检索结果-内蒙古大学图书馆

7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Liu, Wanning Qin, Guihe Yang, Lifeng Liang, Yanhua College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819927296

As control modules in cars become intelligent and automated, electrical systems become more complex. Dozens or even hundreds of electronic control units (ECUs) are used in many cars. In this case, the information security problem of the car is also latent in the dark. When the actual application load ratio of the CAN bus exceeds more than 30%, the delay of data communication and the probability of error frames multiply, causing serious communication congestion. A malicious intruder only needs to affect the bus data traffic to cause the collapse of the CAN bus network, which in turn changes the behavior of the car and has irreversible consequences. In order to enable the CAN bus to maintain its advantages, it can adapt to the new needs of contemporary in-vehicle networks with higher performance and more reliable security protection. In the paper, we propose a flow monitoring alarm module built into the in-vehicle CAN. This module embeds a statistical algorithm based on information theory. The algorithm has a small amount of computation and clear logic, which meets the lightweight requirements of embedded algorithms. Through multiple experiments, the module can monitor all Dos for in-vehicle CAN, as well as other traffic abnormalities with a monitoring alarm rate of more than 98%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

SS-MAF: Semi-Supervised Echocardiographic Segmentation Using Multi-Atlas Fusion 8

SS-MAF: Semi-Supervised Echocardiographic Segmentation Using...

引用

8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Yang, Shuaihua Zheng, Caixia College of Information Science and Technology Northeast Normal University Changchun130117 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

ISBN: (纸本)9798350305838

Left ventricle (LV) segmentation in echocardiography is of paramount significance in cardiac function analysis. Recently, semi-supervised segmentation has garnered considerable attention due to its potential mitigation of labeling costs. Despite this, most semi-supervised methodologies tend to overlook the specialized anatomical priors specific to echocardiography, often yielding suboptimal outcomes. Simultaneously, atlas priors have been validated on other organs and imaging modalities. However, their direct application in echocardiography is fraught with challenges, primarily attributed to the indistinct boundaries of the LV and the presence of fan-shaped image edges, which impede atlas image registration. This paper presents a new method of semi-supervised segmentation using multi-atlas fusion, named SS- MAF, which is designed to incorporate atlas priors tailored for echocardiography while circumventing the intricate image registration process in traditional atlas-based methods. The SS-MAF comprises two components: a Multi-Task Learning (MTL) module and a Multi-Atlas Fusion (MAF) module. The MTL module serves precise landmark localization and segmentation estimation. The MAF module is primarily focused on the generation of probabilistic atlases, utilizing annotated atlas data with the guidance of key landmarks to facilitate the automatic annotation of unlabeled data, avoiding the traditional image-level registration, thus enhancing the performance of the MTL module. Our SS- MAF is rigorously evaluated on the publicly available CAMUS dataset, with results demonstrating its superiority over other state-of-the-art semi-supervised segmentation techniques in echocardiography. © 2023 IEEE.

关键词： Image registration

来源：评论

学校读者我要写书评

暂无评论

Strengthened Initialization of Adaptive Cross-Generation Differential Evolution

引用

Computer Modeling in engineering & Sciences 2022年第3期130卷 1495-1516页

作者： Wei Wan Gaige Wang Junyu Dong Department of Computer Science of Technology Ocean University of ChinaQingdao266100China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for NationalitiesNanning530006China

Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest *** order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant *** this paper,an enhanced version is proposed,namely *** incorporates a strengthened initialization strategy and optimized parameters in contrast to its *** improvements make the direction of crossgeneration mutation more clearly and the ability of searching more *** experiments show that the new algorithm has better diversity and improves convergence to a certain *** the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.

关键词： Differential Evolution(DE) multi-objective optimization(MO) opposition-based learning parameter adaptation

来源：评论

学校读者我要写书评

暂无评论

Osteoporotic Vertebral Fracture Classification in X-rays Based on a Multi-modal Semantic Consistency Network

引用

Journal of Bionic engineering 2022年第6期19卷 1816-1829页

作者： Yuzhao Wang Tian Bai Tong Li Lan Huang College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Department of Orthopedics The Second Hospital of Jilin UniversityChangchun130012China

Osteoporotic Vertebral Fracture(OVFs)is a common lumbar spine disorder that severely affects the health of *** a clear bone blocks boundary,CT images have gained obvious advantages in OVFs *** with CT images,X-rays are faster and more inexpensive but often leads to misdiagnosis and miss-diagnosis because of the overlapping *** how to transfer CT imaging advantages to achieve OVFs classification in X-rays is *** this purpose,we propose a multi-modal semantic consistency network which could do well X-ray OVFs classification by transferring CT semantic consistency *** from existing methods,we introduce a feature-level mix-up module to get the domain soft labels which helps the network reduce the domain offsets between CT and *** the meanwhile,the network uses a self-rotation pretext task on both CT and X-ray domains to enhance learning the high-level semantic invariant *** employ five evaluation metrics to compare the proposed method with the state-of-the-art *** final results show that our method improves the best value of AUC from 86.32 to 92.16%.The results indicate that multi-modal semantic consistency method could use CT imaging features to improve osteoporotic vertebral fracture classification in X-rays effectively.

关键词： Osteoporotic vertebral fracture classification Cross-modality Unsupervised domain adaptation Transfer learning Convolutional neural network Computer-aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

ETFIDS: An Entropy-Driven, Time-Frequency Analysis Framework for In-Vehicle CAN Signal Intrusion Detection

引用

IEEE Internet of Things Journal 2025年

作者： Liu, Wanning Qin, Guihe Liang, Yanhua Song, Jiaru Liu, Qingxin Zhou, Xue Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Changchun130012 China

In recent years, cyberattacks against automobiles have exposed significant security threats to in-vehicle networks. The vulnerability of communication signals to malicious interference and manipulation can lead to serious system failures or abnormal behavior. The existing in-vehicle network intrusion detection methods do not fully exploit the time-frequency characteristics of Controller Area Network (CAN) signals. This limitation reduces their effectiveness in capturing subtle changes and signal complexity. Based on the above motivation, from the perspective of signal perception, we propose an entropy-driven, time-frequency analysis framework for in-vehicle network intrusion detection. The framework integrates a signal sampler, a frequency-domain detector, and a time-domain detector. The signal sampler, as the system's front-end module, extracts real-time physical signal data streams from CAN messages. The frequency-domain detector identifies frequency components, detecting high-frequency disturbances and cyclic variations. Meanwhile, the time-domain detector captures instantaneous changes and sudden anomalies. It analyzes signal complexity and anomalies through both stream and block detection. Experimental results demonstrate that the proposed method performs well under various attack scenarios, offering superior detection and real-time performance. It effectively senses multiple signal anomalies, providing a robust intrusion detection solution for modern in-vehicle networks. © 2025 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Positive and Unlabeled Learning with Controlled Probability Boundary Fence 41

Positive and Unlabeled Learning with Controlled Probability ...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Li, Changchun Dai, Yuanchao Feng, Lei Li, Ximing Wang, Bing Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

Positive and Unlabeled (PU) learning refers to a special case of binary classification, and technically, it aims to induce a binary classifier from a few labeled positive training instances and loads of unlabeled instances. In this paper, we derive a theorem indicating that the probability boundary of the asymmetric disambiguation-free expected risk of PU learning is controlled by its asymmetric penalty, and we further empirically evaluated this theorem. Inspired by the theorem and its empirical evaluations, we propose an easy-to-implement two-stage PU learning method, namely Positive and Unlabeled Learning with Controlled Probability Boundary Fence (PUL-CPBF). In the first stage, we train a set of weak binary classifiers concerning different probability boundaries by minimizing the asymmetric disambiguation-free empirical risks with specific asymmetric penalty values. We can interpret these induced weak binary classifiers as a probability boundary fence. For each unlabeled instance, we can use the predictions to locate its class posterior probability and generate a stochastic label. In the second stage, we train a strong binary classifier over labeled positive training instances and all unlabeled instances with stochastic labels in a self-training manner. Extensive empirical results demonstrate that PUL-CPBF can achieve competitive performance compared with the existing PU learning baselines. Copyright 2024 by the author(s)

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Learning Stable Task-Level Manifold for Few-Shot Learning

Learning Stable Task-Level Manifold for Few-Shot Learning

引用

2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Yu, Siyi Luo, Wei Li, Gang Yang, Bo Jilin University College of Computer Science and Technology China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Deakin University School of Information Technology Geelong Australia

ISBN: (纸本)9798350382204

Few-shot learning (FSL) aims to learn to new concepts based on very limited data. One of the main challenges in FSL is the use of pretrained embeddings whose dimension is too high for the small sample size. While the large dimensionality helps capture diverse features in the training example, not all the features are relevant to the task at hand. Such task-irrelevant features from pre-trained embeddings can negatively impact the performance of the FSL model. To address this challenge, we propose a simple yet effective way to learn task-level manifolds that explicitly eliminate the negative effect of nuisance features. One key novelty of our approach is to address class imbalance in the training data by injecting supervised information through key virtual samples, which we named Manifold Support Points. Empirically, we demonstrate that the stability of the learnt task-level manifolds and their effectiveness in challenging tasks in FSL. Our approach shows competitive performance on several benchmark datasets, including tieredImageNet and CUB. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal Transformer for Data Inference and Long Prediction in Sparse Mobile CrowdSensing 42

Spatiotemporal Transformer for Data Inference and Long Predi...

引用

42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, En Liu, Weiting Liu, Wenbin Xiang, Chaocan Yang, Bo Yang, Yongjian Jilin University College of Computer Science and Technology China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Chongqing University College of Computer Science China

ISBN: (纸本)9798350334142

Mobile CrowdSensing (MCS) is a data sensing paradigm that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has been further proposed for large-scale and fine-grained sensing task with the advantage of collecting only a few data to infer unsensed data. However, in many real-world scenarios, such as early prevention of epidemic, people are interested in not only the data at the current, but also in the future or even long-term future, and the latter may be more important. Long-term prediction not only reduces sensing cost, but also identifies trends or other characteristics of the data. In this paper, we propose a spatiotemporal model based on Transformer to infer and predict the data with sparse sensed data by utilizing spatiotemporal relationships. We design a spatiotemporal feature embedding to embed the prior spatiotemporal information of sensing map into the model to guide model learning. Moreover, we also design a novel multi-head spatiotemporal attention mechanism to dynamically capture spatiotemporal relationships among data. Extensive experiments have been conducted on three types of typical urban sensing tasks, which verify the effectiveness of our proposed algorithms in improving the inference and long-term prediction accuracy with the sparse sensed data. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ke Han, Xiaosong Li, Xiaoran Liang, Yanchun Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350386226

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy. © 2024 IEEE.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

A Driving Area Detection Algorithm Based on Swin Transformer 5

A Driving Area Detection Algorithm Based on Swin Transformer

引用

5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Liu, Shuang Li, Ying Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China

ISBN: (纸本)9798350309034

The detection of drivable areas holds immense significance within the perception system of autonomous vehicles. This capability enables intelligent vehicles to gain a comprehensive understanding of the current road conditions and effectively identify safe areas suitable for safe navigation. Convolutional Neural Networks (CNNs) have achieved significant progress in the application of drivable areas detection, particularly in semantic segmentation. However, most existing CNN-based methods for drivable area detection only consider information within a local neighborhood, thus failing to effectively capture long-range dependencies. To tackle this concern, we propose a novel U-shaped network based on the Swin Transformer. The Swin Transformer block was employed as the fundamental element of both the encoder and decoder. Additionally, an upsampling layer was introduced in the decoder to effectively restore the resolution of the feature map. We evaluate our approach on the Cityscapes dataset, a publicly available benchmark for autonomous driving. Experimental results verify the exceptional performance of our proposed method, achieving an impressive mean Intersection over Union (mIoU) score of 91.61%. Our method outperforms existing state-of-the-art techniques in road segmentation detection, further demonstrating its superiority. © 2023 IEEE.

关键词： CNNS Driving Area Detection Semantic segmentation Swin Transformer

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：