This paper presents the building process of an interactive instrument called the Colombian Solar Atlas able to easily visualize meteorological data but also assess the current and future potentials of solar photovolta...
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This paper presents the building process of an interactive instrument called the Colombian Solar Atlas able to easily visualize meteorological data but also assess the current and future potentials of solar photovoltaic generation throughout the whole territory of Colombia,South *** new tool is based on two different meteorological *** first one is done with historical data extracted from satellite imagery information,and the other one corresponds to data issues from regional-scale climate change projection *** satellite database was validated with different in-situ *** Colombian Solar Atlas uses basic and advanced photovoltaic generation models to estimate the generation of a custom solar *** this tool,a user selects a point on the map and can have directly pertinent information to search for an optimal location with a spatial resolution of 4 *** tool is the first open interactive online tool particularly adapted to study the photovoltaic power potential in Colombia,considering the country’s needs and native language.
This paper presents a miniature (1.7 cm3) and zero static power magnetostatic surface wave tunable notch filter with a tuning range from 3.3 GHz - 10.3 GHz. The notch frequency can be directly tuned by transient volta...
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The paper deals with a single-jet twin-spool turbo-engine (TSSJE) for aircraft use. Based on non-linear engine's motion equations, the mathematical model was established. New forms of model's equations were is...
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The global health landscape has been persistently challenged by the emergence and re-emergence of infectious *** epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into diseas...
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The global health landscape has been persistently challenged by the emergence and re-emergence of infectious *** epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease ***,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive *** is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease *** paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological *** harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced *** comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by *** essence,Al4S represents a paradigm shift in infectious disease *** addresses the limitations of traditional models and paves the way for a more proactive and informed response to future *** we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.
Short-term load forecasting has continued to grow in importance, prompting the development of new forecasting methods. However, access to historical data is often limited, especially for some areas in which smart mete...
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Jet engines for aircraft use are mandatorily assisted by fuel installations (fuel control units-FCUs);their main equipments are the pumps, whose drive can be done mechanically or electrically, depending on the engine&...
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We consider the consensus problem for 2nd-order MIMO multi-agent systems with unknown nonlinear terms. We propose a novel control algorithm based on the Barrier Integral Control (BRIC) that combines reciprocal barrier...
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Linear regression is a very simple machine learning model that is supposed to find linear relations between input and output data. Its use is limited since real-world random variables are almost never linearly correla...
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In this paper, we focus on the problem of conformal prediction with conditional guarantees. Prior work has shown that it is impossible to construct nontrivial prediction sets with full conditional coverage guarantees....
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In this paper, we focus on the problem of conformal prediction with conditional guarantees. Prior work has shown that it is impossible to construct nontrivial prediction sets with full conditional coverage guarantees. A wealth of research has considered relaxations of full conditional guarantees, relying on some predefined uncertainty structures. Departing from this line of thinking, we propose Partition Learning Conformal Prediction (PLCP), a framework to improve conditional validity of prediction sets through learning uncertainty-guided features from the calibration data. We implement PLCP efficiently with alternating gradient descent, utilizing off-the-shelf machine learning models. We further analyze PLCP theoretically and provide conditional guarantees for infinite and finite sample sizes. Finally, our experimental results over four real-world and synthetic datasets show the superior performance of PLCP compared to state-of-the-art methods in terms of coverage and length in both classification and regression scenarios. Copyright 2024 by the author(s)
Conditional validity and length efficiency are two crucial aspects of conformal prediction (CP). Conditional validity ensures accurate uncertainty quantification for data subpopulations, while proper length efficiency...
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