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20th RoEduNet Conference: Networking in Education and Research, RoEduNet 2021

作者： Staniloiu, Eduard Nitu, Razvan Aron, Robert Rughinis, Razvan Politehnica University of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9781665413510

Google web applications have become an integral component in the day to day life of both organizations and individuals alike. These may be accessed through the graphical user interface (GUI) or through the application programming interface (API). The latter is primarily used by programmers to integrate such services into their applications. Most of the languages used to implement such applications are designed with performance in mind, often neglecting security. However, security has become a major concern for such systems thus increasing demand for memory safe languages. Unfortunately, languages such as D and Rust, known to be memory safe, are lacking support for Google services. To that end, we develop a methodology of integrating Google services with safe programming languages. We show that the D programming language can easily and successfully integrate such services bringing a boost in security and productivity. © 2021 IEEE.

关键词： Web services

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Virtual and Remote Robotic Laboratory Using EJS, MATLAB and LabVIEW

arXiv

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arXiv 2024年

作者： Chaos, Dictino Chacón, Jesús Lopez-Orozco, Jose Antonio Dormido, Sebastián Department of Computer Science and Automatic Control UNED Juan del Rosal 16 Madrid28040 Spain Department of Computers Architecture and Automatic Control Complutense University Ciudad Universitaria Madrid28040 Spain

This paper describes the design and implementation of a virtual and remote laboratory based on Easy Java Simulations (EJS) and LabVIEW. The main application of this laboratory is to improve the study of sensors in Mobile Robotics, dealing with the problems that arise on the real world experiments. This laboratory allows the user to work from their homes, tele-operating a real robot that takes measurements from its sensors in order to obtain a map of its environment. In addition, the application allows interacting with a robot simulation (virtual laboratory) or with a real robot (remote laboratory), with the same simple and intuitive graphical user interface in EJS. Thus, students can develop signal processing and control algorithms for the robot in simulation and then deploy them on the real robot for testing purposes. Practical examples of application of the laboratory on the inter-University Master of Systems Engineering and automatic control are presented. © 2024, CC BY-NC-ND.

关键词： Laboratories

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Condition Monitoring of Manufacturing Production Lines Using Fractal Analysis of Energy Consumption Datasets 23

Condition Monitoring of Manufacturing Production Lines Using...

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Mihai, Viorel Dragana, Cristian Popescu, Dan Ichim, Loretta University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9781665439398

Many plants use a combination of maintenance strategies across their production facilities. Under condition monitoring strategy, a continuous analyze of a combination of machine health parameters is performed, aiming the observation of changing trends. Online condition monitoring systems generally include measure of vibration, thermography, ultrasound, oil analysis or energy parameters. In the case of energy analysis, an abnormal behaviour of the manufacturing units generates deviations from the reference models by altering the energy consumption footprint. In this paper an approach based on fractal analysis of energy consumption datasets is evaluated on a real testbed assembly line. By computing the Hurst exponent for each data subset which corresponds to different assembly operations, for several manufacturing cycles, it was found that it is possible to perform an energy consumption pattern matching for each operation. © 2021 IEEE.

关键词： Energy utilization

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Adaptive Models for Microscopic Traffic Modelling 23

Adaptive Models for Microscopic Traffic Modelling

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Nae, Andrei C. Dumitrache, Ioan University Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9781665439398

This paper presents, investigates and further develops microscopic simulations, using advanced capabilities from today programing environment and HPC resources, in an attempt to further develop this type of modelling towards realistic traffic configurations and large-scale applications, in the overall context of CPS - Cyber Physical Systems and future autonomous driving concepts. This is based on the attempt to address and further develop the CPS concepts in the transportation domain, towards a future smart mobility (SM) environment where T-CPS takes into account technologies able to exploit advanced capabilities at both vehicle level and the smart infrastructure. Proposed contributions were assigned on the vehicle level. Autonomous vehicles based on an advanced driver model, able to face all challenges from the current traffic: urban traffic rules, lane changing needs, congestion, passenger comfort. We introduce a new model and characterize this with respect to current expectations. This is considered to be the kernel of the new generation autonomous car computer system. © 2021 IEEE.

关键词： Cyber Physical System

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Adaptive Driving Models for Intelligent Traffic Cyber Physical Systems 3

Adaptive Driving Models for Intelligent Traffic Cyber Physic...

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3rd International Conference on Electrical, Communication and computer Engineering, ICECCE 2021

作者： Nae, Corneliu Andrei Dumitrache, Ioan University Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9781665438971

In current paper we introduce new developments related to the microscopic vehicular traffic flow simulations and specific modelling, where we also exploit latest achievements in state-of-the-art capabilities in HPC. Our major goal is to further extend concepts from CPS-Cyber Physical Systems for the future autonomous vehicles to be part of the ITS-Intelligent Transportation System of tomorrow. In our global vision, T-CPS-Transportation Cyber Physical Systems is the framework where we consider those technologies that are able to fully exploit capabilities at both vehicle level and at the infrastructure level, including synergies. Here we present our advanced driver model, adapted for major challenges in both urban and highway environment: safety dynamic longitudinal model, lane changing options, congestion mitigation solutions and passenger safety and comfort needs. The overall set of parameters defining our models are introduced and a complex test matrix is presented for our numerical experiments. We fully simulate our model in realistic environments and we assess performance, trying to suggest integration specifications for the next generation autonomous car computer system. © 2021 IEEE.

关键词： Cyber Physical System

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Classification of Sunflower Foliar Diseases Using Convolutional Neural Network 23

Classification of Sunflower Foliar Diseases Using Convolutio...

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Dawod, Rodica Gabriela Dobre, Ciprian University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9781665439398

Sunflower is an economically important crop which provides a significant source of vegetable oil for human consumption and industrial use. It is the third largest producing oilseed in the world, after soybean and rapeseed. More than half of world cultivated surface is concentrated primarily in Ukraine and Russia. Many diseases, biotic and abiotic can attack this crop, some causing significant economic damage. This paper proposes a deep learning approach to sunflower disease identification. There are detected 4 foliar diseases, caused by multiple pathogens, with different manifestations, which often cause misidentification. ResNet152 was selected as Convolutional Neural Network (CNN) and options are explored to increase performance metrics by processing and improving the available dataset, by tuning network hyperparameters such as number of epochs and batch size, but also by understanding the plant pathology, disease manifestation and explaining why the model fails on specific images. © 2021 IEEE.

关键词： Crops

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Applicability of Raspberry Pi IOT Device as Back Up Protection Device in Medium Voltage Power Systems 10

Applicability of Raspberry Pi IOT Device as Back Up Protecti...

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10th International Conference on ENERGY and ENVIRONMENT, CIEM 2021

作者： Kandamulla Arachchige, Buddhika Priyashantha Mariana, Mocanu Petrescu, Catalin Zafiu, Claudiu Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Bucharest Romania

ISBN: (纸本)9781665445849

This paper demonstrates applicability of cost efficient IoT board as a backup protection device in a power system protection architecture. In conventional power system protection, there are at least 2 independent protection devices for redundancy. Can an IoT board be used as an efficient solution to replace back-up device? Real time signal processing algorithm and SCADA communication protocol were implemented in a Raspberry Pi (RPI) board. Frequency computed by the RPI was compared with the actual frequency of the analog signal to measure its accuracy. Once basic protection functions are implemented, it is possible to demonstrate applicability of RPI as a backup protection device. This applicability would greatly reduce cost and complexity of power system architecture. By developing this concept, it will be able to use more IoT devices in power system protection to upscale substation into a cloud connected architecture. © 2021 IEEE.

关键词： Real time systems

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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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Split Cost Function based Single-Horizon Finite-Set Model Predictive control for Boost Converter with Loading Current Estimation

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Tatari, Fatemeh Rezayof Bizhani, Hamed Iwanski, Grzegorz Warsaw University of Technology Faculty of Electrical Engineering Warsaw Poland Kielce University of Technology Faculty of Electrical Engineering Automatic Control and Computer Science Kielce Poland

This paper presents a robust single-horizon finite-set model predictive control (FS-MPC) method for controlling the output voltage of boost converters. Unlike the long-horizon FS-MPC approach, which addresses the non-minimum phase (NMP) behavior of the converter by extending the prediction horizon (thereby increasing computational effort), the proposed method ensures stable voltage regulation by incorporating inductor current regulation into the cost function. To achieve this, the desired inductor current is calculated based on input-output power balance theory. Unlike recent studies that require an additional sensor to measure the loading current, this paper develops a loading current estimation approach to eliminate the need for extra current sensors, thereby enhancing the robustness of the proposed FS-MPC method. Additionally, over-current protection, which is typically absent in conventional FS-MPC used for direct voltage regulation, is also incorporated into the proposed method. The conventional cost function is also modified to tackle the NMP behavior more effectively. The performance of the proposed method in regulating the output voltage is evaluated through simulation results. Finally, its effectiveness across a wide range of output power and various load characteristics, including resistive, constant power, and constant current loads, is validated via experimental results. The results demonstrate that the proposed method is not only able to eliminate the overshoot and undershoot, but can also improve the settling time by 41% compared to a well-tuned conventional cascaded PI controller. In addition, the proposed method can reduce the computational burden by 84% compared to the conventional long-horizon FS-MPC approach. © 2013 IEEE.

关键词： Cost functions

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Predictive maintenance for electrical motors: Current approach and usage of artificial intelligence algorithms

Predictive maintenance for electrical motors: Current approa...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Tomasz Herzog Krzysztof Bartecki Faculty of Electrical Engineering Automatic Control and Computer Science Opole University of Technology Opole Poland Rockwell Automation Power Control Business Katowice Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

The article presents the current approach in artificial intelligence methods used in predictive maintenance for electrical motors. The study focuses on presenting data classification methods for fault detection and isolation using data from condition monitoring systems. We discuss the advantages and disadvantages of using machine learning methods like perceptron and Support Vector Machine (SVM) for fault classification problems. Our conclusion shows great interest in machine learning methods combined with condition monitoring systems. Our future research will focus on comparing SVM, perceptron networks, and other methods, such as K-nearest neighbors, with their possibility for implementation in embedded firmware systems.

关键词： Support vector machines Condition monitoring Accuracy Fault detection Artificial neural networks Machine learning Prediction algorithms

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