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24th International Conference on control Systems and computer Science, CSCS 2023

作者： Andronic, Cristian Fagarasan, Ioana Arghira, Nicoleta Iliescu, Sergiu Stelian Tiab Constanta Romania University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania MC-AOSR MO-ASTR University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania

ISBN: (纸本)9798350313390

Using photovoltaic (PV) systems connected in distribution and even transport grids increase the need for energy supply security. Energy Efficiency of PV power plants is a performance indicator as well as a fault detection method. control system topologies for grid connected PV power plants can increase the overall generation performance. Three types of plant control topologies with their benefits and drawbacks are compared. The paper also synthesizes the control requirements for each of the plant components cells/panels, inverter and grid connection point. The PV panel faults issue is tackled practically by designing a PVPP test bed for further application of the proposed fault detection algorithm. A 80kW rooftop PV plant is proposed for efficiency evaluation. A decision tree algorithm for power plant efficiency and fault detection is designed. Computation of the capacity factor is an energy efficiency indicator, as well as a detector for PV panel fault initiation by dust or objects covering the cells. A hierarchical control system architecture with a central level PLC and 4 inverters, each with several MPPT modules, one for each string is proposed. The fault tree algorithm will be implemented on the practical PV power plant test bed. © 2023 IEEE.

关键词： Decision trees

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System Based on Efficient Neural Network Fusion for Negative Emotion Classification 32

System Based on Efficient Neural Network Fusion for Negative...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Bustan, R. Boriceanu, R. Popescu, D. Ichim, L. National University of Science and Technology POLITEHNICA Bucharest Faculty of Automatic Control and Computer Science Bucharest060042 Romania

ISBN: (纸本)9798350395440

Face emotion recognition is an important subject in fields such as psychology and cognitive sciences, as well as in applications that use machine learning methods to create intelligent models capable of understanding human behavior. This paper addresses the challenge of negative facial emotion recognition by employing a comprehensive framework. Our approach leverages 68 facial landmarks and integrates three pre-trained convolutional neural network models (VGG19, DenseNet201, Inception V3) into a unified weighted voting system. The efficacy of our system is evaluated using the Extended Cohn-Kanade (CK+) and Karolinska Directed Emotional Faces (KDEF) databases. The results showcase the effectiveness of our approach in accurately identifying negative facial emotions. This research contributes to the advancement of emotion recognition systems., offering a promising methodology with implications for fields such as psychology., cognitive sciences., and the broader spectrum of intelligent systems reliant on facial expression analysis. © 2024 IEEE.

关键词： Emotion Recognition

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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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Representation of Coherent Structures from Volume Data Using Quality-oriented Features and Genetic Optimization 27

Representation of Coherent Structures from Volume Data Using...

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

作者： Gavrilescu, Marius Leon, Florin Floria, Sabina-Adriana Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

ISBN: (纸本)9798350337983

Representing relevant information from volume data sets is a problem often faced in visualization. Generating meaningful images from highly-complex volume data sets is a challenging, tedious task requiring specialized knowledge of the distribution and properties of the data. Traditionally, this task has been carried out manually via specialized user interfaces. We propose a volume visualization pipeline which facilitates the automatic generation of high-quality images from volume data sets. Our method involves a direct volume renderer which generates images from volume data based on visual mappings provided by a transfer function. Central to our approach is a quality-focused descriptor which exploits the properties of the distribution of gradient orientations of an alpha-bounded surface within the volume. This feature is useful for determining transfer functions that result in the rendering of corresponding images depicting various details from the volume. We show that by using this feature as an optimization objective, the generation of high quality images can be automated. Using simple genetic algorithms, we can automatically generate sets of images illustrating coherent, easily-distinguishable and high-quality surfaces of relevant structures from volume data. © 2023 IEEE.

关键词： Volume rendering

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Transformer-based spectro-temporal fusion for Sentinel-2 super-resolution 30

Transformer-based spectro-temporal fusion for Sentinel-2 sup...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Pogodzinski, Bartlomiej Tarasiewicz, Tomasz Kawulok, Michal Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland

ISBN: (纸本)9798350337297

Multi-image super-resolution consists in fusing multiple images of the same scene to generate an image of higher spatial resolution. Recently, it has been demonstrated that for remotely sensed multispectral Sentine1-2 images, fusion performed in both spectral and temporal dimensions improves the reconstruction quality. However, the deep networks elaborated for this purpose are quite large and influence of the input data is difficult to interpret. In this paper, we show how to exploit transformer-based image fusion to reduce the number of trainable parameters by 30% and allow for increased interpretability with means of attention rollout. The reported experimental results performed over simulated and real-world data indicate that this does not affect the reconstruction quality, while at the same time the visualization tools may help in developing techniques for input data selection and preprocessing. © 2023 IEEE.

关键词： Image fusion

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INCREASING THE OBSERVATION CAPABILITIES OF SMALL SOLAR TELESCOPES USING NEURAL NETWORKS 38

INCREASING THE OBSERVATION CAPABILITIES OF SMALL SOLAR TELES...

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38th Annual European Simulation and Modelling Conference, ESM 2024

作者： Jóźwik-Wabik, Piotr Popowicz, Adam Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9789492859334

Small solar telescopes play an important role in monitoring dynamic heliophysical phenomena on the Sun, such as solar flares, which affect satellites and ground-based electronic systems. Unfortunately, the quality of the data they obtain is significantly affected by atmospheric turbulence, so they require further processing. Currently, this problem is mainly solved by using a state-of-the-art deterministic algorithm, multi-frame blind deconvolution (MFBD). However, this approach is sensitive to manually adjusted system parameters and requires high computational power and long processing time. In this paper, we explore the possibility of using fully convolutional networks (FCNs) as an alternative to MFBD. In our research, we focus on the data processing time, the quality of the results obtained, as well as the hardware resources required for the practical application of the FCNs by small observatories. We show that the proposed networks can achieve results comparable to MFBD, but in almost real time and using more energy-efficient equipment. © 2024 Modelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024. All rights reserved.

关键词： Telescopes

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Massive Natural Language Processing in Distributed Environment 20th

Massive Natural Language Processing in Distributed Environm...

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20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Kielce Poland

ISBN: (纸本)9783031383175

Natural Language Processing (NLP) models are one of the most promising topics nowadays. Applications like ChatGPT uncover the power of such models and their broad applications. However, developing such models requires an extensive amount of training data sets to operate on. As such data sets grow, it becomes impossible to process them on a single machine, even using parallel computations. In this paper, the problem of distributing the NLP computations is taken into consideration. The Scalable Distributed Two-Layer Data Structures were used to organize the distributed environment. Stanford Core NLP library was used to perform accurate NLP computations. The experiments focused on the efficiency of the computation in the distributed environment and the accuracy of the predictions in relation to the scale of the distributions. The results clearly show that an increase in performance can be obtained without a significant decrease in accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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STGNN: a new self-trained generalized neural networks task space control for robot manipulators

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Neural Computing and Applications 2025年 1-26页

作者： Elmogy, Ahmed Elawady, Wael El-Ghaish, Hany Computer Engineering Department Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta31527 Egypt

The inherent uncertainties in the dynamic and kinematic parameters of robot manipulators pose significant challenges for their control in task space. This paper introduces an innovative adaptive sliding mode control strategy, leveraging self-trained generalized neural networks to enhance the precision and robustness of robot manipulator control. The proposed framework called STGNN integrates deep neural networks for the real-time estimation and adjustment of dynamics’ and kinematics’ parameters. Key contributions of this work include the development of semi-supervised learning models for adaptive dynamics and kinematics estimation. By training the generalized neural network on different trajectories, the same models can be utilized to operate on a variety of tasks, significantly improving the robustness, scalability, and adaptability of the control system. The STGNN approach allows the models to leverage both labeled and unlabeled data, incorporating a self-training mechanism that enhances their ability to generalize and adapt to diverse operational scenarios. This capability is particularly beneficial for real-time applications, where the ability to learn and adapt on the fly is crucial. Simulation results demonstrate the superior performance of the STGNN framework in controlling robot manipulators in operational space, showcasing its potential for real-world applications. The proposed method significantly advances the adaptability, accuracy, and efficiency of robot control systems, offering notable improvements over conventional approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self adjusting control systems

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Real-Time Communication Model for IoT Systems 18

Real-Time Communication Model for IoT Systems

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18th Conference on computer Science and Intelligence Systems, FedCSIS 2023

作者： Deniziak, Stanislaw Plaza, Miroslaw Arcab, Lukasz Kielce University of Technology Faculty of Electrical Engineering Automatic Control and Computer Science Kielce Poland

ISBN: (纸本)9788396744784

Internet of Things solutions typically involve interaction between sensors, actuators, the cloud, embedded systems and user applications. Often in such cases, there are time constraints specifying the maximum response time to a request. This time depends on the calculation time and transmission time. Existing Internet communication solutions do not ensure the implementation of transmissions in a way that guarantees meeting the set time constraints. This paper proposes a new model of Internet communication dedicated to real-time Internet of Things systems, which includes a communication protocol, as well as a transmission scheduling and routing method. The protocol takes into account information about transmission time constraints, which is used for packet scheduling by routers, allowing to increase quality of service. In addition, the proposed static routing mechanism makes it possible to parallelize transmissions if time constraints are still exceeded. Also presented are preliminary results of experiments showing to what extent the proposed methods allow improving the quality of service in real-time Internet of Things systems. © 2023 Polish Information Processing Society.

关键词： Internet of things

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Integrating photovoltaic systems into HVAC control using fuzzy algorithms for optimizing thermal comfort 30

Integrating photovoltaic systems into HVAC control using fuz...

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30th IEEE International Symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Boicu, Mihaela-Gabriela Dobrea, Marius-Alexandru Vasluianu, Mihaela Olteanu, Gabriela-Valentina Neculoiu, Giorgian University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Bucharest Romania

ISBN: (纸本)9798331539511

This article presents a study on the use of photovoltaic panels for storing electrical energy intended to power an HVAC unit placed in a residential room. To highlight this aspect, two specific scenarios will be considered. Thus, we aim to analyze whether the generated and stored energy is sufficient to fulfil the thermal comfort requirements of the user, while also considering the primary objective of minimizing energy consumption. To achieve this goal, the HVAC unit will be controlled by implementing a fuzzy system that determines its optimal activation based on the comfort level obtained from the user. The fuzzy system presents as inputs both environmental parameters and specific personal perception parameters of the user. The research results provide an insight into the efficiency of implementing photovoltaic energy storage systems for residential applications and propose solutions for optimizing energy consumption in the context of ensuring adequate thermal comfort. Thus, the study contributes to the development of sustainable and personalized energy solutions for smart homes. © 2024 IEEE.

关键词： Thermal comfort

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