A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery

Neurophysiological studies have shown that the hippocampus, striatum, and prefrontal cortex play different roles in animal navigation, but it is still less clear how these structures work together. In this paper, we establish a navigation learning model based on the hippocampal–striatal circuit (NLM-HS), which provides a possible explanation for the navigation mechanism in the animal brain. The hippocampal model generates a cognitive map of the environment and performs goal-directed navigation by using a place cell sequence planning algorithm. The striatal model performs reward-related habitual navigation by using the classic temporal difference learning algorithm. Since the two models may produce inconsistent behavioral decisions, the prefrontal cortex model chooses the most appropriate strategies by using a strategy arbitration mechanism. The cognitive and learning mechanism of the NLM-HS works in two stages of exploration and navigation. First, the agent uses a hippocampal model to construct the cognitive map of the unknown environment. Then, the agent uses the strategy arbitration mechanism in the prefrontal cortex model to directly decide which strategy to choose. To test the validity of the NLM-HS, the classical Tolman detour experiment was reproduced. The results show that the NLM-HS not only makes agents show environmental cognition and navigation behavior similar to animals, but also makes behavioral decisions faster and achieves better adaptivity than hippocampal or striatal models alone.

Download Full-text

Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Sensors ◽

10.3390/s21030885 ◽

2021 ◽

Vol 21 (3) ◽

pp. 885 ◽

Cited By ~ 1

Author(s):

Zhongzheng Fu ◽

Xinrun He ◽

Enkai Wang ◽

Jun Huo ◽

Jian Huang ◽

...

Keyword(s):

Transfer Learning ◽

Activity Recognition ◽

Human Activity ◽

Sensor Node ◽

Learning Algorithm ◽

Joint Probability ◽

Human Activity Recognition ◽

Sensor Technology ◽

Measurement Unit ◽

Wearable Sensor

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