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

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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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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Integrating Blockchain Technology in a Cyber Physical System to Secure Data 24

Integrating Blockchain Technology in a Cyber Physical System...

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

作者： Beatrix-May, Balaban Stefan, Sacala Ioan Alina-Claudia, Petrescu-Nita Razvan, Nita Politehnica University of Bucharest Faculty of Automatic Control and Computers Department of System Engineering Romania

ISBN: (纸本)9798350313390

Considering the constant growth and evolution of technology, being able to secure data is a challenge. This is why researchers find this a top research topic. Cyber-Physical Systems are complex, large-scale systems that can be found in domains like healthcare, transportation, industrial controls systems or smart grids. Having many components that interact with each other, we can observe here the challenge in maintaining a secure environment for the data as data tampering or data extraction can occur, for example, in the actual transportation of data from one device to another. The main focus of this paper is to give an answer if blockchain technology provides a solution in building a secure network. We give a short review of the related work regarding blockchain integration in large-scale systems like Cyber-Physical Systems. It was also briefly analyzed the layered structure of a blockchain and discussed some solutions for each layer. We then discussed about consensus algorithms for validation and authentication process. In general, consensus algorithms are implemented to assure the validation process of the nodes in a network. In this paper we tried to analyze and propose a model that offers an extra layer of security by adding the authentication step. In this way, the nodes that are validated are also considered trusted nodes in the network. © 2023 IEEE.

关键词： Blockchain

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Nonlinear Identification for control by Using HARMAX Models 9

Nonlinear Identification for Control by Using HARMAX Models

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Culita, Janetta Stefanoiu, Dan Nica, Andreea-Maria University 'Politehnica' of Bucharest Department of Automatic Control and Systems Engineering Romania University 'Politehnica' of Bucharest Faculty of Automatic Control and Computers Romania

ISBN: (纸本)9798350311402

The purpose of this article is to perform a comparison between two identification models: a nonlinear one referred to as HARMAX and a classical linear one, of Box-Jenkins (BJ) type. The first model modifies a classical ARMAX model by applying Hammerstein polynomials on input, output, and noise signals. Identification of models was performed by means of Multi-Step Least Squares Method, as described into the article. The simulations on a real-world plant, namely ASTANK2, a fluidic system with two inputs, two outputs and nonlinear static characteristic, have proven that the HARMAX model performs better than the BJ model and provides more accurate useful models to be integrated into closed loop configurations. © 2023 IEEE.

关键词： Optimization

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Real-Time Notifier for Blockchain Events 22

Real-Time Notifier for Blockchain Events

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22nd RoEduNet Conference: Networking in Education and Research, RoEduNet 2023

作者： Tanase-Paris, Cristian Carabas, Costin Tapus, Nicolae University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350393651

Blockchain technology gained much traction in the last few years. These decentralized databases offer security, immutability, and scalability across various applications. Decentralized applications generate vast amounts of data, known as events, that are recorded on the blockchain and are public to anyone. Some people may see opportunities for financial gains in these events and would like to know when they occur. This paper proposes a solution to process and deliver those events as real-time alerts to the users. It uses existing technologies such as message queues, multithreading, and asynchronous processing and integrates them into a scalable architecture. The results we achieved in this paper show that for an evenly distributed network traffic, which does not entirely consists of transaction bursts, the proposed solution offers reliability, efficiency, and a suitable delivery time to those wishing to integrate it into their projects. With time, this solution, or improved architectures, may form the basis of the following big-data architectures for processing blockchain events. © 2023 IEEE.

关键词： Blockchain

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A Distributed automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem 22

A Distributed Automatic Domain-Specific Multi-Word Term Reco...

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Truica, Ciprian-Octavian Istrate, Neculai-Ovidiu Apostol, Elena-Simona University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350341270

automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem;(2) perform an in-depth analysis of our architecture in terms of accuracy and scalability;(3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets. © 2023 IEEE.

关键词： Ecosystems

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UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles

UPB at IberLEF-2023 AuTexTification: Detection of Machine-Ge...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Preda, Andrei-Alexandru Cercel, Dumitru-Clementin Rebedea, Traian Chiru, Costin-Gabriel Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest060042 Romania

This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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A Comprehensive Patient Triage Algorithm Incorporating ChatGPT API for Symptom-Based Healthcare Decision-Making 4th

A Comprehensive Patient Triage Algorithm Incorporating ChatG...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Roșca, Cosmina-Mihaela Bold, Răzvan-Alexandru Gerea, Alexandru-Eduard Department of Automatic Control Computers and Electronics Faculty of Mechanical and Electrical Engineering Petroleum-Gas University of Ploiesti 39 Bucharest Avenue Ploieşti100680 Romania

ISBN: (纸本)9789819757022

In health care, it is crucial to organize patient care and resources based on the urgency level so that patients whose lives are in danger are treated as a priority. This study introduces a new algorithm, created in C# using new technologies such as ChatGPT, to improve the sorting and prioritizing of patients based on the National Urgency Level. The algorithm aims to demonstrate the possibility of real-time reporting of the occupancy level of all hospitals. This way, patients can head to the nearest and most available hospital based on both proximity and urgency level. To identify the urgency level, the algorithm utilizes a widely used external service, ChatGPT. Additionally, the algorithm calculates the average waiting time based on the number of patients, their urgency level, and the number of on-duty doctors. To achieve these goals, the algorithm's implementation has been carried out using modern technologies, such as C# programming language, ranging from the artificial intelligence service ChatGPT to entity framework for managing data in the Microsoft database. The article addresses two major issues, namely text-based classification into a specific category based on common characteristics, such as a patient's symptoms concerning a specific urgency level. The second issue is represented by the proposed calculation formula for real-time estimation of waiting time, based on the identified urgency level. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： C++ (programming language)

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A Comparative Analysis of Sorting Algorithms for Large-Scale Data: Performance Metrics and Language Efficiency 4th

A Comparative Analysis of Sorting Algorithms for Large-Scale...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Roșca, Cosmina-Mihaela Cărbureanu, Mădălina Department of Automatic Control Computers and Electronics Faculty of Mechanical and Electrical Engineering Petroleum-Gas University of Ploiesti 39 Bucharest Avenue Ploieşti100680 Romania

ISBN: (纸本)9789819757022

In the era of artificial intelligence and real-time applications, the demand for swift data processing on large volumes has become paramount. This article delves into the critical role of programming languages and frameworks in meeting these requirements. With a focus on web development, the study conducts a comparative analysis of eight classic sorting algorithms—Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, Heap Sort, Introspective Sort, and Shell Sort—evaluated in two prominent programming languages, PHP and C#. The research emphasizes the importance of selecting programming tools based on performance criteria in the context of modern applications. Next, a new formula for assessing the complexity of sorted words was introduced, aiming to scrutinize its impact on sorting time. Furthermore, a comparative analysis was conducted, evaluating the implementation of sorting in C# using arrays versus lists. The article sought to delineate the intricacies associated with each approach, considering computational efficiency and resource utilization. The discourse delved into the nuanced aspects of word complexity measurement and its consequential effects on the performance of sorting algorithms in the context of C# programming, contributing valuable insights to the ongoing discourse on algorithmic optimization and language-specific implementation choices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： C (programming language)

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Security issues in MCPS when using Wireless Sensor Networks 10

Security issues in MCPS when using Wireless Sensor Networks

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Béatrix-May, Balaban Ştefan, Sacală Ioan Alina-Claudia, Petrescu-Niţă Radu, Simen Department of System Engineering Faculty of Automatic Control and Computers UPB Bucharest Romania

ISBN: (纸本)9781665485579

Considering the evolution of technology, the need to secure data is growing fast. When we turn our attention to the healthcare field, securing data and assuring privacy are critical conditions that must be accomplished. The information is sensitive and confidential, and the exchange rate is very fast. Over the years, the healthcare domain has gradually seen a growth of interest regarding the interconnectivity of different processes to optimize and improve the services that are provided. Therefore, we need intelligent complex systems that can collect and transport sensitive data in a secure way. These systems are called cyber-physical systems. In healthcare domain, these complex systems are named medical cyber physical systems. The paper presents a brief description of the above-mentioned intelligent systems. Then, we focus on wireless sensor networks and the issues and challenges that occur in securing sensitive data and what improvements we propose on this subject. In this paper we tried to provide a detailed overview about cyber-physical systems, medical cyber-physical systems, wireless sensor networks and the security issues that can appear. © 2022 IEEE.

关键词： Wireless sensor networks

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