检索结果-内蒙古大学图书馆

39th IEEE International Conference on data engineering, ICDE 2023

作者： Fan, Zhuochen Guo, Jiarui Li, Xiaodong Yang, Tong Zhao, Yikai Wu, Yuhan Cui, Bin Xu, Yanwei Uhlig, Steve Zhang, Gong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Theory Lab Hong Kong Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom

ISBN: (纸本)9798350322279

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate"the two machine learning models through end-to-end experiments. We have released our source code at GitHub. © 2023 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

Deep U-Net Architecture with Curriculum Learning for Left Atrial Segmentation 1

引用

1st Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

作者： Jiang, Lei Li, Yan Wang, Yifan Cui, Hengfei Xia, Yong Zhang, Yanning National Engineering Laboratory for Integrated Aero -Space -Ground -Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

ISBN: (数字)9783031317781

ISBN: (纸本)9783031317774

Segmentation of the late-stage gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is a critical step in the ablation therapy for atrial fibrillation (AF). In this work, we propose an end-to-end deep learning-based segmentation method for delineating 3D left atrial (LA) structures in multiple domains. The proposed method uses the 6 layers deep U-Net architecture as the segmentation backbone. Curriculum learning is integrated into the deep U-Net architecture, helping the network learn step by step from easy to difficult scene. We have tested normal and strong version of data augmentation methods, to verify the effect of reducing domain shifts. Other techniques like Fourier-based data augmentation and Swin Transformer Block have also been explored to further improve the segmentation performance. The experimental results demonstrate that the strong version of data augmentation method can reduce the domain shifts and achieve more accurate result, with mean Dice score of 0.881 on the validation set of LAScarQS 2022 challenge. The evaluation results demonstrate our method’s effectiveness on left atrial segmentation in multi-sequence cardiac magnetic resonance (CMR) data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Progress and prospects of data-driven stock price forecasting research

引用

International Journal of Cognitive Computing in engineering 2023年第1期4卷 100-108页

作者： Zhao, Chuanjun Wu, Meiling Liu, Jingfeng Duan, Zening li, Jie Shen, Lihua Shangguan, Xuekui Liu, Donghang Wang, Yanjie School of Information Shanxi University of Finance and Economics Shanxi Taiyuan030006 China School of Journalism and Mass Communication University of Wisconsin-Madison MadisonWI53706 United States Financial Innovation and Big Data Statistical Analysis Laboratory Shanxi University of Finance and Economics Shanxi Taiyuan030006 China Shanxi Information Technology Application Innovation Engineering Research Center Shanxi Taiyuan030006 China

With the rapid development of social economy and the continuous improvement of stock market, stock investment has become more and more widely concerned. Stock price prediction has become an important research direction in the field of cognitive computing in engineering. data-driven stock price forecasting aims to predict future stock price trends based on historical values and textual data, which can effectively help people reduce risks and improve returns in the process of stock investment. The article reviews the literature on stock price forecasting methods, and classifies stock price forecasting methods from two different perspectives of model and feature. According to different model angles, the existing stock price prediction methods can be divided into statistical analysis methods, traditional machine learning methods and deep learning methods. According to different characteristic angles, the existing stock price prediction methods can be divided into those based on numerical data and those based on text mixed with numerical data. Finally, we summarize the research challenges faced by stock price prediction and provide future research directions. © 2023

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

Advances and prospects in SAR microwave vision three-dimensional imaging

引用

national Science Open 2024年第5期3卷 53-83页

作者： Xiaolan Qiu Zekun Jiao Zhe Zhang Qiancheng Yan Chibiao Ding National Key Laboratory of Microwave Imaging Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100094China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou 215123China

Synthetic Aperture Radar three-dimensional(3D)imaging enables the acquisition of more comprehensive information,making it a recent hotspot in radar *** 3D imaging methods have evolved from 2D and interferometric imaging,combining elevation aperture extension with signal processing *** such as long acquisition or complex system from its imaging mechanism restrict its *** recent years,rapid development of artificial intelligence has led to a swift advancement in radar,injecting new vitality into SAR 3D *** microwave vision 3D imaging theory,which is built upon advanced technologies,has emerged as a new interdisciplinary field for radar *** paper reviews SAR 3D imaging’s history and present situation,and introduces SAR microwave *** establish a theoretical framework covering representation models,computational models,processing paradigms and evaluation ***,our research progress in this area is discussed,along with future prospects for SAR microwave vision 3D imaging.

关键词： SAR microwave vision 3D imaging visual semantics scattering mechanism

来源：评论

学校读者我要写书评

暂无评论

HyperCalm Sketch: One-Pass Mining Periodic Batches in data Streams

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

引用

International Conference on data engineering

作者： Zirui Liu Chaozhe Kong Kaicheng Yang Tong Yang Ruijie Miao Qizhi Chen Yikai Zhao Yaofeng Tu Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China ZTE Corporation

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Blind Information Extraction of Machine Faults Based on Separating Matrix

Blind Information Extraction of Machine Faults Based on Sepa...

引用

IEEE International Conference on Intelligent Transportation engineering (ICITE)

作者： Hao Li Yifan Tan Yun Pu Officers College of PAP Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology

ISBN: (数字)9781728194097

ISBN: (纸本)9781728194103

Blind signal processing is an effective feature extraction method for mechanical vibration signals. However due to noise corruption, independent source signals can't always be accurately recovered or separated from the acquired sensor observations. Then feature information extracted from source signals can't naturally represent machine states of the detected mechanical equipment. Generally in blind signal processing, the separating matrix may contain as much information contents as the separated source signals. The separating matrix can directly be processed to extract its singular values as useful feature information by the singular value decomposition (SVD) method. Thus a blind information extraction method was proposed to extract singular values of separating matrix as the desired feature information of the detected machine. The experimental results of gear pump indicate that this method can be applied to feature extraction of mechanical equipment.

关键词： Feature extraction Gears Vibrations Signal processing data mining Pumps Time-domain analysis

来源：评论

学校读者我要写书评

暂无评论

Research on Cyberattack Detection of Connected Automated Vehicles Based on Support Vector Machine

Research on Cyberattack Detection of Connected Automated Veh...

引用

IEEE International Conference on Intelligent Transportation engineering (ICITE)

作者： Runzhe Liu Xin Peng Zhanbo Sun Zhihang Huang Haitao Hu National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China

Connected automated vehicles (CAVs) are facing the threat of cyberattacks while exchanging state information. Most of the existing research focuses on the communication technologies and the network security technologies, with the purpose of providing corresponding preventive solutions for the specific technical means of attack behavior, which are highly targeted but weak in universality of application. In this paper, a type of adversary model based on attack effects, namely, disturbance attack, was established. A numerical simulation platform for CAVs cyberattack experiment was built to quantitatively analyze the impacts of CAVs cyberattacks on traffic. 9 measurement indicators were constructed based on traffic safety indicators and traffic flow parameters for the characteristic impacts of various attacks. Using the cyberattack experiment simulation platform to generate attack feature datasets, A cyberattack detection model of CAVs on the basis of nonlinear Support Vector Machine was established. The model showed a sound classification performance, with a detection rate of more than 95% within 3s of the attack. The research method in this paper has a universal reference value for cyberattacks detection of CAVs. The results help better understand the effects of CAV-related cyberattacks and shed light on proactive defense against such attacks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Polarization imaging bi-attentional recursive residual super-resolution method 9

Polarization imaging bi-attentional recursive residual super...

引用

9th Symposium on Novel Photoelectronic Detection technology and applications

作者： Liu, Jiaqing Li, Yi Liang, Dong Xu, Guoming Zhou, Pucheng Ma, Jian School of Internet Anhui University Hefei China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei China Anhui Provincial Key Laboratory of Polarized Light Imaging Detection Hefei China School of Electrical and Electronics Engineering Anhui Wenda University of Information Engineering Hefei China

ISBN: (纸本)9781510664432

Due to the limitation of the detector, the spatial resolution of the polarization image obtained by space-modulated full-polarization computed imaging is low. In general, there are a lot of valuable high-frequency components in low resolution space, but it is difficult to reconstruct high frequency information by deep learning method. Through the research of the previous super-resolution reconstruction method based on deep learning, it is found that it is difficult to obtain better lifting effect only by stacking residual blocks to build a deeper network. A polarization imaging bi-attention recursive residual network super-resolution reconstruction method is proposed. In the network structure, bi-attention recursive residual group is used as the deep feature extraction module, and the bi-attention module contained in this module is used to adaptively learn image features of different channels and different transformation Spaces in the deep network. In order to provide the concentrated learning efficiency of high-frequency information, jump connections are introduced into the network structure, and the shallow feature extraction and reconstruction of images are completed by a convolution layer and a sub-pixel convolution algorithm respectively. The experimental verification is carried out under the actual imaging system and simulation data, and compared with other methods in terms of visual effect and quantitative results. Experimental results verify that the proposed method can effectively reconstruct the contour structure and texture details of the detection target in subjective image quality, and is superior to the comparison method in objective evaluation index. © 2023 SPIE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Novel Control Algorithm for Interaction Between Surface Waves and A Permeable Floating Structure

引用

China Ocean engineering 2016年第2期30卷 161-176页

作者： Pei-Wei TSAI A.ALSAEDI T.HAYAT Cheng-Wu CHEN College of information Science and Engineering Fujian University of TechnologyFuzhou 350118China Fujian Provincial Key Laboratory of Big Data Mining and Applications(Fujian University of Technology) Fuzhou 350118China Nonlinear Analysis and Applied Mathematics(NAAM)Research Group King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mathematics Quaid-I-Azam UniversityIslamabad 44000Pakistan Department of Maritime Information and Technology National Kaohsiung Marine UniversityKaohsiung 80543China Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic *** the design procedure of the controller,a parallel distributed compensation（PDC） scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers.A stability analysis is carried out for a real structure system by using Lyapunov *** corresponding boundary value problems are then incorporated into scattering and radiation *** are analytically solved,based on separation of variables,to obtain series solutions in terms of the harmonic incident wave motion and surge *** dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structure properties including platform width,thickness and mass has been thus drawn with a parametric *** which mathematical models are applied for the wave-induced displacement of the surge motion.A nonlinearly inverted pendulum system is employed to demonstrate that the controller tuned by swarm intelligence method can not only stabilize the nonlinear system,but has the robustness against external disturbance.

关键词： fuzzy Lyapunov function Takagi?Sugeno form swarm intelligence algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

引用

14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information Research Institute.

关键词： Long short-term memory

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：