scholarly journals Study on transfer learning ability for classifying marsh vegetation with multi-sensor images using DeepLabV3+ and HRNet deep learning algorithms

2021 ◽  
Vol 103 ◽  
pp. 102531
Author(s):  
Man Liu ◽  
Bolin Fu ◽  
Donglin Fan ◽  
Pingping Zuo ◽  
Shuyu Xie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Algorithms ◽  
Learning Ability ◽  
Marsh Vegetation

scholarly journals Sequential Network with Residual Neural Network for Rotatory Machine Remaining Useful Life Prediction Using Deep Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hao Zhang ◽  
Qiang Zhang ◽  
Siyu Shao ◽  
Tianlin Niu ◽  
Xinyu Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Life Prediction ◽  
Feature Learning ◽  
High Accuracy ◽  
Remaining Useful Life ◽  
Learning Ability ◽  
Model Parameters ◽  
Deep Model ◽  
Useful Life

Deep learning has a strong feature learning ability, which has proved its effectiveness in fault prediction and remaining useful life prediction of rotatory machine. However, training a deep network from scratch requires a large amount of training data and is time-consuming. In the practical model training process, it is difficult for the deep model to converge when the parameter initialization is inappropriate, which results in poor prediction performance. In this paper, a novel deep learning framework is proposed to predict the remaining useful life of rotatory machine with high accuracy. Firstly, model parameters and feature learning ability of the pretrained model are transferred to the new network by means of transfer learning to achieve reasonable initialization. Then, the specific sensor signals are converted to RGB image as the specific task data to fine-tune the parameters of the high-level network structure. The features extracted from the pretrained network are the input into the Bidirectional Long Short-Term Memory to obtain the RUL prediction results. The ability of LSTM to model sequence signals and the dynamic learning ability of bidirectional propagation to time information contribute to accurate RUL prediction. Finally, the deep model proposed in this paper is tested on the sensor signal dataset of bearing and gearbox. The high accuracy prediction results show the superiority of the transfer learning-based sequential network in RUL prediction.

A Broad Transfer Learning Algorithm for Classification of Bearing Faults

2020 ◽  
Author(s):  
Guokai Liu ◽  
Liang Gao ◽  
Weiming Shen ◽  
Andrew Kusiak
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Manufacturing Industry ◽  
Domain Adaptation ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Training Data ◽  
Training Time ◽  
Bearing Faults

Abstract Condition monitoring and fault diagnosis are of great interest to the manufacturing industry. Deep learning algorithms have shown promising results in equipment prognostics and health management. However, their success has been hindered by excessive training time. In addition, deep learning algorithms face the domain adaptation dilemma encountered in dynamic application environments. The emerging concept of broad learning addresses the training time and the domain adaptation issue. In this paper, a broad transfer learning algorithm is proposed for the classification of bearing faults. Data of the same frequency is used to construct one- and two-dimensional training data sets to analyze performance of the broad transfer and deep learning algorithms. A broad learning algorithm contains two main layers, an augmented feature layer and a classification layer. The broad learning algorithm with a sparse auto-encoder is employed to extract features. The optimal solution of a redefined cost function with a limited sample size to ten per class in the target domain offers the classifier of broad learning domain adaptation capability. The effectiveness of the proposed algorithm has been demonstrated on a benchmark dataset. Computational experiments have demonstrated superior efficiency and accuracy of the proposed algorithm over the deep learning algorithms tested.

scholarly journals Review on anomalous gait behavior detection using machine learning algorithms

2020 ◽  
Vol 9 (5) ◽  
pp. 2090-2096
Author(s):  
Hana’ Abd Razak ◽  
M. Ahmed M. Saleh ◽  
Nooritawati Md Tahir
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Gait Recognition ◽  
Learning Algorithms ◽  
Anomalous Behavior ◽  
Machine Learning Algorithms ◽  
Behavior Detection ◽  
Detection And Recognition

A review on anomalous behavior in crime by other researchers is discussed in this study that focused specifically on the linkage between anomalous behaviors. Next, comprehensive reviews related to gait recognition in utilizing machine learning algorithms for detection and recognition of anomalous behavior is elaborated too. The review begins with the conventional approach of gait recognition that includes feature extraction and classification using PCA, OLS, ANN, and SVM. Further, the review focused on utilization of deep learning namely CNN for anomalous gait behavior detection and transfer learning using pre-trained CNNs such as AlexNet, VGG, and a few more. To the extent of our knowledge, very few studies investigated and explored crime related anomalous behavior based on their gaits, hence this will be the next study that we will explore.

scholarly journals Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Yan Du ◽  
Aiming Wang ◽  
Shuai Wang ◽  
Baomei He ◽  
Guoying Meng
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Classification Accuracy ◽  
Working Condition ◽  
Learning Ability ◽  
Residual Network ◽  
Time Frequency ◽  
Residual Learning

Fault diagnosis plays a very important role in ensuring the safe and reliable operations of machines. Currently, the deep learning-based fault diagnosis is attracting increasing attention. However, fault diagnosis under variable working conditions has been a significant challenge due to the domain discrepancy problem. This problem is also unavoidable in deep learning-based fault diagnosis methods. This paper contributes to the ongoing investigation by proposing a new approach for the fault diagnosis under variable working conditions based on STFT and transfer deep residual network (TDRN). The STFT was employed to convert vibration signal to time-frequency image as the input of the TDRN. To address the domain discrepancy problem, the TDRN was developed in this paper. Unlike traditional deep convolutional neural network (DCNN) methods, by combining with transfer learning, the TDRN can make a bridge between two different working conditions, thereby using the knowledge learned from a working condition to achieve a high classification accuracy in another working condition. Moreover, since the residual learning is introducing, the TDRN can overcome the problems of training difficulty and performance degradation existing in traditional DCNN methods, thus further improving the classification accuracy. Experiments are conducted on the popular CWRU bearing dataset to validate the effectiveness and superiority of the proposed approach. The results show that the developed TDRN outperforms those methods without transfer learning and/or residual learning in terms of the accuracy and feature learning ability for the fault diagnosis under variable working conditions.

Multi-Source Transfer Learning Based on Inductive Knowledge-Leveraged for Medical Datasets

2020 ◽  
Vol 10 (7) ◽  
pp. 1615-1620
Author(s):  
Jingxiang Zhang ◽  
Weijie Wu ◽  
Yanqing Shao
Keyword(s):  
Probability Distribution ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Multiple Source ◽  
Learning Ability ◽  
Source Domain ◽  
Domain Transfer ◽  
Domain Transfer Learning ◽  
Multiple Domains

Transfer learning changes the limitation of the same probability distribution among domains. There are many innovative ideas of those models which are fully used the information and the knowledge from different domains. Additional knowledge by transferring learning is beneficial to improve the learning ability in target tasks. However, most multiple source domain transfer learning algorithms are developed for the specified model. The existing transfer TGHRR algorithm is suitable to one source only. Given this problem, a new multiple source transfer learning algorithm integrated with the TGHRR and the inductive knowledge of multiple domains (MS-TGHRR in brevity) is proposed. Furthermore, MS-TGHRR algorithm has been evaluated by experiments on medical datasets for classification task. Extensive experiments demonstrate the classification accuracies trained by the newly designed MS-TGHRR algorithm over the existing multiple source transfer learning algorithms.

scholarly journals Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms

2021 ◽  
Author(s):  
Halgurd Maghdid ◽  
Aras T. Asaad ◽  
Kayhan Zrar Ghafoor Ghafoor ◽  
Ali S. Sadiq ◽  
Seyedali Mirjalili ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Algorithms ◽  
Ct Images ◽  
X Ray

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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