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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Pei Zhang Yunpeng Bai Dong Wang Bendu Bai Ying Li School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Shaanxi Xi’an China

While achieving remarkable success in remote sensing (RS) scene classification for the past few years, convolutional neural network (CNN) based methods suffer from the demand for large amounts of training data. The bottleneck in prediction accuracy has shifted from data processing limits toward a lack of ground truth samples, usually collected manually by experienced experts. In this work, we provide a metalearning framework for few-shot classification of RS scene. Under the umbrella of meta-learning, we show it is possible to learn much information about a new category from only 1 or 5 samples. The proposed method is based on Prototypical Networks with a pre-trained stage and a learnable similarity metric. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN, on two challenging datasets: NWPU-RESISC45 and RSD46-WHU.

关键词： Measurement Image analysis Conferences Training data Signal processing algorithms Signal processing data processing

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Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

arXiv

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arXiv 2022年

作者： Feng, Xin-Ru Li, Heng-Chao Wang, Rui Du, Qian Jia, Xiuping Plaza, Antonio School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS39762 United States School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10071 Spain

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Time-aware bike flow prediction framework with dynamic edge fusion and memory integration

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Knowledge-Based Systems 2025年 322卷

作者： M.Rajeh, Taha Li, Tianrui Luo, Zhpeng Javed, Muhammad Hafeez Alhaek, Fares School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Stirling College Chengdu University Chengdu610106 China Computing Science Faculty of Natural Sciences University of Stirling StirlingFK9 4LA United Kingdom

Bike-Sharing Systems (BSSs) have become a popular solution to urban sustainability concerns, providing affordable and environmentally friendly transportation. However, modeling and predicting shared-bike flow patterns are essential for developing intelligent transportation systems and facilitating city management. Despite the existence of several inflow and outflow prediction models, certain unresolved issues remain, including the inability to differentiate and capture heterogeneous dynamic relationships within daily flows, and the suboptimal utilization of historical flow data for forecasting. To address these limitations, we propose MVRC (Multi-View Relational Framework based on Clustering), a novel deep-learning framework for precise inflow and outflow prediction. The MVRC framework models three types of heterogeneous dynamic flow relationships during different periods of the day. Initially, we introduce a novel clustering method to group nearby stations exhibiting similar patterns, followed by creating a dynamic memory module to store and update edge attributes based on the time of day. Furthermore, the dynamic edge fusion mechanism effectively integrates heterogeneous, time-evolving flow features, capturing their varying impacts. To further enhance the model's capabilities, an offline component that leverages historical data is incorporated, improving performance during testing. Extensive experiments conducted on real datasets from New York City and Washington, D.C., demonstrate the superior prediction efficiency and framework effectiveness of the MVRC framework compared to existing state-of-the-art techniques. These results highlight the practicality and efficacy of MVRC in terms of prediction accuracy and stability. © 2025

关键词： Urban transportation

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Web Service Discovery Based on Knowledge Graph and Similarity Network

Web Service Discovery Based on Knowledge Graph and Similarit...

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IEEE World Congress on Services (SERVICES)

作者： Yang Yu Jun Zeng Juan Yao Junhao Wen Bin Xing School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Innovation Center of Industrial Big-Data Co. Ltd National Engineering Laboratory for Industrial Big-data Application Technology Chongqing China

The following topics are dealt with: cloud computing; mobile computing; Internet; learning (artificial intelligence); Internet of Things; Web services; software engineering; optimisation; workflow management software;... 详细信息

ISBN: (数字)9781728182032

ISBN: (纸本)9781728182049

关键词： Knowledge engineering Web services Semantics Neural networks Feature extraction data mining Context modeling

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Life cycle assessment of comprehensive utilization of calcium carbide slag in cement kiln 20th

Life cycle assessment of comprehensive utilization of calciu...

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20th Chinese Materials Conference, CMC 2019

作者： Lin, Xinping Liu, Aiwei Feng, Yunfa Chen, Qiling Chen, Tao Hao, Liwei Li, Xiaoqing National Engineering Laboratory for Industrial Big-data Application Technology Beijing100124 China Beijing University of Technology The Key Laboratory of Advanced Functional Materials Ministry of Education of China Beijing100124 China Beijing Building Materials Academy of Sciences Research State Key Laboratory of Solid Waste Reuse for Building Materials Beijing100041 China

ISBN: (纸本)9783035715668

The recycling utilization of solid waste is an important technical means for the sustainable development of the cement industry in China. Calcium carbide slag is a special solid waste in China, which can be used for cement production with a great advantage on CO2 emission reduction. With an view to providing methodological and data support for the development of policies in the cement industry, this paper quantitatively analyzes the environmental effects/environmental benefits of the comprehensive utilization of calcium carbide slag in cement kiln by comparing the traditional system of Portland cement clinker completely produced by natural resources with the system of cement clinker produced by calcium carbide slag based on the life cycle assessment (LCA) method given in standards and specifications of ISO 14040 series. The results show that the latter system has a better effect in material saving and carbon emission reduction, will increase the energy consumption in cement production process, and also slightly increase other pollutants (e.g. SOx, NOx, etc.) emission. The GWP, AP and EP indicators of the calcium carbide slag cement clinker system decrease compared with those of the Portland cement clinker system, while other indicators do not differ much or even slightly increase. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Slags

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Integrated Public Transport Timetable Coordination and Vehicle Scheduling with Even Headways

SSRN

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SSRN 2022年

作者： Liu, Tao Ji, Wen Gkiotsalitis, Konstantinos Cats, Oded National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China Institute of System Science and Engineering School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China Department of Civil Engineering Faculty of Engineering Technology University of Twente Horst Complex Z222 P.O. Box 217 Enschede 7500 AE Netherlands Department of Transport & Planning Delft University of Technology Netherlands

Timetabling and vehicle scheduling are two important activities in public transport (PT) operations planning. Traditionally, the timetabling problem is solved first before proceeding to the vehicle scheduling problem. The integration of these two problems can help further reduce the total operation cost and improve the level of service, especially when timetables of different bus lines are well-coordinated at transfer stations. This work addresses the integrated PT timetable coordination and vehicle scheduling problem while ensuring that each PT line is dispatched with an even headway. We first separately formulate two integer linear programming models for the timetable coordination and vehicle scheduling problems. Next, the two models are combined into a bi-objective integer linear programming model for the integrated timetable coordination and vehicle scheduling problem. For small size PT networks, the model can be solved by using an ɛ -constraint method, together with off-the-shelf optimization solvers. For large-size problems, two constraint reduction procedures are developed to reduce the number of redundant constraints so as to reduce the computation complexity and improve the solution process. Finally, the models and solution method are applied to a numerical example and a real-world bus rapid transit (BRT) network in Chengdu, China. Computation results show that the solution generated by the sequential optimization approach is usually dominated by the Pareto-optimal solutions generated by the integrated optimization approach. Our findings suggest that it is not a wise decision to use the solution generated by the sequential optimization approach or the solution with the minimum fleet size generated by the integrated optimization approach. For practical implementation, it is recommended to choose the solution that has a fleet size of one more vehicle than the minimum fleet size. © 2022, The Authors. All rights reserved.

关键词： Integer programming

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Recent developments on life cycle assessment of building materials 20th

Recent developments on life cycle assessment of building mat...

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20th Chinese Materials Conference, CMC 2019

作者： Liu, Ning Liu, Yu Gong, Xianzheng Hao, Liwei Gao, Feng Li, Xiaoqing Zheng, Yan National Engineering Laboratory for Industrial Big-data Application Technology Beijing100124 China Beijing University of Technology the Key Laboratory of Advanced Functional Materials Ministry of Education of China Beijing100124 China Beijing Building Materials Academy of Sciences Research State Key Laboratory of Solid Waste Reuse for Building Materials Beijing100041 China

ISBN: (纸本)9783035715668

In recent years, the building materials industry in China has made great progress in the R&D of energy conservation, emission reduction and cleaner production technologies, in order to implement sustainable development policy. Life cycle assessment (LCA) is one of the mainstream method to analyze the environmental impact of product during its life cycle, which plays an important role on ecological design of building materials and development of green manufacture technology in recent year. This paper reviewed the LCA studies of building materials. Firstly, the development of China's building materials industry and technical framework of LCA standardized by ISO14040/14044 were introduced. Moreover, the typical LCA case studies of cement, glass, ceramics, wall materials, insulation materials and other building materials were reviewed. At last, some prospects for future research and development in this field were put forward. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Life cycle

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COVID-19大流行期间流感活动呈“断崖式”下降——佩戴口罩、人员流动变化及SARS-CoV-2干扰的作用

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engineering 2023年第2期21卷 195-202,M0008页

作者：韩莎莎张婷吕岩赖圣杰戴佩希郑建东杨维中周晓华冯录召 Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Harvard Medical School Harvard UniversityBostonMA 02115USA School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijing 100871China WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthampton SO171BJUK Division for Infectious Diseases Chinese Center for Disease Control and PreventionBeijing 102206China Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijing 100871China National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijing 100871China

一般情况下,每年冬季是季节性流感高发季节,但在当前2019冠状病毒病(COVID-19)大流行期间,全球季节性流感活动呈“断崖式”下降。为应对即将到来的流感季节,亟需弄清这种前所未有的流感低水平流行的原因。本文中,我们探索了一种国家特... 详细信息

一般情况下,每年冬季是季节性流感高发季节,但在当前2019冠状病毒病(COVID-19)大流行期间,全球季节性流感活动呈“断崖式”下降。为应对即将到来的流感季节,亟需弄清这种前所未有的流感低水平流行的原因。本文中,我们探索了一种国家特异性推理模型,利用该模型估计中国、英国和美国这三个国家中佩戴口罩、人员流动变化(国际和国内)以及严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)干扰的影响。结果发现,在这些地区增加一周佩戴口罩的干预措施,流感活动可减少11.3%~35.2%。实施一周的限制人员流动措施对国际(1.7%~6.5%)和国内社区(1.6%~2.8%)的影响较小。2020年至2021年,仅佩戴口罩这一项干预措施就可使阳性率下降13.3%至19.8%。仅人员流动变化可使阳性率降低5.2%至14.0%,其中79.8%~98.2%归因于国际旅行限制。SARS-CoV-2干扰仅在2019年至2020年具有统计学显著效应。中国北方地区及英国的阳性百分比分别降低7.6%(2.4~14.4)和10.2%(7.2~13.6)。探索所得出的结果对了解非药物干预措施和其他呼吸系统疾病背景下的流感演变有一定意义,有助于制定卫生政策,并可为公共卫生措施的个性化设计提供信息。

关键词：卫生政策非药物干预呼吸系统疾病流行期间季节性流感流动变化个性化设计模型估计

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A new classification method of SAR image based on OGMRF-RC model 5

A new classification method of SAR image based on OGMRF-RC m...

引用

IET International Radar Conference 2020, IET IRC 2020

作者： Li, Yuan Guo, Zhishun Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475000 China College of Environment and Planning Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) image classification is a key technique and research focus in the application of remote sensing. This paper proposes a new classification method of SAR image based on Object-based Gaussian-Markov Random Field model with Region Coefficients (OGMRF-RC), the new method use Fuzzy Probability (FP) represents the region's category probability, FP is obtained by regional posterior probability and edge information of neighborhood. Use FP to replace the unique class label of the region, which avoids the parameters estimation inaccurate of the feature field caused by the unique initial class label. And the parameter calculation method of feature field takes into account the feature information of all regions in the image. In this paper, we take the area around Baliwan in Kaifeng as study area, Sentinel-1 is used as the remote sensing data sources. FCM, K-means, classic MRF and OGMRF-RC are used as contrast methods, the experimental result demonstrates the accuracy of our method is better than these methods. © 2020 Institution of engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation

arXiv

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arXiv 2021年

作者： Liao, Zehui Hu, Shishuai Xie, Yutong Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Australian Institute for Machine Learning The University of Adelaide AdelaideSA5000 Australia

Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can have multiple annotators and fuse their annotations to reduce stochastic errors, we cannot use this strategy to handle the bias caused by annotators’ preferences. In this paper, we highlight the issue of annotator-related biases on medical image segmentation tasks, and propose a Preference-involved Annotation Distribution Learning (PADL) framework to address it from the perspective of disentangling an annotator’s preference from stochastic errors using distribution learning so as to produce not only a meta segmentation but also the segmentation possibly made by each annotator. Under this framework, a stochastic error modeling (SEM) module estimates the meta segmentation map and average stochastic error map, and a series of human preference modeling (HPM) modules estimate each annotator’s segmentation and the corresponding stochastic error. We evaluated our PADL framework on two medical image benchmarks with different imaging modalities, which have been annotated by multiple medical professionals, and achieved promising performance on all five medical image segmentation tasks. © 2021, CC BY-NC-ND.

关键词： Stochastic systems

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