检索结果-内蒙古大学图书馆

IAENG International Journal of computer science 2024年第11期51卷 1720-1730页

作者： Li, Xudong Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

This paper introduces an advanced road damage detection algorithm that effectively addresses the shortcomings of existing models, including limited detection performance and large parameter sizes, by utilizing the YOLOv8n model. Key enhancements are integrated into the proposed algorithm to bolster its efficacy. First, the ConvNeXt V2 backbone network is integrated to improve the extraction of contextual features, thereby enhancing the effectiveness of road damage detection. Second, the algorithm employs a C2f_GhostNetV2 block structure to strengthen feature representation while simultaneously reducing computational costs. Additionally, PConv is utilized in the neck region to optimize spatial feature extraction, thereby minimizing redundant computations. The experimental results indicate that the proposed algorithm performs effectively on the Chinese subset of the RDD2022 dataset. Specifically, detection accuracy improved by 1.9%, recall by 1.8%, and mAP@0.5 by 2.1%, while the number of parameters decreased by 24% compared to the initial model. The optimized algorithm increases FPS by 4, meeting the dual requirements of mobile devices for accuracy and real-time object detection. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Research on Network Traffic Anomaly Detection for Class Imbalance 3rd

Research on Network Traffic Anomaly Detection for Class Imba...

引用

3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Wang, Zhurong Zhou, Jing Wang, Zhanmin Hei, xinhong School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9789819903009

It is actually a classification that the anomalistic detection on traffic of network belongs to. The imbalance of data about network traffic, leading to the disability of anomaly model on learning the characteristics of the minority class samples, makes the detection accuracy in the minority samples poor. Aiming at the imbalance of network flow data, a network traffic anomaly detection method combining Deep Convolutional Generative Adversarial Networks (DCGAN) and Vision Transformer is proposed to make the detection accuracy of network traffic data more perfect. The network traffic dataset is sampled, and the sampled imbalanced dataset is class balanced by DCGAN. The class balanced network traffic data is fed into Vision Transformer for prediction. To simplify the model structure, the vision Transformer model is proposed, and only N encoders are included in the vision Transformer, and the decoder structure is removed. Using residual network in encoder to solve the problem of model degradation. The dataset uses CIC-IDS-2017 network intrusion detection data. After experiments, it was enough to hold that the model which was proposed can positively perform class balancing of data and increase the prediction precision of data about network traffic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Multi-lesion Segmentation of Fundus Images using Improved UNet++

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2024年第10期51卷 1587-1595页

作者： Jiang, Haoyan Zhao, Ji School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Diabetes retinopathy (DR) is one of the complications of diabetes. Early diagnosis of retinopathy is helpful to avoid vision loss or blindness. The difficulty of this task lies in the significant differences in the size and shape of lesions between different DR samples, with a higher proportion of small lesions. We propose a new multi-disease segmentation method based on UNet++ to improve the segmentation accuracy of DR lesions. We chose Resnet50 as the backbone network and introduced a new hybrid residual module to replace the original residual module. At the same time, to compensate for the loss of information in DR small lesions during the feature extraction process, we introduce the Across Feature Map Attention (AFMA) is an auxiliary branch that enhances the segmentation accuracy of small-scale lesions. Finally, in response to the difficulty in extracting DR lesions in shallow models, the model abandoned the deep supervision structure in UNet++. In addition, we use a weighted mixed loss function to train the model. We conducted experiments on IDRID and DDR public datasets, simultaneously segmenting four typical DR lesions. The results on intersection over union (IOU) and dice similarity coefficient (Dice) showed that our method achieved competitive performance compared to other research methods. © (2024), (International Association of Engineers). All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

First-principles calculations study of valley polarization in antiferromagnetic bilayer systems

引用

Physical Review B 2025年第14期111卷 L140404-L140404页

作者： San-Dong Guo Ping Li Guangzhao Wang School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi'an 710121 China State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Xi'an Shaanxi 710049 China Key Laboratory of Extraordinary Bond Engineering and Advanced Materials Technology of Chongqing School of Electronic Information Engineering Yangtze Normal University Chongqing 408100 China

Superior to ferromagnetic (FM) valleytronics, antiferromagnetic (AFM) counterparts exhibit ultradense and ultrafast potential due to their intrinsic advantages of zero stray field, terahertz dynamics, and compensated moments of antiferromagnets. However, the physics of spontaneous valley polarization is mainly rooted in FM hexagonal lattices and is rarely used to explore the simultaneous spin and valley polarizations in AFM materials. Here, we propose a general stacking method to achieve valley polarization in AFM bilayer systems. The hexagonal ferrovalley material is used as the basic building unit, and then the space-inversion centrosymmetric bilayer system with interlayer AFM ordering is constructed by horizontal mirror and twofold rotational operations, which can exhibit spontaneous valley polarization. In this construction process, the rarely explored layer-locked hidden valley polarization, hidden Berry curvature, and layer Hall effect are involved, and an out-of-plane electric field can be used to detect hidden valley polarization and to realize layer-locked anomalous valley Hall effect. We use three examples to illustrate our proposal. First, the Janus GdBrI is used to illustrate concepts and effects involved in our design process. Second, the RuBr2 is used to demonstrate other phenomena, including valley polarization transition and near-ideal quantum spin Hall insulators. Finally, we use our design principles to understand the valley polarization of experimentally synthesized MnSe from a new perspective. Our works establish a robust general scheme to achieve valley polarization in AFM bilayer systems, thereby opening up new avenues for AFM valleytronics.

关键词： Landforms

来源：评论

学校读者我要写书评

暂无评论

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

引用

computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

来源：评论

学校读者我要写书评

暂无评论

Adaptive Broad Network With Graph-Fuzzy Embedding for Imbalanced Noise Data

引用

IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1949-1962页

作者： Chen, Wuxing Yang, Kaixiang Yu, Zhiwen Nie, Feiping Chen, C. L. Philip South China University of Technology School of Future Technology Guangdong510650 China Pengcheng Laboratory Shenzhen518066 China South China University of Technology School of Computer Science and Engineering Guangdong510641 China Xi'an710072 China

Broad learning system (BLS) is renowned for its excellent generalization and high efficiency in data classification. However, when confronted with class imbalance problems, BLS treats all samples as equally important, resulting in performance degradation. Additionally, the presence of noise and outliers in imbalanced data further complicates BLS's ability to handle real-world classification problems. To address these challenges, this paper proposes a Graph-Embedding Intuitionistic Fuzzy Adaptive Broad Learning System (GEIB). The graph embedding strategy proposed by GEIB leverages the geometric topology of the data and class-specific information, effectively capturing variability among imbalanced samples and improving class separability. Furthermore, we introduce intuitionistic fuzzy (IF) theory. The BLS integrated with it considers both the homogeneity and heterogeneity of sample neighborhoods, enabling it to address uncertainty and imprecision in the data. It further differentiates clean samples from noisy ones in imbalanced datasets, thereby enhancing model robustness. To further investigate the prior distribution information of imbalanced data, we design an adaptive class-specific penalty mechanism based on global distribution and local density information. This mechanism accounts for both intra- and interclass density information and class global distribution. We verify the superiority of our method by conducting a comparison with current approaches using real-world datasets that include both Gaussian noise and noise-free versions. © 1993-2012 IEEE.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

Bearing Small Sample Fault Diagnosis Based on DCGAN and CNN 3rd

Bearing Small Sample Fault Diagnosis Based on DCGAN and CNN

引用

3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Gao, Miao Hei, xinhong Zhang, Kuan School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9789819903009

It is suggested to use deep convolutional generative adversarial networks and convolutional neural networks to diagnose faults in small samples of bearings. The CWRU bearing dataset is pre-processed with data and used as input to DCGAN, which is trained in DCGAN. When the number of training sessions reaches the experimentally set number, the training is stopped and the samples generated by the generator are mixed with the original samples and used as input to CNN, on which the classification model is trained to achieve fault classification. Comparative studies were carried out on the dataset of taken bearings to show the efficacy of the suggested method. The findings demonstrate how well the suggested diagnosis approach diagnoses bearing faults while outperforming competing algorithms and models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

引用

science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

来源：评论

学校读者我要写书评

暂无评论

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

引用

engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

来源：评论

学校读者我要写书评

暂无评论

A Digital Twin Framework for Logical Range

A Digital Twin Framework for Logical Range

引用

2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Dang, Zheng Chen, Hao Hei, xinhong Liu, Yilong School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9798350327380

The Logical Range is introduced into the joint flight test because it can call cross-area test resources and improve the utilization rate of test resources. However, the Logical Range also has the problems of low reusability of test resources and low efficiency in cross regional range data acquisition and communication. For this problem, a digital twin framework for logical range is proposed. In the framework, a Logical Range system based on digital twin is built, and the data network communication methods and virtual-real mapping method are designed in detail. The feasibility of this framework has been effectively verified through flight test examples. This study lays a solid foundation for the combination of flight test and digital twin technology. © 2023 IEEE.

关键词： Reusability

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：