检索结果-内蒙古大学图书馆

Atmospheric fine particles in a typical coastal port of Yangtze River Delta

Journal of Environmental Sciences 2020年第12期32卷 62-70页

作者： Qi Yuan Xiaomi Teng Shaoxuan Tu Binxin Feng Zhiyu Wu Hang Xiao Qiuliang Cai Yinxiao Zhang Qiuhan Lin Zhaoce Liu Mengmeng He Xiaokun Ding Weijun Li Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province Department of Atmospheric SciencesSchool of Earth SciencesZhejiang UniversityHangzhou 310027China Center for Excellence in Regional Atmospheric Environment&Key Lab of Urban Environment and Health Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China Key Laboratory of Urban Environmental Processes and Pollution Control of Zhejiang Province Ningbo Urban Environment Observation and Research StationChinese Academy of SciencesNingbo 315830China School of Earth Science and Engineering Hebei University of EngineeringHandan 056038China Department of Chemistry Zhejiang UniversityHangzhou 310027China

In recent decades, coastal ports have experienced rapid development and become an important economic and ecological hub in China. Atmospheric particle is a research hotspot in atmospheric environmental sciences in inland regions. However, few studies on the atmospheric particle were conducted in coastal port areas in China, which indeed suffers atmospheric particle pollution. Lack of the physicochemical characteristics of fine particles serves as an obstacle toward the accurate control for air pollution in the coastal port area in China. Here, a field observation was conducted in an important coastal port city in Yangtze River Delta from March 6 to March 19, 2019. The average PM2.5 concentration was 63.7 ±27.8 μg/m^3 and NO3^-, SO4^2-, NH4^+, and organic matter accounted for ?60% of PM 2.5. Fe was the most abundant trace metal element and V as the ship emission indicator was detected. Transmission electron microscopy images showed that SK-rich, soot, Fe, SK-soot and SK-Fe were the major individual particles in the coastal port. V and soluble Fe were detected in sulfate coating of SK-Fe particles. We found that anthropogenic emissions, marine sea salt, and secondary atmosphere process were the major sources of fine particles. Backward trajectory analysis indicated that the dominant air masses were marine air mass, inland air mass from northern Zhejiang and inland-marine mixed air mass from Shandong and Shanghai during the sampling period. The findings can help us better understand the physicochemical properties of atmospheric fine particles in the coastal port of Eastern China.

关键词： Coastal port Water-soluble ion Organic matter Trace metal Individual particle

来源：评论

学校读者我要写书评

暂无评论

Developing Global Digital Economy after COVID-19: Preface for ITQM 2020 & 2021

引用

Procedia Computer Science 2021年 199卷 1-9页

作者： Liu, Yubin Shi, Yong Wang, Yong Ergu, Daji Berg, Daniel Tien, James Li, Jianping Tian, Yingjie Southwest Minzu University Sichuan Chengdu610041 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Engineering University of Miami Coral GablesFL33124 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Welcome to the Eighth International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021), July 9-11, Chengdu, China. The theme of ITQM 2020 & 2021 is “Developing Global Digital Economy after COVID-19”. ITQM 2020 & 2021 is organized by International Academy of Information Technology and Quantitative Management (IAITQM), Southwest Minzu University, Chinese Academy of Sciences and University of Nebraska at Omaha. IAITQM was formally inaugurated on June 3, 2012 with more than 50 founding members from China, United States, Australia, Japan, Lithuania, Poland, Romania, Spain, Singapore, South Korea, The Netherlands, Turkey and other countries. The International Conference on Information Technology and Quantitative Management (ITQM), established by IAITQM, is a global forum for exchanging research findings and case studies that bridge the latest information technology and quantitative management techniques. It explores how the use of information technology techniques to improve quantitative management and how the development of management tools can reshape the development of information technology. The First International Conference on Information Technology and Quantitative Management (ITQM 2013) took place in Suzhou, China. The Second International Conference on Information Technology and Quantitative Management (ITQM 2014) was held in Moscow, Russia. The Third International Conference on Information Technology and Quantitative Management (ITQM 2015) was held at Rio de Janeiro, Brazil. The Forth International Conference on Information Technology and Quantitative Management (ITQM 2016) was held at Asan, Korea. The Fifth International Conference on Information Technology and Quantitative Management (ITQM 2017) was held at New Delhi, India. The Sixth International Conference on Information Technology and Quantitative Management (ITQM 2018) was hosted at Omaha, USA. The Seventh International Conference on Information Technology and Quantitative

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quantitative monitoring of sugarcane typhoon disaster based on multi-source remote sensing data 3

Quantitative monitoring of sugarcane typhoon disaster based ...

引用

3rd International Conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

作者： Qian, Lisha Chen, Shuisen Jiang, Hao Dai, Xuemei Jia, Kai Guangzhou Institute of Geography Guangdong Academy of Sciences Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Res. Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data Guangzhou China Shaoguan ShenBay Low Carbon Digital Technology Co. Ltd. Guangdong Shaoguan China Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665485951

ISBN: (纸本)9781665485951

With a foreseen increase in the number of agrometeorological disasters due to climate change, especially in the field of crop lodging. This paper presented an innovative monitoring technique to explore the application potential of muti-source remote sensing data. Based on the sugarcane planting area extracted from sentinel-1 time-series data and combination of Landsat-8 and sentinel-2 MSI images before and after Super Typhoon Hato, a vegetation index distance leveling method was come out and then was applied to assess the sugarcane lodging in Dagang Town, Nansha District, Guangzhou City. The region was caused by strong wind and rainstorm on August 23, 2017. The validation results showed that the multi-temporal Sentinel-1 image data can effectively extract the sugarcane planted area before and after lodging with an accuracy of 87.83%. Compared with other vegetation indices (RVI/LSWI/NBR/EVI/DVI), NDVI was the most sensitive in response to sugarcane lodging. The validation accuracy of extracting farmland damage extent reached 71.64%, among them, the affected area of sugarcane reached 711.33 ha. The study further illustrates the capability of the image vegetation index difference method on monitoring of sugarcane lodging degree at the reginal scale. © 2022 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Gray Learning from Non-IID data with Out-of-distribution Samples

arXiv

引用

arXiv 2022年

作者： Zhao, Zhilin Cao, Longbing Wang, Chang-Dong The Data Science Lab School of Computing and DataX Research Centre Macquarie University SydneyNSW2109 Australia The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-IID datasets comprising both in- and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in- and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed Gray Learning (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization

arXiv

引用

arXiv 2021年

作者： Zhou, Ziqi Qi, Lei Yang, Xin Ni, Dong Shi, Yinghuan The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University China The School of Computer Science and Engineering Southeast University China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center The Medical Ultrasound Image Computing Lab The Marshall Laboratory of Biomedical Engineering Shenzhen University China

For medical image segmentation, imagine if a model was only trained using MR images in source domain, how about its performance to directly segment CT images in target domain? This setting, namely generalizable cross-modality segmentation, owning its clinical potential, is much more challenging than other related settings, e.g., domain adaptation. To achieve this goal, we in this paper propose a novel dual-normalization model by leveraging the augmented source-similar and source-dissimilar images during our generalizable segmentation. To be specific, given a single source domain, aiming to simulate the possible appearance change in unseen target domains, we first utilize a nonlinear transformation to augment source-similar and source-dissimilar images. Then, to sufficiently exploit these two types of augmentations, our proposed dual-normalization based model employs a shared backbone yet independent batch normalization layer for separate normalization. Afterward, we put forward a style-based selection scheme to automatically choose the appropriate path in the test stage. Extensive experiments on three publicly available datasets, i.e., BraTS, Cross-Modality Cardiac, and Abdominal Multi-Organ datasets, have demonstrated that our method outperforms other state-of-the-art domain generalization methods. Code is available at https://***/zzzqzhou/Dual-Normalization. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Vehicle Segmentation with Coarse Distance Estimation Based on Monocular Vision

Vehicle Segmentation with Coarse Distance Estimation Based o...

引用

IEEE International Conference on e-Business engineering (ICEBE)

作者： Fangyuan Xie Pengshuai Yin Zehui Ke Ruizhou Sun Ke Ding Jian Chen Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

With the increasing population of drivers and the popularity of intelligent cars, traffic safety is becoming more concerned. Aiming to reduce the probability of road accidents, many works have explored how to provide an early warning when the distances between the current car (or the in-car camera) and vehicles ahead are dangerous. In this paper, benefiting from the robust pixel-level understanding of cutting-edge deep networks, we introduce a vehicle segmentation method based on monocular vision, detecting vehicles in front of the current one and generating a segmentation mask for each vehicle instance detected. Instead of concentrating our efforts on measuring distance precisely, we categorize distances into three classes (“near”, “medium”, and “far”), which are pretty enough for an early collision warning. Our model is trained and evaluated on our hand-made dataset, after which we transfer and deploy it onto an Android TV virtual machine to test its performance. All the results we got show that our method does a good job in vehicle segmentation with coarse distance estimation, being applicable to aided driving.

关键词： TV Sociology Estimation Cameras Virtual machining Safety Automobiles

来源：评论

学校读者我要写书评

暂无评论

Improved Luminescence Properties in a Yb: YAG Crystal-Derived Fiber by Thermal Treatment

Improved Luminescence Properties in a Yb: YAG Crystal-Derive...

引用

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Kai Zou Jianxiang Wen Ying Wan Fufei Pang Tingyun Wang Key Lab of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai China

The luminescence properties of Yb: YAG crystal-derived fiber (YDF) have been improved by thermal treatment. Its center position of the emission wavelength shifted from 1017 to 1030 nm and luminescence intensity is enh... 详细信息

ISBN: (纸本)9781665410953

关键词： Asia Luminescence Crystals Optical fiber communication High power fiber lasers Photonics

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based computational method for soft matter dynamics: Deep Onsager-Machlup method

arXiv

引用

arXiv 2023年

作者： Li, Zhihao Zou, Boyi Wang, Haiqin Su, Jian Wang, Dong Xu, Xinpeng Department of Civil Engineering Shantou University 243 Daxue Road Guangdong Shantou515063 China Department of Physics MATEC Key Lab Guangdong Technion - Israel Institute of Technology 241 Daxue Road Guangdong Shantou515063 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Technion – Israel Institute of Technology Haifa32000 Israel

A deep learning-based computational method has been proposed for soft matter dynamics – the deep Onsager-Machlup method (DOMM), which combines the brute forces of deep neural networks (DNNs) with the fundamental physics principle – Onsager-Machlup variational principle (OMVP). In the DOMM, the trial solution to the dynamics is constructed by DNNs that allow us to explore a rich and complex set of admissible functions. It outperforms the Ritz-type variational method where one has to impose carefully-chosen trial functions. This capability endows the DOMM with the potential to solve rather complex problems in soft matter dynamics that involve multiple physics with multiple slow variables, multiple scales, and multiple dissipative processes. Actually, the DOMM can be regarded as an extension of the deep Ritz method constructed using variational formulations of physics models to solve static problems in physics as discussed in our former work [Wang et al, Soft Matter, 2022, 18, 6015-6031]. In this work, as the first step, we focus on the validation of the DOMM as a useful computational method by using it to solve several typical soft matter dynamic problems: particle diffusion in dilute solutions, and two-phase dynamics with and without hydrodynamics. The predicted results agree very well with the analytical solution or numerical solution from traditional computational methods. These results show the accuracy and convergence of DOMM and justify it as an alternative computational method for solving soft matter dynamics. © 2023, CC BY-NC-ND.

关键词： Computational methods

来源：评论

学校读者我要写书评

暂无评论

Noise-Tolerant Learning for Audio-Visual Action Recognition

arXiv

引用

arXiv 2022年

作者： Han, Haochen Zheng, Qinghua Luo, Minnan Miao, Kaiyao Tian, Feng Chen, Yan The National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Ministry of Education Xi’an710049 China The School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide semantically relevant information. Unfortunately, the widely used video datasets are usually coarse-annotated or collected from the Internet. Thus, it inevitably contains a portion of noisy labels and noisy correspondence. To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence. Specifically, our method consists of two phases that aim to rectify noise by the inherent correlation between modalities. First, a noise-tolerant contrastive training phase is performed to make the model immune to the possible noisy-labeled data. Despite the benefits brought by contrastive training, it would overfit the noisy correspondence and thus provide false supervision. To alleviate the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. As the noisy correspondence existed at the instance level, we further propose a category-level contrastive loss to reduce its interference. Second, in the hybrid-supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision to guide the training. Furthermore, due to the lack of suitable datasets, we establish a benchmark of real-world noisy correspondence in audio-visual data by relabeling the Kinetics dataset. Extensive experiments on a wide range of noisy levels demonstrate that our method significant

关键词： Modal analysis

来源：评论

学校读者我要写书评

暂无评论

A Fuzzy ESO-based Joint Angle Control Design of Snake Robots

A Fuzzy ESO-based Joint Angle Control Design of Snake Robots

引用

IEEE International Conference on Robotics and Biomimetics

作者： Lili Wu Yang Liu Junfang Zhou Zhigang Wang Yushuang Wang Yongchen Tang Beijing Electro-Mechanical Engineering Institute Beijing China 91039 Army School of Electrical and Information Engineering Tianjin University Tianjin China Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan China

ISBN: (纸本)9781665405362

The joint angle control of snake robot is an important component for the path tracking control. However, existing controllers can not realize the accurate control of the joint angle of snake robots since they rely on model information and can not adapt to the uncertain environment. Here, aiming at the above problems, a fuzzy extended state observer (FESO)-based joint angle controller for snake robots is designed. The total system disturbance including model uncertainty and external disturbance is estimated by FESO and then rejected by adding a corresponding part to the control law. Moreover, the peaking phenomenon caused by initial estimation errors can be suppressed in the proposed FESO. Finally, the effectiveness of the designed joint angle controller is verified by simulations.

关键词： Adaptation models Estimation error Uncertainty Control design Conferences Biomimetics Biological system modeling

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：