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Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data

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Journal of Geographical Sciences 2022年第10期32卷 1886-1910页

作者： ZHANG Li LEI Jun WANG Changjian WANG Fei GENG Zhifei ZHOU Xiaoli Xinjiang Institute of Ecology and Geography CASUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China Fujian Urban and Rural Planning&Design Institute Fuzhou 350003China Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China School of Resources and Planning Guangzhou Xinhua UniversityGuangzhou 510520China Suzhou Honglan Data Technology Co. LtdSuzhou 215000JiangsuChina Urumqi Meteorological Satellite Ground Station Urumqi 830011China

This essay combines the Defense Meteorological Satellite Program Operational Linescan system(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic DMSP”dataset,from 1992 to 2020,to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang,***,this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector *** reveal that(1)total carbon emissions continued to grow,while the growth rate slowed down in the past five years.(2)Large regional differences exist in total carbon emissions across various *** carbon emissions of these regions in descending order are the northern slope of the Tianshan(Mountains)>the southern slope of the Tianshan>the three prefectures in southern Xinjiang>the northern part of Xinjiang.(3)Economic growth,population size,and energy consumption intensity are the most important factors of spatial differentiation of carbon *** interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’spatial *** paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps.

关键词： carbon emissions nighttime light data spatio-temporal variations influencing factors Xinjiang

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IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT of DUNHUANG WALL PAINTING IMAGES

IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT of DUNHUANG WA...

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ISPRS geospatial Week 2017

作者： Hu, K. Huang, X. You, H. Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences No. 19 North 4th Ring Road West Beijing100190 China Wuhan Engineering Science and Technology Institute Jiangda Road 30 Wuhan Hubei430019 China

Bundle adjustment with additional parameters is identified as a critical step for precise orthoimage generation and 3D reconstruction of Dunhuang wall paintings. Due to the introduction of self-calibration parameters and quasi-planar constraints, the structure of coefficient matrix of the reduced normal equation is banded-bordered, making the solving process of bundle adjustment complex. In this paper, Conjugate Gradient Bundle Adjustment (CGBA) method is deduced by calculus of variations. A preconditioning method based on improved incomplete Cholesky factorization is adopt to reduce the condition number of coefficient matrix, as well as to accelerate the iteration rate of CGBA. Both theoretical analysis and experimental results comparison with conventional method indicate that, the proposed method can effectively conquer the ill-conditioned problem of normal equation and improve the calculation efficiency of bundle adjustment with additional parameters considerably, while maintaining the actual accuracy. © Authors 2017.

关键词： Calculus of variations Conjugate gradient bundle adjustment (CGBA) Dunhuang wall painting images Improved incomplete Cholesky factorization Space complexity Time complexity

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Networked predictive control of Hammerstein systems

Networked predictive control of Hammerstein systems

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第三十三届中国控制会议

作者： Tian-tian Wang Yu Kang Yun-Bo Zhao Department of Automation and CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China College of Information Engineering Zhejiang University of Technology

ISBN: (纸本)9781479947249

This article mainly deals with the control and stability problems of networked Hammerstein with nonlinear input. A novel predictive controller design method is proposed to offset the effect of network delay and data dropout. The controller gain which depends on the time delay of the feedback channel is time-variant. Since we assume that the state is not measurable, the control signal is based on the state estimated by the observer. As for the nonlinear part of the input,We assume it satisfies a sector constraint and treat it as a input inaccuracy. Theoretical results are presented for the closed-loop stability by modeling the system as time-delay Hammerstein system with nonlinear inputs. A second-order Hammerstein system is implemented to show the enhanced performance of this control method.

关键词： Networked control system,Hammerstein,nonlinear,delay,dropout

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Holocene millennial-scale megaflood events point to ENSO-driven extreme climate changes

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Science China Earth Sciences 2023年第11期66卷 2530-2545页

作者： Cong CHEN Yanwei ZHENG Zhuo ZHENG Yongqiang ZONG Kangyou HUANG Barry VROLETT Huanhuan PENG Xiao ZHANG Yongjie TANG Qiuchi WAN Guifang ZHANG Rachid CHEDADDI Guangdong Key Lab of Geodynamics and Geohazards School of Earth Sciences and EngineeringSun Yat-sen UniversityZhuhai 519082China Key Laboratory of Separation and Comprehensive Utilization of Rare Metals Institute of Resources Utilization and Rare Earth DevelopmentGuangdong Academy of SciencesGuangzhou 510650China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519082China Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China Department of Anthropology University of Hawai’i at ManoaHonoluluHawai’i 96822USA College of Ocean and Meteorology Guangdong Ocean UniversityZhanjiang 524088China Institute of Evolutionary Sciences of Montpellier University of MontpellierCNRSMontpellier 34095France

Reconstructing the Holocene megaflood history is a key component of understanding the mechanism of past climate change and assessing the potential impact of future catastrophic *** Pearl River is the longest watercourse in southern China,and its lower reach has been identified as one of the world's most vulnerable regions for flood ***,there is a complete lack of millennial-scale geological records of paleomegafloods for the future prediction of once-in-a-hundred(even once-in-a-thousand)year floods in southern ***,we identified a series of paleomegaflood deposits interbedded with wood-rich peat layers in the lower West Pearl River *** paleoflood layers have been well dated using AMS~(14)C dating *** to the regional correlation of the flood sequence,sediment characteristics and provenance analysis,there have been at least 7 megafloods corresponding to once-in-a-thousand-year events in the lower reaches of the West Pearl River during the past 6000 years,with an average return period of approximately 855 *** identified paleomegafloods were coeval with periods of strong El Ni?o-Southern Oscillation(ENSO),indicating that weakening of the Asian summer monsoon,associated with enhanced ENSO variability,may have triggered abnormally high precipitation leading to flooding of exceptional magnitude in southern *** addition,the most prominent paleomegafloods identified in the lower Pearl River coincided with intervals of lower precipitation and fewer storms in central-eastern China,indicating the intensification of the meridional“tripole”pattern of precipitation across eastern China during the latter half of the *** land use and deforestation over the last 2000 years have resulted in soil loss and rapid degradation of local primeval forest ecosystems,leading to more catastrophic *** amounts of rice pollen in the uppermost flood layer during the Song Dynasty indicate that this megaflood may have inu

关键词： Holocene megaflood Asian monsoon ENSO Vegetation change Early farming West Pearl River

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Journal of Computational information systems 2014年第17期10卷 7259-7267页

作者： Qi, Xiang Wu, Wei Huang, Yu Huang, Tinglei Fu, Kun Wang, Hongqi Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics University of Chinese Academy of Sciences Beijing China University of Science and Technology of China Hefei China

In this paper we propose the rating Correlated Topic Model for rating-based collaborative filtering, which improves the performance of the state-of-the-art Latent Semantic Models in two aspects: (1), making the prediction accuracy more robust to the topic number K;(2), improving the recommendation quality for users with few existed ratings. We achieve our goals by employing the Logistic Normal distribution to capture the correlation between latent topics following the Correlated Topic Model, as well as modifying the generative process to meet the requirement of rating-based collaborative filtering. We derive a parameter estimation algorithm based on variational inference for the proposal. Experiment results on the Movielens data set demonstrate our model's advantages on both referred aspects.

关键词： Collaborative filtering

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A Hybrid and Explainable Deep Learning Framework for SAR Images

A Hybrid and Explainable Deep Learning Framework for SAR Ima...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhongling Huang Mihai Datcu Zongxu Pan Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR)

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Deep learning based patch-wise Synthetic Aperture Radar (SAR) image classification usually requires a large number of labeled data for training. Aiming at understanding SAR images with very limited annotation and taking full advantage of complex-valued SAR data, this paper proposes a general and practical framework for quad-, dual-, and single-polarized SAR data. In this framework, two important elements are taken into consideration: image representation and physical scattering properties. Firstly, a convolutional neural network is applied for SAR image representation. Based on time-frequency analysis and polarimetric decomposition, the scattering labels are extracted from complex SAR data with unsupervised deep learning. Then, a bag of scattering topics for a patch is obtained via topic modeling. By assuming that the generated scattering topics can be regarded as the abstract attributes of SAR images, we propose a soft constraint between scattering topics and image representations to refine the network. Finally, a classifier for land cover and land use semantic labels can be learned with only a few annotated samples. The framework is hybrid for the combination of deep neural network and explainable approaches. Experiments are conducted on Gaofen-3 complex SAR data and the results demonstrate the effectiveness of our proposed framework.

关键词： Deep learning Training Annotations Scattering Image representation Radar polarimetry Synthetic aperture radar

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Residual objectness for imbalance reduction

arXiv

引用

arXiv 2019年

作者： Chen, Joya Liu, Dong Luo, Bin Peng, Xuezheng Xu, Tong Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Tencent TaiQ Team

For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds. While several sampling/reweighting schemes have been explored to alleviate the imbalance, they are usually heuristic and demand laborious hyper-parameters tuning, which is hard to achieve the optimality. In this paper, we first reveal that such the imbalance could be addressed in a learning-based manner. Guided by this illuminating observation, we propose a novel Residual Objectness (ResObj) mechanism that addresses the imbalance by end-to-end optimization, while no further hand-crafted sampling/reweighting is required. Specifically, by applying multiple cascaded objectness-related modules with residual connections, we formulate an elegant consecutive refinement procedure for distinguishing the foregrounds from backgrounds, thereby progressively addressing the imbalance. Extensive experiments present the effectiveness of our method, as well as its compatibility and adaptivity for both region-based and one-stage detectors, namely, the RetinaNet-ResObj, YOLOv3-ResObj and FasterRCNN-ResObj achieve relative 3.6%, 3.9%, 3.2% Average Precision (AP) improvements compared with their vanilla models on COCO, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Quantum-behaved PSO-based Lyapunov control of closed quantum systems

Quantum-behaved PSO-based Lyapunov control of closed quantum...

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第40届中国控制会议

作者： Song Liu Sen Kuang Department of Automation University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

A Lyapunov-based control scheme is presented to drive closed quantum systems into any target eigenstate with as high population as possible by the quantum-behaved particle swarm optimization（PSO） algorithm. Based on a Lyapunov function with a Hermitian operator to be constructed, a control law with the unknown parameters contained in the Hermitian operator is designed. To achieve high-population state transfer to the target state, we first initialize those unknown parameters by choosing a path to the target state in its energy-level connectivity graph and setting their values along the path. Then, a set of optimal parameters is found by the quantum-behaved PSO algorithm. Finally, numerical simulation experiments are performed on a five-level quantum system and a four-qubit system to demonstrate the effectiveness of the control scheme in this paper.

关键词： Quantum systems Lyapunov control quantum-behaved PSO algorithm energy-level connectivity graph high-population transfer

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Evolution of AVS video coding standards: twenty years of innovation and development

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Science China(information Sciences) 2022年第9期65卷 50-73页

作者： Siwei MA Li ZHANG Shiqi WANG Chuanmin JIA Shanshe WANG Tiejun HUANG Feng WU Wen GAO Institute of Digital Media School of Computer Science Peking University Bytedance Inc. Department of Computer Science City University of Hong Kong CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China

Ever since the founding of the Audio and Video Coding Standard(AVS) Workgroup of China in 2002, it has been dedicated to advancing and innovating the digital audio-video industry with highly efficient and economical encoding/decoding technologies. Three representative generations of video coding standards have been finalized and published, consistently improving the coding performance in the past two *** series of AVS standards establish solid foundations for ubiquitous video applications in the areas including acquisition, coding, production, delivery, integrated system, public service, general screen content, and mixed reality media. Along with the standardization process, an extensive amount of studies have been carried out on efficiency-aware designation, algorithm optimization, and hardware implementation of these innovative video coding techniques. This paper explains how those developed techniques provide a lasting impact on the video coding community, extensively, technologically, and systematically. In particular, we provide a comprehensive survey of the three generations of the standards, and timely and in-depth summarize the efforts of the AVS video coding standards in the twenty years. The rate-distortion performance comparisons,in particular in terms of the 8K ultra-high-definition(UHD) contents, reflect the elegant design of the state-of-the-art AVS3 standards. We have also elaborated on a variety of well-established and promising applications, including commercial level real-time 8K encoder, high-frame-rate decoder chip for cell phone,and live streaming solution for sports. In addition, the China Central Television(CCTV) of China Media Group(CMG), the state television of China, has officially launched the first 8K broadcasting channel(CCTV-8K) since 2021 using AVS3. Given the significant success realized by the AVS standards, it is envisioned that a new era of 8K UHD video is arriving.

关键词： video coding AVS standards video applications intelligent coding

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Offline and Online Optical Flow Enhancement for Deep Video Compression

arXiv

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arXiv 2023年

作者： Tang, Chuanbo Sheng, Xihua Li, Zhuoyuan Zhang, Haotian Li, Li Liu, Dong CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possible, but the optical flows themselves may cost too many bits to encode. Second, the optical flow estimation networks were trained on synthetic data, and may not generalize well enough to real-world videos. We address the twofold limitations by enhancing the optical flows in two stages: offline and online. In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e.g. H.266/VVC, as we believe the motion information of H.266/VVC achieves a better rate-distortion tradeoff. In the online stage, we further optimize the latent features of the optical flows with a gradient descent-based algorithm for the video to be compressed, so as to enhance the adaptivity of the optical flows. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC. Experimental results demonstrate that the proposed offline and online enhancement together achieves on average 12.8% bitrate saving on the tested videos, without increasing the model or computational complexity of the decoder side. Copyright © 2023, The Authors. All rights reserved.

关键词： Image coding

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