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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Alexander L. Shestakov Dmitrii V. Galyshev Victoria A. Eremeeva Olga L. Ibryaeva laboratory of self-diagnostics and self-control of devices and systems South Ural State University Chelyabinsk Russia dept. of Applied Mathematics and Programming South Ural State University Chelyabinsk Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The problem of diagnostics of broken rotor bars in an induction (IM) motor based on its current signals is considered. The proposed fault detection technique is based on filtering the current signal in the region around the frequency 7f, where f is the power supply frequency, and further obtaining its envelope using the Hilbert Transform. Scalograms are constructed from the envelopes using a Continuous Wavelet Transform and form a training dataset for the Convolutional Neural Network (CNN) classifier. In real industrial conditions, we have a lack of defective data which is simulated in this paper. It is shown that a CNN model trained on normal data obtained under different motor operating conditions and defective data obtained under one operating mode will poorly detect a defect when changing the operating mode. The reason for this lies in the characteristic defect frequencies change with the change in the operating mode. A data augmentation method based on spectrum distortion is proposed to solve this problem. We distort the spectrum of a real defective signal taking into account its structure and create synthetic data that simulate missing defective signals with different operating conditions. The paper presents the experimental rig and dataset, and shows that the spectral distortion-based augmentation technique significantly improves the performance of the CNN model under conditions of insufficient defective training data.

关键词： Training Power supplies Rotors Training data Distortion Data augmentation Data models Convolutional neural networks Bars Synthetic data

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Model-based dynamic optimization with openmodelica and CasADi

Model-based dynamic optimization with openmodelica and CasAD...

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7th IFAC Symposium on Advances in Automotive Control, AAC 2013

作者： Shitahun, Alachew Ruge, Vitalij Gebremedhin, Mahder Bachmann, Bernhard Eriksson, Lars Andersson, Joel Diehl, Moritz Fritzson, Peter PELAB-Programming Environment Lab Dept. Computer Science Linköping University SE-581 83Linköping Sweden Dept. Mathematics and Engineering University of Applied Sciences D-33609 Bielefeld Germany Vehicular Systems Dept. Electrical Engineering Linköping University SE-581 83 Linköping Sweden K.U. Leuven Belgium

ISBN: (纸本)9783902823434

This paper demonstrates model-based dynamic optimization through the coupling of two open source tools: OpenModelica, which is a Modelica-based modeling and simulation platform, and CasADi, a framework for numerical optimization. The coupling uses a standardized XML format for exchange of differential-algebraic equations (DAE) models. OpenModelica supports export of models written in Modelica and the optimization language extension using this XML format, while CasADi supports import of models represented in this format. This allows users to define optimal control problems (OCP) using Modelica and optimization language specification, and solve the underlying model formulation using a range of optimization methods, including direct collocation and direct multiple shooting. The proposed solution has been tested on several industrially relevant optimal control problems, including a dieselelectric power train.

关键词： Optimization

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Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic

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Materials Today: Proceedings 2022年 56卷 2213-2216页

作者： Boddu, Raja Sarath Kumar Karmakar, Partha Bhaumik, Ankan Nassa, Vinay Kumar Vandana Bhattacharya, Sumanta Department of CSE Lenora College of Engineering Andhra Pradesh Rampachodavaram India Government of West Bengal Bikash Bhawan Salt Lake W.B. Kolkata India Dept. of Applied Mathematics With Oceanology and Computer Programming Vidyasagar University Midnapore India Department of Computer Science Engineering South Point Group of Institutions Sonepat Haryana India Department of Mathematics Dasmesh Khalsa College Zirakpur India Department of Science and Technology and Biotechnology MAKAUT India

Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quic

关键词： COVID-19

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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Robust initialization of differential algebraic equations

Robust initialization of differential algebraic equations

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1st International Workshop on Equation-Based Object-Oriented Languages and Tools, EOOLT 2007 - In Conjunction with the ECOOP 2007 Conference

作者： Bachmann, Bernhard Aronsson, Peter Fritzson, Peter Dept. Mathematics and Engineering University of Applied Sciences D-33609 Bielefeld Germany MathCore Engineering AB Teknikringen 1F SE-583 30 Linköping Sweden Department of Computer Science PELAB Programming Environments Lab Linköping University SE-581 83 Linköping Sweden

This paper describes a new solution method applied to the problem initializing DAEs using the Modelica language. Modelica is primarily an object- oriented equ-tion-based modeling language that allows specification of mathematical models of complex natural or man-made systems. Major features of Modelica are the multidomain modeling capability and the reusability of model components corresponding to physical objects, which allow to build and simulate highly complex systems. However, initializing such models has been quite cumbersome, since initial equations have to be pro-vided at the system level, where the user needs to know details on the underlying transformation and index-reduction algorithms, that in general are applied to simulate a Modelica model.

关键词： Modeling languages

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Model-Based Dynamic Optimization with OpenModelica and CasADi

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IFAC Proceedings Volumes 2013年第21期46卷 446-451页

作者： Alachew Shitahun Vitalij Ruge Mahder Gebremedhin Bernhard Bachmann Lars Eriksson Joel Andersson Moritz Diehl Peter Fritzson PELAB-Programming Environment Lab Dept. Computer Science Linköping University SE-581 83 Linköping Sweden Dept. Mathematics and Engineering University of Applied Sciences D-33609 Bielefeld Germany Vehicular Systems Dept. Electrical Engineering Linköping University SE-581 83 Linköping Sweden Dept. of Electrical Engineering and Optimization in Engineering Center (OPTEC) K.U. Leuven Belgium

关键词： Modelica OpenModelica model-based optimization dynamic optimization CasADi

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Development of OpenFOAM Solver for Compressible Viscous Flows Simulation Using Quasi-Gas Dynamic Equations

Development of OpenFOAM Solver for Compressible Viscous Flow...

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Ivannikov ISPRAS Open Conference (ISPRAS)

作者： Matvey V. Kraposhin Daniil A. Ryazanov Elena V. Smirnova Tatiana G. Elizarova Maria A. Istomina Ivannikov Institute for System Programming of Russian Academy of Sciences Moscow Russia Dept. of wave and gas dynamics Lomonosov Moscow State University Moscow Russia Bauman Moscow State Technical University Moscow Russia Keldysh Institute of Applied Mathematics (Russian Academy of Sciences) Moscow Russia

Results of QGD (quasi-gas dynamics) equations approximation using OpenFOAM Finite Volume Method library are presented. The new developed solver QGDFoam compared to standard OpenFOAM solver rhoCentralFoam. Two approaches for the approximation of QGD terms are considered and compared - (1) least square method and (2) face normal derivative. Qualitative analysis showed that the first approach is more accurate compared with the second one and is preferable for reproducing complex shock interactions during unsteady flow. It was shown that the second approach produces the solution, similar to rhoCentralFoam solver with upwind interpolation. Being more diffusive than rhoCentralFoam solver with TVD schemes, QGDFoam, allows, however, to reproduce flows at wider Mach number range without introducing too much numerical diffusion comparing to upwind interpolation. Parallel scalability of developed solver has been checked. It scales for meshes down to approximately 1000 cells per node.

关键词： Mathematical model Face Numerical models Viscosity Interpolation Heating systems Shape

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Galerkin Method and Qualitative Approach for the Investigation and Numerical Analysis of Some Dissipative Nonlinear Physical Systems

Galerkin Method and Qualitative Approach for the Investigati...

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International Scientific and Technical Conference on Computer Science and Information Technologies

作者： Petro Pukach Volodymyr Il'kiv Zinovii Nytrebych Myroslava Vovk Natalia Shakhovska Pavlo Pukach Dept. of Computational Mathematics and Programming Lviv Polytechnic National University Lviv Ukraine Dept. of Mathematics Lviv Polytechnic National University Lviv Ukraine Dept. of Artifical Intelligence Systems Lviv Polytechnic National University Lviv Ukraine Dept. of Applied Mathematics Lviv Polytechnic National University Lviv Ukraine

The mathematical models of the oscillations for the important classes of the nonlinear physical systems with dissipation are considered in the paper. It is impossible to apply the asymptotic analytical methods to construct the solutions in the mathematical models of the dynamical processes in these systems. Consequently the qualitative approach is used, the solution existence and uniqueness is substantiated and estimated. The qualitative methods enable to use the corresponding specified numerical methods for the investigation of the mathematical model and solution construction. Basing on the numerical analysis and the fourth-order Runge-Kutta method there are analyzed some singularities of the dynamical processes in the considered systems classes. The effective combination of the theoretic and numerical approach allows to build the innovative procedure to analyze the mathematical models for the wide class of the nonlinear physical systems applied in the engineering.

关键词： Mathematical model Physical system Dynamical process Dissipation Nonlinear oscillations Galerkin method

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CONTROL SYSTEM DESIGN CONSIDERING A TRADEOFF BETWEEN EVALUATED UNCERTAINTY RANGES AND CONTROL PERFORMANCE

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Asian Journal of Control 2008年第1期1卷

作者： Y. Wakasa Y. Yamamoto Dept. of Applied Analysis and Complex Dynamical Systems Graduate School of Informatics Kyoto University Kyoto Japan. Yuji Wakasa was born in Okayama Japan in 1968. He received the B.S. and M.S. degrees in engineering from Kyoto university Japan in 1992 and 1994 respectively. From 1994 to 1998 he was a Research Associate in the Department of Information Technology Okayama University. Since April 1998 he has been a Research Associate in the Graduate School of Informatics Kyoto University. His current research interests include robust control and control system design via mathematical programming. Yutaka Yamamoto received his B.S. and M.S. degrees in engineering from Kyoto University Kyoto Japan in 1972 and 1974 respectively and the M.S. and Ph.D. degree in mathematics from the University of Florida in 1976 and 1978 respectively. From 1978 to 1987 he was with Department of Applied Mathematics and Physics Kyoto University and from 1987 to 1997 with Department of Applied System Science. Since 1998 he is a professor at the current position. His current research interests include realization and robust control of distributed parameter systems learning control sampled-data systems and digital signal processing. Dr. Yamamoto is a receipient of the Sawaragi memorial paper award (1985) the Outstanding Paper Award of SICE (1987) Best Author Award of SICE (1990) the George Axelby Outstanding Paper Award of IEEE CSS in 1996 Takeda Paper Prize of SICE in 1997. He is a Fellow of IEEE. He was an associate editor of Automatica. He is currently an associate editor of IEEE Transactions on Automatic Control Systems and Control Letters and Mathematics of Control Signals and Systems. He is a member of the IEEE the Society of Instrument and Control Engineers (SICE) and the Institute of Systems Control and Information Engineers.

This paper presents a design method of control systems such that a designer can flexibly take account of tradeoffs between evaluated uncertainty ranges and the level of control performance. The problem is reduced to a BMI problem and approximately solved by LMIs. The convergence of the proposed approximation is proved in a modified sense. A numerical example shows the effectiveness of the proposed method in comparison with the standard robust control.

关键词： Control system design LMI BMI tradeoff curves

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