scholarly journals System Error Compensation Methodology Based on a Neural Network for a Micromachined Inertial Measurement Unit

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 175 ◽  
Author(s):  
Shi Liu ◽  
Rong Zhu
Keyword(s):  
Neural Network ◽  
Error Compensation ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
System Error ◽  
Inertial Measurement

scholarly journals A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Changhui Jiang ◽  
Yuwei Chen ◽  
Shuai Chen ◽  
Yuming Bo ◽  
Wei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inertial Measurement Unit ◽  
Short Term Memory ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Short Term ◽  
Inertial Measurement ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

Currently, positioning, navigation, and timing information is becoming more and more vital for both civil and military applications. Integration of the global navigation satellite system and /inertial navigation system is the most popular solution for various carriers or vehicle positioning. As is well-known, the global navigation satellite system positioning accuracy will degrade in signal challenging environments. Under this condition, the integration system will fade to a standalone inertial navigation system outputting navigation solutions. However, without outer aiding, positioning errors of the inertial navigation system diverge quickly due to the noise contained in the raw data of the inertial measurement unit. In particular, the micromechanics system inertial measurement unit experiences more complex errors due to the manufacturing technology. To improve the navigation accuracy of inertial navigation systems, one effective approach is to model the raw signal noise and suppress it. Commonly, an inertial measurement unit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play an important role in the accuracy of the inertial navigation system’s navigation solutions. Motivated by this problem, in this paper, an advanced deep recurrent neural network was employed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset from a micromechanics system inertial measurement unit was employed in the experiments. The results showed that, compared to the two-layer long short term memory, the three-axis attitude errors of the mixed long short term memory–gated recurrent unit decreased by 7.8%, 20.0%, and 5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed 15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the performance of designed method, specifically, the mixed deep recurrent neural networks outperformed than the two-layer gated recurrent unit and the two-layer long short term memory recurrent neural networks.

scholarly journals Simulation of Breathing Patterns and Classification of Sensor Data for the early detection of impending Sudden Infant Death

2019 ◽  
Vol 5 (1) ◽  
pp. 401-403
Author(s):  
Michael Munz ◽  
Nicolas Wolf
Keyword(s):  
Neural Network ◽  
Infant Death ◽  
Inertial Measurement Unit ◽  
Sudden Infant Death ◽  
Sensor Data ◽  
Measurement Unit ◽  
Sudden Infant ◽  
Breathing Patterns ◽  
Inertial Measurement

AbstractIn this work, a methodology for the classification of breathing patterns in order to prevent sudden infant death (SID) incidents is presented. The basic idea is to classify breathing patterns which might lead to SID prior to an incident. A thorax sensor is proposed, which is able to simulate breathing patterns given by certain parameters. A sensor combination of conductive strain fabric and an inertial measurement unit is used for data acquisition. The data is then classified using a neural network.

MEMS Gyroscope Null Drift and Compensation Based on Neural Network

2011 ◽  
Vol 255-260 ◽  
pp. 2077-2081 ◽  
Author(s):  
Jaw Kuen Shiau ◽  
Der Ming Ma ◽  
Chen Xuan Huang ◽  
Ming Yu Chang
Keyword(s):  
Neural Network ◽  
Inertial Measurement Unit ◽  
Temperature Compensation ◽  
Micro Electro Mechanical System ◽  
Measurement Unit ◽  
Temperature Calibration ◽  
Inertial Measurement ◽  
Mems Gyroscope ◽  
Measurement Units ◽  
Effects Of Temperature

This study investigates the effects of temperature on micro-electro mechanical system (MEMS) gyroscope null drift and methods and efficiency of temperature compensation. First, this study uses in-house-designed inertial measurement units (IMUs) to perform temperature effect testing. The inertial measurement unit is placed into the temperature control chamber. Then, the temperature is gradually increased from 25 °C to 80 °C at approximately 0.8 degrees per minute. After that, the temperature is decreased to -40 °C and then returning to 25 °C. During these temperature variations, the temperature and static gyroscope output observes the gyroscope null drift phenomenon. The results clearly demonstrate the effects of temperature on gyroscope null voltage. A temperature calibration mechanism is established by using a neural network model. With the temperature calibration, the attitude computation problem due to gyro drifts can be improved significantly.

scholarly journals A Deep Neural Network-Based Fault Detection Scheme for Aircraft IMU Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yiming Zhang ◽  
Hang Zhao ◽  
Jinyi Ma ◽  
Yunmei Zhao ◽  
Yiqun Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Inertial Measurement Unit ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Measurement Unit ◽  
Short Term ◽  
Detection Scheme ◽  
Inertial Measurement ◽  
Long Short Term Memory

A new fault detection scheme for aircraft Inertial Measurement Unit (IMU) sensors is developed in this paper. This scheme adopts a deep neural network with a CNN-LSTM-fusion architecture (CNN: convolution neural network; LSTM: long short-term memory). The fault detection network (FDN) developed in this paper is irrelative to aircraft model or flight condition. Flight data is reformed into a 2D format for FDN input and is mapped via the net to fault cases directly. We simulate different aircrafts with various flight conditions and separate them into training and testing sets. Part of the aircrafts and flight conditions appears only in the testing set to validate robustness and scalability of the FDN. Different architectures of FDN are studied, and an optimized architecture is obtained via ablation studies. An average detecting accuracy of 94.5% on 20 different cases is achieved.

Fusing and refining convolutional neural network models for assembly action recognition in smart manufacturing

2020 ◽  
pp. 095440622093154
Author(s):  
Md. Al-Amin ◽  
Ruwen Qin ◽  
Wenjin Tao ◽  
David Doell ◽  
Ravon Lingard ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Action Recognition ◽  
Inertial Measurement Unit ◽  
Network Models ◽  
Measurement Unit ◽  
Smart Manufacturing ◽  
Neural Network Models ◽  
Inertial Measurement

Assembly carries paramount importance in manufacturing. Being able to support workers in real time to maximize their positive contributions to assembly is a tremendous interest of manufacturers. Human action recognition has been a way to automatically analyze and understand worker actions to support real-time assistance for workers and facilitate worker–machine collaboration. Assembly actions are distinct from activities that have been well studied in the action recognition literature. Actions taken by assembly workers are intricate, variable, and may involve very fine motions. Therefore, recognizing assembly actions remains a challenging task. This paper proposes to simply use only two wearable devices that respectively capture the inertial measurement unit data of each hand of workers. Then, two convolutional neural network models with an identical architecture are independently trained using the two sources of inertial measurement unit data to respectively recognize the right-hand and the left-hand actions of an assembly worker. Classification results of the two convolutional neural network models are fused to yield a final action recognition result because the two hands often collaborate in assembling operations. Transfer learning is implemented to adapt the action recognition models to subjects whose data have not been included in dataset for training the models. One operation in assembling a Bukito three-dimensional printer, which is composed of seven actions, is used to demonstrate the implementation and assessment of the proposed method. Results from the study have demonstrated that the proposed approach effectively improves the prediction accuracy at both the action level and the subject level. Work of the paper builds a foundation for building advanced action recognition systems such as multimodal sensor-based action recognition.

Optical Computer Mouse Referenced Calibration of an Inertial Measurement Unit for Use in Unmanned Underwater Vehicles

2014 ◽  
Vol 664 ◽  
pp. 274-278
Author(s):  
Serhat Ikizoğlu ◽  
Yaver Kamer
Keyword(s):  
Neural Network ◽  
Inertial Measurement Unit ◽  
Underwater Vehicles ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Unmanned Underwater Vehicles ◽  
Computer Mouse ◽  
Dynamic Errors ◽  
Optical Computer ◽  
Nonlinear Estimators

In this study an inertial measurement unit (IMU) used in unmanned underwater vehicles has been taken into consideration.The main objective of this study is to improvethe measurements obtained from an IMU used in the position detection by minimizing the effect of its static and dynamic errors on the output. To enhance the IMU data optical computer mouse (OCM) is proposed as calibrator. The data received from the OCM is used to train an artificial neural network (ANN) which would improve the IMU outputs by trying to estimate the reference data from the actual sensor outputs. The ANN performance is compared with that of classic low pass filtering methods to provide a relative performance criterion. The ANN trained with OCM data has given satisfactory results. During the training of ANNs the effects of several parameters such as neural network architecture, activation functions, training algorithm, layer and cell number have been investigated. Thus, the results, findings and insights obtained in this study can be applied in research areas where this kind of nonlinear estimators are used.

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