Counterfactual inference to predict causal knowledge graph for relational transfer learning by assimilating expert knowledge --Relational feature transfer learning algorithm

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

Download Full-text

Event detection of different English data sources based on transfer learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189798 ◽

2021 ◽

pp. 1-11

Author(s):

Yanan Huang ◽

Yuji Miao ◽

Zhenjing Da

Keyword(s):

Transfer Learning ◽

Event Detection ◽

Visual Analysis ◽

Learning Algorithm ◽

Data Sources ◽

Data Set ◽

Data Source ◽

Single Data Source ◽

The Difference ◽

Single Data

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.

Download Full-text

Transfer Learning algorithm in image analysis with Augmented Reality headset for Industry 4.0 technology

2020 International Conference Mechatronic Systems and Materials (MSM) ◽

10.1109/msm49833.2020.9201739 ◽

2020 ◽

Author(s):

Mateusz Kozek

Keyword(s):

Image Analysis ◽

Augmented Reality ◽

Transfer Learning ◽

Industry 4.0 ◽

Learning Algorithm

Download Full-text

Effective Transfer Learning Algorithm in Spiking Neural Networks

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2021.3079097 ◽

2021 ◽

pp. 1-13

Author(s):

Qiugang Zhan ◽

Guisong Liu ◽

Xiurui Xie ◽

Guolin Sun ◽

Huajin Tang

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Learning Algorithm ◽

Spiking Neural Networks ◽

Effective Transfer

Download Full-text

Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

Applied Sciences ◽

10.3390/app8122416 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2416 ◽

Cited By ~ 34

Author(s):

Ansi Zhang ◽

Honglei Wang ◽

Shaobo Li ◽

Yuxin Cui ◽

Zhonghao Liu ◽

...

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Recurrent Neural Networks ◽

Prediction Models ◽

Learning Algorithm ◽

Remaining Useful Life ◽

Operating Conditions ◽

Training Data ◽

Good Prediction ◽

Useful Life

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

Download Full-text