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Air pollution and population morbidity forecasting with artificial neural networks

Air pollution and population morbidity forecasting with arti...

International Conference and Early Career Scientists School on Environmental Observations, Modeling and Information systems, ENVIROMIS 2018

作者： Gornov, A Yu Zarodnyuk, T.S. Efimova, N.V. Institute for System Dynamics and Control Theory Siberian Branch of Russian Academy of Sciences Lermontov str. 134 Post Box 64033 Irkutsk Russia East-Siberian Institute of Medical and Ecological Research 12a District 3 Angarsk665827 Russia

Incidence prediction models for urban population have not yielded consistent or highly accurate results. The complex nature of the interrelationship between "environmental factors and incidence" has many nonlinear associations with outcomes. We explore artificial neural networks (ANNs) to predict the complex interactions between the risk factors of incidence among the urban population. ANN modeling using a standard feed-forward, back-propagation neural network with three layers (i.e., an input layer, a hidden layer, and an output layer) is used to predict the incidences of diseases of children and adults. A receiver-operating characteristic (ROC) analysis is used to assess the model accuracy. We develop a mathematical model taking into account factors of natural, anthropogenic, and social environments. The model effectiveness is proved by computing experiments for the Bratsk industrial centre (Irkutsk region, Russia). Optimal air pollution levels are offered to achieve a background morbidity level among different age groups of the population. The prediction of incidence is most accurate when using the ANN model with several univariate influences on the outcome. An incorporation of some computerized learning systems might improve decision making and outcome prediction. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Reduction theory in dynamics and control

Reduction theory in dynamics and control

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2005中国控制与决策学术年会

作者： Vassilyev S N Institute for System Dynamics and Control Theory Siberian Branch of the Russian Academy of SciencesIrkutskRussia.

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The Computer Architecture and Hardware Description Language

The Computer Architecture and Hardware Description Language

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Proceedings of the International Convention MIPRO

作者： A.S. Burlakov A.E. Khmelnov Siberian Branch of Russian Academy of Sciences Institute for System Dynamics and Control Theory Irkutsk Russian Federation

ISBN: (纸本)9781479981748

The paper introduces CAHDL, the Computer Architecture and Hardware Description Language. CAHDL is aimed to specify processor instructions and computer hardware formally. Although CAHDL supports RTL modeling to some extent, it is does not describe digital circuits. CAHDL is designed first to specify CPU and hardware behavior, rather than their structure. CAHDL can be applied in simulators, compilers, disassemblers, testers and other programs requiring specification of computer architecture. In the end of the paper we observe how CAHDL is applied in computer simulation. The paper brings some CAHDL syntactic constructions as examples and explains their semantics. Parsing methods are also briefly reviewed.

关键词： Computer architecture Registers Hardware design languages Syntactics Computers Random access memory Keyboards

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A novel deployment strategy of monitors for vehicle emission remote sensing system

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ISA Transactions 2022年

作者： Liu, Jun Yu, Cheng Jiang, Peng Wang, Ben Hu, Hua Bychkov, Igor Hmelnov, Alexei School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Hangzhou Dianzi University Hangzhou310018 China Matrosov Institute for System Dynamics and Control Theory of Siberian Branch of Russian Academy of Sciences Irkutsk664033 Russia

A novel deployment strategy for monitors is presented in this paper. Firstly, the real road networks are modeled though graph-theoretic analysis with limited information like the topology of the road networks and the direction of traffic flows;secondly, an appropriate visiting order of vertexes is obtained by the breadth-first search algorithm;thirdly, a minimum set of roads to deploy monitors is calculated based on the balance attribute of traffic flows at each junction. Experiment results demonstrate that the proposed deployment strategy can not only decrease the redundancy of the deployed monitors, but also consider the real unbalanced vertexes in the road networks. © 2022

关键词： Roads and streets

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Particle identification in ground-based gamma-ray astronomy using convolutional neural networks 8

Particle identification in ground-based gamma-ray astronomy ...

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Selected Papers of the 8th International Conference "Distributed Computing and Grid-Technologies in Science and Education", GRID 2018

作者： Postnikov, E.B. Bychkov, I.V. Dubenskaya, J.Y. Fedorov, O.L. Kazarina, Y.A. Korosteleva, E.E. Kryukov, A.P. Mikhailov, A.A. Nguyen, M.D. Polyakov, S.P. Shigarov, A.O. Shipilov, D.A. Zhurov, D.P. Leninskie gory GSP-1 Moscow119991 Russia Matrosov Institute for System Dynamics and Control Theory Siberian Branch of Russian Academy of Sciences 134 Lermontov St. P.O. Box 292 Irkutsk664033 Russia Irkutsk State University 1 Karl Marx St. Irkutsk664003 Russia 20 Bul'var Gagarina Irkutsk664003 Russia

Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed. © 2018 Evgeny B. Postnikov, Igor V. Bychkov, Julia Y. Dubenskaya, Oleg L. Fedorov, Yulia A. Kazarina, Elena E. Korosteleva, Alexander P. Kryukov, Andrey A. Mikhailov, Minh-Duc Nguyen, StanislavP. Polyakov, Alexey O. Shigarov, Dmitry A. Shipilov, Dmitry P. Zhurov.

关键词： Cameras

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Pseudoscalar meson transition form factors in nonperturbative QCD approach

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Nuclear Physics B - Proceedings Supplements 2012年 225-227卷 141-145页

作者： Dorokhov, A.E. Radzhabov, A.E. Zhevlakov, A.S. Joint Institute for Nuclear Research Bogoliubov Laboratory of Theoretical Physics 141980 Dubna Moscow region Russian Federation Institute for System Dynamics and Control Theory 664033 Irkutsk Russian Federation Russian Federation Institute for Theoretical Problems of Microphysics Moscow State University RU-119899 Moscow Russian Federation

In this communication we discuss a few topics related to modern experimental data on physics of light pseudoscalar mesons. It includes the contribution of the pseudoscalar mesons to the muon anomalous magnetic moment (AMM), . g-. 2, rare decays of light pseudoscalar mesons to a lepton pair, transition form factors of pseudoscalar mesons at large momentum transfer, the pion transversity form *** the muon anomalous magnetic moment g-2 and rare decays of light pseudoscalar mesons into a lepton pair P→l ⁺l ^- serve as important test of the standard model. To reduce the theoretical uncertainty in the standard model predictions, the data on the transition form factors of light pseudoscalar mesons play significant role. Recently new data on behavior of these form factors at large momentum transfer was supplied by the BABAR collaboration. Within the nonlocal chiral quark model it is shown how to describe these data and how the meson distribution amplitude evolves as a function of the dynamical quark masses and meson masses. © 2012 Elsevier B.V.

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Distributed data storage for modern astroparticle physics experiments

arXiv

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arXiv 2019年

作者： Kryukov, Alexander Nguyen, Minh-Duc Bychkov, Igor Mikhailov, Andrey Shigarov, Alexey Dubenskaya, Julia Skobeltsyn Institute of Nuclear Physics Lomonosov Moscow State University Moscow119992 Russia Matrosov Institute for System Dynamics and Control Theory Siberian Branch of Russian Academy of Sciences Lermontov st. 134 Irkutsk Russia

The German-Russian Astroparticle Data Life Cycle Initiative is an international project launched in 2018. The Initiative aims to develop technologies that provide a unified approach to data management, as well as to demonstrate their applicability on the example of two large astrophysical experiments - KASCADE and TAIGA. One of the key points of the project is the development of a distributed storage, which, on the one hand, will allow data of several experiments to be combined into a single repository with unified interface, and on the other hand, will provide data to all participants of experimental groups for multi-messenger analysis. Our approach to storage design is based on the single write-multiple read (SWMR) model for accessing raw or centrally processed data for further analysis. The main feature of the distributed storage is the ability to extract data either as a collection of files or as aggregated events from different sources. In the last case the storage provides users with a special service that aggregates data from different storages into a single sample. Thanks to this feature, multi-messenger methods used for more sophisticated data exploration can be applied. Users can use bothWeb-interface and Application Programming Interface (API) for accessing the storage. In this paper we describe the architecture of a distributed data storage for astroparticle physics and discuss the current status of our work. Copyright © 2019, The Authors. All rights reserved.

关键词： Application programming interfaces (API)

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Reversible integer approximation of color space transforms for lossless compression of big color raster data

Computer Optics

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Computer Optics 2022年第3期46卷 492-505页

作者： Hmelnov, A.E. ISDCT SB RAS – Matrisov Institute for System Dynamics and Control Theory of Siberian Branch of RAS Lermontova 134 Irkutsk664033 Russia

Reversible integer transforms are or great importance for lossless compression algorithms. To perform reversible decorrelation of color channels we propose an algorithm for calculating parameters of a reversible integer transform, which approximates such continuous mappings as a discrete Karunen-Loeve transform. We propose a method for estimating the approximation errors, which makes it possible to choose an optimal approximation of the original transform that minimizes these errors. Using the MRG file format, intended for storing large amounts of integer raster data, as an example, we show that after applying decorrelation, it is possible to increase the compression ratio of multichannel raster images using lossless compression algorithms. © 2022, Institution of Russian Academy of Sciences. All rights reserved.

关键词： Color

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Data-Driven Predictive Disturbance Observer for Quasi Continuum Manipulators

Data-Driven Predictive Disturbance Observer for Quasi Contin...

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IEEE Conference on Decision and control

作者： Daniel Müller Justinus Feilhauer Jennifer Wickert Julian Berberich Frank Allgöwer Oliver Sawodny Institute for System Dynamics University of Stuttgart Stuttgart Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Deriving accurate and time efficient models for soft robots has proven to be a difficult task. This is mainly due to the behaviour of the soft materials and the interaction with other parts of the robot, resulting in complicated models with many states that require complex sensor *** robots such as the Bionic Soft Arm (BSA) are subject to uncertainties. The resulting mismatch between plant and model can be viewed as a disturbance. Disturbance observers significantly improve the control performance of the model-based controllers. However, they assume an underlying model of the disturbance that is usually unknown. To address this problem, we propose a novel Data-Driven Predictive Disturbance Observer (DPDO), which predicts the future disturbance based on past measurements that are updated online and, thereby, enables the model-based controller to achieve zero control error. The approach is first tested in simulation, examining the influence of noise, time delays and typical model uncertainties for the BSA. Afterwards, the algorithm is applied to the real system, demonstrating its practicability.

关键词： Uncertainty Trajectory tracking Biological system modeling Soft robotics Predictive models Prediction algorithms Manipulators

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A distributed data warehouse system for astroparticle physics

arXiv

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arXiv 2018年

作者： Nguyen, Minh-Duc Kryukov, Alexander Dubenskaya, Julia Korosteleva, Elena Polyakov, Stanislav Postnikov, Evgeny Bychkov, Igor Mikhailov, Andrey Shigarov, Alexey Fedorov, Oleg Kazarina, Yulia Shipilov, Dmitry Zhurov, Dmitry Lomonosov Moscow State University Skobeltsyn Institute of Nuclear Physics Moscow Russia Matrosov Institute for System Dynamics and Control Theory Siberian Branch of Russian Academy of Sciences Irkutsk Russia Applied Physics Institute Irkutsk State University Irkutsk Russia

A distributed data warehouse system is one of the actual issues in the field of astroparticle physics. Famous experiments, such as TAIGA, KASCADE-Grande, produce tens of terabytes of data measured by their instruments. It is critical to have a smart data warehouse system on-site to store the collected data for further distribution effectively. It is also vital to provide scientists with a handy and user-friendly interface to access the collected data with proper permissions not only on-site but also online. The latter case is handy when scientists need to combine data from different experiments for analysis. In this work, we describe an approach to implementing a distributed data warehouse system that allows scientists to acquire just the necessary data from different experiments via the Internet on demand. The implementation is based on CernVM-FS with additional components developed by us to search through the whole available data sets and deliver their subsets to users' computers. Copyright © 2018, The Authors. All rights reserved.

关键词： Data warehouses

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