Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function

Rodríguez-Quiñonez, Julio C.; Sergiyenko, Oleg; Hernández-Balbuena, Daniel; Rivas-López, Moisés; Flores-Fuentes, Wendy; Básaca-Preciado, Luís C.

doi:10.2478/s11772-014-0203-1

Cited by 39 publications

(12 citation statements)

References 28 publications

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“…In the literature different optimization algorithms have been proposed to train the ANN. These range from the Levenberg-Marquardt methods [39] to the Widrow-Hoff rule [40]. RNNs are typically trained by using a variant of the standard backpropagation algorithm, which is named backpropagation through time (BPTT).…”

Section: B Recurrent Neural Networkmentioning

confidence: 99%

Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

Martínez-García

Zhang

Suzuki

et al. 2021

IEEE Trans. Ind. Inf.

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The aim of this paper is to develop a methodology for measuring the degree of unpredictability in dynamical systems with memory, i.e., systems with responses dependent on a history of past states. The proposed model is generic, and can be employed in a variety of settings, although its applicability here is examined in the particular context of an industrial environment: gas turbine engines. The given approach consists in approximating the probability distribution of the outputs of a system with a deep recurrent neural network; such networks are capable of exploiting the memory in the system for enhanced forecasting capability. Once the probability distribution is retrieved, the entropy or missing information about the underlying process is computed, which is interpreted as the uncertainty with respect to the system's behaviour. Hence the model identifies how far the system dynamics are from its typical response, in order to evaluate the system reliability and to predict system faults and/or normal accidents. The validity of the model is verified with sensor data recorded from commissioning gas turbines, belonging to normal and faulty conditions.

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Section: B Recurrent Neural Networkmentioning

confidence: 99%

Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

Martínez-García

Zhang

Suzuki

et al. 2021

IEEE Trans. Ind. Inf.

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“…The consequence of removing the unwanted coefficients is the elimination of noise; however, the held coefficients signify the wavelet coefficients of the signal after removal of noise. By considering the inverse wavelet transform, denoising is achieved [27,28].…”

Section: Preprocessingmentioning

confidence: 99%

“…This tool is also used to know the cause of unconsciousness in comatose patients [3]. Spectral information of EEG signal can be obtained by focusing on the frequency bands, namely, Alpha waves (8)(9)(10)(11)(12), Beta waves (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), Gamma waves (above 30 Hz), Theta waves (4)(5)(6)(7)(8) and Delta waves (1)(2)(3)(4). These enable easy understanding for an accurate diagnosis of the classifications as mentioned above, signifing a different mental state of a patient.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Prediction Model for Automated Brain Abnormalities Detection

Anand

Jaiswal

Ghosh

2018

CSENG

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Background: The rapid improvement in technology enables an Electroencephalogram (EEG) to detect a diverse range of brain disorders easily. The design of sophisticated signal processing methods for an efficient analysis of the EEG signals is exceptionally essential. Raw EEG signal is contaminated by noise and artefacts that modify the spectral-spatial and temporal information of the signal and renders inaccurate clinical interpretation. Denoising of the signal is the first step to refine the signal quality and identify patient's mental state from the signal although it is not an easy task because of high dimensionality and complexity of EEG signal. The present study highlights three conditions of the brain namely stroke, brain death, and a healthy state. The primary concern is to detect the most abnormal conditions of the brain, i.e., an EEG with a critical stage.Method: This paper introduces a neoteric technique for the analysis of EEG signals of the three conditions using filters such as Fuzzy filter and wavelet orthogonal filter to obtain highly accurate resultant signals. Further, the resultant filter is trained in Neural Network for predicting the brain abnormalities. The proposed system is found to be efficient in denoising the EEG waves. Results:The result shows that the classification accuracy of multiclass EEG dataset achieved and the performance of ANN is high and it was found to be the best validation performance of ANN which is 0.2303. Conclusion:This paper comprehensively describes the denoising of the EEG signals that will provide accuracy in the diagnosis of the EEG to detect brain disorders. The Fuzzy filter pre-processes the signals by considering the noisy signal by an ideal value in such a way that the desired metric (the filtered output) is reduced. The orthogonal wavelet filter produces a single scaling function and wavelet function. The EEG features are extracted from multiple-level decompositions of EEGs by DWT. Finally, the features are classified using Back propagation artificial neural network that categorizes the EEGs to make the diagnosis easier for the brain abnormalities.

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“…[1][2][3][4][5] In these applications, the accuracy is essential for the tasks that must be developed; therefore, there are methods to improve the accuracy of the 3-D measurements. 6,7 In autonomous navigation systems, the aim is to move an autonomous object through a 3-D environment with classic interpolation methods (inertial sensors) or external references (computer vision, ultrasonic sensors, GPS, CCD, and CMOS). [8][9][10][11] Recently, research in autonomous navigation applications has focused on stereo vision, which is used for 3-D mapping, detection, and location of objects.…”

Section: Introductionmentioning

confidence: 99%

Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Ramírez-Hernández

Rodríguez-Quiñonez

Castro-Toscano

et al. 2020

International Journal of Advanced Robotic Systems

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Computer vision systems have demonstrated to be useful in applications of autonomous navigation, especially with the use of stereo vision systems for the three-dimensional mapping of the environment. This article presents a novel camera calibration method to improve the accuracy of stereo vision systems for three-dimensional point localization. The proposed camera calibration method uses the least square method to model the error caused by the image digitalization and the lens distortion. To obtain particular three-dimensional point coordinates, the stereo vision systems use the information of two images taken by two different cameras. Then, the system locates the two-dimensional pixel coordinates of the three-dimensional point in both images and coverts them into angles. With the obtained angles, the system finds the three-dimensional point coordinates through a triangulation process. The proposed camera calibration method is applied in the stereo vision systems, and a comparative analysis between the real and calibrated three-dimensional data points is performed to validate the improvements. Moreover, the developed method is compared with three classical calibration methods to analyze their advantages in terms of accuracy with respect to tested methods.

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Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function

Cited by 39 publications

References 28 publications

Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

A Machine Learning Prediction Model for Automated Brain Abnormalities Detection

Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

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