Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

Shen, Chong; Liu, Xiaochen; Cao, Huiliang; Zhou, Yuchen; Liu, Jun; Tang, Jun; Guo, Xiaoting; Huang, Haoqian; Chen, Xuemei

doi:10.3390/app9081708

Cited by 54 publications

(25 citation statements)

References 28 publications

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“…In recent years, methods based on signal processing [3] or deep learning [4,5] are widely used to solve practical engineering problems [6][7][8][9]. Moreover, in the field of compound fault diagnosis of rotating machinery, with the continuous exploration of many researchers, novel intelligent fault diagnosis methods emerge in an endless flow.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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Section: Introductionmentioning

confidence: 99%

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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“…With the rapid development of industry, materials, energy, printing and agriculture, a large amount of sewage and other pollutants is directly discharged without being purified or purification treatment . This fails to meet the discharge standards and causes severe damages to the water environment.…”

Section: Introductionmentioning

confidence: 99%

Research Progress in the Field of Adsorption and Catalytic Degradation of Sewage by Hydrotalcite‐Derived Materials

Pan

Tian

et al. 2019

The Chemical Record

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With the rapid development of industry and agriculture and the greatly improved living conditions, the resultant gradually deteriorated environments threaten the human beings. Refractory or even toxic pollutants, which are from different industries such as printing and dyeing, pesticides, chemicals, petrochemicals, plastics and rubber, seriously threat the ecosystems and human health. Having the advantages of flexible composition, unique structure, high stability, memory effect, easy preparation and low cost, hydrotalcite compounds have a great potential in sewage degradation and environmental protection. This study focuses on the adsorption and catalytic properties (such as photocatalysis, electrocatalysis and photoelectrocatalysis) of hydrotalcite-derived materials for treating organic, inorganic and heavy metal ion sewage. The types of adsorption and catalysis, and the effects of various influencing factors on the degradation efficiency were discussed as well.Wiley Online Library 355 Figure 13. The photoelectrocatalytic mechanism of organic pollutant MB over the ZNI-1-MMO-1.5 V photoelectrode.

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“…It is a part of a current research trend. Temperature compensation can improve the accuracy of an accelerometer and its output signal [12,13]. For this purpose, we develop a fusion algorithm that combines EMD with wavelet thresholding and temperature compensation.…”

Section: Introductionmentioning

confidence: 99%

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

Shen

Cao

et al. 2019

Shock and Vibration

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In recent years, High-G MEMS accelerometers have been widely used in aviation, medicine, and other fields. So it is extremely important to improve the accuracy and performance of High-G MEMS accelerometers. For this purpose, we propose a fusion algorithm that combines EMD, wavelet thresholding, and temperature compensation to process measurement data from a High-G MEMS accelerometer. In the fusion algorithm, the original accelerometer signal is first decomposed by EMD to obtain the intrinsic mode function (IMF). Then, sample entropy (SE) is used to divide the IMF components into three segments. The noise segment is directly omitted, wavelet thresholding is performed on the mixing segment, and a GA-BP performs temperature compensation on the drift segment. Finally, signal reconstruction is implemented. Later, a comparative analysis is carried out on the results from four models: EMD, wavelet thresholding, EMD + wavelet thresholding, and EMD + wavelet thresholding + temperature compensation. The experimental data show that the acceleration random walk change from 1712.66 g/h/Hz0.5 to 79.15 g/h/Hz0.5 and the zero-deviation stability change from 49275 g/h to 774.7 g/h. This indicates that the fusion algorithm (EMD + wavelet thresholding + temperature compensation) not only effectively suppresses the noise of high-frequency components but also compensates for temperature drift in the accelerometer.

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Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

Cited by 54 publications

References 28 publications

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Research Progress in the Field of Adsorption and Catalytic Degradation of Sewage by Hydrotalcite‐Derived Materials

Fusion Algorithm‐Based Temperature Compensation Method for High‐G MEMS Accelerometer

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