An assembly precision prediction method for customized mechanical products based on GAN-FTL

2021 ◽  
pp. 095440542110213
Author(s):  
Heng Li ◽  
Lemiao Qiu ◽  
Zili Wang ◽  
Shuyou Zhang ◽  
Jianrong Tan ◽  
...  
Keyword(s):  
Sample Size ◽  
Prediction Method ◽  
Quality Data ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Guide Rail ◽  
Target Domain ◽  
Source Domain ◽  
Mechanical Products ◽  
Assembly Precision

Reliable prediction of assembly precision is important for quality control of customized mechanical products characterized by individual customization, small batch size, and multiple varieties, resulting in insufficient samples for predicting assembly performance. A customized mechanical product assembly precision prediction method based on generative adversarial networks and feature transfer learning (GAN-FTL) is proposed in this paper. A GAN is built based on high quality data (source domain) to generate auxiliary samples with high fidelity and large sample size. A support vector machine is used to generate pseudo-tags for auxiliary samples. Features of source domain, target domain and auxiliary samples from different distributions are transferred to the same distribution to achieve multi-source fusion of measured and simulated data using FTL. Data after FTL is used to train the assembly precision prediction model. The elevator guide rail assembly is taken as the case study. T70/B and T90/B guide rail assembly are selected as the source and target domains, respectively. FTL was performed between the source and target domains, with different sample sets for comparison and compared with five different methods. Experimental results show that the prediction accuracy of the target domain is improved when the auxiliary sample size is 300, 400, and 500, and the accuracy improvement of the five methods are 15.37%, 12.17%, 9.68%, 6.29%, and 4.31%, respectively, which verified the effectiveness and usability of the proposed assembly precision prediction method based on GAN-FTL.

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 One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Networks

2019 ◽  
Author(s):  
Tao He ◽  
Yuan-Fang Li ◽  
Lianli Gao ◽  
Dongxiang Zhang ◽  
Jingkuan Song
Keyword(s):  
Adaptive Learning ◽  
Digital Data ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Source Domain ◽  
Learning Framework ◽  
Common Space ◽  
Adversarial Networks ◽  
Shared Space ◽  
Benchmark Datasets

With the recent explosive increase of digital data, image recognition and retrieval become a critical practical application. Hashing is an effective solution to this problem, due to its low storage requirement and high query speed. However, most of past works focus on hashing in a single (source) domain. Thus, the learned hash function may not adapt well in a new (target) domain that has a large distributional difference with the source domain. In this paper, we explore an end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images. Our method encodes two domains images into a semantic common space, followed by two independent generative adversarial networks arming at crosswise reconstructing two domains’ images, reducing domain disparity and improving alignment in the shared space. We evaluate our framework on four public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.

scholarly journals Application of Offshore Visibility Forecast Based on Temporal Convolutional Network and Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhenyu Lu ◽  
Cheng Zheng ◽  
Tingya Yang
Keyword(s):  
Transfer Learning ◽  
Forecast Error ◽  
Model Performance ◽  
Prediction Method ◽  
Small Data ◽  
Data Sets ◽  
Target Domain ◽  
Convolutional Network ◽  
Data Set ◽  
Source Domain

Visibility forecasting in offshore areas faces the problems of low observational data and complex weather. This paper proposes an intelligent prediction method of offshore visibility based on temporal convolutional network (TCN) and transfer learning to solve the problem. First, preprocess the visibility data sets of the source and target domains to improve the quality of the data. Then, build a model based on temporal convolutional network and transfer learning (TCN_TL) to learn the visibility data of the source domain. Finally, after transferring the knowledge learned from a large amount of data in the source domain, the model learns the small data set in the target domain. After completing the training, the model data of the European Mid-Range Weather Forecast Center (ECMWF) meteorological field were selected to test the model performance. The method proposed in this paper has achieved relatively good results in the visibility forecast of Qiongzhou Strait. Taking Haikou Station in the spring and winter of 2018 as an example, the forecast error is significantly lower than that before the transfer learning, and the forecast score is increased by 0.11 within the 0-1 km level and the 24 h forecast period. Compared with the CUACE forecast results, the forecast error of TCN_TL is smaller than that of the former, and the TS score is improved by 0.16. The results show that under the condition of small data sets, transfer learning improves the prediction performance of the model, and TCN_TL performs better than other deep learning methods and CUACE.

scholarly journals Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks

2021 ◽  
Author(s):  
Marlen Runz ◽  
Daniel Rusche ◽  
Martin R Weihrauch ◽  
Jürgen Hesser ◽  
Cleo-Aron Weis
Keyword(s):  
Image Acquisition ◽  
Similarity Measures ◽  
Staining Intensity ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Inverse Mapping ◽  
Generative Adversarial Network ◽  
Source Domain ◽  
Adversarial Network ◽  
Histological Images

Abstract Background: Histological images show huge variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. The variance can impede many image analyzes such as staining intensity evaluation or classification. Methods to reduce these variances are gathered under the term image normalization. Methods: We present the application of CylceGAN - a cycle consistent Generative Adversarial Network for color normalization in hematoxylin-eosin stained histological images using typical clinical data including variability of internal staining. The network consists of a generator network GB that learns to map an image X from a source domain A to a target domain B, i.e. GB : XA → XB. In addition, a discriminator network DB is trained to distinguish whether an image from domain B is an original or generated one. The same process is applied to another generator-discriminator pair (GA, DA), for the inverse mapping GA : XB → XA. Cycle consistency ensures that the generated image is close to the original image when being mapped backwards (GA(GB(XA)) ≈ XA and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma dataset for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. Results: We present qualitative results of the images generated by our network compared to the original color distributions. Our evaluation shows that by mapping images from a source domain to a target domain, the similarity to original images from the target domain improve up to 96%. We also achieve a high cycle consistency for the inverse mapping by obtaining similarity indices bigger than 0.9. Conclusions: CycleGANs have proven to efficiently normalize HE-stained images. The approach enables to compensate for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions. The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch. The dataset supporting the solutions is available at https://heidata.uni-heidelberg. de/privateurl.xhtml?token=12493b50-1538-4bdf-aca5-03352a1399a8.

scholarly journals Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks

2020 ◽  
Vol 10 (3) ◽  
pp. 1092 ◽  
Author(s):  
Bilel Benjdira ◽  
Adel Ammar ◽  
Anis Koubaa ◽  
Kais Ouni
Keyword(s):  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Aerial Imagery ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Source Domain ◽  
Adversarial Networks ◽  
Semantic Label ◽  
Global Accuracy ◽  
The Cost

Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.

scholarly journals TriGAN: image-to-image translation for multi-source domain adaptation

2021 ◽  
Vol 32 (1) ◽  
Author(s):  
Subhankar Roy ◽  
Aliaksandr Siarohin ◽  
Enver Sangineto ◽  
Nicu Sebe ◽  
Elisa Ricci
Keyword(s):  
Domain Adaptation ◽  
Image Features ◽  
Generative Adversarial Networks ◽  
Source Image ◽  
Multiple Sources ◽  
Target Domain ◽  
Invariant Representation ◽  
Source Domain ◽  
Practical Applications ◽  
Adversarial Networks

AbstractMost domain adaptation methods consider the problem of transferring knowledge to the target domain from a single-source dataset. However, in practical applications, we typically have access to multiple sources. In this paper we propose the first approach for multi-source domain adaptation (MSDA) based on generative adversarial networks. Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style (characterized in terms of low-level features variations) and the content. For this reason, we propose to project the source image features onto a space where only the dependence from the content is kept, and then re-project this invariant representation onto the pixel space using the target domain and style. In this way, new labeled images can be generated which are used to train a final target classifier. We test our approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.

The Concept of Animals in Balinese Proverbs

2017 ◽  
Vol 3 (1) ◽  
pp. 87
Author(s):  
I Wayan Budiarta ◽  
Ni Wayan Kasni
Keyword(s):  
Short Story ◽  
Cognitive Processes ◽  
Qualitative Data ◽  
Syntactic Structure ◽  
The Body ◽  
Biological Characteristic ◽  
Speech Community ◽  
Target Domain ◽  
Source Domain ◽  
Mapping Process

This research is aimed to figure out the syntactic structure of Balinese proverbs, the relation of meaning between the name of the animals and the meaning of the proverbs, and how the meanings are constructed in logical dimension. This research belongs to a qualitative as the data of this research are qualitative data which taken from a book entitled Basita Paribahasa written by Simpen (1993) and a book of Balinese short story written by Sewamara (1977). The analysis shows that the use of concept of animals in Balinese proverbs reveal similar characteristics, whether their form, their nature, and their condition. Moreover, the cognitive processes which happen in resulting the proverb is by conceptualizing the experience which is felt by the body, the nature, and the characteristic which owned by the target with the purpose of describing event or experience by the speech community of Balinese. Analogically, the similarity of characteristic in the form of shape of source domain can be proved visually, while the characteristic of the nature and the condition can be proved through bodily and empirical experiences. Ecolinguistics parameters are used to construct of Balinese proverbs which happen due to cross mapping process. It is caused by the presence of close characteristic or biological characteristic which is owned by the source domain and target domain, especially between Balinese with animal which then are verbally recorded and further patterned in ideological, biological, and sociological dimensions.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

Correlation alignment with attention mechanism for unsupervised domain adaptation

2021 ◽  
pp. 1-7
Author(s):  
Rong Chen ◽  
Chongguang Ren
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Transfer Process ◽  
Negative Transfer ◽  
Domain Adaptation ◽  
Attention Mechanism ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to solve the problems of lacking labels. Most existing works of domain adaptation mainly focus on aligning the feature distributions between the source and target domain. However, in the field of Natural Language Processing, some of the words in different domains convey different sentiment. Thus not all features of the source domain should be transferred, and it would cause negative transfer when aligning the untransferable features. To address this issue, we propose a Correlation Alignment with Attention mechanism for unsupervised Domain Adaptation (CAADA) model. In the model, an attention mechanism is introduced into the transfer process for domain adaptation, which can capture the positively transferable features in source and target domain. Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the domain discrepancy by aligning the second-order statistics of the positively transferable features extracted by the attention mechanism. Extensive experiments on the Amazon review dataset demonstrate the effectiveness of CAADA method.

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