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检索条件"机构=Department for Automation and Applied Informatics"

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Identifying Developer Positions in AGILE Projects Using BERT Architecture

Identifying Developer Positions in AGILE Projects Using BERT...

引用

International Symposium on Electronics and Telecommunications (ISETC)

作者： Traian-Radu Ploscă Bianca-Valeria Rus Alexandru-Mihai Pescaru Daniel-Ioan Curiac Department of Automation and Applied Informatics Politehnica University of Timisoara Timisoara Romania Department of Computer and Information Technology Politehnica University of Timisoara Timisoara Romania

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

The paper is a solution based on machine learning for automatically classifying job titles in AGILE software industry projects. Our methodology incorporates fine-tuning techniques to accurately identify job titles from task descriptions. By utilizing the BERT model, fine-tuned on a dataset of job postings in the software industry, we achieve good performance in understanding contextual information and the semantic nuances of job skills and requirements. The results indicate the model's ability to adapt to various industry projects and accurately identify the project contributors title. These findings highlight the potential of transformer-based models in improving the efficiency and effectiveness of project management practices.

关键词： Industries Adaptation models Social networking (online) Transfer learning Semantics Project management Transformers Software Telecommunications Qualifications

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Machine Learning Modeling and Simulation of Asynchronous Electric Drive 6

Machine Learning Modeling and Simulation of Asynchronous Ele...

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6th IEEE International Symposium on Logistics and Industrial informatics, LINDI 2024

作者： Kulikov, Anatoliy Kaverin, Vladimir Ardabili, Sina Mosavi, Amir Nabipour, Narjes Kalinin, Alexey Abylkas Saginov Karaganda Technical University Automation of Production Processes Department Karaganda Kazakhstan Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary University of Mohaghegh Ardabili Ardabil Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary Ai Techlife TorontoON Canada Ai Techlife Edmonton Canada Abylkas Saginov Karaganda Technical University Department of Information computing systems Karaganda Kazakhstan

ISBN: (纸本)9798331516192

This study focuses on simulating and modeling the behavior of a frequency-controlled asynchronous electric drive using MATLAB/Simulink. The control system is designed using the root locus method along with the Vyshnegradsky diagram. The research examines the system's dynamic response during startup under normal load. The results indicate that the electromagnetic torque can be optimized, with minimal overshot, by selecting specific Vyshnegradsky parameters. Experimental validation shows that the control system achieves an acceptable level of accuracy. Additionally, machine learning techniques such as support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN) are used to predict the behavior of key system variables. Evaluation of the models was conducted using two frequently used criteria. i.e., root mean square error and correlation coefficient. RF provided lower RMSE and higher CC in comparison with SVM and k-NN. On the other hand, k-NN provided higher CC, but it generated higher RMSE in comparison with RF. Accordingly, in the training phase, RF was selected for the optimum modeling. © 2024 IEEE.

关键词： MATLAB

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Offensive text detection on English Twitter with deep learning models and rule-based systems 13

Offensive text detection on English Twitter with deep learni...

引用

Working Notes of FIRE - 13th Forum for Information Retrieval Evaluation, FIRE-WN 2021

作者： Gémes, Kinga Kovács, Ádám Reichel, Markus Recski, Gábor TU Wien Austria Budapest University of Technology and Economics Department of Automation and Applied Informatics Hungary

This paper describes the systems the TUW-Inf team submitted for the HASOC 2021 shared task on identifying offensive comments in social media. Besides a simple BERT-based classifier that achieved one of the highest F-scores on the binary classification task, we also build a high-precision rule-based classifier using a custom framework for human-in-the-loop learning. Both of our approaches are also evaluated qualitatively by manual analysis of 150 tweets, which also highlights possible controversies in the ground truth labels of the HASOC dataset. © 2021 Copyright for this paper by its authors.

关键词： Classification (of information)

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Control of Wind Turbines Operating at Significantly Variable Wind Speeds Over Time

Control of Wind Turbines Operating at Significantly Variable...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Emil-Ronald Edu Mircea-Bogdan Radac Department of Mechanical Engineering Politehnica University of Timisoara (PUT) Timisoara Romania Dept. of Automation and Applied Informatics PUT Timisoara Romania

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

In the present paper, the operation of high-power wind turbines of the order of mega-watts (MW), is analysed at significantly variable wind speeds over time. Experimental data from a wind turbine operating in the Dobrogea region are processed, at a higher sampling frequency of measurements. The current operating condition is investigated from an efficiency viewpoint and compared with an optimized controlled setting, in which the electric generator power is controlled dependent on the wind speed value. We find that wind speed sampling frequency is an important factor impacting control quality, while the improved control is mandatory for maximal operation efficiency.

关键词： Wind energy Wind speed Current measurement Electric generators Wind turbines Frequency measurement Frequency control

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

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

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International Symposium on applied Computational Intelligence and informatics ( SACI)

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

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.

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

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

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Peter Sarcevic Dominik Csík Richard Pesti Massimo Stefanoni József Sárosi Ákos Odry Department of Mechatronics and Automation University of Szeged Szeged Hungary Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary

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.

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

Particle swarm optimization aided calibration of sensor inst...

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International Symposium on applied Computational Intelligence and informatics ( SACI)

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

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.

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Adaptive Control Lyapunov Function-Based Controller Using Fixed Point Iteration

Adaptive Control Lyapunov Function-Based Controller Using Fi...

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International Symposium on applied Computational Intelligence and informatics ( SACI)

作者： Massimo Stefanoni Dominik Csík Peter Sarcevic Ákos Odry József K. Tar Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary Department of Mechatronics and Automation Faculty of Engineering University of Szeged Szeged Hungary John von Neumann Faculty of Informatics Obuda University Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

The Proportional Integral Derivative (PID), the Computed Torque Control (CTC), the Fuzzy Logic (FL), and the Artificial Neural Network (ANN) controllers are some examples of methods widely used in the industrial sectors for control purposes. All of them have been largely developed and investigated, but besides their advantages, they have some drawbacks which have not been completely solved yet. Other types of approaches, especially used in Robotics, are classical adaptive methods such as the Adaptive Inverse Dynamics Control (AIDC), the Adaptive Slotine-Li Control (ASCL), and the Model Reference Adaptive Controller (MRAC). They need a formally accurate robot model and they fail in the presence of unknown external disturbances or friction. This work aims to propose a new Fixed Point Iteration-based (FPI) adaptive controller that applies Control Lyapunov Function for kinematic design, and a multi-dimensional generalization of the Rodrigues formula for adaptation. The first one is derived using the error of the second derivative of the generalized coordinates and tries to move the error to zero exponentially. The second one is used to develop an Adaptive Deformation function to compensate for the inaccurate dynamic model where the new output for the vector of the second derivative of the generalized coordinates is obtained by smooth rotations. The concept was simulated by using Julia code which demonstrated the feasibility of the idea providing after the transient an oscillating error which ranges between -0.005 and 0.004 rad.

关键词： Fuzzy logic Adaptation models Deformation Service robots Robot kinematics Torque control Artificial neural networks

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

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

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International Symposium on Computational Intelligence and informatics

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

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.

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

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

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International Symposium on Computational Intelligence and informatics

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 $0.3 \mu \mathrm{T}$, and a maximum error in the range from $1.8767 \mu \mathrm{T}$ to $2.5252 \mu \mathrm{T}$. 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.

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