April 1, 2019 Thesis Open Access

Tewodros Kibatu

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{
  "description": "<p>Global network infrastructures are increasing with the development of new technologies and growth in Internet traffic. As network infrastructures increases, maintaining and monitoring them will become very challenging since thousands of alarms are generated every day. Clearing those alarms by corrective maintenance activities require considerable effort and resources (car, labor, and budget). In mobile networks, a Base Transceiver Station (BTS) is one key infrastructure element performing the task of connecting customer equipment with the cellular network. BTS services may be interrupted due to transmission, optical fiber cut, power system failure, natural disaster or many more. In the case of Ethio Telecom (ET), the sole telecom service provider in Ethiopia, power system failure takes the biggest share for interruption of BTS services. Minimizing power system failure will reduce downtime of the BTS thereby, guarantee customer satisfaction and maximize revenue. Recently, machine learning algorithms are used to predict failure in various areas like power distribution, hydropower generation plants, solar power generation plants, high voltage transmission grid and many more.</p>", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "creator": [
    {
      "@type": "Person", 
      "name": "Tewodros Kibatu"
    }
  ], 
  "headline": "Recurrent Neural Network-based Base Transceiver Station Power System  Failure Prediction", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2019-04-01", 
  "url": "https://nadre.ethernet.edu.et/record/2107", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.20372/nadre:2107", 
  "@id": "https://doi.org/10.20372/nadre:2107", 
  "@type": "ScholarlyArticle", 
  "name": "Recurrent Neural Network-based Base Transceiver Station Power System  Failure Prediction"
}