scholarly journals A New Lightweight In-situ Adversarial Sample Detector for Edge Deep Neural Network

Author(s):  
Si Wang ◽  
Wenye Liu ◽  
Chip-Hong Chang
2021 ◽  
Vol 27 (S1) ◽  
pp. 262-264
Author(s):  
Joshua Vincent ◽  
Sreyas Mohan ◽  
Ramon Manzorro ◽  
Binh Tang ◽  
Dev Sheth ◽  
...  

2021 ◽  
Author(s):  
Fahad Ali Milaat ◽  
Zhuo Yang ◽  
Hyunwoong Ko ◽  
Albert T. Jones

Abstract Selective laser melting (SLM) is modernizing the production of highly complex metal parts across the manufacturing industry. However, achieving material homogeneity and controlling thermal deformation remain major challenges for metal-based, additive manufacturing. Therefore, adequate control systems are needed to monitor build processes, and ensure part quality throughout production. Traditionally, control designs relied on physics-based knowledge in analyzing, characterizing, and modeling complex, nonstationary patterns. Recent advancements in machine learning techniques harness the abundance of data to discover effective control designs. In this paper, we investigate the efficacy of a data-driven approach towards in-situ modeling of melt-pool geometry. Specifically, we propose a new methodology that uses a deep neural network architecture to predict melt pool geometries with linear regression models, which manifest during in-situ processes. Experimental results show that our deep neural network model with multiple input features produced 84% goodness of fit score, outperforming the model with a single feature that scored 37% for the given dataset, and the monitored regression models. These outcomes promote further investigation into new and efficient ways for acquiring real-time data from in-situ processes. Our contribution complements the way we understand properties of in-situ data, and predict patterns of melt pools, based on artificial cognition.


Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler

Author(s):  
P.L. Nikolaev

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.


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