scholarly journals A Source Domain Extension Method for Inductive Transfer Learning Based on Flipping Output

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 95 ◽  
Author(s):  
Yasutake Koishi ◽  
Shuichi Ishida ◽  
Tatsuo Tabaru ◽  
Hiroyuki Miyamoto
Keyword(s):  
Data Collection ◽  
Transfer Learning ◽  
High Performance ◽  
Learning Method ◽  
Source Domain ◽  
Wide Range ◽  
Domain Extension ◽  
Inductive Transfer Learning ◽  
Better Than ◽  
Real World Problems

Transfer learning aims for high accuracy by applying knowledge of source domains for which data collection is easy in order to target domains where data collection is difficult, and has attracted attention in recent years because of its significant potential to enable the application of machine learning to a wide range of real-world problems. However, since the technique is user-dependent, with data prepared as a source domain which in turn becomes a knowledge source for transfer learning, it often involves the adoption of inappropriate data. In such cases, the accuracy may be reduced due to “negative transfer.” Thus, in this paper, we propose a novel transfer learning method that utilizes the flipping output technique to provide multiple labels in the source domain. The accuracy of the proposed method is statistically demonstrated to be significantly better than that of the conventional transfer learning method, and its effect size is as high as 0.9, showing high performance.

scholarly journals Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jun He ◽  
Xiang Li ◽  
Yong Chen ◽  
Danfeng Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Learning Method ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

scholarly journals Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 298 ◽  
Author(s):  
Linyi Liu ◽  
Yingying Dong ◽  
Wenjiang Huang ◽  
Xiaoping Du ◽  
Juhua Luo ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Transfer Learning ◽  
Kappa Coefficient ◽  
Learning Method ◽  
Training Process ◽  
Wheat Powdery Mildew ◽  
Field Samples ◽  
Monitoring Accuracy ◽  
Sample Data ◽  
Better Than

In order to monitor the prevalence of wheat powdery mildew, current methods require sufficient sample data to obtain results with higher accuracy and stable validation. However, it is difficult to collect data on wheat powdery mildew in some regions, and this limitation in sampling restricts the accuracy of monitoring regional prevalence of the disease. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region. By taking into account the representativeness of contributions of auxiliary samples to adjust the weight placed on auxiliary samples, an optimized TrAdaBoost algorithm, named OpTrAdaBoost, was generated to map regional wheat powdery mildew. The algorithm conducts this by: (1) producing uncertainty associated with each prediction based on the similarities, and calculating the representativeness contribution of all auxiliary samples by taking into account the overall uncertainty of the wheat powdery mildew map; (2) calculating the errors of the weak learners during the training process and using boosting to filter out the unreliable auxiliary samples by adjusting the weights of auxiliary samples; (3) combining all weak learners according to the weights of training instances to build a strong learner to classify disease severity. OpTrAdaBoost was tested using a dataset with 39 study area samples and 106 auxiliary samples. The overall monitoring accuracy was 82%, and the kappa coefficient was 0.72. Moreover, OpTrAdaBoost performed better than other algorithms that are commonly used to monitor wheat powdery mildew at the regional level. Experimental results demonstrated that OpTrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples.

scholarly journals Improving the Capabilities of Strategic Competence and Mathematical Disposition Using the Inquiri Model of Alberta Method

2018 ◽  
Vol 1 (3) ◽  
pp. 218
Author(s):  
Supiyanto Supiyanto ◽  
Heris Hendriana ◽  
Rippi Maya
Keyword(s):  
Data Collection ◽  
Nonparametric Tests ◽  
Usual Method ◽  
Learning Method ◽  
Experiment Data ◽  
Mathematical Learning ◽  
Strategic Competence ◽  
Inquiry Method ◽  
Written Test ◽  
Better Than

This study aims to improve the ability of strategic competence and mathematical disposition in mathematical learning using inquiry method of alberta model and correlation between the two. The subjects of this study are students of SMP class VII as many as two classes with a total of 70 students. The instruments used in data collection are written test of description for strategic competence and mathematical disposition and alberta model inquiry method. The research used a quasi experiment. Data on strategic competence and mathematical disposition were analyzed using Mann Whitney nonparametric tests. The results of this study are: (1) Improvement of students' strategic competency skills whose learning method using Inquiry Model Alberta is better than the usual method; (2) The mathematical disposition of students whose learning method using the Inquiry Model Alberta is better than the usual method; (3) There is a correlation between the ability of strategic competence with mathematical disposition of students whose learning method using Inquiry Model Alberta

scholarly journals Session to Session Transfer Learning Method Using Independent Component Analysis with Regularized Common Spatial Patterns for EEG-MI Signals

2019 ◽  
Vol 15 (1) ◽  
pp. 13-27
Author(s):  
Zaineb Alhakeem ◽  
Ramzy Ali
Keyword(s):  
Independent Component Analysis ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Component Analysis ◽  
Independent Component ◽  
Learning Method ◽  
Common Spatial Pattern ◽  
Brain Signals ◽  
The Brain ◽  
Better Than

Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.

scholarly journals Improving Communication Skills and Mathematical Disposition by Inquiry Model Alberta Method

PRISMA ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Supiyanto Supiyanto ◽  
Heris Hendriana ◽  
Rippi Maya
Keyword(s):  
Data Collection ◽  
Communication Skills ◽  
Research Method ◽  
Nonparametric Tests ◽  
Usual Method ◽  
Learning Method ◽  
Communication Ability ◽  
Inquiry Method ◽  
Written Test ◽  
Better Than

ABSTRACTThis study aims to improve communication skills and mathematical disposition in mathematical learning using inquiry method of alberta model and assosiation between the two. The The subjects of this study are students of SMP class VII as many as two classes with a total of 64 students. The instrument used in data collection is a written test for communication and mathematical disposition. The research method used in this research is using quasi experiment. Communication data and mathematical dispositions were analyzed using Mann Whitney nonparametric tests. The results obtained from this study were obtained: (1) Improvement of students' communication skills whose learning method using Inquiry Model Alberta is better than the usual method; (2) The mathematical disposition of students whose learning method using the Inquiry Model Alberta is better than the usual method; (3) There is a assosiation between communication ability with mathematical disposition of students whose learning method using Inquiry Model AlbertaKeywords: Communication, Mathematical Disposition, Alberta Model Inquiry Method. 

scholarly journals Censuses of agriculture and COVID-19: Global situation and lessons

2020 ◽  
Vol 36 (4) ◽  
pp. 861-865
Author(s):  
Jairo Castano
Keyword(s):  
Data Collection ◽  
Census Data ◽  
Working Environment ◽  
Physical Contact ◽  
Training Methods ◽  
Wide Range ◽  
The Status ◽  
Statistical Systems ◽  
The Impact ◽  
Better Than

A review of the status of censuses of agriculture in 150 countries and territories shows that the impact of COVID-19 has not discriminated between developed and developing countries. However, some countries have fared better than others when faced with the challenges posed by the pandemic. Earlier improvements in national statistical systems, a wide range of ICT solutions and the sourcing of census data from administrative registers have enabled these countries to significantly reduce their reliance on physical contact for tasks such as final preparation of field activities, training and data collection. The experience has confirmed the usefulness of these efforts and will likely further accelerate the pace of innovation, even though most of these countries expect that farmers’ non-response rates will be higher than in the past. At the same time, the COVID-19 crisis has been a lesson to other countries on the need to improve the working environment, diversify census data collection and training methods, and make use of administrative registers in future census rounds.

scholarly journals Heterogeneous Transfer Learning with Weighted Instance-Correspondence Data

2020 ◽  
Vol 34 (04) ◽  
pp. 4099-4106
Author(s):  
Yuwei He ◽  
Xiaoming Jin ◽  
Guiguang Ding ◽  
Yuchen Guo ◽  
Jungong Han ◽  
...  
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Learning Method ◽  
Target Domain ◽  
High Quality ◽  
Source Domain ◽  
Data Generator ◽  
Data Pair ◽  
Quality Variation

Instance-correspondence (IC) data are potent resources for heterogeneous transfer learning (HeTL) due to the capability of bridging the source and the target domains at the instance-level. To this end, people tend to use machine-generated IC data, because manually establishing IC data is expensive and primitive. However, existing IC data machine generators are not perfect and always produce the data that are not of high quality, thus hampering the performance of domain adaption. In this paper, instead of improving the IC data generator, which might not be an optimal way, we accept the fact that data quality variation does exist but find a better way to use the data. Specifically, we propose a novel heterogeneous transfer learning method named Transfer Learning with Weighted Correspondence (TLWC), which utilizes IC data to adapt the source domain to the target domain. Rather than treating IC data equally, TLWC can assign solid weights to each IC data pair depending on the quality of the data. We conduct extensive experiments on HeTL datasets and the state-of-the-art results verify the effectiveness of TLWC.

Transfer Learning Method for Rolling Bearing Fault Diagnosis under Different Working Conditions Based on CycleGAN

2021 ◽  
Author(s):  
Jiantong Zhao ◽  
Wentao Huang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Working Condition ◽  
Learning Method ◽  
Target Domain ◽  
Simulation Data ◽  
Source Domain ◽  
Bearing Fault Diagnosis ◽  
Cross Domain

Abstract In practical bearing fault diagnosis tasks, the available labelled data are often not from the equipment to be diagnosed and cannot cover all manner of working conditions. The adopted data-driven method is required to have a certain degree of cross-domain and cross-working condition transfer learning diagnosis ability. However, limited by the performance of existing transfer learning methods, the potential difference between the source domain and the target domain poses a challenge for the accuracy of transfer diagnosis. In this paper, a cross-working condition data supplement method based on the cycle generative adversarial network (CycleGAN) and a dynamics model is proposed, which can use limited available data to approximate the missing parts of existing data and be used for diagnosis of the target domain. First, we considered the limited experimental data as the target domain, the simulation data corresponding to the working condition as the source domain and used the working condition as the benchmark to constrain the data correspondence between the two datasets. We then used the CycleGAN model to learn the feature mapping from simulation to experiment. Second, based on the working condition of the data to be tested, the corresponding simulation data were input into the trained generator to obtain labeled data with experimental characteristics under the corresponding working conditions, and transferred the dataset as the source domain data to the data to be tested. In the test using self-made simulation and experimental datasets, combined with the transfer learning method based on the probability distribution adaptation, it was shown that the proposed method could effectively improve the diagnostic impact of the single transfer learning method in cross-domain and cross-working conditions when the working condition span was large.

Advances in imaging SIMS studies of stained and BrdU-labelled human metaphase chromosomes

1995 ◽  
Vol 53 ◽  
pp. 932-933
Author(s):  
R. Levi-Setti ◽  
J. M. Chabala ◽  
R. Espinosa ◽  
M. M. Le Beau
Keyword(s):  
High Performance ◽  
Secondary Ion Mass Spectrometry ◽  
Analytical Sensitivity ◽  
Metaphase Chromosomes ◽  
Banding Patterns ◽  
Sector Mass Spectrometer ◽  
Staining Methods ◽  
The University ◽  
Secondary Ion ◽  
Better Than

We have shown previously that isotope-labelled nucleotides in human metaphase chromosomes can be detected and mapped by imaging secondary ion mass spectrometry (SIMS), using the University of Chicago high resolution scanning ion microprobe (UC SIM). These early studies, conducted with BrdU- and 14C-thymidine-labelled chromosomes via detection of the Br and 28CN- (14C14N-> labelcarrying signals, provided some evidence for the condensation of the label into banding patterns along the chromatids (SIMS bands) reminiscent of the well known Q- and G-bands obtained by conventional staining methods for optical microscopy. The potential of this technique has been greatly enhanced by the recent upgrade of the UC SIM, now coupled to a high performance magnetic sector mass spectrometer in lieu of the previous RF quadrupole mass filter. The high transmission of the new spectrometer improves the SIMS analytical sensitivity of the microprobe better than a hundredfold, overcoming most of the previous imaging limitations resulting from low count statistics.

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