scholarly journals Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection

2021 ◽  
Vol 11 (24) ◽  
pp. 11905
Author(s):  
Yunhee Lee ◽  
Manbok Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Hough Transform ◽  
Line Detection ◽  
Angle Error ◽  
Parallel Parking ◽  
Parking System ◽  
Automatic Parking ◽  
Heading Angle

This paper introduces an automatic parking method using an around view monitoring system. In this method, parking lines are extracted from the camera images, and a route to a targeted parking slot is created. The vehicle then tracks this route to park. The proposed method extracts lines from images using a line filter and a Hough transform, and it uses a convolutional neural network to robustly extract parking lines from the environment. In addition, a parking path consisting of curved and straight sections is created and used to control the vehicle. Perpendicular, angle, and parallel parking paths can be created; however, parking control is applied according to the shape of each parking slot. The results of our experiments confirm that the proposed method has an average offset of 10.3 cm and an average heading angle error of 0.94°.

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

scholarly journals A High-Precision Magnetic-Assisted Heading Angle Calculation Method Based on a 1D Convolutional Neural Network (CNN) in a Complicated Magnetic Environment

Micromachines ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 642
Author(s):  
Guanghui Hu ◽  
Hong Wan ◽  
Xinxin Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Precision ◽  
Geomagnetic Field ◽  
Field Data ◽  
Time Window ◽  
Magnetic Data ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Heading Angle

Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading to reduced positioning accuracy of magnetic-assisted navigation systems. Therefore, there is an urgent need for methods which screen out undisturbed geomagnetic field data for realizing the high accuracy pedestrian inertial navigation indoors. In this paper, we propose an algorithm based on a one-dimensional convolutional neural network (1D CNN) to screen magnetic field data. By encoding the magnetic data within a certain time window to a time series, a 1D CNN with two convolutional layers is designed to extract data features. In order to avoid errors arising from artificial labels, the feature vectors will be clustered in the feature space to classify the magnetic data using unsupervised methods. Our experimental results show that this method can distinguish the geomagnetic field data from indoors disturbed magnetic data well and further significantly improve the calculation accuracy of the heading angle. Our work provides a possible technical path for the realization of high-precision indoor pedestrian navigation systems.

scholarly journals Pedestrian Re-Identification Monitoring System Based on Deep Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 86162-86170
Author(s):  
Wenzheng Qu ◽  
Zhiming Xu ◽  
Bei Luo ◽  
Haihua Feng ◽  
Zhiping Wan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Deep Convolutional Neural Network

scholarly journals Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Qiushuang Lin ◽  
Chunxiang Li ◽  
Chao Wu
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
High Precision ◽  
Health Monitoring ◽  
Wind Engineering ◽  
Stage Model ◽  
Health Monitoring System ◽  
Non Gaussian

Wind signal forecasting has become more and more crucial in the structural health monitoring system and wind engineering recently. It is a challenging subject owing to the complicated volatility of wind signals. The robustness and generalization of a predictor are significant as well as of high precision. In this paper, an adaptive residual convolutional neural network (CNN) is developed, aiming at achieving not only high precision but also high adaptivity for various wind signals with varying complexity. Afterwards, reinforced forecasting is adopted to enhance the robustness of the preliminary forecasting. The preliminary forecast results by adaptive residual CNN are integrated with historical observed signals as the new input to reconstruct a new forecasting mapping. Meanwhile, simplified-boost strategy is applied for more generalized results. The results of multistep forecasting for five kinds of nonstationary non-Gaussian wind signals prove the more excellent adaptivity and robustness of the developed two-stage model compared with single models.

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