scholarly journals Temperature Prediction of Heating Furnace Based on Deep Transfer Learning

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4676
Author(s):  
Naiju Zhai ◽  
Xiaofeng Zhou
Keyword(s):  
Transfer Learning ◽  
Heating Temperature ◽  
Direct Method ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Heating Process ◽  
Heating Zone ◽  
Billet Temperature ◽  
Source Domain ◽  
Target Model

Heating temperature is very important in the process of billet production, and it directly affects the quality of billet. However, there is no direct method to measure billet temperature, so we need to accurately predict the temperature of each heating zone in the furnace in order to approximate the billet temperature. Due to the complexity of the heating process, it is difficult to accurately predict the temperature of each heating zone and each heating zone sensor datum to establish a model, which will increase the cost of calculation. To solve these two problems, a two-layer transfer learning framework based on a temporal convolution network (TL-TCN) is proposed for the first time, which transfers the knowledge learned from the source heating zone to the target heating zone. In the first layer, the TCN model is built for the source domain data, and the self-transfer learning method is used to optimize the TCN model to obtain the basic model, which improves the prediction accuracy of the source domain. In the second layer, we propose two frameworks: one is to generate the target model directly by using fine-tuning, and the other is to generate the target model by using generative adversarial networks (GAN) for domain adaption. Case studies demonstrated that the proposed TL-TCN framework achieves state-of-the-art prediction results on each dataset, and the prediction errors are significantly reduced. Consistent results applied to each dataset indicate that this framework is the most advanced method to solve the above problem under the condition of limited samples.

scholarly journals Domain Adversarial Transfer Learning for Generalized Tool Wear Prediction

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Peng (Edward) Wang ◽  
Matthew Russell
Keyword(s):  
Tool Wear ◽  
Transfer Learning ◽  
Network Performance ◽  
Generative Adversarial Networks ◽  
Smart Manufacturing ◽  
Successful Implementation ◽  
Target Domain ◽  
Source Domain ◽  
Network Training ◽  
And Task

Given its demonstrated ability in analyzing and revealing patterns underlying data, Deep Learning (DL) has been increasingly investigated to complement physics-based models in various aspects of smart manufacturing, such as machine condition monitoring and fault diagnosis, complex manufacturing process modeling, and quality inspection. However, successful implementation of DL techniques relies greatly on the amount, variety, and veracity of data for robust network training. Also, the distributions of data used for network training and application should be identical to avoid the internal covariance shift problem that reduces the network performance applicability. As a promising solution to address these challenges, Transfer Learning (TL) enables DL networks trained on a source domain and task to be applied to a separate target domain and task. This paper presents a domain adversarial TL approach, based upon the concepts of generative adversarial networks. In this method, the optimizer seeks to minimize the loss (i.e., regression or classification accuracy) across the labeled training examples from the source domain while maximizing the loss of the domain classifier across the source and target data sets (i.e., maximizing the similarity of source and target features). The developed domain adversarial TL method has been implemented on a 1-D CNN backbone network and evaluated for prediction of tool wear propagation, using NASA's milling dataset. Performance has been compared to other TL techniques, and the results indicate that domain adversarial TL can successfully allow DL models trained on certain scenarios to be applied to new target tasks.

scholarly journals Generating Synthetic Sidescan Sonar Snippets Using Transfer-Learning in Generative Adversarial Networks

2021 ◽  
Vol 9 (3) ◽  
pp. 239
Author(s):  
Yannik Steiniger ◽  
Dieter Kraus ◽  
Tobias Meisen
Keyword(s):  
Transfer Learning ◽  
Real Data ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Sidescan Sonar ◽  
Adversarial Networks ◽  
Image Dataset ◽  
Few Data ◽  
Simulated Images ◽  
Realistic Images

The training of a deep learning model requires a large amount of data. In case of sidescan sonar images, the number of snippets from objects of interest is limited. Generative adversarial networks (GAN) have shown to be able to generate photo-realistic images. Hence, we use a GAN to augment a baseline sidescan image dataset with synthetic snippets. Although the training of a GAN with few data samples is likely to cause mode collapse, a combination of pre-training using simple simulated images and fine-tuning with real data reduces this problem. However, for sonar data, we show that this approach of transfer-learning a GAN is sensitive to the pre-training step, meaning that the vanishing of the gradients of the GAN’s discriminator becomes a critical problem. Here, we demonstrate how to overcome this problem, and thus how to apply transfer-learning to GANs for generating synthetic sidescan snippets in a more robust way. Additionally, in order to further investigate the GAN’s ability to augment a sidescan image dataset, the generated images are analyzed in the image and the frequency domain. The work helps other researchers in the field of sonar image processing to augment their dataset with additional synthetic samples.

scholarly journals Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4850 ◽  
Author(s):  
Carlos S. Pereira ◽  
Raul Morais ◽  
Manuel J. C. S. Reis
Keyword(s):  
Transfer Learning ◽  
Climatic Conditions ◽  
Fine Tuning ◽  
Variety Identification ◽  
Test Accuracy ◽  
Accuracy Score ◽  
Learning Techniques ◽  
Four Corners ◽  
Integrated Software ◽  
Grape Varieties

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.

scholarly journals Design and Manufacturing of a Disposable, Cyclo-Olefin Copolymer, Microfluidic Device for a Biosensor †

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1178 ◽  
Author(s):  
Jorge Prada ◽  
Christina Cordes ◽  
Carsten Harms ◽  
Walter Lang
Keyword(s):  
Microfluidic Device ◽  
Heating Temperature ◽  
Low Cost ◽  
Reaction Chamber ◽  
Microfluidic System ◽  
Heating Process ◽  
Design And Manufacturing ◽  
On Chip ◽  
Working Parameters ◽  
Auto Fluorescence

This contribution outlines the design and manufacturing of a microfluidic device implemented as a biosensor for retrieval and detection of bacteria RNA. The device is fully made of Cyclo-Olefin Copolymer (COC), which features low auto-fluorescence, biocompatibility and manufacturability by hot-embossing. The RNA retrieval was carried on after bacteria heat-lysis by an on-chip micro-heater, whose function was characterized at different working parameters. Carbon resistive temperature sensors were tested, characterized and printed on the biochip sealing film to monitor the heating process. Off-chip and on-chip processed RNA were hybridized with capture probes on the reaction chamber surface and identification was achieved by detection of fluorescence tags. The application of the mentioned techniques and materials proved to allow the development of low-cost, disposable albeit multi-functional microfluidic system, performing heating, temperature sensing and chemical reaction processes in the same device. By proving its effectiveness, this device contributes a reference to show the integration potential of fully thermoplastic devices in biosensor systems.

scholarly journals New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Jae Kim ◽  
Jang Pyo Bae ◽  
Jun-Won Chung ◽  
Dong Kyun Park ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Test Data ◽  
State Of The Art ◽  
Early Stage ◽  
Statistical Significance ◽  
Recall Rate ◽  
Training Data ◽  
Fine Tuning ◽  
Accuracy Evaluation

AbstractWhile colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.

scholarly journals COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader

2021 ◽  
Vol 29 (1) ◽  
pp. 19-36
Author(s):  
Çağín Polat ◽  
Onur Karaman ◽  
Ceren Karaman ◽  
Güney Korkmaz ◽  
Mehmet Can Balcı ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Critical Time ◽  
Image Data ◽  
Mapping Method ◽  
Fine Tuning ◽  
Learning Stage ◽  
X Ray ◽  
Testing Dataset ◽  
X Ray Imaging ◽  
Chest X Ray

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

scholarly journals Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Tam ◽  
Mounir Boukadoum ◽  
Alexandre Campeau-Lecours ◽  
Benoit Gosselin
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Control Strategy ◽  
High Density ◽  
Fine Tuning ◽  
Mode Switching ◽  
Correct Prediction ◽  
Learning Approach ◽  
Predictive Values ◽  
Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

Combining generative adversarial networks and agricultural transfer learning for weeds identification

2021 ◽  
Vol 204 ◽  
pp. 79-89
Author(s):  
Borja Espejo-Garcia ◽  
Nikos Mylonas ◽  
Loukas Athanasakos ◽  
Eleanna Vali ◽  
Spyros Fountas
Keyword(s):  
Transfer Learning ◽  
Generative Adversarial Networks ◽  
Adversarial Networks
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