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27th International Conference on System Theory, control and Computing, ICSTCC 2023

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

ISBN: (纸本)9798350337983

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. © 2023 IEEE.

关键词： Vehicles

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From Model Predictive control to Data Driven control to Meet High Efficiency Objective of a PMSM Drive System 16

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

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

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

ISBN: (纸本)9798350388107

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. © 2024 IEEE.

关键词： MATLAB

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Enhancing Urban Cooperative Transportation Systems using the V2X-based MOSAIC Platform

Enhancing Urban Cooperative Transportation Systems using the...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Sebastian I. Alupoaei Constantin F. Caruntu Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Iasi Romania

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

Rapid urban growth and increased vehicle use present major challenges for traditional transportation systems, causing congestion, accidents, and pollution. Traffic jams lead to delays, economic losses, and accidents. This study explores the V2X-based MOSAIC platform for traffic data analysis in Iasi, Romania's second-largest city, assessing indicators like density and speed. The aim is to enhance efficiency and safety in urban transport, offering insights to reduce congestion with future intelligent systems.

关键词： Electric potential Pollution Urban areas Transportation Telecommunications Safety Delays Intelligent systems Traffic congestion Accidents

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Coalitional control for Mobile Robots Formation in Logistic Applications

Coalitional Control for Mobile Robots Formation in Logistic ...

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

作者： Ovidiu Pauca Razvan-Gabriel Lazar Anca Maxim Constantin-Florin Caruntu Department of Automatic Control and Applied Informatics “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Autonomous Mobile Robots (AMRs) are essential in various industries due to their efficacy and their usefulness in securing the transportation of goods. These robots autonomously execute tasks in intra-logistics operations, spanning manufacturing, warehousing, and healthcare settings. Moreover, AMRs can interact with humans as collaborative assistants. This paper proposes a longitudinal coalitional control (CC) approach for an interconnected AMRs group. The CC method ensures that the AMRs are controlled so that, both position and velocity errors are minimised, thereby maintaining the shape of the group. Additionally, the method allows for dynamic communication topology switching by enabling or disabling communication links to reduce communication usage. The results show that the coalitional algorithm with switching topologies yields a better cost performance compared to individual topologies, proving the efficacy of the CC method in achieving satisfactory performances with minimal communication usage.

关键词： Heuristic algorithms Warehousing Transportation Switches control systems Mathematical models Topology Mobile robots Robots Logistics

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FedBayes: An Aggregation Method for Federated Learning that uses Bayesian Regression

FedBayes: An Aggregation Method for Federated Learning that ...

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

作者： Iuliana Bejenar Lavinia Ferariu Carlos Pascal Constantin F. Caruntu Department of Automatic Control and Applied Informatics “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Over the years, the concept of Federated Learning (FL) has gained popularity based on compliance with the General Data Protection Regulation (GDPR). This framework allows multiple participants to collaborate on training a shared global model, without exchanging training samples. In real-world applications, the data of different participants are usually non-independently and identically distributed (non-IID); in these cases, the clients are vulnerable to producing biased local models, for which FL-based aggregation could be improper. Several problems may also arise when some clients work with an insufficient amount of data or are exposed to potential attacks. To address these main issues, this paper proposes a new algorithm that evaluates the accuracy of models derived from participating clients and uses Bayesian regression to aggregate the local models. Through this strategy, the number of clients who can negatively affect the global model can be reduced, whether they have been attacked or have obtained poor performance for other reasons (e.g., an insufficient amount of data). The experiments are performed using a previously proposed algorithm, FedAccSize, together with this new extension, FedBayes. In the considered scenarios, both independent and identically distributed data (IID) and non-IID data are used to demonstrate the ability of the proposed algorithms to obtain an aggregation of the global model which can be done safely. Based on the comparison with the established algorithms FedAvg and FedAvgM, it results that assessing the quality of the local models is crucial for aggregation.

关键词： Training Analytical models Accuracy Federated learning Distributed databases Training data Data models Bayes methods Protection General Data Protection Regulation

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Skin Cancer Diagnosis Using CNN with Attention Mechanisms Based on Grad-CAM

Skin Cancer Diagnosis Using CNN with Attention Mechanisms Ba...

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

作者： Ana-Maria Prisăcariu Lavinia Ferariu Department of Automatic Control and Applied Informatics "Gheorghe Asachi" Technical University of Iasi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Skin cancer is one of the most prevalent types of cancer, with approximately 2 to 3 million cases diagnosed globally each year. Typically originating in skin areas exposed to the sun, deviations in the deoxyribonucleic acid (DNA) of skin cells can also precipitate its emergence in other regions. The paper discusses an application for the diagnosis of a diverse spectrum of dermatological conditions, focusing on the design of convolutional neural network (CNN) architectures capable of providing an accurate classification. The input data consists of dermoscopic images, and the associated classes indicate the type of lesions. CNN architectures with single or multiple attention mechanisms are analyzed in this context. Spatial attention mechanisms are applied to different layers of the feature extractor, to highlight local and global features from the most relevant areas of the images. This paper also introduces attention mechanisms that exploit Grad-Classification Activation Maps (Grad-CAMs) resulting from previously trained models, for an effective transfer of knowledge to the attention weights. The experimental demonstration was done for the AlexNet neural network, using images from the Human Against Machine (HAM10000) dataset, corresponding to 5 dermatological conditions. The best accuracy on testing data was obtained for models with single attention mechanisms related to global features. In addition, these models demonstrate a better understanding of dermoscopic images, as illustrated by the mean Grad-CAMs.

关键词： Training Attention mechanisms Accuracy Systematics DNA Feature extraction Skin Convolutional neural networks Skin cancer Testing

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Policy Transfer with Maximum Entropy Deep Reinforcement Learning for Real Cart-Pole control

Policy Transfer with Maximum Entropy Deep Reinforcement Lear...

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IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Tudor-Andrei Paleu Adrian Burlacu Carlos Pascal Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Iasi Romania

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

Transferring learned controllers from simulation to real-world domains is rarely observed for deep reinforcement learning applications, even physics-based tasks like the inverted pendulum being particularly difficult when transitioning to the physical world. Model-free controllers based on deep reinforcement learning have been a successful choice for many tasks. They are preferred over model-based ones due to their adaptive capability to situations not included in the initial design and for requiring less advanced expertise in control. This paper aims to apply learning-based model-free control to a real cart-pole system using a deep reinforcement learning policy transferred from simulation to a real environment without any fine-tuning involved with the dynamics model. In a simulation, the baseline deep Q-learning, an off-policy model-free algorithm, delivered an inferior performance on the Cart-Pole environment compared to the soft Q-learning, keeping the same hyper-parameters and ensuring reproducible outcomes. In the real experimental setup, besides a PD controller feasible for under-actuated systems, the soft version policy was transferred to accelerate the design process and enhance the control. Additionally, limitations related to the brittle robustness of PD controllers and the absence of accurate models in the learning phase are studied and addressed.

关键词： Adaptation models Q-learning Heuristic algorithms Process control Deep reinforcement learning Robustness Hardware Entropy Data models PD control

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Model-Free Adaptive Pitch control for a Nonlinear Aerospace Laboratory Equipment

Model-Free Adaptive Pitch Control for a Nonlinear Aerospace ...

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

作者： Andrei Baciu Corneliu Lazar Department of Automatic Control and Applied Informatics "Gheorghe Asachi" Technical University of Iasi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The Model Free Adaptive control (MFAC) control laws are attractive for applications involving complex plants. This is mainly due to the fact that no process modeling is required, which becomes more and more cumbersome, but only a dynamic linearization through the concept of pseudo partial derivatives (PPD), which it introduces. Using PPD, the control law uses only the input and output data of the process to understand and control it. Systems in the aerospace category are increasingly complex, using a multitude of sensors, information that can be used very easily for applications involving MFAC. The paper proposes a test of an MFAC law conjugated with Sliding Mode control (SMC), to test this type of law in comparison with the classic MFAC-CFDL variant.

关键词： Adaptation models Process modeling Heuristic algorithms Process control Aerodynamics Sensor systems and applications Pitch control (position) Sliding mode control Oscillators Aerospace control

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Impact of Data Packet Loss for DSRC Vehicular Safety Communication in Connected Vehicle Platoons 28

Impact of Data Packet Loss for DSRC Vehicular Safety Communi...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Lazar, Razvan-Gabriel Militaru, Andreea-Valentina Caruntu, Constantin-Florin Gheorghe Asachi Technical University of Iasi Department of Automatic Control and Applied Informatics Iasi Romania Gheorghe Asachi Technical University of Iasi Department of Telecommunications and Information Technologies Iasi Romania

ISBN: (纸本)9798350339918

In the context of the development of wireless networks in the field of connected vehicles, platooning becomes more common on the road, and vehicular safety communication is crucial to ensure driver and passenger safety. Communication between vehicles within platoons relies on Dedicated Short-Range Communication (DSRC) and Cooperative Adaptive Cruise control (CACC). The DSRC allows vehicles to communicate by exchanging safety information like location, speed, and acceleration. As a result, the CACC control solution maintains a speed-independent inter-vehicle distance of the followers' vehicles based on this information. The platoon systems are susceptible to data packet loss that determine delayed and incomplete responses which can lead to collisions or other safety issues. This paper analyzes indicators such as the distance between platoon members, the front delay, and the relative speed of vehicles that have an impact on vehicular safety communication. © 2023 IEEE.

关键词： Cooperative communication

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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