Satellite battery fault detection using Naïve Bayesian classifier

Galal, Mohamed Ahmed; Hussein, Walid; Abdelkawy, Ezz Eldin; Sayed, Mahmoud M.

doi:10.1109/aero.2019.8741963

Cited by 9 publications

(1 citation statement)

References 10 publications

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“…With the classified data, a data prediction model was created using the Predictions widget connected to the Orange Naive Bayes classifier model [31]. We use Naive Bayesian algorithm for fault detection in satellite batteries because it presents good results as demonstrated in [29]. The classifier Naive Bayesian presents a good result as a classifier for fault detection in satellite batteries.…”

Section: ) Classificationmentioning

confidence: 99%

Automated Planning With Invalid States Prediction

et al. 2021

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The increase of automated systems in space missions raises concerns about safety and reliability in operations carried out by satellites due to performance degradation. There have been several studies about the automatic planning process, but many approaches are generated with invalid states. The invalid state can be understood as a prohibited, degraded or risky scenario for the domain. This paper proposes an automated planning process with restrictions that enables automatic planners to not generate plans with invalid states. We implement a validator method for the planner software which proves that plan generation matches the restrictions imposed on the domain. In the experiments, we test an automatic planning process that is specific to the aerospace area, where a knowledge base with invalid states is available in the context of the operation of a satellite. Our proposal to carry out the verification of invalid states in automatic planning, can contribute to plans being generated with higher quality, ensuring that the goal of a plan is only achieved through valid intermediate states. It is also expected that the generated plans will be executed with better performance and will require less computational resources, since the search space is reduced.

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Section: ) Classificationmentioning

confidence: 99%

Automated Planning With Invalid States Prediction

et al. 2021

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Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives

Abdolrasol,

Ayob,

Lipu

et al. 2024

eTransportation

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Intrusion detection in cluster‐based wireless sensor networks: Current issues, opportunities and future research directions

John,

Isnin,

Hamid Hussain Madni

et al. 2024

IET Wireless Sensor Systems

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Wireless sensor network (WSN) cluster‐based architecture is a system designed to control and monitor specific events or phenomena remotely, and one of the important concerns that need quick attention is security risks such as an intrusion in WSN traffic. At the same time, a high‐level security method may refer to an intrusion detection system|intrusion detection systems (IDS), which may be employed effectively to achieve a higher level of security in detecting an intruder attack or any attack initiated within a WSN system. The significance of the detection of network intrusions on heterogeneous cluster‐based sensor networks with wireless connections, as well as the approaches to machine learning utilised in IDS model development, were discussed. In addition, this research conducted several comparative studies of feature selection techniques and machine learning methodologies in the development of intrusion detection systems. The authors used a bibliometric indicator to identify the leading trends when it comes to IDS, and the VOS viewer was used to create a spatial mapping of co‐authorship, co‐occurrence, and citation types of analysis with their respective units of study. The purpose of this research paper is to generate relevant findings and a research problem formulation that can lead to a research gap in the research topic's domain area.

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Satellite battery fault detection using Naïve Bayesian classifier

Cited by 9 publications

References 10 publications

Automated Planning With Invalid States Prediction

Automated Planning With Invalid States Prediction

Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives

Intrusion detection in cluster‐based wireless sensor networks: Current issues, opportunities and future research directions

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