Interference and deception detection technology of satellite navigation based on deep learning

2017 ◽  
Author(s):  
Weiyi Chen ◽  
Pingke Deng ◽  
Xiaoguang Zhang ◽  
Yi Qu ◽  
Yaping Li
Keyword(s):  
Deep Learning ◽  
Deception Detection ◽  
Satellite Navigation ◽  
Detection Technology

scholarly journals Deep-Learning-Based Pupil Center Detection and Tracking Technology for Visible-Light Wearable Gaze Tracking Devices

2021 ◽  
Vol 11 (2) ◽  
pp. 851
Author(s):  
Wei-Liang Ou ◽  
Tzu-Ling Kuo ◽  
Chin-Chieh Chang ◽  
Chih-Peng Fan
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Tracking System ◽  
Recall Rate ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Tracking Errors ◽  
Gaze Tracking ◽  
Detection Technology ◽  
Pupil Tracking

In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.

scholarly journals Neural Network for Metal Detection Based on Magnetic Impedance Sensor

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4456
Author(s):  
Sungjae Ha ◽  
Dongwoo Lee ◽  
Hoijun Kim ◽  
Soonchul Kwon ◽  
EungJo Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Deep Learning ◽  
Short Term Memory ◽  
Magnetic Impedance ◽  
Detection Technology ◽  
Metal Detection ◽  
Long Short Term Memory ◽  
Impedance Sensor ◽  
The Magnetic Field

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.

scholarly journals Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5446
Author(s):  
Hyojung Ahn ◽  
Inchoon Yeo
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Detection System ◽  
Noisy Environments ◽  
Acoustic Data ◽  
Detection Techniques ◽  
Machine Operation ◽  
Detection Technology ◽  
Environment Noise

As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.

scholarly journals Research on Zero-Sequence Insulator Detection Technology Based on Deep Learning

2019 ◽  
Vol 1325 ◽  
pp. 012011
Author(s):  
Guo Tao ◽  
Xu Lianggang ◽  
Shi Hongyun ◽  
Chen Fengxiang ◽  
Wang Shichun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Technology ◽  
Zero Sequence

scholarly journals Railway Subgrade Defect Automatic Recognition Method Based on Improved Faster R-CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xinjun Xu ◽  
Yang Lei ◽  
Feng Yang
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Model Performance ◽  
Improvement Strategy ◽  
Detection Technology ◽  
Improvement Strategies ◽  
New Thinking ◽  
Defect Recognition ◽  
Improved Model ◽  
Ground Penetrating

Railway subgrade defect is the serious threat to train safety. Vehicle-borne GPR method has become the main railway subgrade detection technology with its advantages of rapidness and nondestructiveness. However, due to the large amount of detection data and the variety in defect shape and size, defect recognition is a challenging task. In this work, the method based on deep learning is proposed to recognize defects from the ground penetrating radar (GPR) profile of subgrade detection data. Based on the Faster R-CNN framework, the improvement strategies of feature cascade, adversarial spatial dropout network (ASDN), Soft-NMS, and data augmentation have been integrated to improve recognition accuracy, according to the characteristics of subgrade defects. The experimental results indicates that compared with traditional SVM+HOG method and the baseline Faster R-CNN, the improved model can achieve better performance. The model robustness is demonstrated by a further comparison experiment of various defect types. In addition, the improvements to model performance of each improvement strategy are verified by an ablation experiment of improvement strategies. This paper tries to explore the new thinking for the application of deep learning method in the field of railway subgrade defect recognition.

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