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arXiv 2023年

作者： Smadu, Razvan-Alexandru Echim, Sebastian-Vasile Cercel, Dumitru-Clementin Marin, Iuliana Pop, Florin University Politehnica of Bucharest Faculty of Automatic Control and Computers Romania University Politehnica of Bucharest Faculty of Engineering in Foreign Languages Romania National Institute for Research Development in Informatics - ICI Bucharest Romania

Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup. © 2023, CC BY-NC-SA.

关键词： Classification (of information)

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Cooperative adaptive cruise control with string stability based on DMPC for vehicle platooning

Cooperative adaptive cruise control with string stability ba...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Ovidiu Pauca Mircea Lazar Constantin F. Caruntu Dept. of Automatic Control and Applied Informatics ”Gheorghe Asachi” Technical University of Iasi Iasi Romania Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands

The cooperative adaptive cruise control (CACC) functionality received significant interest in the state-of-the-art due to its advantages in optimizing traffic flow. The model-based predictive control (MPC) strategy was used in various studies due to its advantages in improving the performance of the vehicles (reducing the travel costs, improving the quality of the travel by reducing sudden accelerations, and ensuring the stability of the platoons). Moreover, MPC solutions are built to maximize the advantages of vehicular communication by sharing predictions on states of vehicles (e.g., velocities, accelerations, trajectories). In addition, MPC is also used to compensate for the disturbances added by communications. Thus, this paper proposes a CACC strategy for a vehicle platoon. The solution is based on the distributed MPC (DMPC) strategy, and the controller is proposed in discrete time, ensuring predecessor-follower string stability for the whole platoon.

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on control and Automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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Stabilization and trajectory tracking of a subactuated aircraft based on a Geometric Algebra approach

Stabilization and trajectory tracking of a subactuated aircr...

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American control Conference (ACC)

作者： Leonardo Escamilla Luis Rodolfo Garcia Carrillo Steven Sandoval Eduardo Steed Espinoza Quesada Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA French-Mexican Laboratory of Informatics and Automatic Control Mexico City Mexico

A novel approach to the modeling and control of a subactuated aircraft is performed based on Geometric Algebra (GA) principles. The selected platform for analysis is a quad rotorcraft. The derived model leverages objects from GA, such as the rotor, to perform rotations, replacing the need for Euler angles and quaternions. controllers, which operate exclusively on GA objects, are developed to regulate the altitude, attitude, and translation of the quad rotorcraft. Numerical examples, including way-point navigation and trajectory tracking, illustrate the feasibility of the GA approach.

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Parameter Adjustments in POMDP-Based Trajectory Planning for Unsignalized Intersections

arXiv

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arXiv 2024年

作者： Kollarčík, Adam Hanzálek, Zdeněk Dept. of Control Engineering Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic

This paper investigates the problem of trajectory planning for autonomous vehicles at unsignalized intersections, specifically focusing on scenarios where the vehicle lacks the right of way and yet must cross safely. To address this issue, we have employed a method based on the Partially Observable Markov Decision Processes (POMDPs) framework designed for planning under uncertainty. The method utilizes the Adaptive Belief Tree (ABT) algorithm as an approximate solver for the POMDPs. We outline the POMDP formulation, beginning with discretizing the intersection’s topology. Additionally, we present a dynamics model for the prediction of the evolving states of vehicles, such as their position and velocity. Using an observation model, we also describe the connection of those states with the imperfect (noisy) available measurements. Our results confirmed that the method is able to plan collision-free trajectories in a series of simulations utilizing real-world traffic data from aerial footage of two distinct intersections. Furthermore, we studied the impact of parameter adjustments of the ABT algorithm on the method’s performance. This provides guidance in determining reasonable parameter settings, which is valuable for future method applications. © 2024, CC BY-NC-ND.

关键词： Markov processes

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POMDP-Based Trajectory Planning for On-Ramp Highway Merging

arXiv

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arXiv 2024年

This paper addresses the trajectory planning problem for automated vehicle on-ramp highway merging. To tackle this challenge, we extend our previous work on trajectory planning at unsignalized intersections using Partially Observable Markov Decision Processes (POMDPs). The method utilizes the Adaptive Belief Tree (ABT) algorithm, an approximate sampling-based approach to solve POMDPs efficiently. We outline the POMDP formulation process, beginning with discretizing the highway topology to reduce problem complexity. Additionally, we describe the dynamics and measurement models used to predict future states and establish the relationship between available noisy measurements and predictions. Building on our previous work, the dynamics model is expanded to account for lateral movements necessary for lane changes during the merging process. We also define the reward function, which serves as the primary mechanism for specifying the desired behavior of the automated vehicle, combining multiple goals such as avoiding collisions or maintaining appropriate velocity. Our simulation results, conducted on three scenarios based on real-life traffic data from German highways, demonstrate the method’s ability to generate safe, collision-free, and efficient merging trajectories. This work shows the versatility of this POMDP-based approach in tackling various automated driving problems. © 2024, CC BY-NC-ND.

关键词： Markov processes

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From Model Predictive control to Data Driven control to Meet High Efficiency Objective of a PMSM Drive System

From Model Predictive Control to Data Driven Control to Meet...

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International Conference on Applied and Theoretical Electricity (ICATE)

作者： Madalin Costin Ion Bivol Dept. of Electrical Engineering and Energy Conversion Systems “Dunarea de Jos” University of Galati Galati Romania Dept. of Automatic Control and Applied Informatics “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798350388107

ISBN: (纸本)9798350388114

In this paper is proposed a Data Driven control (DDC) law of a Permanent Magnet Synchronous Machine (PMSM) drive as an alternative to the Model Predictive control (MPC) strategy. The DDC method is designed for the inner control structure of the PMSM drive, the outer speed loop being employed by a classical linear PI controller. Thus, the DDC is based on build up a database of the relevant quantities as electromagnetic torque, speed, voltages and current, all obtained in steady - state regime with MPC strategy, being selected for the highest efficiency values for a given pair of speed and torque. The learning process of the DDC strategy by a mutidimesional interpolation method leads to obtain high performances in both steady - state and dynamic regimes, without any additional optimal current reference command. A comparative analysis done in Matlab simulation environment of the results obtained by both MPC and DDC control laws shows the effectiveness of the last strategies. The DDC method provide a robust optimal control under energetic constraints, being more suitable then the classical MPC law that can not offers reliable results for a large area of conditions in practice.

关键词： Permanent magnet machines Interpolation Torque Stator windings Prediction algorithms Permanent magnets Temperature control Steady-state Reliability Predictive control

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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Energy Quality Analysis of electrical Drive Systems with Induction Machine

Energy Quality Analysis of Electrical Drive Systems with Ind...

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International Conference on engineering of Modern Electric Systems (EMES)

作者： Helga Silaghi Viorica Spoiala Claudiu Costea Liliana Magdoiu Jan Gamec Alexandru Marius Silaghi Control Systems Engineering and Management Department University of Oradea Oradea Romania Faculty of Electrical Engineering and Informatics Technical University of Kosice Kosice Slovakia Electrical Engineering Department University of Oradea Oradea Romania

Inthis paper many improvements in dealing with energy quality issues that could arise in electrical drive systems using asynchronous machines are presented. A series active power filter is used in electrical drive systems with induction motors to reduce harmonic current and voltage. We also propose a variety of active power filters with hysteresis current value control. Lastly, some experimental results for an electrical drive system utilizing an induction motor and a PWM converter are provided. These findings are followed by a discussion of the system's energy quality issues.

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A Deep Learning Approach For Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants

A Deep Learning Approach For Faults Recognition of Dissolved...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Liliana Maria Ghinea Mihaela Miron Harsha Ratnaweera Department of Automatic Control and Electrical Engineering Department Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Faculty of Science and Technology Norwegian University of Life Sciences Oslo Norway

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing.

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