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20th Chinese Materials Conference, CMC 2019

作者： Liu, Yangsi Xi, Xiaoli Nie, Zuoren Zhao, Linyan Fan, Youshu College of Materials Science and Engineering Key Laboratory of Advanced Functional Materials Education Ministry of China Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783035715668

Tungsten trioxide (WO3) nanostructures were synthesized by a hydrothermal method, and the influence of essential hydrothermal conditions, temperature and time, on their crystal structure, morphology and visible-light driven photocatalysis was studied. The hydrothermal temperature was varied from 120 °C to 200 °C, and the hydrothermal time changed from 12 h to 32 h. The crystal structure, morphology and photocatalytic performance of WO3 nanostructures were characterized by XRD, SEM and UV-Vis. The crystal structure of WO3 nanostructure was triclinic phase and their morphology was mainly one dimensional nanorods. Methylene blue was used as the target to evaluate their photocatalytic performance under visible light (λ>420 nm). The photocatalytic results suggest the suitable hydrothermal conditions to synthesize WO3 nanostructures for the wastewater treatment application. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Crystal structure

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Green synthesis of tungsten ditelluride and the magnetoresistance property 20th

Green synthesis of tungsten ditelluride and the magnetoresis...

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20th Chinese Materials Conference, CMC 2019

作者： Cao, Mingxing Ma, Liwen Xi, Xiaoli Wang, Zhihong National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China College of Materials Science and Engineering Key Laboratory of Advanced Functional Materials Education Ministry of China Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783035715668

As a new type of unsaturated linear magnetoresistive materials, tungsten ditelluride series material has potential applications in the fields of strong magnetic detection, information recording and magnetic storage devices. The current development concepts of "green materials", "green chemical technology" and "environmentally friendly technology" require us to pursue the non-toxic, low-emission and non-emission in material synthesis technology. At the same time, the prepared products show excellent performance and high versatility and efficiency. According to our previous work, the study attempts to use a low-volatile amine solvent as a pretreatment raw material to synthesize a tungsten germanium-based linear magnetoresistive material by hydrothermal/solvothermal method and self-fluxing method. The obtained materials were subjected to XRD, Raman, SEM and Magnetoresistance. Experimental results show that when the mass ratio of W and Te is 1:4, the prepared material is pure phase and the sample is the layered structure. The Magnetoresistance Property points out that the maximum value is about 190 %, which appears in the condition of 10 K and 7 T. Those test projects include phase, structure and morphology characteristics, and provide technical parameters and methods for the development of green synthesis and potential applications of tungsten ditelluride series linear magnetoresistive materials. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Virtual storage

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Mean-field theory for the Nagel-Schreckenberg model with overtaking strategy 5

Mean-field theory for the Nagel-Schreckenberg model with ove...

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5th International Workshop on Statistical Physics and Mathematics for Complex Systems, SPMCS 2017

作者： Su, Zhu Deng, Weibing Han, Jihui Li, Wei Cai, Xu National Engineering Laboratory for Technology of Big Data Applications in Education Central China Normal University Wuhan China Complexity Science Center Institute of Particle Physics Central China Normal University Wuhan China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, a modified model considered overtaking strategy (NSOS) has been proposed [1, 2]. In this paper, we focus on the theoretical analysis of traffic flow for NSOS model by using mean-field method. In the special case of vmax = 1 where vehicles can not overtake preceding ones, the features of stationary state can be obtained exactly. However, in the case of vmax > 1 where overtaking happens, some approximative methods have to be took into account. The main results are that we find the reason why traffic flow is increased in the regime where densities exceed the maximum flow density, and the influence of traffic flow on the transition density is dominated by the braking probability p. © Published under licence by IOP Publishing Ltd.

关键词： Mean field theory

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Life cycle assessment of the ndfeb recycling process route for oil sludge waste treatment 21st

Life cycle assessment of the ndfeb recycling process route f...

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21st Chinese Materials Conference, CMC 2020

作者： Fan, Yujie Gao, Feng Sun, Boxue Yue, Ming Yin, Xiaowen National Engineering Laboratory of Industrial Big Data Application Technology Beijing University of Technology No.100 Pingleyuan Beijing China Key Laboratory of New Functional Materials of Ministry of Education Beijing University of Technology No.100 Pingleyuan Beijing China

ISBN: (纸本)9783035717389

Based on the life cycle concept of oil sludge waste from the NdFeB manufacturing process, the process technology route was investigated to recover valuable elements from the oil sludge waste by hydro-metallurgical refining and calcium reduction to fabricate a sintered NdFeB magnet. The life cycle list was calculated and the environmental impact of the main processes was examined using the ReCiPe method, and the main factors of environmental impact were identified. The results showed that the hydrometallurgical technology route, compared with the calcium reduction technology route, resulted in 119.84% of human health hazards, 119.84% of ecological damage and 119.84% of resource. The consumption was found to be 13.08% higher than that of FDP. The sensitivity analysis revealed that the production of hydrochloric acid was highly sensitive to FDP, and power consumption was high to WTP in the hydrometallurgical technology route, and WTP in the calcium reduction unit process, and HTP in all processes in the calcium reduction technology route. The overall results show that reducing the environmental impact in the calcium reduction recovery process and accelerating the clean production of China's power resources are of great significance for the recovery of oil sludge waste for the preparation of recycled sintered NdFeB. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Life cycle

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A Framework for Multiclass Contour Visualization

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IEEE Transactions on Visualization and Computer Graphics 2023年第1期29卷 353-362页

作者： Li, Sihang Yu, Jiacheng Li, Mingxuan Liu, Le Zhang, Xiaolong Luke Yuan, Xiaoru Laboratory of Machine Perception Ministry of Education School of AI Peking University China Center for Data Science Academy for Advanced Interdisciplinary Studies Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China School of Computer Science Northwestern Polytechnical University China College of Information Sciences and Technology Pennsylvania State University United States

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization. © 2022 IEEE.

关键词： data visualization

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Time series forecast of sales volume based on XGBoost 2

Time series forecast of sales volume based on XGBoost

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2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021

作者： Zhang, Lingyu Bian, Wenjie Qu, Wenyi Tuo, Liheng Wang, Yunhai School of Computer Science and Technology Shandong University Qingdao China Didi AI Labs Didi Chuxing Beijing China Smart Transportation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing China

Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models. © Published under licence by IOP Publishing Ltd.

关键词： Time series

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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