Quantifying tectonic and glacial controls on topography in the Patagonian Andes (46.5° S) from integrated thermochronometry and thermo‐kinematic modeling

NEW EVIDENCE FOR LONG-LIVED CENOZOIC TOPOGRAPHY IN THE PATAGONIAN ANDES

10.1130/abs/2017am-305550 ◽

2017 ◽

Author(s):

David Auerbach ◽

◽

Mark T. Brandon ◽

Michael T. Hren ◽

Jeremy K. Hourigan ◽

...

Keyword(s):

Patagonian Andes ◽

New Evidence

Download Full-text

RESOLVING THE GEODYNAMIC EFFECTS OF SPREADING RIDGE SUBDUCTION IN THE PATAGONIAN ANDES USING MULTICHRONOMETER THERMOCHRONOLOGY

10.1130/abs/2019am-337250 ◽

2019 ◽

Author(s):

Andrea Stevens Goddard ◽

◽

Julie C. Fosdick

Keyword(s):

Spreading Ridge ◽

Ridge Subduction ◽

Patagonian Andes

Download Full-text

CONVERGENT OROGENESIS IN THE SOUTHERN PATAGONIAN ANDES AND MAGALLANES-AUSTRAL BASIN

10.1130/abs/2019am-336071 ◽

2019 ◽

Author(s):

Julie C. Fosdick ◽

Keyword(s):

Patagonian Andes

Download Full-text

Kinematic modeling and constraint analysis for robotic excavator operations in piling construction

Automation in Construction ◽

10.1016/j.autcon.2021.103666 ◽

2021 ◽

Vol 126 ◽

pp. 103666

Author(s):

Dong Guan ◽

Nan Yang ◽

Jerry Lai ◽

Ming-Fung Francis Siu ◽

Xingjian Jing ◽

...

Keyword(s):

Kinematic Modeling ◽

Robotic Excavator ◽

Constraint Analysis

Download Full-text

Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211010569 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110105

Author(s):

Jnana Sai Abhishek Varma Gokaraju ◽

Weon Keun Song ◽

Min-Ho Ka ◽

Somyot Kaitwanidvilai

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Convolutional Neural Networks ◽

Doppler Radar ◽

Autonomous Vehicle ◽

Sampling Point ◽

Body Segment ◽

Kinematic Modeling ◽

Deep Convolutional Neural Networks ◽

Kinematic Models

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

Download Full-text

Thermo-kinematic modeling of detachment-dominated extension, northeastern Death Valley area, USA: Implications for mid-crustal thermal-rheological evolution

Tectonophysics ◽

10.1016/j.tecto.2021.228755 ◽

2021 ◽

pp. 228755

Author(s):

Brandon M. Lutz ◽

Richard A. Ketcham ◽

Gary J. Axen ◽

Mengesha A. Beyene ◽

Michael L. Wells ◽

...

Keyword(s):

Death Valley ◽

Kinematic Modeling ◽

Valley Area

Download Full-text

Forward Kinematic Modeling of Conical-Shaped Continuum Manipulators

Robotica ◽

10.1017/s0263574720001484 ◽

2021 ◽

pp. 1-19

Author(s):

A. H. Bouyom Boutchouang ◽

Achille Melingui ◽

J. J. B. Mvogo Ahanda ◽

Othman Lakhal ◽

Frederic Biya Motto ◽

...

Keyword(s):

Neural Networks ◽

Data Collection ◽

Robot Control ◽

Deep Neural Networks ◽

Forward Kinematics ◽

Kinematic Modeling ◽

Experimental Platform ◽

Continuum Manipulators ◽

Forward Kinematic

SUMMARY Forward kinematics is essential in robot control. Its resolution remains a challenge for continuum manipulators because of their inherent flexibility. Learning-based approaches allow obtaining accurate models. However, they suffer from the explosion of the learning database that wears down the manipulator during data collection. This paper proposes an approach that combines the model and learning-based approaches. The learning database is derived from analytical equations to prevent the robot from operating for long periods. The database obtained is handled using Deep Neural Networks (DNNs). The Compact Bionic Handling robot serves as an experimental platform. The comparison with existing approaches gives satisfaction.

Download Full-text