Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

AbstractThe purpose of this work was to develop an open-source deep learning-based algorithm for motion capture marker labelling that can be trained on measured or simulated marker trajectories. In the proposed algorithm, a deep neural network including recurrent layers is trained on measured or simulated marker trajectories. Labels are assigned to markers using the Hungarian algorithm and a predefined generic marker set is used to identify and correct mislabeled markers. The algorithm was first trained and tested on measured motion capture data. Then, the algorithm was trained on simulated trajectories and tested on data that included movements not contained in the simulated data set. The ability to improve accuracy using transfer learning to update the neural network weights based on labelled motion capture data was assessed. The effect of occluded and extraneous markers on labelling accuracy was also examined. Labelling accuracy was 99.6% when trained on measured data and 92.8% when trained on simulated trajectories, but could be improved to up to 98.8% through transfer learning. Missing or extraneous markers reduced labelling accuracy, but results were comparable to commercial software. The proposed labelling algorithm can be used to accurately label motion capture data in the presence of missing and extraneous markers and accuracy can be improved as data are collected, labelled, and added to the training set. The algorithm and user interface can reduce the time and manual effort required to label optical motion capture data, particularly for those with limited access to commercial software.

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Investigating Transfer Learning for Characterization and Performance Prediction in Unconventional Reservoirs

10.2118/204563-ms ◽

2021 ◽

Author(s):

Jodel Cornelio ◽

Syamil Mohd Razak ◽

Atefeh Jahandideh ◽

Behnam Jafarpour ◽

Young Cho ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Transfer Learning ◽

Method Development ◽

Simulated Data ◽

Production Performance ◽

Unconventional Reservoirs ◽

Eagle Ford ◽

Input Parameters

Abstract Transfer learning is a machine learning concept whereby the knowledge gained (e.g., a model developed) in one task can be transferred (applied) to solve a different but related task. In the context of unconventional reservoirs, the concept can be used to transfer a machine learning model that is learned from data in one field (or shale play) to another, thereby significantly reducing the data needs and efforts to build a new model from scratch. In this work, we study the feasibility of developing deep learning models that can capture and transfer common features in a rich dataset pertaining to a mature unconventional play to enable production prediction in a new unconventional play with limited available data. The focus in this work is on method development using simulated data that correspond to the Bakken and Eagle Ford Shale Plays as two different unconventional plays in the US. We use formation and completion parameter ranges that correspond to the Bakken play with their simulated production responses to explore different approaches for training neural network models that enable transfer learning to predict production responses of input parameters corresponding to the Eagle Ford play (previously unseen input parameters). We explore different schemes by accessing the internal components of the model to extrapolate and categorize salient features that are represented in the trained neural network. Ultimately, our goal is to use these new mechanisms to enable effective sharing and reuse of discovered features from one unconventional well to another. To extract salient trends from formation and completion input parameters and their corresponding simulated production responses, we use deep learning architectures that consist of convolutional encoder-decoder networks. The architecture is then trained with rich simulated data from one field to generate a robust mapping between the input and the output feature spaces. The "learned" parameters from this network can then be "transferred" to develop a different predictive model for another field that may lack sufficient historical data. The results show that using standard training approaches, a neural network model that is trained with sufficiently large data samples from Bakken could produce reliable prediction models for typical wells that may be found in that field. The same neural network, however, could not produce reliable predictions for a typical Eagle Ford well. Furthermore, we observe that a neural network trained with insufficient data samples from Eagle Ford produces a poor prediction model for typical wells that may be found in Eagle Ford. However, when extrapolated feature components of the Bakken neural network were integrated into the training process of the Eagle Ford neural network, the resulting predictions for typical Eagle Ford wells improved significantly. Moreover, we observe that the ability to transfer learning can improve when specialized training strategies are adopted to enable transfer learning. Using several numerical experiments, the paper presents and assesses various transfer learning strategies to predict the production performance of unconventional wells in a new area with limited information by integrating knowledge from more mature plays.

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Implementation of Object Recognition Algorithm to enhance Manufacturing and Maintenance Tasks on an Aircraft

10.32920/ryerson.14637387.v1 ◽

2021 ◽

Author(s):

Abhinav Sundar

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Recognition ◽

Object Detection ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Learning Algorithm ◽

Recognition Algorithm ◽

Recognition Algorithms ◽

Aerospace Assembly

The objective of this thesis was to evaluate the viability of implementation of an object recognition algorithm driven by deep learning for aerospace manufacturing, maintenance and assembly tasks. Comparison research has found that current computer vision methods such as, spatial mapping was limited to macro-object recognition because of its nodal wireframe analysis. An optical object recognition algorithm was trained to learn complex geometric and chromatic characteristics, therefore allowing for micro-object recognition, such as cables and other critical components. This thesis investigated the use of a convolutional neural network with object recognition algorithms. The viability of two categories of object recognition algorithms were analyzed: image prediction and object detection. Due to a viral epidemic, this thesis was limited in analytical consistency as resources were not readily available. The prediction-class algorithm was analyzed using a custom dataset comprised of 15 552 images of the MaxFlight V2002 Full Motion Simulator’s inverter system, and a model was created by transfer-learning that dataset onto the InceptionV3 convolutional neural network (CNN). The detection-class algorithm was analyzed using a custom dataset comprised of 100 images of two SUVs of different brand and style, and a model was created by transfer-learning that dataset onto the YOLOv3 deep learning architecture. The tests showed that the object recognition algorithms successfully identified the components with good accuracy, 99.97% mAP for prediction-class and 89.54% mAP. For detection-class. The accuracies and data collected with literature review found that object detection algorithms are accuracy, created for live -feed analysis and were suitable for the significant applications of AVI and aircraft assembly. In the future, a larger dataset needs to be complied to increase reliability and a custom convolutional neural network and deep learning algorithm needs to be developed specifically for aerospace assembly, maintenance and manufacturing applications.

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Deep learning the holographic black hole with charge

International Journal of Modern Physics D ◽

10.1142/s0218271819501530 ◽

2019 ◽

Vol 28 (12) ◽

pp. 1950153 ◽

Cited By ~ 2

Author(s):

Jing Tan ◽

Chong-Bin Chen

Keyword(s):

Neural Network ◽

Black Hole ◽

Deep Learning ◽

Deep Neural Network ◽

Learning Algorithm ◽

Equation Of Motion ◽

Batch Size ◽

Training Process ◽

Data Set ◽

Deep Learning Algorithm

We use the deep learning algorithm to learn the Reissner–Nordström (RN) black hole metric by building a deep neural network. Plenty of data are determined in boundary of AdS and we propagate them to the black hole horizon through AdS metric and equation of motion (e.o.m). We label these data according to the values near the horizon, and together with initial data they constitute a data set. Then we construct corresponding deep neural network and train it with the data set to obtain the Reissner–Nordström (RN) black hole metric. Finally, we discuss the effects of learning rate, batch-size and initialization on the training process.

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Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

IEEE Access ◽

10.1109/access.2021.3062748 ◽

2021 ◽

pp. 1-1

Author(s):

Allison L. Clouthier ◽

Gwyneth B. Ross ◽

Matthew P. Mavor ◽

Isabel Coll ◽

Alistair Boyle ◽

...

Keyword(s):

Deep Learning ◽

Open Source ◽

Motion Capture ◽

Learning Algorithm ◽

Deep Learning Algorithm ◽

Development And Validation

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Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021865201 ◽

2021 ◽

Vol 8 (6) ◽

pp. 1293

Author(s):

Mohammad Farid Naufal ◽

Selvia Ferdiana Kusuma

Keyword(s):

Neural Network ◽

Computer Vision ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Transfer Learning ◽

Cross Validation ◽

Learning Algorithm ◽

Computing Time ◽

Computation Time

Pada tahun 2021 pandemi Covid-19 masih menjadi masalah di dunia. Protokol kesehatan diperlukan untuk mencegah penyebaran Covid-19. Penggunaan masker wajah adalah salah satu protokol kesehatan yang umum digunakan. Pengecekan secara manual untuk mendeteksi wajah yang tidak menggunakan masker adalah pekerjaan yang lama dan melelahkan. Computer vision merupakan salah satu cabang ilmu komputer yang dapat digunakan untuk klasifikasi citra. Convolutional Neural Network (CNN) merupakan algoritma deep learning yang memiliki performa bagus dalam klasifikasi citra. Transfer learning merupakan metode terkini untuk mempercepat waktu training pada CNN dan untuk mendapatkan performa klasifikasi yang lebih baik. Penelitian ini melakukan klasifikasi citra wajah untuk membedakan orang menggunakan masker atau tidak dengan menggunakan CNN dan Transfer Learning. Arsitektur CNN yang digunakan dalam penelitian ini adalah MobileNetV2, VGG16, DenseNet201, dan Xception. Berdasarkan hasil uji coba menggunakan 5-cross validation, Xception memiliki akurasi terbaik yaitu 0.988 dengan waktu total komputasi training dan testing sebesar 18274 detik. MobileNetV2 memiliki waktu total komputasi tercepat yaitu 4081 detik dengan akurasi sebesar 0.981. AbstractIn 2021 the Covid-19 pandemic is still a problem in the world. Therefore, health protocols are needed to prevent the spread of Covid-19. The use of face masks is one of the commonly used health protocols. However, manually checking to detect faces that are not wearing masks is a long and tiring job. Computer vision is a branch of computer science that can be used for image classification. Convolutional Neural Network (CNN) is a deep learning algorithm that has good performance in image classification. Transfer learning is the latest method to speed up CNN training and get better classification performance. This study performs facial image classification to distinguish people using masks or not by using CNN and Transfer Learning. The CNN architecture used in this research is MobileNetV2, VGG16, DenseNet201, and Xception. Based on the results of trials using 5-cross validation, Xception has the best accuracy of 0.988 with a total computation time of training and testing of 18274 seconds. MobileNetV2 has the fastest total computing time of 4081 seconds with an accuracy of 0.981.

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Adaptive Transfer Learning Based on a Two-Stream Densely Connected Residual Shrinkage Network for Transformer Fault Diagnosis over Vibration Signals

Electronics ◽

10.3390/electronics10172130 ◽

2021 ◽

Vol 10 (17) ◽

pp. 2130

Author(s):

Xiaoyan Liu ◽

Yigang He ◽

Lei Wang

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Transfer Learning ◽

Learning Algorithm ◽

Operating Conditions ◽

Data Set ◽

Vibration Signals ◽

Time Frequency ◽

Diagnosis Method ◽

Transformer Fault

Vibration signal analysis is an efficient online transformer fault diagnosis method for improving the stability and safety of power systems. Operation in harsh interference environments and the lack of fault samples are the most challenging aspects of transformer fault diagnosis. High-precision performance is difficult to achieve when using conventional fault diagnosis methods. Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized S-transform) are proposed to convert vibration signals into different images, effectively expanding the samples and extracting effective features of signals. Second, a Two-stream Densely Connected Residual Shrinkage (TSDen2NetRS) network is presented to achieve a high accuracy fault diagnosis under different working conditions. Furthermore, the Residual Shrinkage layer (RS layer) is applied as a nonlinear transformation layer to the deep learning framework to remove unimportant features and enhance anti-interference performance. Lastly, an adaptive transfer learning algorithm that can automatically select the source data set by using the domain measurement method is proposed. This algorithm accelerates the training of the deep learning network and improves accuracy when the number of samples is small. Vibration experiments of transformers are conducted under different operating conditions, and their results show the effectiveness and robustness of the proposed method.

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Classification of Image using Convolutional Neural Networks

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37581 ◽

2021 ◽

Vol 9 (8) ◽

pp. 1212-1214

Author(s):

Dr. Abhay E Wagh

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Bench Mark ◽

Exact Order ◽

Gray Scale ◽

Data Set

Abstract: Now a day, with the rapid advancement in the digital contents identification, auto classification of the images is most challenging job in the computer field. Programmed comprehension and breaking down of pictures by framework is troublesome when contrasted with human visions. A Several research have been done to defeat issue in existing classification system,, yet the yield was limited distinctly to low even out picture natives. Nonetheless, those approach need with exact order of pictures. This system uses deep learning algorithm concept to achieve the desired results in this area like computer. Our framework presents Convolutional Neural Network (CNN), a machine learning algorithm is used for automatic classification the images. This system uses the Digit of MNIST data set as a bench mark for classification of gray-scale images. The gray-scale images are used for training which requires more computational power for classification of those images. Using CNN network the result is near about 98% accuracy. Our model accomplishes the high precision in grouping of images. Keywords: Convolutional Neural Network (CNN), deep learning, MINIST, Machine Learning.

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Implementation of Object Recognition Algorithm to enhance Manufacturing and Maintenance Tasks on an Aircraft

10.32920/ryerson.14637387 ◽

2021 ◽

Author(s):

Abhinav Sundar

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Recognition ◽

Object Detection ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Learning Algorithm ◽

Recognition Algorithm ◽

Recognition Algorithms ◽

Aerospace Assembly

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Face recognition based on full convolutional neural network based on transfer learning model

Computer Science and Information Systems ◽

10.2298/csis200922028f ◽

2021 ◽

pp. 28-28

Author(s):

Zhongkui Fan ◽

Ye-Peng Guan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Face Recognition ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Test Accuracy ◽

Great Success ◽

Data Set ◽

Face Features ◽

The Face

Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.

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Share Market Data Prediction Strategies using Deep Learning Algorithm

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191209093139 ◽

2019 ◽

Vol 13 ◽

Author(s):

A John. ◽

D. Praveen Dominic ◽

M. Adimoolam ◽

N. M. Balamurugan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Stock Market ◽

Predictive Analytics ◽

Learning Algorithm ◽

Market Price ◽

Stochastic Gradient ◽

Stochastic Gradient Descent ◽

Mining Machine ◽

Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

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