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32 篇 会议

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检索条件"任意字段=26th International Conference on Methods and Models in Automation and Robotics, MMAR 2022"

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Application of Tiny-ML methods for face recognition in social robotics using OhBot robots 26

Application of Tiny-ML methods for face recognition in socia...

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26th international conference on methods and models in automation and robotics

作者： Probierz, Eryka Bartosiak, Natalia Wojnar, Martyna Skowronski, Kamil Galuszka, Adam Grzejszczak, Tomasz Kgdziora, Olaf Silesian Tech Univ Dept Automat Control & Robot Gliwice Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

the aim of this paper is to show the possible application of Tiny-ML family neural networks to social robots for face recognition. Social robotics is a constantly developing field that allows the production and development of robots whose task is to accompany humans, participate in social situations and perform specific educational, entertainment and therapeutic tasks. One of the fundamental problems of social robotics is the proper recognition of humans by robots. this poses a critical problem because it is the moment when human-robot contact is initiated. Widespread solutions, in addition to high efficiency, also require adequate computing power, which in social robots cannot always be provided. For this purpose, solutions from the Tiny-ML stream are used, i.e. such a construction of neural networks and machine learning that would be adapted to limited technological resources and, at the same time, equally effective. the paper uses a YOLOv4-tiny network, which was compared to a YOLOv5s solution, both in terms of efficiency and processing time. the proposed networks were tested on social robots of the OhBot type and with extended capabilities, by using Neural Sticks. the results obtained show the highest efficiency of the implemented YOLOv5s network using a Raspberry Pi along with an accelerator. the presented research is an opportunity to draw attention to the problem of computational complexity in robotic applications, and also has the potential to popularize social robots and their use in everyday life.

关键词： Social robots OhBot robots Face detection Tiny-YOLO Neural networks

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Sensitivity Analysis of Time Delay Parabolic-Hyperbolic Optimal Control Problems with Boundary Conditions Involving Time Delays

Sensitivity Analysis of Time Delay Parabolic-Hyperbolic Opti...

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18th international conference on methods and models in automation and robotics (mmar)

作者： Emirsajlow, Zbigniew Krakowiak, Anna Kowalewski, Adam Sokolowski, Jan West Pomeranian Univ Technol Inst Control Engn Ul 26 Kwietnia 10 PL-71126 Szczecin Poland

ISBN: (纸本)9781467355087;9781467355063

In the paper the first order sensitivity analysis is performed for a class of optimal control problems for parabolic-hyperbolic systems in which time delays appear both in the state equations and in the Neumann boundary conditions. A singular perturbation of geometrical domain of integration is introduced in the form of a circular hole. the Steklov-Poincare operator on a circle is defined in order to reduce the problem to regular perturbations in the truncated domain. the optimality system is differentiated with respect to the small parameter and the directional derivative of the optimal control is obtained as a solution to an auxiliary optimal control problem.

关键词： Time delay

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Patterns Tracking using Local Lacunarity and Track-Before-Detect Approach 20

Patterns Tracking using Local Lacunarity and Track-Before-De...

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20th international conference on methods and models in automation and robotics (mmar)

作者： Mazurek, Przemyslaw West Pomeranian Univ Technol Szczecin Dept Signal Proc & Multimedia Engn 26 Kwietnia 10 St PL-71126 Szczecin Poland

ISBN: (纸本)9781479987016

Line tracking is important for numerous applications of the machine vision, including control of line following robots. Local lacunarity for binary images allows the estimation of line by the detection of spatial differences using statistical properties of textures. Additional tracking algorithm improves the line detection by application of motion model. Such approach allows the extended width line tracking. the properties of proposed local lacunarity are analyzed for the perturbed pattern located on the random background. Monte Carlo approach is applied for the analysis of such lines for two smoothing coefficient values of Spatio-Temporal Track-Before-Detect algorithm. Obtained horizontal mean error value is comparable to the width of the line.

关键词： on line detection robot vision Binary images tracking mean errors

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Interpretable deep learning approach for classification of breast cancer - a comparative analysis of multiple instance learning models 27

Interpretable deep learning approach for classification of b...

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27th international conference on methods and models in automation and robotics (mmar)

作者： Buler, Jakub Buler, Rafal Bobowicz, Maciej Ferlin, Maria Rygusik, Marlena Kwasigroch, Arkadiusz Grochowski, Michal Gdansk Univ Technol Dept Intelligent Control Syst & Decis Support Gdansk Poland Med Univ Gdansk Dept Radiol 2 Gdansk Poland

ISBN: (纸本)9798350311075

Breast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening analysis and support radiologists and oncologists in diagnosing breast cancer. the design of a deep learning-based system for automated breast cancer diagnosis is not easy due to the lack of annotated data, especially at pixel level, the large size of the images with relatively small cancer lesion sizes and class imbalance, a wide diversity of cancerous lesions, a variety of breasts, both in size and density, make the training of the neural models challenging. Moreover, clinicians are often concerned about using these black-box models because of the lack of transparency in their inference. To address these issues, we propose an approach taking advantage of Multiple Instance Learning (MIL), supported by attention mechanisms. We researched Attention-based MIL (AMIL), Gated AMIL (GAMIL), Dual Stream MIL (DSMIL) and CLustering-constrained AMIL (CLAM) models trained in a weakly-supervised manner and compared them with a common model in image classification tasks, ResNet18. the approach described in this paper is multimodal and combines two mammographic projections (CC and MLO) in the training process. the developed neural system achieved high classification efficiency. Furthermore, exploiting the generated attentional maps allowed the localisation of cancerous lesions, thus increasing the interpretability of the algorithm. thanks to this mechanism, we were also able to detect artifacts in the analyzed database, difficult to spot but drastically skewing the algorithm's performance.

关键词： breast lesion classification weakly-supervised learning decision support diagnosis multimodal learning attention maps dataset biases trustworthy AI

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On the attainability of an estimate of a number of Perron exponents for discrete linear diagonal systems with bounded coefficients 20

On the attainability of an estimate of a number of Perron ex...

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20th international conference on methods and models in automation and robotics (mmar)

作者： Konyukh, Alexander Niezabitowski, Michal Belarus State Econ Univ Partyzanski Praspiekt 26 Minsk 220070 BELARUS Silesian Tech Univ Fac Automat Control Elect & Comp Sci PL-44100 Gliwice Poland

ISBN: (纸本)9781479987016

the Lyapunov, Perron and Bohl exponents are the most important numerical characteristics of dynamic systems used in control theory. Properties of the first one are well investigated. Properties of the Perron and Bohl exponents are much less described in the literature, for example the questions about the structure of the sets of values of these quantities for general discrete-time systems are still open. In this paper we give an example of a two-dimensional system with bounded coefficients that have three different Perron exponents. In addition, we prove that the s-dimensional diagonal discrete-time system with bounded coefficients can have any number of different Perron exponents between 1 and 2(s) - 1.

关键词： Perron exponent Lyapunov exponent diagonal discrete time-varying systems

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Sensitivity Analysis of Time Delay Parabolic-Hyperbolic Optimal Control Problems

Sensitivity Analysis of Time Delay Parabolic-Hyperbolic Opti...

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17th international conference on methods and models in automation and robotics (mmar)

作者： Emirsajlow, Zbigniew Krakowiak, Anna Kowalewski, Adam Sokolowski, Jan W Pomeranian Univ Technol Inst Control Engn Ul 26Kwietnia 10 PL-71126 Szczecin Poland Krakow Tech Univ PL-31155 Krakow Poland AGH Univ Sci & Technol Fac Elect Engn Automat Comp Sci & Elect Inst Automat PL-30059 Krakow Poland Univ Henri Poincare Inst Elie Cartan F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9781467321242

In the paper the first order sensitivity analysis is performed for a class of optimal control problems for time delay parabolic-hyperbolic equations. A singular perturbation of geometrical domain of integration is introduced in the form of a circular hole. the Steklov-Poincare operator on a circle is defined in order to reduce the problem to regular perturbations in the truncated domain. the optimality system is differentiated with respect to the small parameter and the directional derivative of the optimal control is obtained as a solution to an auxiliary optimal control problem.

关键词： Optimal control Sensitivity analysis time delay Directional derivative Circles perturbation

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Sensitivity Analysis of Parabolic-Hyperbolic Optimal Control Problems

Sensitivity Analysis of Parabolic-Hyperbolic Optimal Control...

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16th international conference on methods and models in automation and robotics (mmar)

作者： Emirsajlow, Zbigniew Krakowiak, Anna Kowalewski, Adam Sokolowski, Jan West Pomeranian Univ Technol Inst Control Engn Ul 26 Kwietnia 10 PL-71126 Szczecin Poland Krakow Tech Univ Math Inst PL-31155 Krakow Poland AGH Univ Sci & Technol Inst Automat PL-30059 Krakow Poland Univ Henri Poincare Inst Elie Cartan Math Lab F-54506 Vandoeuvre Les Nancy France Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

ISBN: (纸本)9781457709142

In the paper the first order sensitivity analysis is performed for a class of optimal control problems for parabolic-hyperbolic equations. A singular perturbation of geometrical domain of integration is introduced in the form of a circular hole. the Steklov-Poincare operator on a circle is defined in order to reduce the problem to regular perturbations in the truncated domain. the optimality system is differentiated with respect to the small parameter and the directional derivative of the optimal control is obtained as a solution to an auxiliary optimal control problem.

关键词： Optimal control Sensitivity analysis Directional derivative Circles perturbation

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mmar 2022 Table of Contents

MMAR 2022 Table of Contents

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international conference on methods and models in automation and robotics (mmar)

Scroll to the title and select a Blue link to open a paper. After viewing the paper, use the bookmarks to the left to return to the Table of Contents.

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596

Scroll to the title and select a Blue link to open a paper. After viewing the paper, use the bookmarks to the left to return to the Table of Contents.

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mmar 2022 Author Index

MMAR 2022 Author Index

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international conference on methods and models in automation and robotics (mmar)

Scroll to the title and select a Blue link to open a paper. After viewing the paper, use the bookmarks to the left to return to the Author Index.

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596

Scroll to the title and select a Blue link to open a paper. After viewing the paper, use the bookmarks to the left to return to the Author Index.

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Improving the detection of noisy labels in image datasets using modified Confidence Learning

Improving the detection of noisy labels in image datasets us...

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international conference on methods and models in automation and robotics (mmar)

作者： Adam Popowicz Krystian Radlak Slawomir Lasota Karolina Szczepankiewicz Michal Szczepankiewicz Faculty of Automatic Control Silesian University of Technology Electronics and Computer Science Gliwice Poland Warsaw University of Technology Warsaw Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596

the effectiveness of machine learning algorithms, including deep neural networks (DNN) for classifying image data, depends on proper preparation of the training dataset. Erroneously labeled images in the training data will degrade algorithmic efficiency and cause unpredictable model behavior, thus reduce its safety. Verifying labels in the numerous available databases remains a complicated and laborious task. In this article, we present a MultiNET approach that allows for efficient verification of labeled image datasets. We adapt a state-of-the-art technique, namely Confidence Learning, extending its flexibility and improving the effectiveness by combining outcomes from various DNN architectures. thanks to the proposed modification, it is possible to automatically detect incorrect labels while minimizing the number of false positives, thus making the verification process much less burdensome. the technique may be of use for researchers and software engineers dealing with externally supplied image datasets.

关键词： Deep learning Training Machine learning algorithms Databases Training data Computer architecture Data models

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