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Immobilized ZnO based nanostructures and their environmental applications

Progress in Natural Science:Materials International 2021年第6期31卷 821-834页

作者： Wei Gaoa Yangsi Liu Junzhe Dong Department of Chemical and Materials Engineering The University of Auckland Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing Guyue New Materials Research Institute Beijing University of Technology

In the research of semiconductor metal oxides, zinc oxide(ZnO) nanomaterials have been committed to design and exploration because of their unique physical and chemical properties. ZnO has many structural characteristics and has considerable practical applications in the field of environmental technology. However, ZnO nanomaterials used for environmental functions are often presented in the form of powders. After use, they need to go through recovery processes such as centrifugation and filtration, which is also easy to cause secondary *** above problems can be avoided by fixing ZnO nanostructures on substrates to form easily movable ***, we present a review of our work and some others on the different synthetic strategies, formation mechanism, modification, performance and diversifications of immobilized ZnO based nanostructures, and highlight their photocatalytic related activities for environmental remediation. Various immobilized ZnO based nanostructures including nanorod arrays, 3D nanostructures, and composite heterostructures have been focused. We hope it may serve as a useful reference for researchers interested in immobilized semiconductor nanostructures and their photocatalysis and other environmental applications.

关键词： ZnO Nanostructure Immobilization Photocatalysis Environmental application

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Triple-Attention Residual Networks for Hyperspectral Image Classification 5

Triple-Attention Residual Networks for Hyperspectral Image C...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Zhang, Lu Ruan, Chao Zhao, Jinling Huang, Linsheng Anhui University Anhui Province Hefei China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui Province Hefei China

ISBN: (纸本)9798350373820

The attention mechanism has been widely applied in hyperspectral image classification, yet most current methods rely on a relatively simple form of attention, which fails to extract features deeply. To address this issue, this paper proposes a triple-attention residual networks (TARN) for hyperspectral image (HSI) classification. This method can not only emphasize the importance of different bands in hyperspectral image classification, but also capture pairwise relationships between spatial feature pixels at the pixel level. By integrating these attention mechanisms, we achieve efficient extraction of spectral-spatial features. Extensive experiments demonstrate that the proposed artificial neural network significantly outperforms state-of-the-art methods, exhibiting its superior performance in hyperspectral image classification. © 2024 IEEE.

关键词： Pixels

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Bridging prediction and decision: Advances and challenges in data-driven optimization

Nexus

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Nexus 2025年第1期2卷

作者： Yanzhi Wang Jianxiao Wang Haoran Zhang Jie Song Department of Industrial Engineering and Management College of Engineering Peking University Beijing 100871 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 100871 China PKU-Changsha Institute for Computing and Digital Economy Changsha 410000 China School of Urban Planning and Design Peking University Shenzhen Guangdong 518055 China

data-driven approaches have revolutionized traditional optimization methods by integrating prediction with decision-making. This review examines the theoretical foundations, strengths, recent advancements, and limitations of three key methods—sequential optimization, end-to-end learning, and direct learning—highlighting their practical applications in power grid scheduling, operations management, and intelligent autonomous control. A multidimensional comparison is presented, followed by a discussion of the challenges in data-centric methodology, optimization methodology, and decision-making application. This paper offers a methodological guide and outlines future directions for academia and industry to enhance decision-making in complex data environments.

关键词： convex optimization data-centric AI data-driven decision-making end-to-end learning intelligent autonomous control machine learning operations management power grid scheduling reinforcement learning uncertainty estimation

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A method for correcting characteristic X-ray net peak count from drifted shadow peak

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Nuclear Science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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Shaping future low-carbon energy and transportation systems: Digital technologies and applications

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iEnergy 2022年第3期1卷 285-305页

作者： Jie Song Guannan He Jianxiao Wang Pingwen Zhang Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China School of Mathematical Sciences Peking Univer-sityBeijing 100871China

Digitalization and decarbonization are projected to be two major trends in the coming *** the already widespread process of digitalization continues to progress,especially in energy and transportation systems,massive data will be produced,and how these data could support and promote decarbonization has become a pressing *** paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure,common mechanisms and algorithms,and system-level impacts,as well as the application of digital technologies to coupled energy and transportation systems with electric *** paper also identifies corresponding challenges and future research directions,such as in the field of blockchain,digital twin,vehicle-to-grid,low-carbon computing,and data security and pri-vacy,especially in the context of integrated energy and transportation systems.

关键词： Digitalization decarbonization energy system transportation system energy and transportation integration.

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Spatial–temporal variations of NDVI and its response to climate in China from 2001 to 2020

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International Journal of Digital Earth 2022年第1期15卷 1463-1484页

作者： Yuanyuan Wei Shougang Sun Dong Liang Zhaohong Jia School of Internet Anhui UniversityHefeiPeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui UniversityHefeiPeople’s Republic of China School of Computer Science and Technology Anhui UniversityHefeiPeople’s Republic of China

Vegetation plays an important role in global or regional environmental *** this study,the spatial–temporal variations of NDVI and its response to climate in China and its seven sub-regions were investigated based on MODIS NDVI data,ERA5-land precipitation(PRE)and temperature(TEM)data from 2001 to *** inter-annual growth rate of NDVI in China was 0.0021/yr in the past 20 *** inter-annual growth rates of NDVI in seven sub-regions had significant differences at regional or seasonal *** ratio of improved vegetation area to the total studied area reached about 70%.In summer,vegetation degradation was concentrated in East China and Southwest *** vegetation in Central China and South China improved more obviously in autumn than in the other *** vegetation of Northeast China had a remarkable degradation in autumn and winter,especially in *** influence degree of PRE(q=0.54,P<0.01)was greater than that of TEM(q=0.27,P<0.01)in the control of the spatial distribution of *** interaction influence degree q of PRE∩TEM was about 0.71 in the last 20 ***,the PRE and TEM played different roles in vegetation growth in seven sub-regions.

关键词： NDVI spatial–temporal variations climate change MK test Geodetector

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ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream data Processing Systems 49th

ContTune: Continuous Tuning by Conservative Bayesian Optimiz...

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49th International Conference on Very Large data Bases, VLDB 2023

作者： Lian, Jinqing Zhang, Xinyi Shao, Yingxia Pu, Zenglin Xiang, Qingfeng Li, Yawen Cui, Bin Beijing University of Posts and Telecommunications China School of Computer Science and National Engineering Laboratory for Big Data Analysis and Applications Peking University China

The past decade has seen rapid growth of distributed stream data processing systems. Under these systems, a stream application is realized as a Directed Acyclic Graph (DAG) of operators, where the level of parallelism of each operator has a substantial impact on its overall performance. However, finding optimal levels of parallelism remains challenging. Most existing methods are heavily coupled with the topological graph of operators, unable to efficiently tune under-provisioned jobs. They either insufficiently use previous tuning experience by treating successively tuning independently, or explore the configuration space aggressively, violating the Service Level Agreements (SLA). To address the above problems, we propose ContTune, a continuous tuning system for stream applications. It is equipped with a novel big-small algorithm, in which the big phase decouples the tuning from the topological graph by decomposing the job tuning problem into sub-problems that can be solved concurrently. We propose a conservative Bayesian Optimization (CBO) technique in the Small phase to speed up the tuning process by utilizing the previous observations. It leverages the state-of-the-art (SOTA) tuning method as conservative exploration to avoid SLA violations. Experimental results show that ContTune reduces up to 60.75% number of reconfigurations under synthetic workloads and up to 57.5% number of reconfigurations under real workloads, compared to the SOTA method DS2. © 2023, VLDB Endowment. All rights reserved.

关键词： Directed graphs

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Optimal Scheduling of Single-Line Electric Buses with Battery Swapping 8

Optimal Scheduling of Single-Line Electric Buses with Batter...

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8th IEEE International Conference on Intelligent Transportation engineering, ICITE 2023

作者： Guo, Yuyan Liu, Tao Institute of System Science and Engineering School of Transportation and Logistics Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

ISBN: (纸本)9798350314212

With the advantages of zero emission and comfortable riding experience, battery-electric buses (BEBs) are widely adopted in public transit agencies as a green alternative to conventional diesel buses. Battery swapping technology is an efficient and promising charging technology to address the issues of battery range anxiety and long battery charging time. This study developed a two-stage optimization model to optimize the single-line battery-electric bus scheduling problem considering battery swapping. At the first stage, a bi-objective integer programming model is established to determine the minimum BEB fleet size, together with the BEB-to-trip assignment and battery swapping schedule. At the second stage, an integer linear programming model is formulated to determine the required minimum number of batteries. An e-constraint method is designed to investigate the trade-off between the required minimum fleet size and the number of back-up batteries. Finally, a real-world case study of a BEB line in Chengdu, China, is conducted to demonstrate the effectiveness of the optimization model and solution method. Sensitivity analyses are further conducted to understand the impacts of some key parameters, including battery capacity, number of trips, cycle time, and battery swapping time. The results show that battery capacity and cycle time have significant influences on BEB-to-trip assignment results and battery swapping schedule. © 2023 IEEE.

关键词： Sensitivity analysis

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Saliency Prediction Based on Lightweight Attention Mechanism 4

Saliency Prediction Based on Lightweight Attention Mechanism

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Wang, Yu Zhu, Ming Electronics and Information Engineering Anhui University Hefei Anhui 230601 China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui University Hefei AnHui230601 China

Saliency prediction refers to an algorithm that extracts salient regions from natural scenes. In the field of deep learning, receptive field limits the accuracy of pixel classification and thus affects the accuracy of saliency prediction. This study proposes a new saliency prediction method that uses the improved feature pyramid attention (FPA) to gain multi-scale context information to solve the above-mentioned problems. FPA's number of parameters and cost of calculation are also decreased. Experimental results show that this method can obtain more accurate results than the existing saliency prediction methods without increasing the calculation amount. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Research on Calculation Methods of Taxi Passenger Volume Based on Satellite Positioning data

Research on Calculation Methods of Taxi Passenger Volume Bas...

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2022 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2022

作者： Xiao, Yun Zhang, Meng Li, Rongqiao Liang, Zijun School of Urban Construction and Transportation Hefei University Anhui Hefei230606 China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory Anhui Hefei230606 China

ISBN: (纸本)9781510655867

Scientific measurement of taxi passenger volume is of great significance for optimizing urban traffic. In this paper, based on the satellite positioning data of taxis in Huangshan City, the identification method of taxi passenger trips is studied with Python as the analysis tool, and the taxi passenger trip trajectory is extracted. Through the sampling survey, the passenger load factor of different periods is calculated, based on which the distribution characteristics of taxis under different time and space is analyzed and visualized through ArcGIS and TransCAD. The research demonstrates that the average daily passenger volume of taxis in Huangshan City is 14, 013 person-times, and the number of taxi passengers on rest days is higher than that on working days, and significantly higher during the day than at night;taxi trips are mainly distributed in the main urban areas of the city, accounting for 87.36% of the total trips, forming two passenger corridors, east-west and north-south. © 2022 SPIE.

关键词： Python

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