scholarly journals State Optimal Estimation for Nonstandard Multi-Sensor Information Fusion System

10.5772/9955 ◽  
2010 ◽  
Author(s):  
Jiong Qi
Keyword(s):  
Information Fusion ◽  
Optimal Estimation ◽  
Fusion System ◽  
Sensor Information

Distributed Multi-Source Spatial Data Fusion Model Construction and Performance Evaluation

2011 ◽  
Vol 460-461 ◽  
pp. 404-408
Author(s):  
Yue Shun He ◽  
Jun Zhang ◽  
Jie He
Keyword(s):  
Performance Evaluation ◽  
Data Fusion ◽  
Information Fusion ◽  
Spatial Data ◽  
Evaluation Model ◽  
Distributed Model ◽  
Fusion System ◽  
Fusion Model ◽  
Test Environment ◽  
Sensor Information

This paper mainly analyzed the principle of multi-source spatial data fusion, and expounded the multi-source spatial data fusion of the distributed model structure. The paper considers a distributed multi-sensor information fusion system factors, A performance evaluation model was established which was suitable for distributed multi-sensor information fusion system, It can estimate the system's precision, track quality, filtering quality, and the relevant between Navigation Paths and so on. Meanwhile, we had a lot of experiments by the datum which generated by the simulation test environment, experiments show that this evaluation model is valid.

scholarly journals Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1440
Author(s):  
Jianping Wu ◽  
Bin Jiang ◽  
Hongtian Chen ◽  
Jianwei Liu
Keyword(s):  
Fault Detection ◽  
Information Fusion ◽  
High Speed ◽  
Euclidean Distance ◽  
Manifold Regularization ◽  
Fusion System ◽  
Electrical Drive ◽  
High Speed Trains ◽  
Sensor Information ◽  
Drive Systems

Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE.

scholarly journals AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1345 ◽  
Author(s):  
Carson Leung ◽  
Peter Braun ◽  
Alfredo Cuzzocrea
Keyword(s):  
Supervised Learning ◽  
Information Fusion ◽  
Smart Cities ◽  
Satellite System ◽  
Sensor Data ◽  
Fusion System ◽  
Sensor Information ◽  
Global Navigation Satellite ◽  
Effective Integration ◽  
The Internet Of Things

In recent years, artificial intelligence (AI) and its subarea of deep learning have drawn the attention of many researchers. At the same time, advances in technologies enable the generation or collection of large amounts of valuable data (e.g., sensor data) from various sources in different applications, such as those for the Internet of Things (IoT), which in turn aims towards the development of smart cities. With the availability of sensor data from various sources, sensor information fusion is in demand for effective integration of big data. In this article, we present an AI-based sensor-information fusion system for supporting deep supervised learning of transportation data generated and collected from various types of sensors, including remote sensed imagery for the geographic information system (GIS), accelerometers, as well as sensors for the global navigation satellite system (GNSS) and global positioning system (GPS). The discovered knowledge and information returned from our system provides analysts with a clearer understanding of trajectories or mobility of citizens, which in turn helps to develop better transportation models to achieve the ultimate goal of smarter cities. Evaluation results show the effectiveness and practicality of our AI-based sensor information fusion system for supporting deep supervised learning of big transportation data.

scholarly journals Design and Implementation of Track and Field Training Information Collection and Feedback System Based on Multi-sensor Information Fusion

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ling Li ◽  
Chengliang Li
Keyword(s):  
Information Fusion ◽  
Feedback System ◽  
Track And Field ◽  
Information Collection ◽  
Control Group ◽  
Average Performance ◽  
Field Training ◽  
Sensor Information ◽  
Experimental Group ◽  
Field Sports

AbstractTrack and field sports are known as the "mother of sports". Whether in the field of athletics, fitness, or education, modern track and field sports have developed rapidly. The field of athletics has reached the point where it challenges the limits of humans. The development of China is inseparable from the support of science and technology, and it is inseparable from human scientific research on track and field sports. In order to improve the scientific level of track and field training methods and develop our country's sports industry, this paper designs a track and field training information collection and feedback system based on multi-sensor information fusion. In the method part, this article briefly introduces the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, using weight coefficient fusion method, D-S evidence theory algorithm and Kalman filter algorithm. This paper designs an information collection and feedback system based on multi-sensor information fusion, and conducts demand analysis, comparative analysis, and data record analysis on this system. By designing the experimental group and the control group, it can be seen that the average performance of the two groups of athletes in the 50-meter run in 8 weeks has improved, and the data of the experimental group and the control group show significant differences. After the experiment, the average performance of the male athletes in the control group increased from around 8.32 to around 8.12, an increase of 4.7%. The performance of male athletes in the experimental group increased from 8.37 to 7.92, an increase of 5.6%. It can also be known that before the experiment, the average performance of the athletes in the selected control group was due to the experimental group, but after 8 weeks of experiment, the increase in the experimental group was higher than that of the control group. This shows that the data collection and feedback system using multi-sensor information fusion can be more accurately and differentiatedly applied to track and field training, and can find problems in athletes, so as to prescribe the right medicine.

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