Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The abili...
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This paper presents a set of ontologies to organize concepts, categories, properties, and relationships that are typically used by designers for characterizing sensing and actuation devices that compose a given system...
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ISBN:
(数字)9781728182865
ISBN:
(纸本)9781728182872
This paper presents a set of ontologies to organize concepts, categories, properties, and relationships that are typically used by designers for characterizing sensing and actuation devices that compose a given system architecture (SA). From the characterization of such devices, modeling can be done using the Architecture Analysis and Design Language (AADL). AADL supports modeling and analysis of functional and nonfunctional properties to evaluate quality attributes from ontology models. The paper also proposes an AADL property set that can be applied to sensors and actuators (S&A), based on previous researches with ontologies applied to sensor networks. Specific characteristics are provided to describe systems' quality attributes. This aims to be used for analyzing the designed architecture, and also its probable future evolution.
The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integratin...
The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.
y associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with poss...
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The classification of vehicles is a matter of great importance for traffic control and management, helping with traffic surveillance as well as in statistical data collection. Among the several vehicular classificatio...
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The classification of vehicles is a matter of great importance for traffic control and management, helping with traffic surveillance as well as in statistical data collection. Among the several vehicular classification techniques, the most popular uses inductive loop sensors, because they achieve high accuracy rate at low cost. This paper proposes 5 different vehicle classification models by inductive waveform analysis: KNN, SVC, Decision Tree, Random Forest, and Voting Classifier. A brief introduction to the mathematical basis of these models and the main forms of vehicle detection are also presented. The obtained results reached an accuracy of 94% and showed how inductive waveform analysis is still a valid option for vehicle classification.
Droughts are natural hazards that exist in nature and can have a serious impact on the environment and society, which includes water shortages, crop failures, fires and, in some cases, soil manipulation. To assess and...
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Droughts are natural hazards that exist in nature and can have a serious impact on the environment and society, which includes water shortages, crop failures, fires and, in some cases, soil manipulation. To assess and predict droughts, various methods, such as the Standardized Precipitation Index (SPI), were designed to segregate drought trends and excess rainfall over a period ranging from 3 to 48 months. This study proposes an innovative approach to predicting drought use, the Evolutionary Polynomial Expansion with Feature Selection (EPEFS) model, a hybrid method that integrates polynomial regression with feature selection to increase accuracy and interpretability. The methodology was applied to historical precipitation data from six meteorological stations in Türkiye, covering the period from 1971 to 2016. The drought index Standardized Precipitation Index (SPI) was used as the primary indicator, with predictions made for three different time scales: SPI-3, SPI-6 and SPI-12. Furthermore, a time series cross-validation strategy was employed to ensure performance assessment. The EPEFS model obtained R 2 coefficients of 0.880, 0.903 and 0.929 for SPI-3, SPI-6 and SPI-12, respectively, surpassing the other models analyzed. Furthermore, the model presented less complexity in the generated expressions. The results suggest that the EPEFS model holds promise as a robust and interpretable tool for drought forecasting, with potential applications in early warning systems and mitigation strategies.
By associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with pos...
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By associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. Control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant.
Without proper coordination, power conditioners within microgrids are prone to suffer from resonance phenomena due to the complex and dynamic interactions among the main grid, nonlinear loads and distributed converter...
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Without proper coordination, power conditioners within microgrids are prone to suffer from resonance phenomena due to the complex and dynamic interactions among the main grid, nonlinear loads and distributed converters. In addition to the detriment of grid-connected devices and loads, harmonic voltage resonances may also lead to microgrid instability. As a consequence, the steering of distributed power conditioners to diminish voltage distortions and suppress undesired currents has been playing a key role on enhancing the operational stiffness of microgrids. In general, such conditioners are driven by the synthesis of sinusoidal currents independently on the status of voltage waveforms, which may not adequately damp harmonic resonances and still jeopardize system stability. Thus, this work proposes the coordination of multiple parallel power conditioners, which are driven as controlled current sources, through a current-based approach that synthesizes resistive loads, enhancing the system capability to damp voltage resonances, as well as improving power quality within microgrids. Simulation results comprising a single-phase microgrid with resonant and nonlinear loads, as well as two distributed power conditioners, are presented to demonstrate the effectiveness of the approach.
The presence of distributed generation systems spread over low-voltage electrical networks is boosting the development of control methodologies aiming at coordinating and cooperatively managing the existing smart inve...
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ISBN:
(数字)9781728141800
ISBN:
(纸本)9781728141817
The presence of distributed generation systems spread over low-voltage electrical networks is boosting the development of control methodologies aiming at coordinating and cooperatively managing the existing smart inverters. Although low-bandwidth data transmission links are constantly described to be required for a considerable number of centralized and decentralized control methodologies, there is a gap in literature concerning the plain understanding of the features of the related communication protocols available for such application. Thus, this paper brings considerations on some of the most relevant communication protocols that can be applied to the cooperative control of multiple smart inverters, taking into account the recent updates on interoperability requirements recommended by the IEEE 1547-2018 standard. The communication infrastructure, topology and features of a low-bandwidth data transmission link are discussed in this paper focusing on the SunSpec, DNP3 and SEP2 protocols. Yet, some critical comments are made regarding the practical interoperability of commercial inverters, also bordering cyber security matters.
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