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Particle swarm optimization aided calibration of sensor installation errors for MEMS accelerometers 17

Particle swarm optimization aided calibration of sensor inst...

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17th IEEE International Symposium on applied Computational Intelligence and informatics, SACI 2023

作者： Pesti, Richard Sarcevic, Peter Csik, Dominik Odry, Akos Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary University of Szeged Department of Mechatronics and Automation Szeged Hungary

ISBN: (纸本)9798350321104

Accelerometers, gyroscopes, and magnetometers, constituting the Inertial Measurement Unit (IMU), are among the most fundamental sensors utilized in localization problems. However, their utilization is a challenging task since the reliability of measurements depends on the precision of the executed calibration method. This paper proposes an industrial robot-based infield calibration process in a numerical optimization aided framework, which does not need turntable nor dedicated expensive laboratory setup. The proposed method utilizes a calibrated industrial robotic arm, which is programmed to execute wide variety of motions with comprehensive dynamical ranges. The end effector of the robotic arm is equipped with IMUs with predefined installation errors. The proposed calibration process records the ground truth accelerations and raw IMU data in a database in Robot Operating System (ROS) framework. Then, realistic sensor models are established, and the Particle Swarm optimization (PSO) algorithm is executed with a custom fitness function to obtain the between sensor misalignment and bias errors. The comprehensive analysis highlights that the proposed method effectively determines the calibration parameters. Mean squared error (MSE) performance metrics shows that the precision of these parameters mainly depends on the complexity of the executed dynamical motions. © 2023 IEEE.

关键词： Calibration

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Fingerprint-based fusion of magnetic field data with multiple wireless technologies for indoor mobile robot positioning 13

Fingerprint-based fusion of magnetic field data with multipl...

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13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Sarcevic, Peter Csik, Dominik Pesti, Richard Stefanoni, Massimo Sarosi, Jozsef Odry, Akos University of Szeged Department of Mechatronics and Automation Szeged Hungary Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary

ISBN: (纸本)9798350320114

Mobile robot pose information is getting utilized in more and more indoor applications. In this paper, a novel fingerprinting-based positioning method is proposed. The method utilizes magnetic field data together with measurements collected using anchors equipped with multiple wireless technologies. The collected fingerprints are used to train MultiLayer Perceptron (MLP) neural networks, which can later be utilized to estimate the 2D position based on sensor readings in an unknown position. Real measurements collected in a laboratory are used to validate the performance of the method. The measurement database consists of 3D magnetometer measurements and four different parameter types measured between the mobile node and the anchor nodes using multiple wireless technologies. The parameters include Time of Flight (ToF) measurements using Ultra-WideBand (UWB) and Received Signal Strength Indicator (RSSI) measured using WiFi, UWB and 433 MHz frequency band. Various combinations of data were tested in the evaluation process. The obtained results showed that significant improvement, even more than 35%, can be achieved in the positioning performance for most combinations by fusing magnetometer measurements and wireless communication data. © 2023 IEEE.

关键词： artificial neural network fingerprinting localization magnetometer RSSI

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A dipole model-based approach for magnetic disturbance compensation of mobile robots using magnetometer arrays 23

A dipole model-based approach for magnetic disturbance compe...

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23rd IEEE International Symposium on Computational Intelligence and informatics, CINTI 2023

作者： Stefanoni, Massimo Odry, Akos Sarcevic, Peter Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary University of Szeged Department of Mechatronics and Automation Szeged Hungary

ISBN: (纸本)9798350342949

Magnetic sensors are used for localization purposes in both outdoor and indoor applications. In indoor applications, three-axis magnetometers cannot be used as compasses due to the presence of hard and soft-iron effects that require to be neglected. This paper proposes a new method for magnetic compensation for an unknown metallic object. It considers the source of the disturbance as a dipole, uses an array of four no-coplanar magnetometers, and exploits an Artificial Neural Network (ANN). The ANN receives as inputs the measurement differences of each pairwise sensor and provides as output the disturbance components related to one sensor of the array. In such a way, by estimating the magnetic disturbances, it is possible to evaluate the real value of the Earth's magnetic field. The ANN is trained by exploiting a database generated by using the dipole model and by evaluating the model for different intensities and orientations. The simulation results show how the method can effectively reduce the introduced disturbances in the surroundings of the object with a Mean Absolute Error (MAE) in the order of 0.3 μ T, and a maximum error in the range from 1.8767 μ T to 2.5252 μ T. The proposed method contributes to the development of improved localization algorithms, where the proposed disturbance rejection technique enables the utilization of reliable measurements for mobile robot pose estimation. © 2023 IEEE.

关键词： Neural networks

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Sharing the IT Educational Experience of Developing 3D Applications for Medical Students Training

Sharing the IT Educational Experience of Developing 3D Appli...

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作者： Nicola, Stelian Stoicu-Tivadar, Lacramioara Department of Automation and Applied Informatics Politehnica University Timisoara Romania

ISBN: (纸本)9781643682501

The importance of using the new technologies to develop educational and training applications for medical students is given by the times we live in and also by the continued development of the IT industry. New concepts used in 3D applications such as gamification bring added value to the use of learning applications. The introduction of technologies-based on virtual and augmented reality contributes to increasing of the interactivity. This paper presents an overview of the students' experience in developing a complex 3D application for medical students. The application development methodology is explained by splitting the design and development of the 3D learning application into steps. The main users' benefits provided by such applications are increased interactivity and learning benefits, and the continuous availability of the application wherein virtual laboratories within a medical clinic are simulated. © 2022 The authors and IOS Press.

关键词： Augmented reality

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Comparison of different radio communication-based technologies for indoor localization using trilateration 17

Comparison of different radio communication-based technologi...

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17th IEEE International Symposium on applied Computational Intelligence and informatics, SACI 2023

作者： Csik, Dominik Sarcevic, Peter Pesti, Richard Odry, Akos Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary University of Szeged Department of Mechatronics and Automation Szeged Hungary

ISBN: (纸本)9798350321104

With the spread of Internet of Things (IoT) different radio communication-based technologies are gaining more attention, and their application in indoor localization problems where GPS signal is not available, becomes more and more relevant. This paper addresses the trilateration algorithm-based position estimation approach in indoor localization;this approach uses Received Signal Strength Indication (RSSI) value with the Free Space Path Loss (FSPL) model. Comprehensive laboratory measurements were performed with different radio communication-based technologies namely 433 MHz RSSI, 2.4 GHz WiFi RSSI, ultra-wideband (UWB) RSSI, and UWB Time of Flight (TOF). Then, numerical optimization using the Particle Swarm optimization (PSO) algorithm to determine the parameters of the FSPL model for each anchor and each technology. This method enabled the utilization of trilateration to calculate the position of the measurement node. The obtained results show that the 433 MHz RSSI and the UWB TOF outperform the other two technologies. Using UWB TOF technology, the achieved accuracy was 165.97 cm. 433 MHz RSSI technology provided the second-best solution with 166.60 cm. WiFi RSSI provided 227.89 cm accuracy, while the worst case was obtained by UWB RSSI with 252.08 cm. The study experimentally validates that the most appropriate measurements are provided by UWB TOF and 433 MHz RSSI, which enables the implementation of these technologies in sensor fusion algorithms. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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A novel WKNN algorithm for fingerprinting-based fusion of different radio communication technologies for indoor positioning 23

A novel WKNN algorithm for fingerprinting-based fusion of di...

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23rd IEEE International Symposium on Computational Intelligence and informatics, CINTI 2023

作者： Csik, Dominik Odry, Akos Pesti, Richard Sarcevic, Peter Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary University of Szeged Department of Mechatronics and Automation Szeged Hungary

ISBN: (纸本)9798350342949

Indoor Positioning Systems (IPSs), which commonly rely on Wireless Sensor Networks (WSN), are gaining attention in various fields due to the emergence of Industry 4.0. In this paper, a novel Weighted K-Nearest Neighbor (WKNN) -based localization algorithm is proposed for the fusion of different wireless parameters. An experiment was set up for evaluation and measurements were performed in a laboratory. The essence of the novel method is that different wireless communication technologies, namely, 2.4 GHz WiFi Received Signal Strength Indicator (RSSI), 433 MHz CC1101 RSSI, Ultra-Wideband (UWB) RSSI, and UWB Time of Arrival (TOA) were combined for localization purposes. The proposed algorithm has two steps. In the first step the technologies are used separately to calculate the searched position. In the second step the average of the estimated coordinates is taken in all possible combination of the technologies. The original WKNN method considers the data together provided by each technology. The proposed WKNN algorithm provided better results in 5 cases than the original WKNN method. The improvement ranged from 0.7% to 15%. The largest improvement was achieved by using WiFi and CC1101 RSSI together, when the average error was reduced from 1.86 m to 1.58 m. The results show that it is worthwhile to adaptively select the appropriate fusion of technologies, as this can significantly reduce the localization error. © 2023 IEEE.

关键词： Indoor positioning systems

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A Neural Network-Based Approach for the Identification and Compensation of Magnetic Disturbances in Mobile Robot Localization 17

A Neural Network-Based Approach for the Identification and C...

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17th IEEE International Symposium on applied Computational Intelligence and informatics, SACI 2023

ISBN: (纸本)9798350321104

Magnetometers are widely used in sensor fusion frameworks of mobile robots due to their ability to work as compasses. The measurements of these sensors are largely influenced by hard iron and soft iron effects that are caused by nearby ferromagnetic objects. In this paper, an artificial neural network (ANN)-based method is proposed for the identification of such effects, which can be compensated from the measurements. The method utilizes both the distance and the angle of the detected object based on the coordinate frame of the mobile robot as the inputs of the Multi-Layer Perceptron (MLP) ANN. The MLP provides on its outputs the disturbance levels in the three axes, which need to be subtracted from the measurements to obtain the compensated values. The method was tested with three different types of objects to evaluate the performance of the method. Measurements were collected using an industrial robotic arm in a grid around the objects. The disturbance levels generated by the objects were computed by subtracting measurements recorded in an undisturbed scenario. Three-layer MLPs were tested with various numbers of hidden layer neurons to find the optimal configurations. The achieved results show that the disturbance levels can be significantly decreased using the proposed method. © 2023 IEEE.

关键词： Mobile robots

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Comparison of Data Classification Results for Leap Motion Recovery Gestures

Comparison of Data Classification Results for Leap Motion Re...

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作者： Nicola, Stelian Chirila, Oana-Sorina Stoicu-Tivadar, Lacramioara Department of Automation and Applied Informatics Politehnica University Timisoara Romania

ISBN: (纸本)9781643682907

Static and dynamic gestures are frequently used in activities supporting learning, recovery healthcare, engineering, and 3D games to increase the interactivity between man and machine. The gestures are detected via hardware devices and data is processed using different software methods. This paper presents the manner of detection and interpretation of two gestures, a hand rotation gesture and a palm closing and opening gesture, using the Leap Motion device. These two dynamic gestures are very often used in hand recovery exercises. For comparing the two gestures we use data classification methods, Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The data for the gesture classification were 80% training data and 20% testing data. The metrics for comparison are precision, recall, F1-score, and the total number of testing cases (support). The SVM classifier gives an accuracy of 99.4% and the MLP classifier a 96.2%. We built two confusion matrices for better visualizing the results. © 2022 The authors and IOS Press.

关键词： Recovery

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AI-Assisted Application for Pediatric Drug Dosing

AI-Assisted Application for Pediatric Drug Dosing

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2024 European Federation for Medical informatics Special Topic Conference, EFMI STC 2024

作者： Mocrii, Andreea-Alexandra Chirila, Oana-Sorina Faculty of Automation and Computers University Politehnica Timişoara Timiş Timişoara Romania Department of Automation and Applied Informatics University Politehnica Timişoara Timiş Timişoara Romania

ISBN: (纸本)9781643685540

Technology in the medical field is continuously advancing due to its numerous subdomains and the ever-growing medical needs of people. Information systems have become integral to doctors' daily routines in patient care, offering flexibility and support in repetitive tasks, thereby allowing more time for critical activities. This paper presents the implementation of a machine learning algorithm, leveraging natural language processing (NLP) and labeling techniques, to analyze medical leaflets from Romania. The aim is to assist pediatricians in determining appropriate treatment doses for children based on various parameters such as age, weight, and other significant factors. © 2024 The Authors.

关键词： Drug dosage

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TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping

TreeSwap: Data Augmentation for Machine Translation via Depe...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Nagy, Attila Lakatos, Dorina Barta, Botond Ács, Judit Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Hungary

ISBN: (纸本)9789544520922

Data augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages. We introduce a novel augmentation method, which generates new sentences by swapping objects and subjects across bisentences. This is performed simultaneously based on the dependency parse trees of the source and target sentences. We name this method TreeSwap. Our results show that TreeSwap achieves consistent improvements over baseline models in 4 language pairs in both directions on resource-constrained datasets. We also explore domain-specific corpora, but find that our method does not make significant improvements on law, medical and IT data. We report the scores of similar augmentation methods and find that TreeSwap performs comparably. We also analyze the generated sentences qualitatively and find that the augmentation produces a correct translation in most cases. Our code is available on Github1. © 2023 Incoma Ltd. All rights reserved.

关键词： Computational linguistics

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