Essays about: "Loss Deployment"
Showing result 11 - 15 of 40 essays containing the words Loss Deployment.
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11. Model-driven development for Microservices : A domain-specific modeling language for Kubernetes
University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)Abstract : In the digital age that we live in today, we are dependent on numerous web applications or services, from dealing with banking, booking air flights, and handling our taxes. We expect these applications and services to support high availability, data loss prevention, and fast response time. READ MORE
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12. Performance Evaluation of WebRTC Server On Different Container Technologies : Kubernetes and Docker Swarm
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background: Cloud computing technology has come a long way with various technological advancements in the past few years. It has been accelerated with the evolution of various virtualization technologies. READ MORE
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13. Investigation of Solar Powered EV Charging StationPotential
University essay from Högskolan Dalarna/Institutionen för information och teknikAbstract : The worldwide fast growth of the transportation sector contributes to a large andgrowing share of global greenhouse gas (GHG) emissions. The Swedish TransportAdministration report indicates that emissions from domestic transport increasedin 2018. READ MORE
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14. Conceptual and Control System Design of a Lagrangian Float
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Lagrangian floats are autonomous drifting buoys that ideally behave identicalto the surrounding water particles in the water column. Existing examples ofLagrangian floats are often costly and heavy, carrying expensive sensor anddata collection equipment, which prevents their scalability to larger fleets orbroader usage. READ MORE
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15. Enforcing low confidence class predictions for out of distribution data in deep convolutional networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Modern discriminative deep neural networks are known to perform high confident predictions for inputs far away from the training data distribution, commonly referred to as out-of-distribution inputs. This property poses security concerns for the deployment of deep learning models in critical applications like autonomous vehicles because it hinders the detection of such inputs. READ MORE