Layout Estimation for Layered Ink of 3D Printer to Reproduce the Desired Line Spread Function of Skin using Simulated Data

2021 ◽  
Author(s):  
Kazuki Nagasawa ◽  
Kensuke Fukumoto ◽  
Wataru Arai ◽  
Kunio Hakkaku ◽  
Satoshi Kaneko ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Three Dimensional ◽  
Simulated Data ◽  
Training Data ◽  
Target Color ◽  
3D Printer ◽  
Line Spread Function ◽  
Spread Function ◽  
Line Spread

In this article, the authors propose a method to estimate the ink layer layout for a three-dimensional (3D) printer. This enables 3D printed skin to be produced with the desired translucency, which they represent as line spread function (LSF). A deep neural network in an encoder–decoder model is used for the estimation. It was previously reported that machine learning is an effective way to formulate the complex relationship between optical properties such as LSF and the ink layer layout in a 3D printer. However, although 3D printers are more widespread, the printing process is still time-consuming. Hence, it may be difficult to collect enough data to train a neural network sufficiently. Therefore, in this research, they prepare the training data, which is the correspondence between an LSF and the ink layer layout in a 3D printer, via computer simulation. They use a method to simulate the subsurface scattering of light for multilayered media. The deep neural network was trained with the simulated data and evaluated using a CG skin object. The result shows that their proposed method can estimate an appropriate ink layer layout that closely reproduces the target color and translucency.

scholarly journals Estimation of Layered Ink Layout to reproduce desired Translucency of skin in Inkjet 3D Printer using deep neural network trained with synthetic simulated data

2020 ◽  
Vol 2020 (28) ◽  
pp. 321-326
Author(s):  
Kensuke Fukumoto ◽  
Kazuki Nagasawa ◽  
Wataru Arai ◽  
Kunio Hakkaku ◽  
Satoshi Kaneko ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Simulated Data ◽  
Training Data ◽  
Target Color ◽  
3D Printer ◽  
Subsurface Scattering ◽  
Line Spread Function ◽  
3D Printed ◽  
Line Spread

In this paper, we propose a method to estimate ink layer layout used as an input for 3D printer. This method makes it possible to reproduce a 3D printed patch that gives a desired translucency, which is represented as Line Spread Function (LSF) in this study. Deep neural networks of encoder decoder model is used for the estimation. In a previous research, it is reported that machine learning method is effective to formulate the complex relationship between the optical property such as LSF and the ink layer layout in 3D printer. However, it may be difficult to collect data large enough to train a neural network sufficiently. Especially, although 3D printer is getting more and more widespread, the printing process is still time consuming. Therefore, in this research, we prepare the training data, which is the correspondence between LSF and ink layer layout in 3D printer, by simulating it on a computer. MCML was used to perform the simulation. MCML is a method to simulate subsurface scattering of light for multi-layered media. Deep neural network was trained with the simulated data, and evaluated using a CG skin object. The result shows that our proposed method can estimate an appropriate ink layer layout which reproduce the appearance close to the target color and translucency.

scholarly journals Prediction of the layered ink layout for 3D printers considering a desired skin color and line spread function

2021 ◽  
Author(s):  
Kazuki Nagasawa ◽  
Junki Yoshii ◽  
Shoji Yamamoto ◽  
Wataru Arai ◽  
Satoshi Kaneko ◽  
...  
Keyword(s):  
Human Skin ◽  
Skin Color ◽  
Three Dimensional ◽  
Estimation Method ◽  
Three Dimensional Printing ◽  
Line Spread Function ◽  
Spread Function ◽  
3D Printers ◽  
Dimensional Printing ◽  
Line Spread

AbstractWe propose a layout estimation method for multi-layered ink using a measurement of the line spread function (LSF) and machine learning. The three-dimensional printing market for general consumers focuses on the reproduction of realistic appearance. In particular, for the reproduction of human skin, it is important to control translucency by adopting a multilayer structure. Traditionally, layer design has depended on the experience of designers. We, therefore, developed an efficient layout estimation to provide arbitrary skin color and translucency. In our method, we create multi-layered color patches of human skin and measure the LSF as a metric of translucency, and we employ a neural network trained with the data to estimate the layout. As an evaluation, we measured the LSF from the computer-graphics-created skin and fabricate skin using the estimated layout; evaluation with root-mean-square error showed that we can obtain color and translucency that are close to the target.

scholarly journals DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Adil Al-Azzawi ◽  
Anes Ouadou ◽  
Highsmith Max ◽  
Ye Duan ◽  
John J. Tanner ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Training Data ◽  
Human Intervention ◽  
Protein Particle ◽  
Particle Shapes

Abstract Background Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Results Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention. Conclusions Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

2020 ◽  
Vol 10 (5) ◽  
pp. 1657 ◽  
Author(s):  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Training Data ◽  
Open Pit ◽  
Percentage Error ◽  
Open Pit Mines ◽  
Determination Coefficient ◽  
Packet Data ◽  
Hidden Layer

This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively.

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