检索结果-内蒙古大学图书馆

4th ISPRS Geospatial Week 2019

作者： Lan, Y. Huang, Z. Guo, R. Zhan, Q. Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services Shenzhen University China Guangdong Key Laboratory of Urban Informatics Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Urban Design Wuhan University Wuhan China

Exploring the spatiotemporal patterns of the relationships between urban indicators and urban temperature is essential to improve the mitigation effectiveness when we intend to adjust built environment for moderating urban thermal environment. In this study, RS, GIS technology and statistical methods were involved to investigate the spatiotemporal patterns of the impacts of urban buildings and vegetation on Air Temperature (AT). Building Density (BD) and Normalized Difference Vegetation Index (NDVI) are the indicators for urban buildings and vegetation respectively. The objectives of this study are: 1) to determine an appropriate scale for examining the building-AT relationships and vegetation-AT relationships;2) to explore the seasonal and daily characteristics of these relationships;and 3) to compare the effects of urban buildings and vegetation. The results show that, for both summer and winter, a scale of 200–250 m is optimal for examining building-AT relationships, and 960–1020 m is the desirable scale for studying vegetation-AT relationships. Based on the optimal scales, we find that for both buildings and vegetation, they only significantly impact night-time temperature in both summer and winter. For seasonal comparison, the building-AT relationships and vegetation-AT relationships are relatively stronger in summer than in winter, which are indicated by R-square of the regression results. When comparing the effects of urban building and vegetation, we find that increasing vegetation is more effective than reduce buildings to achieve the same air temperature reduction. Our findings are conducive to generating space-time targeted Urban Heat Island (UHI) mitigation strategies. © Authors 2019.

关键词： Vegetation

来源：评论

学校读者我要写书评

暂无评论

Using NSGA-Ⅲ for optimising biomedical ontology alignment

引用

CAAI Transactions on Intelligence Technology 2019年第3期4卷 135-141页

作者： Xingsi Xue Jiawei Lu Junfeng Chen College of Information Science and Engineering Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Intelligent Information Processing Research Center Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of TechnologyFuzhouFujianPeople’s Republic of China College of IOT Engineering Hohai UniversityChangzhouJiangsuPeople’s Republic of China

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.

关键词： ontology ontologies transform together Ontology complex context effects

来源：评论

学校读者我要写书评

暂无评论

DSRGAN: Explicitly learning disentangled representation of underlying structure and rendering for image generation without tuple supervision

arXiv

引用

arXiv 2019年

作者： Hao, Guang-Yuan Yu, Hong-Xing Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Collaborative Innovation Center of High Performance Computing Nudt Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou

We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision. The setting is significant since tuple supervision is costly and sometimes even unavailable. However, the task is highly unconstrained and thus ill-posed. To address this problem, we propose to introduce an auxiliary domain which shares a common underlying-structure space with the target domain, and we make a partially shared latent space assumption. The key idea is to encourage the partially shared latent variable to represent the similar underlying spatial structures in both domains, while the two domain-specific latent variables will be unavoidably arranged to present renderings of two domains respectively. This is achieved by designing two parallel generative networks with a common Progressive Rendering Architecture (PRA), which constrains both generative networks' behaviors to model shared underlying structure and to model spatially dependent relation between rendering and underlying structure. Thus, we propose DSRGAN (GANs for Disentangling Underlying Structure and Rendering) to instantiate our method. We also propose a quantitative criterion (the Normalized Disentanglability) to quantify disentanglability. Comparison to the state-of-The-Art methods shows that DSRGAN can significantly outperform them in disentanglability. Copyright © 2019, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Improved small gain conditions for input-to-state stability with respect to measurement functions: Discrete time networked system

Improved small gain conditions for input-to-state stability ...

引用

IEEE Conference on Decision and Control

作者： Yuanqiu Mo Changbin Yu Soura Dasgupta Westlake Institute for Advanced Study Westlake University Hangzhou China College of Artificial Intelligence and Big Data Shandong First Medical University & Shandong Academy of Medical Sciences Jinan China Institute for Intelligent Robots Fudan University Shanghai China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks China Faculty of Engineering & the Built Environment University of Johannes-Burg Johannesburg South Africa University of Iowa Iowa City Iowa USA

ISBN: (纸本)9781665436601

In this paper we study input-to-state stability with respect to measurement functions for discrete time networked systems. In such a networked system, the trajectory of each subsystem is affected by another in each time step, and this constraining system may change from step to step. We derive small gain conditions of input-to-state stability with respect to measurement functions for this type of discrete time networked system relying on the construction of dissipative-form finite time Lyapunov function. These conditions loosen those presented in [1] and [2]. Finally, we demonstrate the applicability of our results by applying them to the stability analysis of a distributed graph algorithm which does not satisfy the conditions of [1] and [2].

关键词： Network topology Conferences Gain measurement Stability analysis Time measurement Trajectory Topology

来源：评论

学校读者我要写书评

暂无评论

Deep semantic dictionary learning for multi-label image classification

arXiv

引用

arXiv 2020年

作者： Zhou, Fengtao Huang, Sheng Xing, Yun School of Big data & Software Engineering Chongqing University Shazheng street NO.174 Shapingba District Chongqing400044 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Shazheng street NO.174 Shapingba District Chongqing400044 China

Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic information of categories for improving multi-label image classification performance. However, these semantic-based methods only take semantic information as type of complements for visual representation without further exploitation. In this paper, we present an innovative path towards the solution of the multi-label image classification which considers it as a dictionary learning task. A novel end-to-end model named Deep Semantic Dictionary Learning (DSDL) is designed. In DSDL, an auto-encoder is applied to generate the semantic dictionary from class-level semantics and then such dictionary is utilized for representing the visual features extracted by Convolutional Neural Network (CNN) with label embeddings. The DSDL provides a simple but elegant way to exploit and reconcile the label, semantic and visual spaces simultaneously via conducting the dictionary learning among them. Moreover, inspired by iterative optimization of traditional dictionary learning, we further devise a novel training strategy named Alternately Parameters Update Strategy (APUS) for optimizing DSDL, which alternately optimizes the representation coefficients and the semantic dictionary in forward and backward propagation. Extensive experimental results on three popular benchmarks demonstrate that our method achieves promising performances in comparison with the state-of-the-arts. Our codes and models have been released1 © 2020, CC BY-NC-SA.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Deep Domain Adaptation for Pavement Crack Detection

arXiv

引用

arXiv 2021年

作者： Liu, Huijun Yang, Chunhua Li, Ao Huang, Sheng Feng, Xin Ruan, Zhimin Ge, Yongxin College of Computer Science Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Chongqing City Management College Chongqing401331 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China College of Computer Science And Engineering Chongqing University of Technology Chongqing400054 China Co. Ltd Chongqing400060 China

Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Specifically, DDACDN first extracts crack features from both the source and target domain by a two-branch weights-shared backbone network. And in an effort to achieve the cross-domain adaptation, an intermediate domain is constructed by aggregating the three-scale features from the feature space of each domain to adapt the crack features from the source domain to the target domain. Finally, the network involves the knowledge of both domains and is trained to recognize and localize pavement cracks. To facilitate accurate training and validation for domain adaptation, we use two challenging pavement crack datasets CQU-BPDD and RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement Multi-label Disease dataset named CQU-BPMDD, which contains 38994 high-resolution pavement disease images to further evaluate the robustness of our model. Extensive experiments demonstrate that DDACDN outperforms state-of-the-art pavement crack detection methods in predicting the crack location on the target domain. Copyright © 2021, The Authors. All rights reserved.

关键词： Crack detection

来源：评论

学校读者我要写书评

暂无评论

Training and inference Time Efficiency Assessment Framework for machine learning algorithms: A case study for hyperspectral image classification

引用

International Journal of Applied Earth Observation and Geoinformation 2025年 141卷

作者： Zheng, Xiaorou Jia, Jianxin Dong, Shoubin Wang, Yawei Lu, Runuo Chen, Yuwei Wang, Yueming Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute Espoo FI-02150 Finland Guangzhou Institute of Geography Guangdong Academy of Sciences Guangdong Guangzhou 311100 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Zhejiang Hangzhou 310024 China Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China The Advanced Laser Technology Laboratory of Anhui Province Hefei 230037 China

The increasing complexity and scale of remote sensing datasets, coupled with the challenges of accurately estimating algorithmic time efficiency, often lead to significant resource waste or even failure when using machine learning algorithms in urgent or resource-constrained scenarios. Accurate time efficiency estimation is critical for deploying effective algorithms, yet it remains challenging due to the many factors influencing computational performance. Traditional methods of evaluating time efficiency often neglect the effects of core model parameters and complex data scales in spectral and temporal dimensions. In addition, inference time, an essential factor in real-world applications, is often overlooked. To address these limitations, we propose the Time Efficiency Assessment Framework (TEAF), a novel method for evaluating the time efficiency of machine learning algorithms. Through mathematical reasoning, TEAF models the training and inference time as functions (ψ) of complex data scales and core model parameters. The strong linear correlation between ψ and the actual runtime allows TEAF to accurately predict the time and cost of machine learning tasks with a low computational overhead before algorithm execution. To validate this framework, we derived TEAF formulations for five classical machine learning algorithms and tested them on state-of-the-art hyperspectral image datasets and Sentinel-2 multispectral datasets. The results demonstrated that TEAF could accurately predict both training and inference time for various algorithms, with a strong linear correlation between ψ and actual runtime (R2>0.942). This study offers a practical solution to the challenges posed by the increasing volume and complexity of data in remote sensing image processing. The code is available at https://***/SCUT-CCNL/TEAF. © 2025 The Authors

关键词： Algorithm derivation Complexity data scale Hyperspectral image Inference time Time efficiency Time prediction

来源：评论

学校读者我要写书评

暂无评论

A robust and generalized framework for adversarial graph embedding

arXiv

引用

arXiv 2021年

作者： Li, Jianxin Fu, Xingcheng Peng, Hao Wang, Senzhang Zhu, Shijie Sun, Qingyun Yu, Philip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China State Key Laboratory of Software Development Environment Beihang University Beijing100083 China School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high quality graph embedding vectors various types of graphs. However, most existing methods usually randomly select the negative samples from the original graph to enhance the training data without considering the noise. In addition, most of these methods only focus on the explicit graph structures and cannot fully capture complex semantics of edges such as various relationships or asymmetry. In order to address these issues, we propose a robust and generalized framework for adversarial graph embedding based on generative adversarial networks. Inspired by generative adversarial network, we propose a robust and generalized framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution, and enables the discriminator and generator to jointly learn each node's robust and generalized representation. Based on this framework, we propose three models to handle three types of graph data and derive the corresponding optimization algorithms, i.e., UG-AGE and DG-AGE for undirected and directed homogeneous graphs, respectively, and HIN-AGE for heterogeneous information networks. Extensive experiments show that our methods consistently and significantly outperform existing state-of-the-art methods across multiple graph mining tasks, including link prediction, node classification, and graph reconstruction. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

来源：评论

学校读者我要写书评

暂无评论

Object tracking by the least spatiotemporal searches

arXiv

引用

arXiv 2020年

作者： Yu, Zhiyong Han, Lei Chen, Chao Guo, Wenzhong Yu, Zhiwen College of Mathematics and Computer Sciences Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China School of Computer Science Chongqing University Chongqing400044 China

Tracking a suspicious car or a person in a city efficiently is crucial in urban safety management. But how can we complete the task with the minimal number of spatiotemporal searches when massive camera records are involved? To this end, this study proposes a strategy named intermediate searching at heuristic moments (IHMs). At each step, we determine which moment is the best one for the search based on a heuristic indicator. Then, at that moment, locations are searched one by one in descending order of predicted appearing probabilities until a search hit is obtained. We iterate this step until we derive the object's current location. Five searching strategies are compared via experiments. Among these strategies, the IHMs strategy is validated as the most efficient. IHMs can save up to 1/3 of the total cost. This result provides evidence that "searching at intermediate moments can save cost. Copyright © 2020, The Authors. All rights reserved.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Integrating ARIMA and Bidirectional LSTM to Predict ETA in Multi-Airport Systems

Integrating ARIMA and Bidirectional LSTM to Predict ETA in M...

引用

Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Lechen Wang Xuechun Li Jianfeng Mao School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen Guangdong China

ISBN: (数字)9781728172705

ISBN: (纸本)9781728172712

Traffic states prediction in air transportation systems is a challenging problem and has not been fully explored because it is subject to many more highly correlated factors and a more complicated traffic management scheme compared to urban transportation systems. It becomes a more formidable task when facing a multi-airport system (MAS), in which several major airports are closely located and tightly coupled with each other through limited terminal airspace. In this work, we propose a novel method using a time series model and recurrent neural network to make the estimated time of arrival (ETA) for a flight to an MAS, which can be potentially utilized for flight delay prediction and congestion analysis. The experiment utilizes two months of 4D trajectories data from Beijing Capital International Airport (PEK) to Shenzhen Bao'an International airport (ZGSZ). The entire prediction work is decomposed into two sub-problems, en-route travel time prediction which is from flight origin to the entering gate of MAS, defined as the location is 300km from the airport in MAS, and terminal maneuvering area (TMA) travel time prediction which is from the entrance to flight's destination. The auto-regressive integrated moving average (ARIMA), a time series prediction model, is used to predict travel time in en-route under given the flight departure time. Bidirectional long short term memory (LSTM), a recurrent neural network, is developed to forecast travel time in the arrival approach by utilizing spatio-temporal features. To design the input features, we use density-based spatial clustering (DBSCAN) with the help of the Voronoi diagram to extract spatial information from every historical flight trajectory of aircraft operated in an MAS, then select the observation time window to capture the temporal information for each flight. The Multivariate Stacked Fully connected-Bidirectional LSTM (MSFCB-LSTM) model is constructed to make shortterm forecasting using spatio-temporal feature

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：