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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

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Dense residual network: Enhancing global dense feature flow for character recognition

arXiv

引用

arXiv 2020年

作者： Zhang, Zhao Tang, Zemin Wang, Yang Zhang, Zheng Zhan, Choujun Zha, Zhengjun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China School of Computer Science and Technology Soochow University Suzhou215006 China Shenzhen China School of Computer South China Normal University Guangzhou510631 China Deparmtment of Computer Science and Technology University of Science and Technology of China Hefei China

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply stacking the convolutional layers fail to enable them to fully discover local and global feature information between layers. In this paper, we mainly investigate how to enhance the local and global feature learning abilities of DenseNet by fully exploiting the hierarchical features from all convolutional layers. Technically, we propose an effective convolutional deep model termed Dense Residual Network (DRN) for the task of optical character recognition. To define DRN, we propose a refined residual dense block (r-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts of inner layers at the same time. After fully capturing local residual dense features, we utilize the sum operation and several r-RDBs to construct a new block termed global dense block (GDB) by imitating the construction of dense blocks to adaptively learn global dense residual features in a holistic way. Finally, we use two convolutional layers to design a down-sampling block to reduce the global feature size and extract more informative deeper features. Extensive results show that our DRN can deliver enhanced results, compared with other related deep models. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Weakly supervised learning for textbook question answering

TechRxiv

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TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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Spatial optimization of rice systems with ratoon rice increases production and reduces methane emissions

引用

European Journal of Agronomy 2025年 170卷

作者： Qian Ding Yuechen Li Ruyin Cao Jin Chen Xiong Yao Wujun Zhang Chongqing Engineering Research Center for Remote Sensing Big Data Application Chongqing Jinfo Mountain National Field Scientific Observation and Research Station for Karst Ecosystem School of Geographical Sciences Southwest University Chongqing 400715 China Key Laboratory of Remote Sensing Application and Innovation Chongqing 401147 China Key Laboratory of Monitoring Evaluation and Early Warning of Territorial Spatial Planning Implementation Ministry of Natural Resources Chongqing 401147 China School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan 611731 China State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing 100875 China Chongqing Rice Ratooning Research Center Chongqing Academy of Agricultural Sciences Chongqing 401329 China

The ratoon rice system (RR) has potential for increasing rice production and reducing environmental impacts. However, research on the spatial optimization of rice systems with RR to balance rice production and environmental protection is limited. We explored the trade-offs in production and methane emissions between RR, the single-season rice system (SR), and the double-season rice system (DR) by employing spatial optimization as a research paradigm. On the basis of 70 field sites, we analyzed the yield and methane emission differences among RR, SR and DR. A random forest model was employed to assess the suitability of RR in China's rice fields. The potential of RR to increase production and reduce methane emissions in medium- and high-suitability areas under different spatial cultivation strategies was quantified through scenario simulations. We found that the annual yield of RR (12.40 ± 1.98 t ha −1 ) was significantly higher than that of SR (73.53 %) but less than that of DR (1.35 %). However, its methane emissions (0.42 ± 0.19 t ha −1 ) were lower than those of DR (38.25 %) but higher than those of SR (27.43 %). Suitability analysis revealed that 73.82 % of rice fields nationwide are suitable for RR, with 99.21 % suitability in DR areas. Scenario simulations indicated that, with the optimal spatial layout of rice systems, RR could increase rice production by 14.55 % without increasing methane emissions or reduce methane emissions by 5.31 % while maintaining rice production. This study demonstrated the significant potential of RR to increase rice production while reducing environmental impacts through spatial optimization and offered an innovative approach for sustainable rice production planning in China.

关键词： China Methane emission Production Ratoon rice Suitability

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Control of epileptiform waves in a neural mass model

Control of epileptiform waves in a neural mass model

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第三十八届中国控制会议

作者： Wei Wei Xiaofang Wei Min Zuo School of Computer and Information Engineering Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University

In order to achieve desired regulation,a regulation algorithm should be independent of the epileptic model and robust enough to *** this paper,by combining the advantages of active disturbance rejection control(ADRC)and iterative learning control(ILC),an active disturbance rejection iterative learning control(ADRILC)is designed to suppress the high amplitude epileptiform waves generated by Jansen's neural mass model(NMM).Simulation results show that,compared with ADRC,better performance can be obtained based on a more desired regulation signal generated by *** may provide a promising closed-loop regulation way for epilepsy in clinics.

关键词： Epilepsy active disturbance rejection control iterative learning control neural mass model closed-loop regulation

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First Insight into Indigenous Microorganisms Involved in Biodegradation in the Coal Slurry

SSRN

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SSRN 2022年

作者： Li, Jianbo Meng, Delong Wang, Xizhuo Lu, Zijing Zhang, Cui Peng, Yonghe Arauz Lara, Bernardo José Luis Song, Shaoxian Xia, Ling Hubei Key Laboratory of Mineral Resources Processing and Environment Luoshi Road 122 Hubei Wuhan430070 China Instituto de Física Universidad Autonoma de San Luis Potosí Av. Manuel Nava 6 S.L.P. San Luis Potosí78290 Mexico School of Resources and Environmental Engineering Wuhan University of Technology Luoshi Road 122 Hubei Wuhan430070 China School of Minerals Processing and Bioengineering Central South University Changsha410083 China Rizhao Big Data Research Institute of Geology and Geographic Information Shandong Rizhao276826 China Wuhan University of Technology China

Indigenous microorganisms can affect sedimentation by polyacrylamide biodegradation in the coal slurry system;however, they have never been examined in detail. In this study, the microbial community composition in coal slurry was explored using 16S rDNA, and the structural stability and functional prediction of the microbial community were analyzed. In the coal slurry, Bacteroidetes and Candidatus Nitrososphaera were the dominant strains at the phylum and genus levels. They maintained the stability of the microbial community by inhibiting the growth of other microorganisms. Based on the apparent organic compound degradation function of the microbial community, the functional bacteria DBY was isolated from the coal slurry and identified as Sphingomonas, which was used to biodegrade polyacrylamide flocculated in coal slurry. The biodegradation rate reached 66.4% within 76 h. In the biodegradation process, the high molecular weight polyacrylamide was degraded into smaller molecules by the bacteria DBY, and the sedimentation of the coal slurry was inhibited. © 2022, The Authors. All rights reserved.

关键词： Sedimentation

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Research on Automatic Proofreading Method of Sensitive Information in Content Security

Research on Automatic Proofreading Method of Sensitive Infor...

引用

第二届计算智能、工程与技术国际研讨会

作者： YonggangGong YuyingLi XiaoqinLian JunyingFu School of Computer and Information Engineering Beijing Technology and Business UniversityBeijing Key Laboratory of Food Safety Big Data Technology

Aiming at the problem of automatic proofreading of sensitive information in mass text content, an automatic proofreading method based on the combination of rule and SVM(Support Vector Machine) is proposed. To classify sensitive information based on important sensitive information provided in the "Newly Prohibited Texts and Cautions in Xinhua News Reports"(newest revision) and related central and online *** to the different categories, the paper constructs the classification processing rule base, designs the corresponding rules automatic Processing algorithm, and realizes the sensitive information automatic proofreading, At the same time, using the SVM model to analyze the result of the rule processing with emotion, which greatly reduces the false alarm rate. The test result shows that the recall rate of method is 89.98%, the accuracy rate is 98.31%, and 100,000 + text content is processed per second, which solves the key difficult problems in the practical engineering application.

关键词： Research on Automatic Proofreading Method of Sensitive Information in Content Security

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Concatenated Attention Neural Network for Image Restoration

arXiv

引用

arXiv 2020年

作者： Tian, YingJie Wang, YiQi Yang, LinRui Qi, ZhiQuan School of Economics and Management University of Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science Technology University of Chinese Academy of Sciences Beijing China Sino-Danish College University of Chinese Academy of Sciences China

In this paper, we present a general framework for low-levelvision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concatenated attention neural network (CANet) is specifically designed for image restoration. The main contributions of this paper are as follows: First, by applying concise but effective concatenation and feature selection mechanism, we establish a novel connection mechanism which connect different modules in the modules stacking network. Second, both pixel-wise and channel-wise attention mechanisms are used in each module convolution layer, which promotes further extraction of more essential information in images. Lastly, we demonstrate that CANet achieves better results than previous state-of-the-art approaches with sufficient experiments in compression artifacts removing and image denoising. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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On-edge multi-task transfer learning: Model and practice with data-driven task allocation

arXiv

引用

arXiv 2021年

作者： Zheng, Zimu Chen, Qiong Hu, Chuang Wang, Dan Liu, Fangming The National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Edge Cloud Innovation Lab. Technical Innovation Department Cloud BU Huawei Technologies Co. Ltd. Shenzhen China The Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Hong Kong

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

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An Optimization-Based Truth Discovery Method with Claim Relation

An Optimization-Based Truth Discovery Method with Claim Rela...

引用

IEEE International Conference on big knowledge (ICBK)

作者： Jiazhu Xia Ying He Yuxin Jin Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

With the advent of the era of big data, the information from multi-sources often conflicts due to that errors and fake information are inevitable. Therefore, how to obtain the most trustworthy or true information (i.e. truth) people need gradually becomes a troublesome problem. In order to meet this challenge, a novel hot technology named truth discovery that can infer the truth and estimate the reliability of the source without supervision has attracted more and more attention. However, most existing truth discovery methods only consider that the information is either same or different rather than the fine-grained relation between them, such as inclusion, support, mutual exclusion, etc. Actually, this situation frequently exists in real-world applications. To tackle the aforementioned issue, we propose a novel truth discovery method named OTDCR in this paper, which can handle the fine-grained relation between the information and infer the truth more effectively through modeling the relation. In addition, a novel method of processing abnormal values is applied to the preprocessing of truth discovery, which is specially designed for categorical data with the relation. Experiments in real dataset show our method is more effective than several outstanding methods.

关键词： Task analysis knowledge engineering big data Crowdsourcing Reliability engineering Meteorology

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