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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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Conjunto de datos para la identificación de problemas de drones y la estimación de la gravedad.

Swardiantara Silalahi1, Tohari Ahmad1, Hudan Studiawan1

  • 1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

Data in brief
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PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta DroSev, un nuevo conjunto de datos para identificar problemas de drones y estimar su gravedad. Permite un mejor monitoreo de la salud de los drones y el mantenimiento predictivo a través del análisis de mensajes de registro.

Palabras clave:
El conjunto de datos de drones.Pruebas forenses de aviones no tripulados.Infraestructura de las infraestructuras.Análisis de registro Análisis de registro.Identificación del problema y identificación del problema.Gravedad del problema La gravedad del problema.

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Área de la Ciencia:

  • Tecnología de drones tecnología de drones.
  • La ciencia de datos es la ciencia de datos.
  • El aprendizaje automático es el aprendizaje automático.

Sus antecedentes:

  • Los registros de vuelo de drones contienen información crítica para la seguridad operativa.
  • La identificación precisa del problema y la estimación de la gravedad son esenciales para el mantenimiento de drones.

Objetivo del estudio:

  • Introducir DroSev, un nuevo conjunto de datos para la identificación de problemas de drones y la estimación de la gravedad.
  • Facilitar la investigación en el monitoreo automatizado de la salud de los drones y el mantenimiento predictivo.

Principales métodos:

  • Ha adquirido mensajes de registro de vuelo de drones de Mendeley Data y AirData.
  • Se desarrollaron dos subtareas: identificación del problema binario y clasificación de la gravedad del problema multiclase.
  • Se utilizó muestreo estratificado para una división de prueba de tren 80:20.

Principales resultados:

  • El conjunto de datos DroSev proporciona un recurso completo para el análisis de registros de drones.
  • El conjunto de datos apoya tanto la identificación como la evaluación de la gravedad de los problemas relacionados con los drones.
  • Se resumen las características sintácticas de los mensajes de registro.

Conclusiones:

  • DroSev es un recurso valioso para avanzar en la seguridad y confiabilidad de los drones.
  • El conjunto de datos se puede utilizar para entrenar modelos de aprendizaje automático para el diagnóstico automatizado de drones.
  • La investigación adicional puede aprovechar DroSev para mejorar la gestión operativa de drones.