Harvesting Domain-Specific Data Resources for Enhanced After-Sales Intelligence in Car Industry

2012 ◽  
pp. 145-167
Author(s):  
Jan Werrmann
Keyword(s):  
Specific Data ◽  
Car Industry ◽  
Domain Specific

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

Domain-specific data mining for residents' transit pattern retrieval from incomplete information

2019 ◽  
Vol 134 ◽  
pp. 62-71 ◽  
Author(s):  
Yongxin Liu ◽  
Jianqiang Li ◽  
Zhong Ming ◽  
Houbing Song ◽  
Xiaoxiong Weng ◽  
...  
Keyword(s):  
Data Mining ◽  
Incomplete Information ◽  
Specific Data ◽  
Domain Specific ◽  
Pattern Retrieval

Visualizing Model-Based Product Definitions in Augmented Reality

2021 ◽  
Author(s):  
Teodor Vernica ◽  
Robert Lipman ◽  
William Z. Bernstein
Keyword(s):  
Augmented Reality ◽  
Three Dimensional ◽  
Major Barrier ◽  
Specific Data ◽  
Domain Specific ◽  
Engineering Data ◽  
Data Representations ◽  
Design And Manufacturing ◽  
Product Manufacturing ◽  
Immense Potential

Abstract Augmented reality (AR) technologies present immense potential for the design and manufacturing communities. However, coordinating traditional engineering data representations into AR systems without loss of context and information remains a challenge. A major barrier is the lack of interoperability between manufacturing-specific data models and AR-capable data representations. In response, we present a pipeline for porting standards-based Product Manufacturing Information (PMI) with three-dimensional (3D) model data into an AR scene. We demonstrate our pipeline by interacting with annotated parts while continuously tracking their pose and orientation. Our work provides insight on how to address fundamental issues related to interoperability between domain-specific models and AR systems.

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