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International symposium on computational intelligence and Informatics

作者： Anna Lántzky Dániel Joó Molnár Csenge Andrea Human modelling department Mollia Zrt. Budapest Hungary Department of Applied Mechanics Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9798350398830

For this study 10 university students were measured, having a variety of age from 18 years old to 25. Every subject was ask to carry on 6 different balancing acts, without any previous training on the board. One main concept for modelling, is describing the motion with the minimum number of coordinates, but different concepts are considered too. In the simplest one, the human body is described with a rigid homogeneous bar and the ankle’s stiffness is modelled with a torsional spring. The main focus in this thesis, is on the computation of the acting torques of the ankle. For this calculation, the Newtonian equations of the simple mechanical model will be derived, then the generalized coordinate values will be obtained from motion captured measurements, and its derivatives from numerical derivations.

关键词： Training Mechatronics Parametric study computational modeling Robot kinematics Mathematical models Numerical models

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Lyapunov-Inspired Deep Reinforcement Learning for Robot Navigation in Obstacle Environments

Lyapunov-Inspired Deep Reinforcement Learning for Robot Navi...

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computational intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Halil Ibrahim Ugurlu Adrian Redder Erdal Kayacan Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Department of Automatic Control Paderborn University Paderborn Germany

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

The inherent black-box nature of deep reinforcement learning (DRL) poses challenges in ensuring safety constraints. This paper, therefore, introduces a DRL reward design inspired by Lyapunov stability theory for safe robot navigation in the presence of obstacles. The navigation problem is formulated as a state-space control problem with close obstacle locations integrated into the state representation. To ensure safe obstacle avoidance, we introduce a novel reward-shaping strategy utilizing a Lyapunov function that discourages fast movement toward obstacles. Our numerical experiments demonstrate the effectiveness of the reward design strategy compared to baselines in achieving consistent superior learning with higher mission completion rates while maintaining speeds closer to a desired target speed. In addition, we show that our reward design enables a generally smaller choice for the discount factor for value-function-based DRL algorithms, which can lead to faster convergence. This is possible since the reward design merely penalizes the one-step decay of the Lyapunov function. Furthermore, policy training simulations employ an early episode termination method to constrain exploration and add more valuable samples to the DRL training replay memory. Finally, real-world experiments with a quadrotor validate the ability of our method to safely navigate around varying densities of obstacles. The proposed method consistently takes cautious maneuvers near obstacles by slowing down, achieving greater obstacle clearance compared to baseline, although with an increase in mission completion time.

关键词： Training Navigation Deep reinforcement learning Safety Planning Collision avoidance Robots Lyapunov methods Quadrotors Convergence

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Torque control in a Two-Mass Resonant System: Simulation and Dynamic Analysis

Torque Control in a Two-Mass Resonant System: Simulation and...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Mansoor Zeinali Sayed Mohammadali Zanjani Somaye Yaghoubi Amir Mosavi Arman Fathollahi Department of Electrical Engineering Najafabad Branch Islamic Azad University Najafabad Iran Department of Mechanical Engineering Najafabad Branch Islamic Azad University Najafabad Iran John von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

A multi-mass system is a mechanical system that consists of several masses such as a motor, load, and gear that are connected by a flexible shaft. The mechanical fluctuations in drives with flexible coupling between the motor and the driven device can no longer be ignored, as they could in the past when requirements for speed control dynamics were low. These prerequisites pertain to the regulation of the rotational speed and position of the servo drive and are quantified by the magnitude of speed step response time and speed fault elimination time caused by the step change of load torque, which, in modern drives, can be measured. In this study, the dynamical behavior of a two-mass resonant system with a three-term controller for control of torque and speed is investigated using eigenvalues analysis. The proposed control strategy aims to eliminate the rotational fluctuations of the motor shaft, dampen the load torque disturbance impact, provide a quick response to changes in the base speed while avoiding increases in the load speed, and be resilient to instability. Finally, numerical outcomes demonstrate the effect of the presented controller application on improving the dynamic demeanor of the two-mass test system. Based on the outcomes, the effectiveness of the proposed control scheme is highly dependent on system parameters. Due to the inherent parameter uncertainty in the multi-mass system, the use of parameter estimators based on artificial neural networks (ANNs) is suggested for future work.

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Algorithmic Design for Embodied intelligence in Synthetic Cells

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ieee TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2021年第3期18卷 864-875页

作者： Pervan, Ana Murphey, Todd D. Northwestern Univ Dept Mech Engn Evanston IL 60208 USA

In nature, biological organisms jointly evolve both their morphology and their neurological capabilities to improve their chances for survival. Consequently, task information is encoded in both their brains and their bodies. In robotics, the development of complex control and planning algorithms often bears sole responsibility for improving task performance. This dependence on centralized control can be problematic for systems with computational limitations, such as mechanical systems and robots on the microscale. In these cases, we need to be able to offload complex computation onto the physical morphology of the system. To this end, we introduce a methodology for algorithmically arranging sensing and actuation components into a robot design while maintaining a low level of design complexity (quantified using a measure of graph entropy) and a high level of task embodiment (evaluated by analyzing the Kullback-Leibler divergence between physical executions of the robot and those of an idealized system). This approach computes an idealized, unconstrained control policy that is projected onto a limited selection of sensors and actuators in a given library, resulting in intelligence that is distributed away from a central processor and instead embodied in the physical body of a robot. The method is demonstrated by computationally optimizing a simulated synthetic cell. Note to Practitioners-As robotic systems approach the micrometer scale, designing them to be fully autonomous will rely less on onboard computation and more on component selection and design. In this article, we are motivated by synthetic cells-microscopic devices with limited actuation, sensing, and memory components. We apply tools from optimal control, graph theory, and information theory to develop a methodology for designing the electronic circuitry that relates actuation to sensing using memory and physically realizable transformations (e.g., simple logical operators). Results indicate that encoding

关键词： Robot sensing systems Robots Sensors Task analysis Complexity theory Actuators Aerospace electronics Design methodology morphological operations information theory

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Digital Medical Devices in Infection Prevention and control

Digital Medical Devices in Infection Prevention and Control

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Száva Bánsághi László Gulácsi Márta Péntek Tamás Haidegger Digital Health Research Group EKIK Óbuda University Budapest Hungary Health Economics Research Center EKIK Óbuda University Budapest Hungary University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary Austrian Center for Medical Innocation and Technology (ACMIT)

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Digital health technologies literally surround us, and recently a major focus has been put on the advancement of preventive technologies to support patient safety. Among the traditional tools of infection prevention, hand hygiene training, monitoring and quality control have probably seen the most digitalization. In the past decade, electronic compliance monitoring systems, smart dispensers and AI-driven quality assessment tools have been developed and marketed. Yet, these remained outside of the medical device realm from the regulatory point of view. Now, with the advent of the Medical Device Regulation (MDR), the more advanced solutions can be channeled into the MDR regulatory pathways. This research reviews the state of the art in digital infection prevention, along with the outstanding requirements and practices towards these digital health tools and technologies. Our aim is to assist current and future development teams to deliver new solutions to support patient safety.

关键词： Training Medical devices Reviews Prevention and mitigation Quality control Regulation Electronic healthcare

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Few-shot Logo Recognition in the Wild

Few-shot Logo Recognition in the Wild

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International symposium on computational intelligence and Informatics

作者： Mikhail Ermakov Ilya Makarov HSE University Moscow Russia AIRI Moscow Russia NITU MISIS Moscow Russia

ISBN: (纸本)9798350398830

Brand logo recognition can be viewed as identification and classification task. It finds many usages such as market discovery, target advertising, etc. The number of logos growth every year and logo itself can appear in vast variety of contexts, therefore we propose a two-step few-shot framework. We describe a novel combination of universal logo detector and few-shot classifier. The logo detector is based on YOLOv5 and is used to find the areas on the image where logos are located. With this state-of-the-art single-stage object detector we achieved higher precision than similar double-stage solutions. To classify detected logos we propose few-shot classifier which consists of ensemble of pretrained feature extractors and fine-tuned head.

关键词： Computer science Mechatronics Head computational modeling Detectors Computer architecture Feature extraction

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Leakage Detection for Pipe Systems with Fuzzy Monitoring Strategy

Leakage Detection for Pipe Systems with Fuzzy Monitoring Str...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Jafari, Raheleh Razvarz, Sina Vargas-Jarillo, Cristobal Gegov, Alexander Ali, Omar Univ Leeds Sch Design Leeds LS2 9JT W Yorkshire England CINVESTAV IPN Natl Polytech Inst Dept Control Automat Mexico City 07360 DF Mexico Univ Portsmouth Sch Comp Buckingham Bldg Portsmouth PO1 3HE Hants England Tech Univ Sofia English Fac Engn Sofia 1756 Bulgaria

ISBN: (纸本)9781728190488

A Pipe is a ubiquitous product in the industries that is used to convey liquids, gases, or solids suspended in a liquid, e.g., a slurry from one location to another. Both internal and external cracking can result in structural failure of the industrial piping system and possibly decrease the service life of the equipment. The chaos and complexity associated with the uncertain behavior inherent in pipeline systems lead to difficulty in detection and localisation of leaks in real-time. The timely detection of leakage is important in order to reduce the loss rate and serious envi-ronmental consequences. To address this issue, in this paper an auto regressive with exogenous input (ARX)-Laguerre fuzzy proportional - derivative (PD) observation system is pro-posed to detect and estimate a leak in pipelines. In this work, the ARX-Laguerre model has been used to generate better performance in the presence of uncertainty. According to the results, the proposed technique can detect leaks accurately and effectively.

关键词： Autoregressive with eXogenous Input Laguerre (ARX-Laguerre) Fuzzy Pipeline PD controller PD observer

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Database for storing and accessing diabetes related data in a standardized way

Database for storing and accessing diabetes related data in ...

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International symposium on computational intelligence and Informatics

作者： Máté Siket András Nándor Kis Lehel Dénes-Fazakas György Eigner Levente Kovács Physiological Controls Research Center Óbuda University Institute for Computer Science and Control Kende utca 13-17 Budapest Hungary Applied Informatics and Applied Mathematics Doctoral School Óbuda University H-1034 Budapest Bécsi street 96/B Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics Óbuda University

Real-world data has a major importance in diabetes related research, especially considering the widespread applications of machine learning algorithms. There are several existing datasets of real-world data in the literature; however, they all have their specific formats, applied devices and file structures. The different charachteristics make it cumbersome to use multiple datasets for research purposes. We developed a pipeline for efficiently storing and accessing diabetes related data in a standardized way. We defined a standardized JSON format for the records and stored them in a MongoDB database. The pipeline is capable of uploading data from various sources by implementing a corresponding transformer script; also it is possible to extend it with algorithms which are called automatically for new entries in the database. The source code for constructing the pipeline is given in https://***/NeuroDiab/CINTI2023Diabdatabase.

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Image Processing-Based Methods to Improve the Robustness of Robotic Gripping

Image Processing-Based Methods to Improve the Robustness of ...

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International symposium on computational intelligence and Informatics

作者： Kristóf Takács Renáta Nagyné Elek Tamás Haidegger Antal Bejczy Center for Intelligent Robotics Óbuda Universtiy Budapest Hungary Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary Austrian Center for Medical Innovation and Technology Wiener Neustadt Austria

ISBN: (纸本)9798350398830

Image processing techniques are having a huge impact on most fields of robotics and industrial automation. Real-time methods are usually employed in complex automation tasks, assisting with decision making or directly guiding robots and machinery, while post-processing is usually used for retrospective assessment of systems and processes. While artificial intelligence-based image processing algorithms (relying usually on neural networks) are more common nowadays, “classical” image processing methods can also be used effectively for most modern applications. This paper focuses on optical flow-based image processing, proving its efficiency by presenting optical flow-based solutions for modern challenges in different fields of robotics, such as robot-assisted surgery and food processing. The application domain introduced in this paper is based on a smart robotic gripper designed to support automated robot cells in the meat industry. The gripper is capable of slip detection and secure gripping of soft, slippery tissues with the help of the implemented real-time algorithm.

关键词： Training Biomedical optical imaging automation Service robots Image processing Surgery Optical computing

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Proposing a New Model for Estimation of Oil Rate Passing Through Wellhead Chokes in an Iranian Heavy Oil Field

Proposing a New Model for Estimation of Oil Rate Passing Thr...

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International symposium on computational intelligence and Informatics

作者： Omid Hazbeh Hamzeh Ghorbani Omid Molaei Milad Shayanmanesh Sahar Lajmorak Rituraj Rituraj Samaneh Bahrami Faculty of Earth sciences Shahid Chamran University Ahwaz Iran Faculty of General Medicine University of Traditional Medicine of Armenia Yerevan Armenia Department of Petroleum Engineering Omidiyeh Branch Islamic Azad University Omidiyeh Iran Department of Petroleum Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Earth Sciences Institute for Advanced Studies in Basic Sciences (IASBS) 444 Prof. Yousef Sobouti Blvd. Zanjan Iran Obudai University Budapest Hungary Faculty of English Litreture Hafez Institute of Higher Education Shiraz Iran

ISBN: (纸本)9798350398830

One of the main duties of production engineers is to maintain the reservoir productivity and keep it at a desirable level during the production time by preventing extra production and controlling the production through wellhead chokes. Wellhead chokes are tools that are installed in flowing pipes to resist the pressure, limit and control the production, prevent water and gas coning, and control the pressure in order to maintain the wellhead equipment in good working order. Wellhead chokes, which are installed in well flow, are divided into two main groups: positive or fixed chokes and adjustable or variable chokes. Passing flow rate through wellhead chokes is a function of wellhead pressure, choke diameter, before choke temperature, and water production rate. The objective of this work is to propose a new model for the estimation of the oil rate passing through wellhead chokes. In this study, 180 actual tested data for 5 wells from a heavy crude oil field were used to develop a new model for estimating oil rate passing through wellhead chokes. The proposed model has an average relative error of about 5.8%.

关键词： Productivity Mechatronics Oils computational modeling Estimation Production Resists

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