Engineering topics

Rose, Derek C

Publications and source records attributed to Rose, Derek C.

3 records

DOE OSTI2019

Peregrine

The purpose of “Peregrine” is to perform in-situ quality control of the additive manufacturing process in powder bed systems. The software is an AI-based method to automatically detect and map multiple classes of defects present in the top layer of a powder bed system, either after material deposition, i.e. raking, or after printing the layer. Relying on image data or sensing modalities that can be converted into image data, the code allows for: (1) various steps of pre-processing of the data (registration, distortion correction, etc), (2) annotation of training datasets, (3) processing of data inline or offline, (4) creating 3D rendering of the defect map, and (5) implementation of corrective actions if allowed by the manufacturing platform.

Paquit, Vincent C↗

DOE OSTI2017

Deep Modeling: Circuit Characterization Using Theory Based Models in a Data Driven Framework

Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilize measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.

Bolme, David S↗

DOE OSTI2017

Age, Gender, and Fine-Grained Ethnicity Prediction using Convolutional Neural Networks for the East Asian Face Dataset

This paper examines the difficulty associated with performing machine-based automatic demographic prediction on a sub-population of Asian faces. We introduce the Wild East Asian Face dataset (WEAFD), a new and unique dataset to the research community. This dataset consists primarily of labeled face images of individuals from East Asian countries, including Vietnam, Burma, Thailand, China, Korea, Japan, Indonesia, and Malaysia. East Asian turk annotators were uniquely used to judge the age and fine grain ethnicity attributes to reduce the impact of the other race effect and improve quality of annotations. We focus on predicting age, gender and fine-grained ethnicity of an individual by providing baseline results with a convolutional neural network (CNN). Finegrained ethnicity prediction refers to predicting ethnicity of an individual by country or sub-region (Chinese, Japanese, Korean, etc.) of the East Asian continent. Performance for two CNN architectures is presented, highlighting the difficulty of these tasks and showcasing potential design considerations that ease network optimization by promoting region based feature extraction.

Srinivas, Nisha↗