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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

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Efficient and Parallel Separable Dictionary Learning

Efficient and Parallel Separable Dictionary Learning

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Cristian Rusu Paul Irofti Faculty of Automatic Control and Computers University Politehnica Bucharest Bucharest Romania Faculty of Mathematics and Computer Science Research Center for Logic Optimization and Security (LOS) University of Bucharest Bucharest Romania

ISBN: (纸本)9781665408790

Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising.

关键词： Training Dictionaries Conferences Machine learning Computational efficiency Distributed algorithms Image denoising

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Water Quality Prediction Based on Improved Multi-Point Joint Learning

SSRN

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SSRN 2023年

作者： Xu, Xinghan Su, Ziyan Song, Zhao Xia, Huijuan Liu, Jianwei Yan, Xiaohui Han, Min Faculty of Infrastructure Engineering Dalian University of Technology Liaoning Dalian116024 China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Liaoning Dalian116024 China School of Water Resources Engineering Faculty of Infrastructure Engineering Dalian University of Technology Liaoning Dalian116024 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Liaoning Dalian116024 China Professional Technology Innovation Center of Distributed Control for Industrial Equipment of Liaoning Province Liaoning Dalian116024 China

Water quality prediction is an effective method that can be used to prevent water pollution incidents. By accurately predicting the concentration of pollutants in waterbodies, water pollution can be more effectively controlled and prevented. In water quality data prediction, people usually predict the concentration of pollutants in future water bodies based on historical single-point water quality data, but this ignores the potential spatial characteristics between multiple water body points with similar geographical spaces. In order to solve this problem, this paper adopts the principle of data association and deep learning technology to establish the concentration prediction model of water quality indicators. The model consists of two parts: a deep learning network model based on Improved Neural Basis Unfolding Analysis of Interpretable Time Series (N-BEATS) and a deep learning network model based on Graph Neural Network (GNN). The GNN network is used to extract the spatial distribution characteristics and meteorological data of the pollutant concentration of multiple water quality collection points, and use the semivariogram function to quantify the data characteristics between © 2023, The Authors. All rights reserved.

关键词： Water quality

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Models of Battle Dynamics in Strategic Computer Games

Models of Battle Dynamics in Strategic Computer Games

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Ivan Dmitrievich Laryushin Dmitrii Sergeevich Pashin Yaroslav Alekseevich Koltochenko Felix Ivanovich Ereshko Plasma Physics Department Gaponov-Grekhov Institute of Applied Physics RAS Nizhny Novgorod Russia Faculty of Physics National Research Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Department of Mathematical Modeling of Complex Systems and Optimization Moscow Institute of Physics and Technology Moscow Russia Department of Information and Computing Systems Federal Research Center "Computer Science and Control" of the RAS Moscow Russia

A set of new models that describe the dynamics of battles in strategic computer games is presented. These models not only have descriptive functions, but also allow solving problems of optimizing army control under given conditions, as well as calculating the most efficient composition of the army for the planned operation within a computer game.

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Robust control Design for Linear Systems via Multiplicative Noise

arXiv

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arXiv 2020年

作者： Gravell, Benjamin Esfahani, Peyman Mohajerin Summers, Tyler Control Optimization and Networks lab DallasUT United States Delft Center for Systems and Control DelftTU Netherlands

Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and variation in the system dynamics which arise in model-based learning control methods such as adaptive control and reinforcement learning. We provide results which guarantee robustness margins in terms of perturbations on the nominal dynamics as well as algorithms which generate maximally robust controllers. Copyright © 2020, The Authors. All rights reserved.

关键词： Phase noise

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Unintended side effect of the coal-to-gas policy in North China Plain: Migration of the sources and health risks of ambient PAHs

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Science of the Total Environment 2025年 958卷 178050页

作者： Li, Zhiyong Liu, Chen Ren, Zhuangzhuang Liu, Jinming Ma, Xiaohua Ning, Zhi Meng, Jianwei Liu, Aiqin Ma, Huichun Wang, Lei Chen, Lan Wang, Hao Kong, Shaofei Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China Hebei Key Lab of Mineral Resources and Ecological Environment Monitoring Hebei Research Center for Geoanalysis Baoding071051 China Department of Atmospheric Sciences School of Environmental Sciences China University of Geosciences Wuhan430074 China

Ongoing coal-to-gas (CTG) largely cut down both coal consumption and associated PM2.5. However, a knowledge gap still existed in CTG impacts on the other energy and organic pollutant emissions. Coupling on-site investigation with statistical yearbooks, we provided a more realistic energy evolutions before (BCTG), during (DCTG), and after (ACTG) the CTG for Hebei Province. Together, we examined the impacts of CTG derived energy conversion on PM2.5-bound PAHs at urban (UA)/suburban rural (SRA)/remote rural (RRA) sites in winter 2022. As expected, the consumptions of coal and natural gas (NG) far decreased and increased from BCTG to ACTG, respectively. Accidentally, biomass usage rose by 60.7%, and rural CTG acted as a main driver. Specially, SRA's NG-shortage and coal-stove demolition should be the main inducements, and RRA's coal-sale ban was another trigger in the early stage of CTG. ∑18PAHs and ∑8TPAHs stand for the sum of 18 PAHs and 8 toxic PAHs, respectively. ∑18PAHs (ng/m3) presented as SRA (81.8) > RRA (46.4) > UA (19.4). Biomass burning (BB) and NG combustion (NGC) contributed most to∑18PAHs of 31.0% and 23.1% at SRA, resulting in the highest ∑18PAHs, ∑18PAHs/PM2.5, and ∑8TPAHs/PM2.5, and incremental lifetime cancer risk values. Also, NGC has become the second largest contributor at UA. Variations in both diagnostic ratios and source-depend isomers further proved the prominence of NGC related PAHs at UA vs. SRA. Notably, RRA was least affected by the CTG, coal combustion (CC, 40.4%) and BB (32.6%) still occupied the top positions. In short, CTG gave rise to an upsurge in biomass usage, and the incremental PAHs emissions from BB vs. NGC. This study underlined that the priorities should be given to rural NG guarantee and subsidy retention, and biomass prohibition for further air quality improvement. © 2024 Elsevier B.V.

关键词： Coal

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In Situ Microplastic Sers Sensor Based on Flexible AU Tapes

SSRN

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SSRN 2024年

作者： Sun, Zhenli Hong, Zijin Zhang, Yiyan Xie, Dan Duan, Fengkui Du, Jingjing MOE Key Laboratory of Resources and Environmental System Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China University of Chinese Academy of Sciences Beijing China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China

A B S T R A C TMicroplastic analysis is crucial for human health. Thus far, research has primarily focused on investigating microplastics within aqueous environments, while in situ techniques for detecting microplastic particles in the atmosphere remain scarce. Herein, we introduce an innovative flexible sensor for the in-situ detection of microplastics in the atmosphere, using surface-enhanced Raman scattering (SERS) effects. Our sensor, composed of Au nanoparticle (AuNP) tapes fabricated via a novel "drop-coating and peel-off" method, demonstrates outstanding SERS performance. The size, morphology, and electromagnetic field enhancement of the AuNP tape were thoroughly characterized using multiple complementary techniques, including scanning electron microscopy, transmission electron microscopy, UV–vis spectroscopy, atomic force microscopy, and the finite-difference time-domain method. Owing to its exceptional transparency and sensitivity, the AuNP tape facilitates the reliable identification of multi-component microplastics in the atmospheric environment. This study bridges a critical gap in microplastic research and enhances our understanding of microplastic pollution. © 2024, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Economic Equilibrium with Random Transactions 2

Economic Equilibrium with Random Transactions

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2nd International Conference on control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2020

作者： Zhukova, Aleksandra Pospelov, Igor G. Moscow Institue of Physics and Technology Federal Research Center Computer Science and Control of Ras Dept. of Mathematical Modelling of Economic Systems Dept. of Mathematical Modelling of Complex Systems and Optimization Dept of Theoretical Economics Moscow Russia

ISBN: (纸本)9781728188409

This paper presents the advances in modelling the system of agents under the conditions of random transactions between them. We face certain challenges in this project which we would like to highlight in this presentation. The agents in the modelled system are a large number of consumers, a large number of producers and the bank as the intermediary. Consumers take loans from the bank to spend on consumption goods and producers borrow to make investment at random moments of time. The moments of time the agents deal form the Poisson flow. We present the approach to agents' optimal control problems by the Lagrange method instead of dynamic programming. This enables us obtaining the expressions for the optimal feedback control which appear to be linear in state variables. The solution to the optimal control problems are obtained using asymptotic methods assuming large frequency of transactions. Using the linearity of the agents' optimal control, we may aggregate description of agents, stock-flow consistent on the average. Instead of an ensemble of independent random transactions by agents, the description of the aggregate dynamics is deterministic. The system of equations for the aggregate dynamics is reduced to one equation. There are two cases of this equation, depending on the description of the bank. The equation has a specific form, its numerical analysis is challenging. We provide numerical results and discuss further directions of research. © 2020 IEEE.

关键词： Random processes

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Few-Reference Image Quality Assessment with Multiple Information Measurement Fusion 9th

Few-Reference Image Quality Assessment with Multiple Inform...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Shi, Shuang Wang, Simeng Liu, Yuchen Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

In the era of media information explosion, there is an urgent need for a fast and reliable image quality assessment (IQA) model to improve the actual application effect of images. To this end, we propose the multiple information measurement fusion metric (MMFM), which innovatively combines two types of information measures (IMs), i.e., local IM and global IM, using only a small number of references for IQA. First, inspired by the free energy theory, we combine 2-dimensional autoregressive model with sparse random sampling method as an inference engine on an input image to generate its associated predicted image. Second, by the inspiration of pixel-wise measurement, we obtain the local IM by calculating the information entropy of the residual error between the input image and its corresponding predicted one. Third, motivated by the histogram-based measurement, we acquire the global IM by computing the two kinds of divergences between the input image and its predicted one. Fourth, we systematically fuse three components, independently including one distance of local IM between the reference and corrupted images and two distances of global IMs between the reference and corrupted images, based on a linear function to derive the final IQA result. The results of experiment on the most popular LIVE database show that our designed algorithm with only one number used as few reference has achieved well performance as compared with several mainstream IQA models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Fabrication of Structurally Improved Knatio3 Pellets Derived from Cheap Rutile Sand for High-Temperature Co2 Capture

SSRN

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SSRN 2023年

作者： Fan, Wenqi Zhang, Tianyu Huang, Liang Li, Hailong Wang, Lidong Wang, Qiang Beijing Key Lab for Source Control Technology of Water Pollution College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China Engineering Research Center for Water Pollution Source Control & Eco-remediation College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China School of Energy Science and Engineering Central South University Changsha410083 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China

For the practical applications of CO2 sorbents, more studies on pellet- rather than powder-type sorbents is highly demanded. In addition, high sorption kinetics, excellent cycling stability, and cheap raw precursors are far more important than its initial CO2 uptake value to certain extent. KNaTiO3 is a promising high-temperature CO2 sorbent due to its high CO2 sorption capacity and excellent cyclic stability. In order to promote its practical application in industry, low-price titanium ore was selected for the synthesis of KNaTiO3, and the optimized KR3 sorbent maintained a rapid and reversible sorption capacity of 15.5 wt% after 100 cycles. Spherical pellets were fabricated by extrusion-spheronization method with cellulose and alumisol additives. The optimal pellets exhibited a relative compression strength of 4.15 MPa and an attrition rate of 1.2 wt%, maintained a sorption capacity of 14.3 wt% after 50 cycles. It also showed superior CO2 capture kinetics, reaching a high CO2 removal rate of 92.5% within only 4 min (under 20% CO2). The role and mechanism of cellulose as pore-former were explored by SEM analysis and diffusion model fitting. In all, the high CO2 uptake, superior sorption kinetics, excellent cyclic stability, physico-chemical properties and the low cost ensure this newly developed KR3 pellets a highly promising high-temperature CO2 sorbents for practical applications in industries. © 2023, The Authors. All rights reserved.

关键词： Titanium dioxide

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