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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Modelo adaptativo de asignación de espacio aéreo para logística urbana de drones mediante optimización multiobjetivo

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  • 1Business School, Yancheng Polytechnic College, Yancheng, 224005, Jiangsu, China. yphz5223@outlook.com.

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Resumen
Este resumen es generado por máquina.

Este estudio presenta un marco híbrido para la logística urbana de vehículos aéreos no tripulados (UAV), que mejora la eficiencia y la seguridad en entornos complejos. El modelo DRL-RO aborda las incertidumbres para una gestión robusta del tráfico de UAV a nivel de ciudad.

Palabras clave:
Asignación adaptativa de espacio aéreoAprendizaje profundo por refuerzoOptimización distributivamente robustaOptimización multiobjetivoLogística urbana de vehículos aéreos no tripulados

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Área de la Ciencia:

  • Ciencia de la logística y el transporte
  • Inteligencia artificial y robótica
  • Planificación y gestión urbana

Sus antecedentes:

  • La logística urbana de vehículos aéreos no tripulados (UAV) se enfrenta a desafíos con espacio aéreo limitado, volatilidad de la demanda e incertidumbres.
  • Los métodos de asignación estática son inadecuados para entornos urbanos dinámicos.

Objetivo del estudio:

  • Desarrollar un marco adaptativo para la gestión de la logística urbana de UAV.
  • Abordar los desafíos de las limitaciones del espacio aéreo, las fluctuaciones de la demanda y las incertidumbres.

Principales métodos:

  • Se desarrolló un marco híbrido DRL-RO (Aprendizaje Profundo por Refuerzo y Optimización Robusta Discreta).
  • Se empleó un sistema de modelado de incertidumbre de tres capas y una red de políticas mejorada con atención.
  • Se utilizó un algoritmo mejorado MOEA/D-DRL para la aproximación de la frontera de Pareto.

Principales resultados:

  • El marco logró una complejidad computacional subcuadrática con una alta tasa de éxito en Shenzhen.
  • Una estrategia jerárquica de gestión del espacio aéreo equilibró la eficiencia de la distribución, la seguridad del vuelo y los costos.
  • Las restricciones de la esfera de Wasserstein garantizaron la robustez y la escalabilidad en escenarios extremos.

Conclusiones:

  • El marco DRL-RO ofrece una solución robusta para la gestión del tráfico de UAV urbanos.
  • Proporciona soporte teórico y soluciones técnicas para sistemas de UAV a nivel de ciudad.
  • El estudio demuestra un equilibrio eficaz entre eficiencia, seguridad y costo en la logística de UAV.