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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Puchalski, Radoslaw Giernacki, Wojciech Ha, Quang Institute of Robotics and Machine Intelligence University of Technology Faculty of Control Robotics and Electrical Engineering of Poznan Poznan Poland School of Electrical and Data Engineering University of Technology Sydney Faculty of Engineering and Information Technology UltimoNSW2007 Australia

ISBN: (纸本)9798350312072

The growing number of applications for unmanned aerial vehicles operating in close proximity to humans calls for strict safety requirements. To ensure reliability and safety, fast and effective diagnosis of possible drone faults is needed. In this paper, a study on the detection of damage to the propellers of a multirotor using inertial sensors was conducted. Measurement data from the publicly available PADRE repository were used. Frequency features and raw measurement data applicability were tested. Two different approaches to data processing during fault detection and classification were tested. The first is to analyze the signals after collecting the entire set of data needed for treatment by the neural network. The second approach processes the data after every single acquisition of sensor measurements. The performance results and processing time of each solution are recorded for analysis. By effectively selecting parameters of the proposed approaches, the processing time and accuracy of UAV fault classification can be significantly improved, as verified over different classes of propeller defects. Early fault detection is essential for safe operations of multirotor drones. © 2024 IEEE.

关键词： Drones

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Predicting the output error of the suboptimal state estimator to improve the performance of the MPC-based artificial pancreas

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Control Theory and Technology 2023年第4期21卷 541-554页

作者： Martin Dodek Eva Miklovičová Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information TechnologySlovak University of Technology in BratislavaIlkovičova 3Bratislava81219Slovakia

The error of single step-ahead output prediction is the information traditionally used to correct the state estimate while exploiting the new measurement of the system ***,its dynamics and statistical properties can be further studied and exploited in other *** is known that in the case of suboptimal state estimation,this output prediction error forms a correlated sequence,hence it can be effectively predicted in real *** a suboptimal scenario is typical in applications where the process noise model is not known or it is ***,the paper deals with the problems of analytical and empirical modeling,identification,and prediction of the output error of the suboptimal state estimator for the sake of improving the output prediction accuracy and ultimately the performance of the model predictive *** improvements are validated on an empirical model of type 1 diabetes within an in-silico experiment focused on glycemia prediction and implementation of the MPC-based artificial pancreas.

关键词： Suboptimal state estimation Prediction Autoregressive model Moving average model Model predictive control Diabetes mellitus Artificial pancreas

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Autonomous Mobile Robot for Transporting Goods in Warehouse and Production

Autonomous Mobile Robot for Transporting Goods in Warehouse ...

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International Conference on Intelligent Manufacturing and robotics, ICIMR 2023

作者： Xuan, Oh Wei Selamat, Hazlina Muslim, Mohd Taufiq Centre for Artificial Intelligence and Robotics Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

ISBN: (纸本)9789819984978

The trend and growth of digitalization in the different industry sectors have increased the demand for automation technologies in warehouse and production operations. The most common and popular automation technologies used in warehouses and production operations are the autonomous mobile robot (AMR). AMR is a self-guided vehicle equipped with different sensors and a navigation system to enable them to navigate through the environment to transport goods with obstacle avoidance features. Compared to traditional methods that involve manual operation or a conveyor system in transporting goods, AMR offers a wide range of flexibility and scalability in the system in which the AMR can support various load sizes, can add different modules on AMR such as a tray, robotic arms, jacking module, and so on. AMR also provides flexibility in communicating with the existing system of an industry such as MES to manage the navigation of the AMR and digitalize the transport of the goods and ensure the traceability of the goods. The AMR in the market nowadays is being sold at a high cost which is unaffordable for certain SMEs to purchase while another type of autonomous vehicle such as an automated guided vehicle system (AGV) has a lower cost compared to the AMR, but it requires the installation of the physical marker in order for the AGV to navigate. Therefore, this paper is purposed to develop a low-cost autonomous mobile robot (AMR) that is suitable to be deployed in the industry to transport goods on the production floor and warehouse. The most important feature of the AMR is its navigation system. A wide variety of types of sensors are used by the company nowadays to develop autonomous vehicles, and each sensor has different working principles and pros and cons. Other than the sensor, navigation algorithms are also significant in determining the behavior, efficiency, and accuracy of the navigation. The navigation algorithm and the sensors work in pairs, and hence, the algorithm ch

关键词： Navigation

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Critical aspects of the uncertainty budget in the shielding effectiveness measurements

Critical aspects of the uncertainty budget in the shielding ...

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2023 International Symposium on Electromagnetic Compatibility - EMC Europe, EMC Europe 2023

作者： Malecka, Karolina Olczyk, Robert Sroka, Jan Urbaniak, Grzegorz Warsaw University of Technology Faculty of Electrical Engineering Warsaw Poland Poznan University of Technology Faculty of Automatic Control Robotics and Electrical Engineering Poznań Poland

ISBN: (纸本)9798350324006

This article deals with the issue of identification and quantification of uncertainty components in shielding effectiveness measurements carried out in accordance with the standard EN 50147-1. It describes problems accompanying such tasks and presents baseline uncertainty budget. Issues related to uncertainty of the antenna factor, uncertainty of measurement receiver and mismatch of the measurement path were taken into consideration. Further topics requiring consideration have also been identified, such as the type A evaluation of uncertainty, variations of the reflection coefficient at the input of antenna and uncertainties of measurements of S-parameters used to calculate mismatch of the measurement path. © 2023 IEEE.

关键词： Scattering parameters

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Classification of Cancer Cells from Mammogram Images Based on GoogLeNet and ResNet-50 Deep Learning Models 1

Classification of Cancer Cells from Mammogram Images Based o...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Jusman, Yessi Nur 'Aini, Masayu Alya Naufal, Zaidan Ahmad Ridho, Prajarino Rushartini, Hesti Wimala, Galuh Ananda, Indra Pradani, Nisha Alifa Universitas Muhammadiyah Yogyakarta Center of Artificial Intelligence and Robotics Studies Department of Electrical Engineering Bantul Indonesia Universiti Malaysia Perlis Faculty of Electrical Engineering and Technology Perlis Malaysia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Bantul Indonesia

ISBN: (纸本)9798350368918

Breast cancer remains one of the most common and deadly forms of cancer affecting women globally. Early detection through mammogram screening plays a vital role in improving survival rates by enabling timely diagnosis and treatment. This study explored the application of deep learning techniques, particularly GoogLeNet and ResNet-50 architectures, to classify mammogram images into benign and malignant categories. The dataset, sourced from Kaggle, consisted of 479 mammogram images. These images were preprocessed and divided into training and testing sets, with 90% allocated for training and 10% for testing. Both GoogLeNet and ResNet-50 models were trained and tested, yielding different performance metrics. GoogLeNet achieved the highest testing accuracy of 70.83%, while ResNet-50 attained 68.75% accuracy. GoogLeNet demonstrated better sensitivity at 92.85%, but ResNet-50 outperformed in terms of precision at 69.69%. Although both models displayed competitive results, GoogLeNet exhibited superior performance in classifying cancer cells based on sensitivity, making it more effective in identifying true positives. These findings demonstrated the potential of deep learning models to enhance breast cancer detection and classification using mammogram images, contributing to improved diagnostic accuracy and patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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A Multi-party Asymmetric Self-play Algorithm and Its Application in Multi-USV Adversarial Game Simulations 24

A Multi-party Asymmetric Self-play Algorithm and Its Applica...

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2024 International Conference on Machine Learning, Pattern Recognition and Automation engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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Deep Learning Classification of Concrete Crack Images using Pre-trained AlexNet and GoogleNet Models

Deep Learning Classification of Concrete Crack Images using ...

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2024 International Conference on Control and Automation, Electronics, robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Jusman, Yessi Tyassari, Wikan Nadia, Liza Kurniawati, Ranti Yossa Sura, H. Nusaiba, Dzulfikar Firza Universitas Muhammadiyah Yogyakarta Center of Artificial Intelligence and Robotics Studies Department of Electrical Engineering Faculty of Engineering D.I. Yogyakarta Indonesia Pauh Faculty of Electrical Engineering and Technology 02600 Malaysia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Faculty of Engineering D.I. Yogyakarta Indonesia

ISBN: (纸本)9798331511166

Cracking in concrete walls is a common issue in construction, often leading to structural weakening. Such damage can significantly impact both maintenance and repair costs. Concrete wall damage can be categorized in various ways, and detecting these cracks is crucial for timely intervention. This study employed deep learning neural networks to accelerate and simplify the detection and classification of concrete crack images. Two deep learning models, AlexNet and GoogleNet, were trained to classify cracks according to three categories: severe cracks (class 1), fine cracks (class 2), and normal concrete (class 3), using a total of 10,601 concrete images. Among the models, GoogleNet performed than AlexNet. The average performance for GoogleNet is 94.04% for accuracy, 91.96% for precision, 91.97% for recall, 95.39% for specificity, and 91.77% for F-score. However, despite having a lower accuracy than GoogleNet, AlexNet exhibited faster training times, which may be advantageous in certain applications where processing speed is prioritized over precision. These results demonstrate that while GoogleNet provides higher accuracy, AlexNet offers a more time-efficient solution. © 2024 IEEE.

关键词： AlexNet Concrete Crack Deep learning GoogleNet

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Low-Cost High Gain Sea Pimp-Shaped Dual Band Monopole Antenna for Mobile 4G/5G/LTE41/WLAN Application

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Progress In Electromagnetics Research C 2024年 140卷 41-51页

作者： Sakulchat, Suwat Ruengwaree, Amnoiy Naktong, Watcharaphon Unahalekhaka, Pramuk Promput, Sommart Department of Electrical Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Phranakhon Si Ayutthaya13000 Thailand Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani 12110 Thailand Department of Telecommunications Engineering Faculty of Engineering and Technology Rajamangala University of Technology Isan Nakhon Ratchasima30000 Thailand Department of Electrical Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi11000 Thailand Department of Mechatronics and Robotics Engineering School of Engineering and Innovation Rajamangala University of Technology Tawan-ok Chonburi 20110 Thailand

This research aimed to design a sea pimp-shaped monopole antenna by using etching and cutting techniques, combined with the addition of reflector, to modify the antenna structure to support the bandwidth standard according to GSM-850 (0.82–0.90 GHz), GSM900 (0.88–0.96 GHz), DCS (1.72–1.88 GHz), PCS (1.85–1.99 GHz), UMTS (1.92–2.17 GHz), 5G Band40 (2.30–2.40 GHz), LTE41 (2.496–2.690 GHz), and WLAN IEEE 802.11b/g/n (2.4–2.48 GHz). This antenna used a galvanized metal sheet with a conductivity of 3.56 × 107 s/m to fabricate the structure of the radiator, ground plane, and reflector. The reflector modifies radiation patterns and increases the gain of the antenna. The antenna structure used the CST program for simulation to determine the optimal parameters and property values. As a result of replication, the antenna had dual bands with a reflection coefficient (S11) at 915 MHz (736–1040 MHz) of −26.70 dB and a frequency at 2.28 GHz (1.68–2.94 GHz) of −20.15 dB. The antenna gains are 6.70 and 8.47 dBi, an increase of 83.56% and 44.04% over the antenna without a reflector, respectively. The antenna had a unidirectional pattern in all the frequency ranges which can be utilized for the purpose of RF energy-harvesting (RF-EH) systems to provide power to low-power electronic systems. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Monopole antennas

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Control Aspects in Programming by Demonstation for Robotic Deep- Micro- Hole Drilling 32

Control Aspects in Programming by Demonstation for Robotic D...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Maric, Bruno Petric, Frano Orsag, Matko University of Zagreb LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (纸本)9798350395440

This paper explores control aspects in a collab-orative robotic system designed to replicate human operators' drilling skills in deep- micro- hole tasks, focusing on the glass-container mould industry. To replicate human performance, we develop a single-DOF Impedance-based drill controller for a standard industrial position-controlled robot, enhancing safe and smooth interactions between the drill bit and the mould. To address high feed rates observed in worker demonstrations and prevent bit breaks, we focus on minimizing non-axial motion during drilling on an off-the-shelf available industrial manipulator KUKA KR 10. This involves analyzing the impact of joint dynamics on drill motion and designing a control system for the robot's low-level control to achieve axial drill motion. We show that the proposed joint control system that takes into account relationship between joint and tool dynamics outperforms available robot low-level control in minimizing non-axial motion, leading to the robot being able to outperform the human in deep- micro- hole drilling. © 2024 IEEE.

关键词： Molds

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Enhancing robotics Performance: Exploring the Role of Cloud Computing in Planning Tasks 29

Enhancing Robotics Performance: Exploring the Role of Cloud ...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Melo, Eduardo Pinto, Rui Tavares, Pedro University of Porto Dept. of Electrical and Computer Engineering Faculty of Engineering Porto Portugal Faculty of Engineering University of Porto Systec Arise Porto Portugal Sarkkis Robotics Porto Portugal

ISBN: (纸本)9798350361230

The rapid growth of the robotics field is driven by increasing demand for enhanced performance, characterized by high reliability and repeatability capabilities. As industries strive to maintain elevated quality performance throughout their production pipelines, the complexity of the challenges they face grows in tandem. This complexity extends to processing algorithms, needing greater computational power for faster and more effective solutions. In this context, emerging technologies such as Cloud Computing present promising opportunities to augment planning tasks in robotics. This literature review explores the intersection of robotics and Cloud Computing, investigating how leveraging cloud resources can enhance planning performance in response to evolving industry demands. © 2024 IEEE.

关键词： Cloud platforms

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