Rail engineering work and maintenance

Abstract An engineering database modeling telephone outside plant networks is developed. Semantic and relational database design methodologies are used with the semantic data model developed based on an extended entity-relationship approach. This logical model is used to generate a normalized relational data structure. This database holds engineering data supporting engineering analyses, engineering work order generation procedures, and network planning activities. The database has been linked to separate network analysis programs and CAD-based network maps by a database application.

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Discussion of “Garbage Disposal: A Symposium: Administrative and Engineering Work in the Collection and Disposal of Garbage: A Review of the Problem”

Transactions of the American Society of Civil Engineers ◽

10.1061/taceat.0003818 ◽

1927 ◽

Vol 91 (2) ◽

pp. 837-848

Author(s):

Walter A. Sperry ◽

Kenneth Allen ◽

Harrison P. Eddy ◽

L. L. Tribus ◽

J. F. Jackson ◽

...

Keyword(s):

Garbage Disposal ◽

Engineering Work

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An overview of deep learning techniques

at - Automatisierungstechnik ◽

10.1515/auto-2018-0076 ◽

2018 ◽

Vol 66 (9) ◽

pp. 690-703 ◽

Cited By ~ 1

Author(s):

Michael Vogt

Keyword(s):

Deep Learning ◽

Single Layer ◽

Visual Image ◽

Image Understanding ◽

Number Of Layers ◽

Box Models ◽

Learning Techniques ◽

Engineering Work ◽

Computational Resources ◽

Black Box Models

Abstract Deep learning is the paradigm that profoundly changed the artificial intelligence landscape within only a few years. Although accompanied by a variety of algorithmic achievements, this technology is disruptive mainly from the application perspective: It considerably pushes the border of tasks that can be automated, changes the way products are developed, and is available to virtually everyone. Subject of deep learning are artificial neural networks with a large number of layers. Compared to earlier approaches with ideally a single layer, this allows using massive computational resources to train black-box models directly on raw data with a minimum of engineering work. Most successful applications are found in visual image understanding, but also in audio and text modeling.

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