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2021 25th international conference on methods and models in automation and robotics, mmar 2021

2021 25th International Conference on Methods and Models in ...

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25th international conference on methods and models in automation and robotics, mmar 2021

ISBN: (纸本)9781728173801

the proceedings contain 67 papers. the topics discussed include: the use of counting peaks method for the purpose of smoothing filtering efficiency assessment in analysis of electroencephalography data;incremental 3D maps server based on feature matching for multi-robot systems;internal model-based lateral and longitudinal control for autonomous electric vehicles;analysis of microphone use for perception of autonomous vehicles;traffic lane-changing modeling and scheduling with game theoretical strategy;continuous-time model predictive control with disturbances compensation for a glass forehearth;accuracy analysis of the fractional order, positive, state space model of heat transfer process;and generalized proportional integral observer approach for trajectory tracking control of autonomous underwater vehicles.

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Comparison of decision support models - a recruitment case study as a multiclass classification problem with limited data 27

Comparison of decision support models - a recruitment case s...

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

作者： Morys-Magiera, Artur Dlugosz, Marek Skruch, Pawel AGH Univ Cracow Fac Elect Engn Automat Comp Sci & Biomed Engn Krakow Poland

ISBN: (纸本)9798350311075

We present a comparative case study of machine learning models, evaluating their efficiency in a practical task of multiclass classification of samples being submissions to a recruitment survey and assigning them scores denoting the match level for a given candidate to a given workgroup (committee) in the AGH Students' Council. this research is based on the Council's recruitment applications that carried candidates' responses to a set of 10 hypothetical Council member activity scenarios, where they were to choose one of four given solutions to the problems. the data was collected from a web quiz in 2020, validated on a voluntary insider control group's responses to these questions and finally the best-performing model was evaluated in practice in the 2021's recruitment inside an in-browser adventure minigame. this work provides insight into how models ranging from classical methods to deep learning perform in a very specific not yet well-explored in literature, practical non-linear problem that is dependent on individual features of the participants, with the data volume being very limited due to a restricted population of candidates. this information may provide a starting point for applications of machine learning in decision support systems in recruitment processes.

关键词： machine learning multiclass classification deep learning recruitment survey covid remote

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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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Design of weighted PID controllers for control of the Stewart-Gough platform 25

Design of weighted PID controllers for control of the Stewar...

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25th international conference on methods and models in automation and robotics

作者： Kolasiewicz, Justyna Perzylo, Anna Piotrowski, Robert Gdansk Univ Technol Fac Elect & Control Engn Narutowicza 11-12 PL-80223 Gdansk Poland

ISBN: (纸本)9781728173818;9781728173801

Stewart-Gough platform (SGP) is a popular parallel type manipulator that involves a 6 degrees of freedom (DOF) motion. In this paper, the process of mathematical modelling of SGP is presented. Two selected control algorithms that use PID controllers and weighted PID controllers are designed. Both control systems using these algorithms are implemented in MATLAB environment as well as on the actual SGP. Parameters of the controllers are optimized with the use of different integral performance criteria. Tests of the designed control systems on the computer model and on the SGP are presented.

关键词： automation Computational modeling Control systems Manipulators Trajectory Mathematical model Matlab

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Accuracy analysis of the fractional order, positive, state space model of heat transfer process 25

Accuracy analysis of the fractional order, positive, state s...

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25th international conference on methods and models in automation and robotics

作者： Oprzedkiewicz, Krzysztof Dziedzic, Klaudia Mitkowski, Wojciech AGH Univ Sci & Technol Dept Automat Control & Robot Al A Mickiewicza 30 PL-30059 Krakow Poland

ISBN: (纸本)9781728173818;9781728173801

In the paper the accuracy estimation for fractional, positive state space model of heat transfer process is presented. the new criterion of the external positivity for this model is presented. Next the parameters of the model are estimated via numerical minimization of the Mean Square Error (MSE) and FIT cost functions. Finally the externally positive models are compared to non-positive. Results of simulations show that the positive model is more accurate in the sense of considered cost functions than non positive.

关键词： Analytical models Robot vision systems Estimation Mean square error methods Cost function Minimization Numerical models

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On the problem of optimised allocation of water quality sensors and actuators in DWDS 25

On the problem of optimised allocation of water quality sens...

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25th international conference on methods and models in automation and robotics

作者： Zubowicz, Tomasz Langowski, Rafal Gdansk Univ Technol Dept Elect Engn Control Syst & Informat Ul G Narutowicza 11-12 PL-80233 Gdansk Poland

ISBN: (纸本)9781728173818;9781728173801

the problems of water quality sensors and actuators placement in drinking water distribution systems (DWDSs) are addressed as separate, primarily. However, against the background of control systems theory, the nature of DWDSs dynamics indicates that these both problems are interdependent and impact the design of related water quality monitoring and control structures and algorithms. the research work presented in this paper is to investigate the state-of-the-art in this field and discuss the problems of water quality sensors and actuators placement within DWDS and to highlight the potential benefits of considering the joint task of their allocation.

关键词： Actuators Heuristic algorithms Water quality Sensor systems Sensors Numerical models Resource management

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Predicting system degradation using Bayesian time series models 25

Predicting system degradation using Bayesian time series mod...

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25th international conference on methods and models in automation and robotics

作者： Baranowski, Jerzy Bauer, Waldemar Kashpruk, Nataliia Zagorowska, Marta AGH Univ Sci & Technol Dept Automat Control & Robot Krakow Poland Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (纸本)9781728173818;9781728173801

Efficient maintenance of industrial equipment requires degradation monitoring and prediction. Currently used prediction models are mostly deterministic and cannot consider uncertainty inherent to degradation measurements. In this paper we propose using time series models obtained using Facebook Prophet algorithm to predict the evolution of degradation of turbomachinery. We Illustrate our considerations with data from large scale industrial centrifugal compressors. Our predictions are promising and confidence intervals cover the predictions well.

关键词： centrifugal compressor statistical modelling time series degradation Facebook Prophet

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Generalized Proportional Integral Observer Approach for Trajectory Tracking Control of Autonomous Underwater Vehicles 25

Generalized Proportional Integral Observer Approach for Traj...

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25th international conference on methods and models in automation and robotics

作者： Guzman-Hernandez, Sandra Chamorro, Harold R. Maestre, Jose M. Camacho, Eduardo F. ETSI Univ Sevilla Dept Ingn Sistemas & Automat Seville Spain

ISBN: (纸本)9781728173818;9781728173801

Marine sciences require the use of Autonomous Underwater Vehicles (AUV) for their missions in order to perform unmanned duties like deep-sea explorations. AUV require to reject turbulence disturbances while regulating their trajectory. In this paper, a position controller applying the Generalized Proportional Integral (GPI) observer to an Autonomous Underwater Vehicle (AUV) in order to maintain the desired trajectory and reject the environment perturbations is given. the mathematical model and the performance of the GPI shows a precise and fast tracking control through simulations.

关键词： Autonomous Underwater Vehicles Generalized Proportional Integral Control Position Control robotics

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A selection of PID type controller settings via LQR approach for two-wheeled balancing robot 25

A selection of PID type controller settings via LQR approach...

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25th international conference on methods and models in automation and robotics

作者： Laddach, Krzysztof Czyzniewski, Mateusz Langowski, Rafal Gdansk Univ Technol Dept Elect Engn Control Syst & Informat Ul G Narutowicza 11-12 PL-80233 Gdansk Poland

ISBN: (纸本)9781728173818;9781728173801

the problem of PID type controller tuning has been addressed in this paper. In particular, a method of selection of PD settings based on the solution of linear-quadratic optimisation problem using the energy criterion has been investigated. thus, the possibility of transforming optimal settings of the linear-quadratic regulator into the settings of the controller in the classical control system has been given. the presented methodology has been used during synthesis of control system for a two-wheeled balancing robot. Finally, the performance of the proposed control system has been validated by simulation in Matlab/Simulink environment with the use of a two-wheeled balancing robot model.

关键词： State feedback Regulators Hardware Numerical models PD control Mathematical model Robots

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General Dynamic Neural Networks for the Adaptive Tuning of an Omni-Directional Drive System for Reactive Swarm robotics 25

General Dynamic Neural Networks for the Adaptive Tuning of a...

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25th international conference on methods and models in automation and robotics

作者： Zhao, Hanqing Dorigo, Marco Allwright, Michael Univ Libre Bruxelles IRIDIA Brussels Belgium

ISBN: (纸本)9781728173818;9781728173801

We demonstrate the use of general dynamic neural networks (GDNNs) for the online tuning of an omni-directional drive system for reactive swarm robots. the drive system used in this work consists of four motor-encoder-microcontroller modules each constituting a single-input single-output (SISO) proportional, integral, and differential (PID) control system. For a given target velocity, a neural network generates the parameters for each PID control system. In this paper, we evaluate and compare two different network structures for generating the PID parameters for the control systems using a hardware platform that we also presented in this paper. We analyze the performance of the system with respect to ISO performance indicators, our results show that both network structures are able to learn and tune the parameters for each PID control system to increase the accuracy of the drive system in comparison to fixed untuned PID parameters that are close to the output of a randomly initialized network.

关键词： Training PI control Neural networks Swarm robotics SISO communication Trajectory PD control

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