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SSRN 2022年

作者： Li, Zhongcan Huang, Ping Wen, Chao Rodrigues, Filipe National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China Kgs. Lyngby2800 Denmark Institute for Transport Planning and Systems ETH Zurich Zurich8093 Switzerland High-speed Railway Research Centre University of Waterloo WaterlooN2L3G1 Canada

This paper develops a heterogeneous graph neural network (HetGNN) model, which can dispose of dissimilarly attributed nodes (i.e., nodes with different input features), to investigate the evolution of railway network delay by predicting the delays of running trains (RTs). A graph architecture combining the HetGNN model and the GraphSAGE homogeneous graph neural network (HomoGNN), called SAGE-Het, is proposed. This architecture considers four kinds of nodes, namely RTs, terminated trains (TTs), passing stations (PSs), and terminated stations (TSs), and can capture the effects of the interactions between trains and other trains (e.g., the train headways), trains and stations (e.g., the scheduled remaining train running times to the downstream station), and stations and other stations (e.g., the minimum running times) on delay evolution. In contrast to the traditional machine learning methods that require the inputs to have the same formats (e.g., in rectangular or grid-like arrays) and the HomoGNNs that do not allow for dissimilarly attributed nodes in the graph, SAGE-Het allows for flexible inputs. The data from two areas of the China railway network, namely the Guangzhou South network (GZS-Net) and the Changsha South network (CSS-Net), are applied to test the performance and robustness of the proposed SAGE-Het model. The experimental results show that SAGE-Het exhibits better performance than the existing delay prediction methods and some advanced HetGNNs used for other prediction tasks for both short and long delays. Subsequently, the influences of train interactions on delay propagation are investigated. The results show that train interactions are insignificant when the train headways are long (e.g., when the headways are over 20 min, cutting the edges does not decrease the prediction performance). Ultimately, it is found that the predictive performances of SAGE-Het under different prediction time horizons (10/20/30 min ahead) all outperform the performances of o

关键词： Graph neural networks

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Differentiable Feature Aggregation Search for Knowledge Distillation

arXiv

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arXiv 2020年

作者： Guan, Yushuo Zhao, Pengyu Wang, Bingxuan Zhang, Yuanxing Yao, Cong Bian, Kaigui Tang, Jian Peking University China Technology Inc National Engineering Laboratory for Big Data Analysis and Applications DiDi AI Labs

Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to provide more supervision to the student network. However, the effectiveness of multi-teacher distillation methods are accompanied by costly computation resources. To tackle with both the efficiency and the effectiveness of knowledge distillation, we introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework by extracting informative supervision from multiple teacher feature maps. Specifically, we introduce DFA, a two-stage Differentiable Feature Aggregation search method that motivated by DARTS in neural architecture search, to efficiently find the aggregations. In the first stage, DFA formulates the searching problem as a bi-level optimization and leverages a novel bridge loss, which consists of a student-to-teacher path and a teacher-to-student path, to find appropriate feature aggregations. The two paths act as two players against each other, trying to optimize the unified architecture parameters to the opposite directions while guaranteeing both expressivity and learnability of the feature aggregation simultaneously. In the second stage, DFA performs knowledge distillation with the derived feature aggregation. Experimental results show that DFA outperforms existing distillation methods on CIFAR-100 and CINIC-10 datasets under various teacher-student settings, verifying the effectiveness and robustness of the design. Copyright © 2020, The Authors. All rights reserved.

关键词： Distillation

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A review on water footprint research of materials industry 20th

A review on water footprint research of materials industry

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20th Chinese Materials Conference, CMC 2019

作者： Chen, Hualong Liu, Yu Gong, Xianzheng Hao, Liwei Sun, Boxue 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 scarcity of water resource has become one of major issues that constrain economic development and urbanization process in China. The water footprint is a comprehensive indicator used to measure water consumption and pollution that is widely used in global or regional studies. The previous practices showed that water footprint analysis was an effective tool to achieve sustainable utilization of water resources by guiding the development of water-saving technology and product. This paper reviewed the progress of water footprint research in materials industry including related theory, method and application. Firstly, the basic concept of water footprint was introduced. Secondly, the current accounting and assessment methods of water footprint and their applicable fields were summarized. Thirdly, the case studies on the water footprint of metallic materials, nonmetallic materials and chemical materials were reviewed to analyze its guidance significance on the sustainable development of water resources. At last, some suggestions for future research on the water footprint of materials were proposed. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Chemical analysis

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Environmental analysis on co-processing of domestic waste in cement kiln 20th

Environmental analysis on co-processing of domestic waste in...

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20th Chinese Materials Conference, CMC 2019

作者： Yang, Xue Lin, Xinping Wang, Yanjing Liu, Yu Hao, Liwei Sun, Boxue 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 annual output of domestic waste has exceeded 400 million tons in China since 2016, which causes an urgent demand on harmless disposal in the current rapid process of urbanization. Compared with traditional disposal means (e.g. incineration and landfill), co-processing with cement production seems to be a better chose to meet the requirements of domestic waste treatment (i.e. harmless, volume reduction, waste utilization). However, co-processing technology also has some negative impact (e.g. extra energy consumption for pretreatment of domestic waste), so the environmental feasibility of domestic waste co-processing in cement kiln should be verified. In this paper, the influences caused by domestic waste co-processing on the cement clinker products including resource/energy consumption, as well as the pollutant emissions were quantify based on the investigation of typical plants in China. Moreover, the environment impact between landfill treatment and co-processing scenario were compared using life cycle assessment (LCA) method. The result shows that the energy consumption and CO2 emission per ton cement clinker production increased by 3.6% and 0.8% after co-process domestic waste, respectively. Furthermore, compared with sanitary landfill treatment, the co-processing in cement kiln will increase the impact of FFP, but reduce GWP and HTP impacts, especially significantly decrease the impact of LOP and SOP, bring in a good beneficial on material saving and energy saving. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Life cycle

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A Solution-Based Tabu Search Algorithm for the Resource-Constrained Project Scheduling Problem with Step Deterioration

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Dai, Huafeng Li, Lei Mao, Run Liu, Xingyue Zhou, Kun School of Mechanical Engineering Chengdu University Chengdu610105 China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Chengdu610031 China CNPC National Engineering Research Center for Oil & Gas Drilling Equipment Co. Ltd. Chengdu610052 China School of Information Science and Engineering Chengdu University Chengdu610105 China Key Lab. of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

This paper integrates the classical resource-constrained project scheduling problem with step deterioration and accordingly presents a solution-based tabu search (SB-TS) algorithm to solve it with challenging computations. First, we choose successor list size as the priority rule to generate the initial solution of the presented methods. Next, the corresponding solution decoding and calculation ways are defined to update the related configuration at each iteration. Compared with the state-of-the-art methods, in terms of the known datasets, the proposed SB-TS finds the optimal solution for each instance in J30 and shows strongly competitive performance in J60, J90, and J120. Then, on newly generated datasets, where the step deterioration is attached to, the comparison experimental data state that the SB-TS with high-quality solution is superior to the move-based tabu search (MB-TS) algorithm. In addition, two key components are investigated to emphasize their attributions to the proposed algorithm. © 2023 Huafeng Dai et al.

关键词： Deterioration

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MSLKCNN: A Simple and Powerful Multi-scale Large Kernel CNN for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Xun Ng, Alex Hay-Man Lei, Fangyuan Ren, Jinchang Guo, Li Du, Zheyuan Guangdong University of Technology School of Information Engineering Guangzhou510006 China Guangdong University of Technology School of Civil and Transportation Engineering Guangzhou510006 China Guangdong Polytechnic Normal University Guangdong Provincial Key Laboratory of Intellectual Property Big Data Guangzhou510665 China Robert Gordon University National Subsea Centre AberdeenAB21 0BH United Kingdom Ministry of Natural Resources Land Satellite Remote Sensing Application Center Beijing100048 China Geoscience Australia Canberra2609 Australia University of New South Wales School of Civil and Environmental Engineering SydneyNSW2052 Australia

Deep learning-based hyperspectral image (HSI) classification models typically utilize multiple feature extraction layers to learn the features of land covers. Nevertheless, they encounter challenges, e.g., 1) Transformers require substantial computational resources, and 2) these layers are carefully assembled and designed. Recently, large kernel convolutional neural networks (LKCNNs) show excellent performance in natural visual tasks. To tackle these limitations and explore the capability of LKCNNs for HSI classification, we present a novel simple and powerful multi-scale large kernel convolutional neural network architecture (MSLKCNN) with the largest kernel size as large as 15 × 15, in contrast to commonly used 3 × 3, for HSI classification. MSLKCNN avoids these specialized designs, comprising a noise suppression module (NSM) and a multi-scale large kernel convolution (MSLKC). Specifically, NSM is first used to suppress the noise and reduce the number of the bands before extracting the features. Then, MSLKC, as the only feature extraction layer of MSLKCNN, joints three parallel convolutions to capture the features of various types (i.e. spectral, spectral-spatial) and ranges (i.e., small local, larger local, and global) from the dimension of scale: (C1) convolution with a kernel size of 1 × 1 is used to extract spectral features;(C2) multi-scale large kernel depthwise separable convolution (MLKDC) is proposed to learn the spectral-spatial features of different ranges including short-range, middle-range, and long-range;and (C3) multi-scale dilated depthwise separable convolution (MDDC) is designed to aggregate the spectral-spatial features between land covers at various distances. Extensive experimental results on three public HSI datasets demonstrate the competitiveness of the proposed MSLKCNN compared with several state-of-the-art methods. © 1980-2012 IEEE.

关键词： Convolutional neural networks

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Learning synergistic attention for light field salient object detection

arXiv

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arXiv 2021年

作者： Zhang, Yi Chen, Geng Chen, Qian Sun, YuJia Xia, Yong Deforges, Olivier Hamidouche, Wassim Zhang, Lu France National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China School of Information Science and Technology University of Science and Technology of China China School of Computer Science Inner Mongolia University China

In this work, we propose Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms. Our SA-Net exploits the rich information of focal stacks via 3D convolutional neural networks, decodes the high-level features of multi-modal light field data with two cascaded synergistic attention modules, and predicts the saliency map using an effective feature fusion module in a progressive manner. Extensive experiments on three widely-used benchmark datasets show that our SA-Net outperforms 28 state-of-the-art models, sufficiently demonstrating its effectiveness and superiority. Our code is available at https://***/PanoAsh/ SA-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Designing preamplifier for sensing atmospheric electrostatic field strength via supercapacitive sensor

Designing preamplifier for sensing atmospheric electrostatic...

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2020 International Symposium on Electronic Information technology and Communication engineering, ISEITCE 2020

作者： Wu, Changhao Xiong, Yi Zeng, Wei National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Hefei230601 China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco-Dyeing and Finishing Wuhan Textile University Wuhan430073 China School of Physics and Technology Moe Key Laboratory of Artificial Micro- and Nano-Structures Center for Electron Microscopy Wuhan University Wuhan430072 China

In order to effectively obtain the signal from sensor, the analogy signal needs to be amplified and then converted into a digital signal for matching to the sensor characteristics. With a supercapacitive electric field sensor based on graphene aerogel, the response current signal from the electric field sensor is weak and unstable. Herein, a high gain and low noise preamplifier is developed, and an amplifier circuit with double T-type feedback network is proposed to reduce the Johnson noise for the amplifier. This design can reduce the thermal noise of resistance by using the smaller resistance under the same gain, and it can effectively reduce the interference of peak noise by adding the feedback capacitance, so as to improve the detection accuracy. The simulation results show that under the same gain condition, the Johnson noise can be reduced by 46% and the detection accuracy can be improved by 12% compared with the traditional T-type feedback network. © Published under licence by IOP Publishing Ltd.

关键词： Feedback amplifiers

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Research on Equipment State Correlation analysis Based on Digital Model of High-speed Railway Public Works Equipment

Research on Equipment State Correlation Analysis Based on Di...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yanhua Wu Ping Li Zhibo Cheng Wenya Xu Chenkun Jin Hairong Dong National Engineering Laboratory of Comprehensive Transportation Big Data Application Technology China Academy of Railway Sciences Corporation Limited Beijing China China Academy of Railway Sciences Corporation Limited Beijing China Beijing Jiaotong University Beijing China

One of the key factors in the safe operation of high-speed railways is the operational status of public works equipment. Constructing the digital model of public works equipment, fusing basic attribute information with detection and monitoring status information, and characterizing the current operating status of public works equipment, will provide more intuitive support for the change law of state change analysis. The correlation analysis between TQI and settlement based on digital model will provide auxiliary decision support for equipment maintenance.

关键词： Analytical models Correlation Digital twin Conferences Maintenance engineering Rail transportation Monitoring

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Enabling and Scaling the HPCG Benchmark on the Newest Generation Sunway Supercomputer with 42 Million Heterogeneous Cores

Enabling and Scaling the HPCG Benchmark on the Newest Genera...

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Supercomputing Conference

作者： Qianchao Zhu Hao Luo Chao Yang Mingshuo Ding Wanwang Yin Xinhui Yuan Center for Data Science Peking University Beijing China School of Mathematical Sciences Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China National Research Center of Parallel Computer Engineering & Technology Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

We study and evaluate performance optimization techniques for the HPCG benchmark on the newest generation Sunway super-computer. Specifically, a two-level blocking scheme is proposed to expose adequate parallelism in the symmetric Gauss-Seidel kernel while keeping a fast convergence rate, a fine-grained kernel fusion technique is developed to alleviate the bandwidth load on local storage with small capacity, and a low overhead thread collaboration method is presented to efficiently move data between threads and hide its cost with data transfer operations. Test results show that the optimized HPCG code is able to exploit 73.0% of the theoretical memory bandwidth, and scale to over 42 million heterogeneous cores with 95.5% weak-scaling efficiency and 5.91 Pflop/s performance. We also study how the performance can be improved if the specific rules of HPCG are not fully obeyed, and design dependency preserving parallelization and vectorization methods, further boosting performance to 27.6 Pflop/ s.

关键词： Instruction sets High performance computing Memory management Bandwidth Benchmark testing Parallel processing data transfer

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