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MIT engineers are on a failure-finding mission

MIT engineers developed a technique to quickly identify a range of potential failures in a system before they are deployed in the real world. ....

United States , Charles Dawson , National Science Foundation , Department Of Aeronautics , Chuchu Fan , Force Office , Mit Aeroastro , Chuchu Fan , Charles Dawson , Autonomous Systems , Failure Prediction , Collision Avoidance , Power Grids , Network Reliability ,

"Cloud failure prediction based on traditional machine learning and dee" by Tengku Nazmi Tengku Asmawi, Azlan Ismail et al.

Cloud failure is one of the critical issues since it can cost millions of dollars to cloud service providers, in addition to the loss of productivity suffered by industrial users. Fault tolerance management is the key approach to address this issue, and failure prediction is one of the techniques to prevent the occurrence of a failure. One of the main challenges in performing failure prediction is to produce a highly accurate predictive model. Although some work on failure prediction models has been proposed, there is still a lack of a comprehensive evaluation of models based on different types of machine learning algorithms. Therefore, in this paper, we propose a comprehensive comparison and model evaluation for predictive models for job and task failure. These models are built and trained using five traditional machine learning algorithms and three variants of deep learning algorithms. We use a benchmark dataset, called Google Cloud Traces, for training and testing the models. We eva ....

Google Cloud Traces , Extreme Gradient Boosting , Decision Tree , Random Forest , Logistic Regression , Cloud Computing , Deep Learning , Failure Prediction , Ob And Task Failure , Machine Learning ,

Aerostructures Market by Material, Component, End-user,

Dublin, April 07, 2022 (GLOBE NEWSWIRE) The "Global Aerostructures Market by Material(Composites, Alloys & Superalloys and Metals), Component, End. ....

United States , South Korea , Collins Aerospace , Company Evaluation Quadrant , Iai Israel Aerospace Industries , Dynamic Companies , Magellan Aerospace Corporation , Kawasaki Heavy Industries Ltd , Progressive Companies , Sonaca Group , Mission Aircraft Increases Demand For Aerostructures , Korea Aerospace Industries , Porter Five Forces Analysis , Technologies In Engines Increases Development Of Nacelles , Triumph Group , Mission Aircraft , Elbit Systems Ltd , Research Methodology , Development Of Cargo Air Vehicles , Spirit Aerosystems Inc , Aerospace Industrial Development Corporation Aidc , Ruag Group , Responsive Companies , Company Profiles , Nordam Group , Small Enterprises ,

Global Aerostructures Market to 2026 - Adoption of 3D Printing for Aerostructure Manufacturing Presents Opportunities

/PRNewswire/ The "Global Aerostructures Market by Material(Composites, Alloys & Superalloys and Metals), Component, End User (OEM, Aftermarket), Platform. ....

United States , South Korea , Laura Wood , Dynamic Companies , Mission Aircraft Increases Demand For Aerostructures , Progressive Companies , Korea Aerospace Industries , Porter Five Forces Analysis , Triumph Group , Mission Aircraft , Elbit Systems Ltd , Office Hours Call , Research Methodology , Small Enterprises , Technology Analysis , Prominent Companies , Collins Aerospace , Iai Israel Aerospace Industries , Company Evaluation Quadrant , Magellan Aerospace Corporation , Kawasaki Heavy Industries Ltd , Sonaca Group , Technologies In Engines Increases Development Of Nacelles , Development Of Cargo Air Vehicles , Aerospace Industrial Development Corporation Aidc , Spirit Aerosystems Inc ,

"Failure Prediction for Large-scale Water Pipe Networks Using GNN and T" by Shuming Liang, Zhidong Li et al.

Pipe failure prediction in the water industry aims to prioritize the pipes that are at high risk of failure for proactive maintenance. However, existing statistical or machine learning models that rely on historical failures and asset attributes can hardly leverage the structure information of pipe networks. In this work, we develop a failure prediction framework for pipe networks by jointly considering the pipes' features, the network structure, the geographical neighboring effect, and the temporal failure series. We apply a multi-hop Graph Neural Network (GNN) to failure prediction. We propose a method of constructing a geographical graph structure depending on not only the physical connections but also geographical distances between pipes. To differentiate the pipes with diverse properties, we employ an attention mechanism in the neighborhood aggregation process of each GNN layer. Also, residual connections and layer-wise aggregation are used to avoid the over-smoothing issue i ....

Graph Neural Network , Failure Prediction , Infrastructure Networks , Point Process , Proactive Maintenance , Emporal Failure Pattern ,