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analysis of Anti-interference Ability of Hyperspectral Sensitive Features to Wheat Powdery Mildew

IOP Conference Series: Earth and Environmental Science 2021年第1期693卷

作者： Jinling Zhao Guomin Chu Hao Yan Lei Hu Yongan Xue National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data Anhui University Hefei 230601 China College of Mining Engineering Taiyuan University of Technology Taiyuan 030024 China

The development of ground-based, airborne and spaceborne remote sensing has greatly facilitated the identification and diagnosis of various objects. Corresponding algorithms and methods of removing interference from remotely sensed imagery have been proposed. Nevertheless, the studies on anti-interference ability of selected features have not been fully considered. In our study, the hyperspectral reflectance of leaf-scale powdery mildew (Erysiphe graminis) on winter wheat were collected as the testing dataset. A total of seven representative spectral features of Landsat-8 Operational Land Imager (OLI) and GaoFen-1 Wide-Field-View (WFV) was selected, namely, original blue, green, red, near-infrared (NIR) bands and normalized difference vegetation index (NDVI), normalized difference greenness index (NDGI), structure insensitive pigment index (SIPI). Four hyperspectral vegetation indices including red edge (MSR) simple ratio index, NDVI, green band and SIPI were also selected. Three primary background noises including soil, cloud and white poplar (Populus alba L.) were added into the spectral signal. The correlation coefficient (R) between disease severities (0, 1, 2, 3 and 4) and spectral features was used to estimate the anti-interference ability. The results show that there is a generally similar spectral performance for the two sensors, but Landsat-8 OLI is superior to GF-1 WVF in terms of spectral response. The green band was greatly affected with the R values decreasing from 0.77 to 0.35. The MSR and NDVI showed a gradual decrease with the increase of three background noises. The study shows that background noises must be removed when acquiring spectral data and stable spectral features should be also selected by evaluating the anti-interference ability.

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Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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EarthquakeAware: Visual Analytics for Understanding Human Impacts of Earthquakes from Social Media data

EarthquakeAware: Visual Analytics for Understanding Human Im...

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IEEE Symposium on Visual Analytics Science and technology (VAST)

作者： Shuai Chen Sihang Li Liwenhan Xie Yi Zhong Yun Han Xiaoru Yuan Key Laboratory of Machine Perception Ministry of Education School of EECS Peking University National Engineering Laboratory for Big Data Analysis and Application Peking University

In this paper, we propose EathquakeAware, a visual analytics system for social media visualization. Our system supports analyzing geo-tagged messages from different perspectives, including location, keyword, account. It can summarize the evolution patterns of topics and reveal the discussing trend of different keywords to help evaluate situations in different regions. We take VAST Challenge 2019 Mini Challenge 3 as case studies to demonstrate its effectiveness in a fictitious analysis scenario.

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Simple is not easy: A simple strong baseline for TextVQA and TextCaps

arXiv

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arXiv 2020年

作者： Zhu, Qi Gao, Chenyu Wang, Peng Wu, Qi School of Computer Science Northwestern Polytechnical University Xi’an China School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China University of Adelaide Australia

Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices. Two tasks – text-based visual question answering and text-based image captioning, with a text extension from existing vision-language applications, are catching on rapidly. To address these problems, many sophisticated multi-modality encoding frameworks (such as heterogeneous graph structure) are being used. In this paper, we argue that a simple attention mechanism can do the same or even better job without any bells and whistles. Under this mechanism, we simply split OCR token features into separate visual- and linguistic-attention branches, and send them to a popular Transformer decoder to generate answers or captions. Surprisingly, we find this simple baseline model is rather strong – it consistently outperforms state-of-the-art (SOTA) models on two popular benchmarks, TextVQA and all three tasks of ST-VQA, although these SOTA models use far more complex encoding mechanisms. Transferring it to text-based image captioning, we also surpass the TextCaps Challenge 2020 winner. We wish this work to set the new baseline for this two OCR text related applications and to inspire new thinking of multi-modality encoder design. Code is available at https://***/ZephyrZhuQi/ssbaseline Copyright © 2020, The Authors. All rights reserved.

关键词： Optical character recognition

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埋入式传感、图像处理技术和机器学习方法在路面监测与分析中应用的最新研究进展

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engineering 2021年第6期 309-334页

作者：侯越李秋晗张晨陆国阳叶周景陈逸涵汪林兵曹丹丹 Beijing Key Laboratory of Traffic Engineering Beijing University of Technology National Center for Materials Service Safety University of Science and Technology Beijing School of Transportation Southeast University Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Department of Civil and Environmental Engineering Virginia Tech

在现代交通系统中,道路作为车辆和行人使用频率最高的民用基础设施之一,其服役状况和使用寿命直接影响通行体验和效率。因此,在路面发生不可逆损伤之前进行路面健康监测和及时养护,对于保障公共交通服务质量以及通行安全至关重要。通过... 详细信息

在现代交通系统中,道路作为车辆和行人使用频率最高的民用基础设施之一,其服役状况和使用寿命直接影响通行体验和效率。因此,在路面发生不可逆损伤之前进行路面健康监测和及时养护,对于保障公共交通服务质量以及通行安全至关重要。通过路面结构动力响应监测和路面状况评估可有效表征路面损伤状况。埋入式传感器、图像处理和机器学习是目前常用的三种路面结构动力响应监测技术和分析方法。本文综述了近年来上述三种技术在路面工程中的应用现状,并阐述了这些技术在未来路面工程监测与分析中的发展方向。

关键词：路面监测与分析最新的研究进展埋入式传感器图像处理技术机器学习方法

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Model-assisted inference for covariate-specific treatment effects with high-dimensional data

arXiv

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arXiv 2021年

作者： Wu, Peng Tan, Zhiqiang Hu, Wenjie Zhou, Xiao-Hua Beijing International Center for Mathematical Research Peking University Beijing100871 China Department of Statistics Rutgers University 110 Frelinghuysen Road PiscatawayNJ08854 United States Department of Probability and Statistics Peking University Beijing100871 China Department of Biostatistics Beijing International Center for Mathematical Research National Engineering Laboratory of Big Data Analysis and Applied Technology Peking University Beijing100871 China

Covariate-specific treatment effects (CSTEs) represent heterogeneous treatment effects across subpopulations defined by certain selected covariates. In this article, we consider marginal structural models where CSTEs are linearly represented using a set of basis functions of the selected covariates. We develop a new approach in high-dimensional settings to obtain not only doubly robust point estimators of CSTEs, but also model-assisted confidence intervals, which are valid when a propensity score model is correctly specified but an outcome regression model may be misspecified. With a linear outcome model and subpopulations defined by discrete covariates, both point estimators and confidence intervals are doubly robust for CSTEs. In contrast, confidence intervals from existing high-dimensional methods are valid only when both the propensity score and outcome models are correctly specified. We establish asymptotic properties of the proposed point estimators and the associated confidence intervals. We present simulation studies and empirical applications which demonstrate the advantages of the proposed method compared with competing ones. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

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Illuminating Recommendation by Understanding the Explicit Item Relations

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Journal of Computer Science & technology 2018年第4期33卷 739-755页

作者： Qi Liu Hong-Ke Zhao Le Wu Zhi Li En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027 China School of Computer and Information Hefei University of Technology Hefei 230009 China School of Software Engineering University of Science and Technology of China Hefei 230051 China

Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from ＂implicit＂ to ＂explicit＂ views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

关键词： recommender system item relation recommendation interpretability

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Where to look and how to describe: Fashion image retrieval with an attentional heterogeneous Bilinear network

arXiv

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arXiv 2020年

作者： Su, Haibo Wang, Peng Liu, Lingqiao Li, Hui Li, Zhen Zhang, Yanning School of Computer Science Northwestern Polytechnical University China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science University of Adelaide Australia MinSheng FinTech Corp. Ltd China

Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extract both appearance (i.e., "how to describe") and localization (i.e., "where to look") information, and their interactions. To this end, we propose a biologically inspired framework for image-based fashion product retrieval, which mimics the hypothesized two-stream visual processing system of human brain. The proposed attentional heterogeneous bilinear network (AHBN) consists of two branches: a deep CNN branch to extract fine-grained appearance attributes and a fully convolutional branch to extract landmark localization information. A joint channel-wise attention mechanism is further applied to the extracted heterogeneous features to focus on important channels, followed by a compact bilinear pooling layer to model the interaction of the two streams. Our proposed framework achieves satisfactory performance on three image-based fashion product retrieval benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

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An approach to the applicability evaluation of moving target tracking algorithm 2nd

An approach to the applicability evaluation of moving target...

引用

2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Xi, Runping Xue, Shaohui Han, Qianqian Chen, Jiaxin School of Computer Science Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Xi’an710129 China

ISBN: (纸本)9783030317256

Objective and effective algorithm performance evaluation results are an important basis for the selection of tracking algorithms. Problems in the existing performance evaluation of moving target tracking algorithms include an enlarge number of trials, and in particular, failure to consider the influence of algorithm performance on the multifactor combination scenario. This study proposes a method based on the orthogonal test to evaluate algorithms. First, the factors and levels of the tracking algorithm are analyzed, and an orthogonal test dataset is constructed by using an orthogonal table. Second, the experiments of the performance evaluation are arranged with the dataset and the results are analyzed via range analysis. Finally, evaluation results show that the strong– weak sequence of factors affect the performance of the algorithm and the combination of levels form the factors that can achieve enhanced algorithm performance. Experimental results show that the proposed method can evaluate algorithms comprehensively, objectively, and effectively with decreased test and data volume. © Springer Nature Switzerland AG 2019.

关键词： Clutter (information theory)

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Novel Hemorrhagic Risk Score in Elderly Patients with Coronary Artery Disease and Gastrointestinal Malignant Tumor Comorbidity:A 10-year Clinical Inpatient data analysis from 2 Medical Centers

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Cardiology Discovery 2021年第3期1卷 163-172页

作者： Nandi Bao Wanling Wang Huitao Wu Yabin Wang Hebin Che Wenwen Meng Jiaxin Miao Dong Han Fan Yin National Clinical Research Center for Geriatric Diseases The Second Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China Medical School of Chinese People's Liberation Army General Hospital Beijing 100853China National Engineering Laboratory for Medical Big Data Application Technology The First Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China Department of Oncology The Second Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China

Objective:Older patients with comorbidity,such as coronary heart disease(CHD)and malignant gastrointestinal tumors,are at a high risk of bleeding ***,risk prediction models based on risk factor assessment remain *** study aimed to establish an individualized bleeding risk assessment system based on the analysis of 10-year inpatient clinical big ***:Total clinical data of 56,819 patients with CHD and 25,988 patients with malignant digestive tract tumors(admitted from January 2008 to December 2017)were retrospectively collected at the First and Second Medical Centers of Chinese People's Liberation Army General *** them,1307 patients with CHD and malignant digestive tract tumors were screened as the derivation *** dependent variable was the occurrence of major clinical bleeding *** statistics and hypothesis tests of differences were performed for independent variables according to the occurrence of *** Tree,eXtreme Gradient Boosting(XGBoost),logistic regression,and random forest models were used for *** accuracy,sensitivity,specificity,and area under the receiver operating characteristic curve(AUC-ROC)were applied as the criteria for evaluating and verifying model *** evaluate this developed model,another cohort comprising 454 patients(admitted from January 2018 to December 2019)was prospectively enrolled as the validation cohort based on the same inclusion and exclusion ***:Among the 64 variables with<50%missing values,the recursive feature elimination method with a random forest model was used to screen the selected *** highest accuracy was obtained following the selection of 10 scalars,and the final model was constructed *** demonstrated the best performance *** AUC-ROC of this model was 0.981,with an accuracy,sensitivity,and specificity of 0.939,0.950,and 0.927,*** the validation cohort,the AUC-ROC,accurac

关键词： Coronary disease Gastrointestinal cancers Bleeding Multivariate analysis Risk factors Machine learning

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