检索结果-内蒙古大学图书馆

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wu, Lin Sun, Mengwei Min, Lin Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image. © 2019 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Encoding physics to learn reaction-diffusion processes

arXiv

引用

arXiv 2021年

作者： Rao, Chengping Ren, Pu Wang, Qi Buyukozturk, Oral Sun, Hao Liu, Yang Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Department of Mechanical and Industrial Engineering Northeastern University BostonMA02115 United States Department of Civil and Environmental Engineering Northeastern University BostonMA02115 United States Department of Civil and Environmental Engineering MIT CambridgeMA02139 United States Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China School of Engineering Sciences University of Chinese Academy of Sciences Beijing101408 China

Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs). However, due to insufficient prior knowledge on some under-explored dynamical systems, such as those in chemistry, biology, geology, physics and ecology, and the lack of explicit PDE formulation used for describing the nonlinear process of the system variables, to predict the evolution of such a system remains a challenging task. Unifying measurement data and our limited prior physics knowledge via machine learning provides us with a new path to solving this problem. Existing physics-informed learning paradigms impose physics laws through soft penalty constraints, whose solution quality largely depends on a trial-and-error proper setting of hyperparameters. Since the core of such methods is still rooted in black-box neural networks, the resulting model generally lacks interpretability and suffers from critical issues of extrapolation and generalization. To this end, we propose a deep learning framework that forcibly encodes given physics structure to facilitate the learning of the spatiotemporal dynamics in sparse data regimes. We show how the proposed approach can be applied to a variety of problems regarding the PDE system, including forward and inverse analysis, data-driven modeling, and discovery of PDEs. The resultant learning paradigm that encodes physics shows high accuracy, robustness, interpretability and generalizability demonstrated via extensive numerical experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

Private Trajectory data Publication for Trajectory Classification 16th

Private Trajectory Data Publication for Trajectory Classific...

引用

16th Web Information Systems and Applications Conference, WISA 2019

作者： Zhu, Huaijie Yang, Xiaochun Wang, Bin Wang, Leixia Lee, Wang-Chien School of Data and Computer Science Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computer Science and Engineering Northeastern University Shenyang China The Pennsylvania State University State College United States

ISBN: (纸本)9783030309510

Trajectory classification (TC), i.e., predicting the class labels of moving objects based on their trajectories and other features, has many important real-world applications. Private trajectory data publication is to anonymize trajectory data, which can be released to the public or third parties. In this paper, we study private trajectory publication for trajectory classification (PTPTC), which not only preserves the trajectory privacy, but also guarantees high TC accuracy. We propose a private trajectory data publishing framework for TC, which constructs an anonymous trajectory set for publication and use in data services to classify the anonymous trajectories. In order to build a "good" anonymous trajectory set (i.e., to guarantee a high TC accuracy), we propose two algorithms for constructing anonymous trajectory set, namely Anonymize-POI and Anonymize-FSP. Next, we employ Support Vector Machine (SVM) classifier to classify the anonymous trajectories. Finally, the experimental results show that our proposed algorithms not only preserve the trajectory privacy, but also guarantee a high TC accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Fashion Editing With Adversarial Parsing Learning

Fashion Editing With Adversarial Parsing Learning

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haoye Dong Xiaodan Liang Yixuan Zhang Xujie Zhang Xiaohui Shen Zhenyu Xie Bowen Wu Jian Yin School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Intelligent Systems Engineering Sun Yat-sen University Petuum Inc. ByteDance AI Lab

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreover, they directly utilize conventional convolution and normalization layers to restore the incomplete image, which tends to wash away the sketch and color information. In this paper, we propose a novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes. FE-GAN consists of two modules: 1) a free-form parsing network that learns to control the human parsing generation by manipulating sketch and color; 2) a parsing-aware inpainting network that renders detailed textures with semantic guidance from the human parsing map. A new attention normalization layer is further applied at multiple scales in the decoder of the inpainting network to enhance the quality of the synthesized image. Extensive experiments on high-resolution fashion image datasets demonstrate that the proposed FE-GAN significantly outperforms the state-of-the-art methods on fashion image manipulation.

关键词： Image color analysis Semantics Convolution Colored noise Feature extraction Task analysis Neural networks

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

引用

Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

来源：评论

学校读者我要写书评

暂无评论

SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

引用

PeerJ Computer Science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Automatic Recognition of Business Process Images

Automatic Recognition of Business Process Images

引用

2019 IEEE International Conference on Computer Science and Educational Informatization, CSEI 2019

作者： Kang, Qingyi Yu, Yong Dai, Fei School of Software Yunnan University Kunming650091 China School of Software Key Laboratory in Software Engineering of Yunnan Province Yunnan University Kunming650091 China School of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781728123080

The Business Process Modelling Notation (BPMN) is a defacto standard for modelling business processes in the early phases of systems development. The increased adoption of business process management approaches has led organizations to model large collections of BPMN business process models. The mix of constructs in BPMN business process models can lead to semantic error. The ability to take a picture of BPMN business process models and analyze the semantic correctness of BPMN business process images is thus a desirable feature. In this paper, we propose an automatic recognition method to transform BPMN business process images into Petri nets, for which efficient analysis techniques are available. The proposed recognition has been implemented as a tool, in conjunction with existing Petri net-based tools, that enables the soundness analysis of business process images. This transformation can led to the identification of deficiencies in the BPMN business process images. © 2019 IEEE.

关键词： Petri nets

来源：评论

学校读者我要写书评

暂无评论

Analysis of the Causes of Cyanobacteria Bloom:A Review

引用

Journal of Resources and Ecology 2020年第4期11卷 405-413页

作者： ZHOU Bing CAI Xiaoli WANG Sen YANG Xinxin Key Laboratory of Analysis and Processing of Big Data Kaifeng 475004HenanChina School of Computer and Information Engineering Henan UniversityKaifeng 475004HenanChina School of Information Management Wuhan UniversityWuhan 430072China

Among water blooms,cyanobacteria bloom occurs over the widest range and is much more harmful than other *** occurrence in inland water bodies is affected by many factors,such as meteorology,hydrology,and human ***,the study of the causes of cyanobacterial bloom has become a major focus of *** China knowledge Network Journal database contains 143 papers from China and abroad from the years 2004 to 2019 that are relevant to the study of cyanobacteria *** begin by analyzing keywords in these studies and creating a keyword distribution map which indicates the factors related to the *** on parameters such as the frequency of words appearing in the text,the full text of each of the 143 papers is analyzed to form a word cloud created by a program written in Python *** irrelevant terms are eliminated,the word cloud map can reveal potential factors that were not identified by keywords *** completing this macro analysis,we examined approximately 100 related papers from the China knowledge Network Journal database and Web of Science database published from 2014 to ***,we summarize the main reasons for the outbreak of water *** factors causing blooms can be divided into natural factors and human *** the natural factors are illumination,water temperature and nutrient salt *** human factors are generally related to large-scale water conservancy *** paper analyzes and summarizes these factors,and provides a reference to aid in the prevention and treatment of algal *** information in the paper has a certain practical significance for the protection of water environments.

关键词： algae bloom eutrophication literature measurement temperature

来源：评论

学校读者我要写书评

暂无评论

Delay-Optimal Edge Cache Replacement with Non-Markovian Content Fetching

Delay-Optimal Edge Cache Replacement with Non-Markovian Cont...

引用

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Nannan Zhang Wei Wang Pan Zhou Aiping Huang College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

Leveraging the content caching technology, popular contents can be in close proximity to the users, which reduces the delay effectively. In this paper, we dedicate to minimizing the long-term average delay for the cache-enabled wireless networks, in which the fetching delay is considered when the uncached contents are retrieved from remote servers. We construct a decision-theoretic framework for this delay-optimal content-caching problem. The most challenging obstacle is that the consideration of imperfect content fetching breaks the Markovian property in the decision-theoretic framework, which makes the problem difficult to solve. To overcome this obstacle, we propose an equivalent transformation by analyzing the queue dynamics during the content fetching delay, so that the transformed problem can be modeled as an infinite horizon semi-Markov decision process (SMDP). To handle the curse of dimensionality for solving the SMDP, we decompose the global optimality equation into several per-content equations, and propose a low-complexity delay-optimal content caching algorithm. Simulation results show that the proposed algorithm achieves significantly lower delay than conventional caching algorithms.

关键词： Delays Servers Mathematical model Wireless networks Queueing analysis Optimization Time measurement

来源：评论

学校读者我要写书评

暂无评论

Dual-channel hypergraph networks in the time–frequency domain for learning advanced spatiotemporal dependencies in multivariate time series

引用

Neurocomputing 2025年 648卷

作者： Ziwei Chen Jianjian Jiang Xiangmin Luo Fangyuan Lei Xiaochen Yuan Jin Zhan Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Polytechnic Normal University Guangzhou 510665 China College of Electronic and Information Engineering Guangdong Polytechnic Normal University Guangzhou 510665 China Faculty of Applied Sciences Macao Polytechnic University 999078 Macao Special Administrative Region of China School of Computer Science Guangdong Polytechnic Normal University Guangzhou 510665 China

Multivariate time series play a crucial role in applications such as human activity recognition and fault diagnosis, where capturing spatiotemporal dependencies is paramount. While recent methods have leveraged graph neural networks (GNNs) or hybrid models that integrate convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to learn these dependencies, two significant challenges remain: First, previous research has neglected the dynamic dependencies and overall correlations among multivariate time series, which are crucial for comprehensive data utilization. Second, when processing both temporal and spatial dimensional information, how to effectively integrate higher-order information while avoiding complexity issues in model design and implementation remains a challenge. To address these challenges, we propose a Dual-channel Temporal–Spatial Hypergraph Network (DTSHN) classification framework based on the time–frequency domain. Specifically, our DTSHN is a time–frequency domain dual-channel interactive learning model that enhances the utilization and diversity of time series data. To capture higher-order relationships among signals, we design a dynamic hypergraph structure learning module and construct temporal and spatial hypergraph structures in the time–frequency domain. Additionally, we propose a temporal–spatial hypergraph network module to learn temporal correlations and spatial dependencies within time series. Furthermore, to address the feature consistency issue in the time–frequency domain under the dual-channel paradigm, we introduce a time–frequency contrastive learning strategy to enable the model to learn effective feature representations across both domains. The experimental results show that our proposed model achieves 96.77%, 94.33% and 97.57% optimal accuracy in HAR, FM and PS datasets, respectively. In addition, we have reduced the FLOPs by 19.8% compared to other optimal models.

关键词：

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：