scholarly journals Application of Hermite splines for load movement on a flexible crane suspension through a curvilinear trajectory

2020 ◽  
Vol 1441 ◽  
pp. 012101
Author(s):  
M S Korytov ◽  
V S Shcherbakov ◽  
V V Titenko
Keyword(s):  
Hermite Splines ◽  
Load Movement ◽  
Curvilinear Trajectory

Prediction, Testing, and Analysis of a 50 m Long Pile in Soft Marine Clay

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Bengt Fellenius
Keyword(s):  
Peak Load ◽  
Pile Head ◽  
Marine Clay ◽  
Loading Test ◽  
Head Displacement ◽  
Floating Pile ◽  
Peak Loads ◽  
Static Loading Test ◽  
Beta Coefficient ◽  
Load Movement

On April 4, 2018, 209 days after driving, a static loading test was performed on a 50 m long, strain-gage instrumented, square 275-mm diameter, precast, shaft-bearing (“floating”) pile in Göteborg, Sweden. The soil profile consisted of a 90 m thick, soft, postglacial, marine clay. The groundwater table was at about 1.0 m depth. The undrained shear strength was about 20 kPa at 10 m depth and increased linearly to about 80 kPa at 55m depth. The load-distribution at the peak load correlated to an average effective stress beta-coefficient of 0.19 along the pile or, alternatively, a unit shaft shear resistance of 15 kPa at 10 m depth increasing to about 65 kPa at 50 m depth, indicating an α-coefficient of about 0.80. Prior to the test, geotechnical engineers around the world were invited to predict the load-movement curve to be established in the test—22 predictions from 10 countries were received. The predictions of pile stiffness, and pile head displacement showed considerable scatter, however. Predicted peak loads ranged from 65% to 200% of the actual 1,800-kN peak-load, and 35% to 300% of the load at 22-mm movement.

scholarly journals The method of taking into the drift in determining the trajectory of the curvinal movement of the tractor

1970 ◽  
pp. 25-29
Author(s):  
M Dovzhyk ◽  
O Solarov ◽  
О Kalnahus ◽  
О Tatsenko
Keyword(s):  
Automatic Control ◽  
Velocity Vector ◽  
Complete Loss ◽  
Neutral Position ◽  
Main Task ◽  
Positive Direction ◽  
Curvilinear Motion ◽  
Curvilinear Trajectory ◽  
Wheeled Vehicles ◽  
Angle Of Rotation

This article discusses how to construct a curvilinear trajectory for a four-wheeled machine with front steered wheels. Important effects on curvilinear motion are phenomena such as slipping and skidding, which in turn influence the construction of the trajectory. Knowing how the movement of the wheels affects the movement, we can accurately construct the trajectory, namely the entrance and exit of the turn. The main task we wanted to solve was to find the easiest ways to construct a curved trajectory of the machine. It is known that the angle between the velocity vector of the wheel and the positive direction of the axis of the tractor corresponding to the neutral position of the wheels is called the angle of withdrawal. It is also known that the deflection angles arising from the curvilinear movement of wheeled vehicles due to tire deformation distort the trajectory of motion and have a significant effect on the controllability of the machine up to its complete loss. Therefore, it is no coincidence that a large number of works is devoted to investigating the causes of the emergence of angles and their dependence on various factors. Tire theory is taught throughout the course of this subject, but nevertheless it cannot be assumed that the phenomenon has been sufficiently studied so that it can be confidently used in determining trajectories, which is especially important in the development of methods of automatic control of the vehicle . The angle of rotation of the wheels depends on many factors, and often these dependencies are quite complex.

scholarly journals Age Influences Loss Aversion Through Effects on Posterior Cingulate Cortical Thickness

2021 ◽  
Vol 15 ◽  
Author(s):  
Zoe R. Guttman ◽  
Dara G. Ghahremani ◽  
Jean-Baptiste Pochon ◽  
Andy C. Dean ◽  
Edythe D. London
Keyword(s):  
Loss Aversion ◽  
Cingulate Cortex ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Structural Magnetic Resonance Imaging ◽  
Posterior Cingulate ◽  
Age Related ◽  
Curvilinear Trajectory ◽  
The Relationship ◽  
Healthy Participants

Decision-making strategies shift during normal aging and can profoundly affect wellbeing. Although overweighing losses compared to gains, termed “loss aversion,” plays an important role in choice selection, the age trajectory of this effect and how it may be influenced by associated changes in brain structure remain unclear. We therefore investigated the relationship between age and loss aversion, and tested for its mediation by cortical thinning in brain regions that are susceptible to age-related declines and are implicated in loss aversion — the insular, orbitofrontal, and anterior and posterior cingulate cortices. Healthy participants (n = 106, 17–54 years) performed the Loss Aversion Task. A subgroup (n = 78) provided structural magnetic resonance imaging scans. Loss aversion followed a curvilinear trajectory, declining in young adulthood and increasing in middle-age, and thinning of the posterior cingulate cortex mediated this trajectory. The findings suggest that beyond a threshold in middle adulthood, atrophy of the posterior cingulate cortex influences loss aversion.

Review Paper 2: Basic Principles of Dry Friction Testing

1967 ◽  
Vol 182 (7) ◽  
pp. 7-15
Author(s):  
T. H. C. Childs ◽  
D. Tabor
Keyword(s):  
Dry Friction ◽  
Review Paper ◽  
Normal Load ◽  
Tangential Force ◽  
Experimental Methods ◽  
Basic Principles ◽  
Friction Process ◽  
Friction Testing ◽  
The Coefficient Of Friction ◽  
Load Movement

Friction is the force resisting relative motion between surfaces in contact. The coefficient of friction is the ratio of the frictional force to the normal load. Consequently the measurement of friction involves measurement of a normal load, movement of a surface, and measurement of a tangential force. The first part of this review paper deals with the basic principles of the friction process. The second part is concerned with experimental methods of measuring the friction.

Discussion of “Effects of Water Temperature on Bed-Load Movement”

1969 ◽  
Vol 95 (2) ◽  
pp. 244-258
Author(s):  
M. Gamal Mostafa ◽  
Brent D. Taylor ◽  
Vito A. Vanoni ◽  
Adel Kamel
Keyword(s):  
Water Temperature ◽  
Bed Load ◽  
Load Movement

Electric Load Movement Forecasting Based on the DFT Interpolation with Periodic Extension

2015 ◽  
Vol 24 (08) ◽  
pp. 1550123
Author(s):  
Zong-Chang Yang
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Arma Model ◽  
Ar Model ◽  
Electric Load ◽  
Ann Model ◽  
Method Performance ◽  
Bp Artificial Neural Network ◽  
Load Movement ◽  
Artificial Neural Network Ann

Electric load forecasting is increasingly important for the industry. This study addresses the load forecasting based on the discrete Fourier transform (DFT) interpolation. As the most common analysis method in the frequency domain, the conventional Fourier analysis cannot be directly applied to prediction. From the perspective of time-series analysis, electric load movement influenced by various factors is also a time-series, which is usually subject to cyclical variations. Then with periodic extension for the load movement, a forecasting approach based on the DFT interpolation is proposed for predicting its movement. The proposed DFT interpolation prediction model is applied to experiments of forecasting the daily EUNITE load movement and annual load movement of State Grid Corporation in China. The experimental results and analysis show potentiality of the proposed method. Performance comparisons indicate that the proposed DFT interpolation model performs better than the three commonly used interpolation algorithms as well as the classical autoregressive (AR) model, the ARMA model, and the BP-artificial neural network (ANN) model on the same forecasting tasks.

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