kang Haibo Yu Li Kang Gangzhu Qiao Dongpeng Guo Jianchao Zeng College of Electrical and Control Engineering North University of China Taiyuan China College of Computer Science and Technology North University of China Taiyuan China College of Environment and Safety Engineering North University of China Taiyuan China College of Environment and Resources Taiyuan University of Science and Technology Taiyuan China

Expensive optimization problem refers to a category of problems for which numerical simulations or physical experiments are expensive and intractable. Evolutionary algorithm (EA) cannot be directly applied to such problems due to the large number of fitness evaluations consumed to determine the global best solution. Hence, this paper proposes a surrogate-assisted evolutionary algorithm based on k-means clustering and Lévy flight (SAEA-KL), where the Radial Basis Function network (RBFN) is selected as a promising surrogate for saving the number of expensive evaluations, and the dual-start Lévy flight and k-means clustering method are deployed to initialize the population of the competitive swarm optimizer in consideration of both optimal and fitness landscape information. Experimental results on common benchmark problems indicate that the proposed method is competitive with seven state-of-the-art algorithms under a limited computational budget.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MedFILIP: Medical Fine-grained Language-Image Pre-training

arXiv

引用

arXiv 2025年

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Thoracic Surgery at No. 4 Affiliated Hospital Harbin Medical University Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of computer and control engineering Northeast Forestry University Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH United States

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestX-ray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%. The code is available in https://***/ PerceptionComputingLab/MedFILIP. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Syndrome Classification Based on Multi-Graph Attention Network 12

Syndrome Classification Based on Multi-Graph Attention Netwo...

引用

12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Ma, Jishun Lu, Weikai Zhou, Changen Teng, Shenghua Li, Zuoyong Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China Fujian University of Technology School of Electronic Electrical Engineering and Physics Fuzhou350118 China Fujian University of Traditional Chinese Medicine Research Base of Traditional Chinese Medicine Syndrome Fuzhou350122 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Syndrome classification is an important step in Traditional Chinese Medicine (TCM) for diagnosis and treatment. In this paper, we propose a multi-graph attention network (MGAT) based method to simulate TCM doctors to infer the syndromes. Specifically, the complex relationships between symptoms and state elements are aggregated using graph attention networks (GAT) and syndromes are classified by a multilayer perceptron (MLP). To verify the effectiveness of the model, extensive experiments are conducted on the Treatise on Febrile Diseases dataset. The experimental results show that the proposed method outperforms several typical methods in terms of accuracy, precision, recall, and F1-score. The MGAT model has high accuracy in syndrome classification and has practical application value. © 2022 IEEE.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Skin-like cryogel electronics from suppressed-freezing tuned polymer amorphization

引用

Nature Communications 2023年第1期14卷 1-10页

作者： Zhang, Xiansheng Yan, Hongwei Xu, Chongzhi Dong, Xia Wang, Yu Fu, Aiping Li, Hao Lee, Jin Yong Zhang, Sheng Ni, Jiahua Gao, Min Wang, Jing Yu, Jinpeng Ge, Shuzhi Sam Jin, Ming Liang Wang, Lili Xia, Yanzhi State Key Laboratory of Bio-Fibers and Eco-Textiles College of Textiles and Clothing Qingdao University Qingdao 266071 China Institute for Future Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao 266071 China College of Materials Science and Engineering Qingdao University Qingdao 266071 China CAS Key Laboratory of Engineering Plastics Institute of Chemistry Chinese Academy of Sciences Beijing 100190 China Department of Chemistry SungKyunKwan University Suwon 16419 South Korea State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China College of Biological Science and Medical Engineering Donghua University Shanghai 201620 China Institute of Environmental Engineering ETH Zürich Zürich 8093 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore

The sole situation of semi-crystalline structure induced single performance remarkably limits the green cryogels in the application of soft devices due to uncontrolled freezing field. Here, a facile strategy for achieving multifunctionality of cryogels is proposed using total amorphization of polymer. Through precisely lowering the freezing point of precursor solutions with an antifreezing salt, the suppressed growth of ice is achieved, creating an unusually weak and homogenous aggregation of polymer chains upon freezing, thereby realizing the tunable amorphization of polymer and the coexistence of free and hydrogen bonding hydroxyl groups. Such multi-scale microstructures trigger the integrated properties of tissue-like ultrasoftness (Young’s modulus <10 kPa) yet stretchability, high transparency (~92%), self-adhesion, and instantaneous self-healing (<0.3 s) for cryogels, along with superior ionic-conductivity, antifreezing (−58 °C) and water-retention abilities, pushing the development of skin-like cryogel electronics. These concepts open an attractive branch for cryogels that adopt regulated crystallization behavior for on-demand functionalities. © 2023, Springer Nature Limited.

关键词： Gels and hydrogels Polymers

来源：评论

学校读者我要写书评

暂无评论

LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Robust Localization Based on Full Tdoa Set

SSRN

引用

SSRN 2023年

作者： Wang, Yuwei Xiong, Wenxin Wang, Zhi The College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310000 China The Department of Computer Science University of Freiburg Freiburg im Breisgau Baden-Württemberg79110 Germany

Time difference of arrival (TDOA) algorithms have attracted extensive attention among researchers in recent years. Instead of its widely studied non-redundant set counterpart, localization based on the full TDOA set is discussed herein. The non-redundant set is a subset of the full TDOA set. In the line-of-sight (LOS) scenarios, projecting the full TDOA set into a nonredundant set is a conventional way to reduce the computational complexity. In the non-LOS (NLOS) scenarios, it is no longer appropriate since the full TDOA set contains exploitable information and can not be transformed to an equivalent subset. Besides, the variety of NLOS errors is also a challenge. Alternatively, robust localization technologies can be used, yet it is not easy to combine the special data structures in the full TDOA set therewith. In this paper, we propose a new robust algorithm to perform NLOS-resistant localization using the full TDOA set. An essential contribution is that the proposed method jointly mitigates the negative effects of multiple types of NLOS bias errors in the optimization formulation. Simulation and experimental results demonstrate that the proposed scheme can outperform the state-of-the-art full TDOA set based approaches. © 2023, The Authors. All rights reserved.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

引用

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

作者： Xiuen Wu Tao Wang Yuanzheng Cai Lingyu Liang George Papageorgiou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians’ future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method.

关键词： Deep learning Pedestrians Computational modeling Predictive models Prediction algorithms Vectors Inference algorithms

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：