A regularization scheme for displacement reconstruction using measured structural acceleration data

2008 ◽  
Author(s):  
H Lee ◽  
Y Hong ◽  
H Park
Keyword(s):  
Regularization Scheme ◽  
Acceleration Data ◽  
Displacement Reconstruction

Distribution of Speed and Acceleration on a Tread Wear Course

1981 ◽  
Vol 9 (1) ◽  
pp. 19-25 ◽  
Author(s):  
G. S. Ludwig ◽  
F. C. Brenner
Keyword(s):  
Traffic Safety ◽  
Highway Traffic ◽  
National Highway Traffic Safety ◽  
Level Crossings ◽  
Acceleration Level ◽  
Longitudinal Acceleration ◽  
Acceleration Data ◽  
National Highway

Abstract Belted bias and radial Course Monitoring Tires were run over the National Highway Traffic Safety Administration tread wear course at San Angelo on a vehicle instrumented to measure lateral and longitudinal accelerations, speed, and number of wheel rotations. The data were recorded as histograms. The distribution of speed, the distributions of lateral and longitudinal acceleration, and the number of acceleration level crossings are given. Acceleration data for segments of the course are also given.

Development of an Algorithm for Detecting Railway Corrugations in Acceleration Data

2021 ◽  
Author(s):  
Ivan V. Belov ◽  
Roman V. Shalymov ◽  
Anna N. Tkachenko ◽  
Daniil Yu. Larionov ◽  
Liudmila N. Podgornaya
Keyword(s):  
Acceleration Data

scholarly journals Detection of Road-Surface Anomalies Using a Smartphone Camera and Accelerometer

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 561
Author(s):  
Taehee Lee ◽  
Chanjun Chun ◽  
Seung-Ki Ryu
Keyword(s):  
Monitoring System ◽  
Poor Performance ◽  
Road Surface ◽  
Monitoring Systems ◽  
Advantages And Disadvantages ◽  
Acceleration Data ◽  
Road Surfaces ◽  
Road Surface Monitoring ◽  
Surface Monitoring ◽  
Acquisition Method

Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction.

Distributed learning with indefinite kernels

2019 ◽  
Vol 17 (06) ◽  
pp. 947-975 ◽  
Author(s):  
Lei Shi
Keyword(s):  
Large Scale ◽  
Substantial Reduction ◽  
Computation Time ◽  
Distributed Learning ◽  
Rates Of Convergence ◽  
Regression Problem ◽  
Data Set ◽  
Regularization Scheme ◽  
Original Algorithm ◽  
Indefinite Kernel

We investigate the distributed learning with coefficient-based regularization scheme under the framework of kernel regression methods. Compared with the classical kernel ridge regression (KRR), the algorithm under consideration does not require the kernel function to be positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods. The distributed learning approach partitions a massive data set into several disjoint data subsets, and then produces a global estimator by taking an average of the local estimator on each data subset. Easy exercisable partitions and performing algorithm on each subset in parallel lead to a substantial reduction in computation time versus the standard approach of performing the original algorithm on the entire samples. We establish the first mini-max optimal rates of convergence for distributed coefficient-based regularization scheme with indefinite kernels. We thus demonstrate that compared with distributed KRR, the concerned algorithm is more flexible and effective in regression problem for large-scale data sets.

scholarly journals IMUMETER—A Convolution Neural Network-Based Sensor for Measurement of Aircraft Ground Performance

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4726
Author(s):  
Jarosław Pytka ◽  
Piotr Budzyński ◽  
Paweł Tomiło ◽  
Joanna Michałowska ◽  
Ernest Gnapowski ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolution Neural Network ◽  
Flight Tests ◽  
Motion Measurement ◽  
Aircraft Flight ◽  
Acceleration Data ◽  
Measurement Results ◽  
Measurement And Analysis ◽  
Ground Distance

The paper presents the development of the IMUMETER sensor, designed to study the dynamics of aircraft movement, in particular, to measure the ground performance of the aircraft. A motivation of this study was to develop a sensor capable of airplane motion measurement, especially for airfield performance, takeoff and landing. The IMUMETER sensor was designed on the basis of the method of artificial neural networks. The use of a neural network is justified by the fact that the automation of the measurement of the airplane’s ground distance during landing based on acceleration data is possible thanks to the recognition of the touchdown and stopping points, using artificial intelligence. The hardware is based on a single-board computer that works with the inertial navigation platform and a satellite navigation sensor. In the development of the IMUMETER device, original software solutions were developed and tested. The paper describes the development of the Convolution Neural Network, including the learning process based on the measurement results during flight tests of the PZL 104 Wilga 35A aircraft. The ground distance of the test airplane during landing on a grass runway was calculated using the developed neural network model. Additionally included are exemplary measurements of the landing distance of the test airplane during landing on a grass runway. The results obtained in this study can be useful in the development of artificial intelligence-based sensors, especially those for the measurement and analysis of aircraft flight dynamics.

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