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Deep Learning for industrial Prognostics & Health Management (PHM)

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Implementation and Results Introduction Conclusion References Deep Auto-Encoders • 4xNvidia K40 GPUs with with 2880 cores and 12 GB device RAM each in Ubuntu OS workstation •Theano based toolchain for Deep Learning • Nvidia K40 with 12 GB device RAM - driving factor for large dataset inhalation, caching and computation - especially the pre-training stage for DBNs Email:{venugov, gierinmj, reddykk}@utrc.utc.com Deep Belief Nets Layer 1 Layer 2 Bottleneck layer Input layer W2 T Layer 1 Layer 2 RBM RBM RBM Recursive pre-training W1 T W3 T • Successful adoption of Deep Learning methodologies to UTC applications in aerospace and building systems as shown in the timeline. Offers customized support agreements to help operators achieve optimal aircraft utilization. Products range from single actuators to complete flight control systems for the fixed wing, rotorcraft and missile segments as well as fly-by-wire cockpit controls, cabin equipment, trimmable horizontal stabilizer actuators and flight safety parts for helicopters. Engine products include electronic engine controllers, fuel systems, engine actuation, thermal management systems, accessory drive gearboxes and transmissions, drive shafts and flexible couplings, engine start systems, turbine blades and vanes.


Distributed systems: A quick and simple definition

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Attend the O'Reilly Velocity Conference to learn the latest tools and techniques of distributed systems. The technology landscape has evolved into an always-on environment of mobile, social, and cloud applications where programs can be accessed and used across a multitude of devices. These always-on and always-available expectations are handled by distributed systems, which manage the inevitable fluctuations and failures of complex computing behind the scenes. "The increasing criticality of these systems means that it is necessary for these online systems to be built for redundancy, fault tolerance, and high availability," writes Brendan Burns, distinguished engineer at Microsoft, in Designing Distributed Systems. "The confluence of these requirements has led to an order of magnitude increase in the number of distributed systems that need to be built."


Systems Of Intelligence And The Future Of Healthcare

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Over the last decade, progress toward the Institute for Healthcare Improvement Triple Aim--improved patient outcomes and increased patient satisfaction, with reduced costs--has been driven by the implementation of systems of record and systems of engagement. Systems of record--predominantly electronic health records (EHRs)--capture the data that healthcare organizations need, while systems of engagement such as mobile monitoring tools, planning and scheduling tools, and patient portals transform the data into actionable information. Healthcare organizations are beginning to accrue benefits from these systems, but the use of machine learning (ML) and artificial intelligence to create systems of intelligence is ready to produce a truly transformative leap forward. Systems of intelligence analyze the data captured in systems of record and the interactions managed with systems of engagement to provide potential insights for review without human prompting. Early iterations of these systems have been in use for some time--for example, Amazon's recommendation system analyzes user interactions to determine which products (i.e., data) to recommend to its users without input from human analysts.


Why Systems Thinking is Not a Natural Act

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Competence in systems thinking is implicitly assumed among the population of engineers and managers -- in fact, most technical people claim to be systems thinkers. But this competence is not as prevalent as these assertions might lead one to assume. Controlled experiments show that systems thinking performance, even among highly educated people, is poor. This presentation provides a set of systems thinking competencies and demonstrates how these are not as common as advertised. We also discuss how these competencies can be measured.


Data Science –The need for a Systems Engineering approach

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As per the International Council on Systems Engineering (INCOSE), "Systems Engineering is an engineering discipline whose responsibility is creating and executing an interdisciplinary process to ensure that the customer and stakeholder's needs are satisfied in a high quality, trustworthy, cost efficient and schedule compliant manner throughout a system's entire life cycle.