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application and development of elastic fibers focusing on composition and structure

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Journal of Physics: Conference Series 2021年第1期2021卷

作者： Jin Zhang Qinyue Sun Yumin Wang Xiongwei Dong Yi Xiong Wei Zeng Department of Pharmacy Zhongnan Hospital Wuhan University Wuhan 430071 People's Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application School of Electronics and Information Engineering Anhui University Hefei 230601 Anhui People's Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro-and Nano-Structures Wuhan University Wuhan 430072 People's Republic of China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Wuhan 430073 Hubei People's Republic of China

Elastic fabric is comfortable to wear, and has been a consumer textile due to its good shape retention and wrinkle resistance after washing. This paper briefly introduces the types and performance characteristics of several common elastic fibers, and analyzes the relationship between the structure and elasticity of different elastic fibers. Finally, the development trend of elastic fibers is proposed.

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Urban flows prediction from spatial-temporal data using machine learning: A survey

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arXiv 2019年

作者： Xie, Peng Li, Tianrui Liu, Jia Du, Shengdong Yang, Xin Zhang, Junbo School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Institute of Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Urban Computing Business Unit JD Finance China

Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems. Copyright © 2019, The Authors. All rights reserved.

关键词： data fusion

Life cycle assessment of lead production in China

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Life cycle assessment of lead production in China

Chinese Materials Conference, 2018

作者： Sun, Wanyi Gong, Xianzheng Sun, Boxue Ding, Qing College of Materials Science and Engineering Beijing University of Technology 100 Ping Le Yuan Chaoyang District Beijing China National Engineering Laboratory for Industrial Big-data Application Technology 100 Ping Le Yuan Chaoyang District Beijing China China National Institute of Standardization 4 Zhi Chun Road Haidian District Beijing China

ISBN: (纸本)9783035713848

This study analyzed the environmental impacts due to lead production in China, which is the largest producer and consumer of lead in the world, by the method of life cycle assessment (LCA). Based on the Chinese refined lead smelting process, a process-based life cycle assessment model was established to assess the environmental load of lead production system which includes the processes of mining, beneficiation, smelting, electrorefining and transportation. The result shows that the cumulative consumption of electricity and the cumulative emission of greenhouse gases for the production of 1t of refined lead are 1111.93kWh and 2.06E+03kg CO2 eq, respectively. Smelting process is the largest contributor to the environmental impact load, accounting for 51.16% of the total environmental impact. The environmental category of human toxicity potential(HTP), accounting for 35.26% of the total environmental impact, is the largest contributor between different environmental categories to the total environmental impact, followed by metal depletion potential(MDP) and fossil depletion potential(FDP), accounting for 27.94% and 11.80% of the total environmental impact, respectively. Improving the resource efficiencies of the processes of smelting and beneficiation, and using cleaner energy to generate electricity are the key approaches to reduce the overall environmental impact of lead production in China. © 2019 Trans Tech Publications, Switzerland.

关键词： Life cycle

Validation of right coronary artery lumen area from cardiac computed tomography against intravascular ultrasound

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Validation of right coronary artery lumen area from cardiac ...

作者： Cui, Hengfei Xia, Yong Zhang, Yanning Zhong, Liang 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 School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China National Heart Centre Singapore 5 Hospital Drive Singapore169609 Singapore

Quantification of coronary artery disease (CAD) from cardiac computed tomography angiography (CTA) is important both structurally (lumen area stenosis) and functionally (combined with computational fluid dynamics to determine fractional flow reserve) for assessment of ischemic stenosis and to guide treatment. Hence, it is important to have CTA image processing technique for segmentation and reconstruction of coronary arteries. In this study, we developed segmentation and reconstruction techniques, based on fast marching and Runge–Kutta methods for centerline extraction, and surface mesh generation. The accuracy of the reconstructed models was validated with direct intravascular ultrasound (IVUS) measurements in 1950 cross sections within 4 arteries. High correlation was found between CTA and IVUS measurements for lumen areas (r= 0.993 , p2, 6 mm 2, 7 mm 2 and 8 mm 2, all ROC values >0.99). We conclude that our technique has sufficient accuracy for quantifying coronary lumen area. The accuracy and efficiency demonstrated that our approach can facilitate quantitative evaluation of coronary stenosis and potentially help in real-time assessment of CAD. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Runge Kutta methods

Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors

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arXiv 2018年

作者： Li, Zhi Zhao, Hongke Liu, Qi Huang, Zhenya Mei, Tao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China School of Software Engineering University of Science and Technology of China JD AI Research

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers’ interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers’ long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users’ historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

Enhancing person-Job fit for talent recruitment: An ability-aware neural network approach

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arXiv 2018年

作者： Qin, Chuan Zhu, Hengshu Xu, Tong Zhu, Chen Jiang, Liang Chen, Enhong Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology an Application China

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person-Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end-to-end Ability-aware Person-Job Fit Neural Network (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, four hierarchical ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large-scale real-world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

Dynamic OD Prediction for Urban Networks Based on Automatic Number Plate Recognition data: Paramertic vs. Non-parametric Approaches

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Dynamic OD Prediction for Urban Networks Based on Automatic ...

International Conference on Intelligent Transportation

作者： Fangfang Zheng Jing Liu Henk van Zuylen Kun Wang Xaobo Liu Jie Li School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University P.R. China School of Transportation and Logistics Southwest Jiaotong University P.R. China Department of Transport and Planning Delft University of Technology Delft the Netherlands Civil Engineering College Hunan University Changsha Hunan Province P.R. China

OD flows provide important information for traffic management and planning. In this paper, we propose four OD prediction models based on the data obtained by Automated Number Plate Recognition (ANPR) cameras. The principal component analysis (PCA) is applied to reduce the dimension of the original OD matrices and to separate the main structure patterns from the noisier components. A state-space model is established for the main structure patterns and the structure deviations, and is incorporated in the Kalman filter framework to make prediction. We further develop three K- Nearest Neighbor (K-NN) based pattern recognition approaches. The proposed four approaches are validated with three days' field ANPR data from Changsha city, P.R. China. The results show that on one hand our proposed approaches are able to make accurate prediction of OD flows under different demand conditions. On the other hand, the prediction accuracy is highly dependent on the quality of the available OD data: the Kalman filter model performs better for regular and periodic OD patterns; while for irregular OD matrices K-NN models could make more accurate prediction.

关键词： Predictive models Principal component analysis Mathematical model data models Kalman filters Market research Covariance matrices

Car Collision Warning System for Cornering on Mountain Roads 4th

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Car Collision Warning System for Cornering on Mountain Roads

4th Euro-China Conference on Intelligent data analysis and applications, ECC 2017

作者： Meng, S.H. Hu, S.B. Huang, A.C. Huang, T.J. Jia, J.J. Huang, Xuhong School of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Key Laboratory of Big Data Mining and Application School of Information Science and Engineering Fujian University of Technology Minhou FuzhouFujian350118 China Institute of Electro-Optical Science and Engineering National Cheng Kung University Tainan70101 Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Kaohsiung Municipal ChungShan Senior High School Kaohsiung Taiwan

ISBN: (纸本)9783319685267

This study proposes an ultrasound-based collision avoidance/warning safety system for vehicles cornering on mountain roads. The design includes a hardware and a software system. The hardware system consists of a single-chip microcontroller with a minimum system board, a power module, and an automotive radar system. The radar system includes a stc98c52 control module, LCD 1602 display module, and hc-sr04 ultrasonic detection module. The software was programmed using C on the Keil uVision4 platform. © 2018, Springer International Publishing AG.

关键词： Liquid crystal displays

A multi-level deep ensemble model for skin lesion classification in dermoscopy images

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arXiv 2018年

作者： Xie, Yutong Zhang, Jianpeng 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

A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images. In this model, four pre-trained ResNet-50 networks are used to characterize the multiscale information of skin lesions and are combined by using an adaptive weighting scheme that can be learned during the error back propagation. The proposed MLDE model achieved an average AUC value of 86.5% on the ISIC-skin 2018 official validation dataset, which is substantially higher than the average AUC values achieved by each of four ResNet-50 networks. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification