Automatically Correcting for Specimen Displacement Error During XRD Search/Match Identification

1982 ◽  
pp. 231-236 ◽  
Author(s):  
Walter N. Schreiner ◽  
Ronald Jenkins
Keyword(s):  
Displacement Error

Design and locomotion analysis of the tetrahedral mobile robot with only revolute joints (TMRR)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhirui Wang ◽  
Yezhuo Li ◽  
Bo Su ◽  
Lei Jiang ◽  
Ziming Zhao ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Main Body ◽  
Dynamics Analysis ◽  
Content Type ◽  
Displacement Error ◽  
Working Space ◽  
Revolute Joints ◽  
Physical Prototype ◽  
Moving Direction ◽  
Rolling Locomotion

Purpose The purpose of this paper is to introduce a tetrahedral mobile robot with only revolute joints (TMRR). By using rotation actuators, the mechanism of the robot gains favorable working space and eliminates the engineering difficulties caused by the multilevel extension compared with liner actuators. Furthermore, the rolling locomotion is improved to reduce displacement error based on dynamics analysis. Design/methodology/approach The main body of deforming mechanism with a tetrahedral exterior shape is composed of four vertexes and six RRR chains. The mobile robot can achieve the rolling locomotion and reach any position on the ground by orderly driving the rotation actuators. The global kinematics of the mobile modes are analyzed. Dynamics analysis of the robot falling process is carried out during the rolling locomotion, and the rolling locomotion is improved by reducing the collision impulse along with the moving direction. Findings Based on global kinematics analysis of TMRR, the robot can realize the continuous mobility based on rolling gait planning. The main cause of robot displacement error and the corresponding improvement locomotion are gained through dynamic analysis. The results of the theoretical analysis are verified by experiments on a physical prototype. Originality/value The work introduced in this paper is a novel exploration of applying the mechanism with only revolute joints to the field of tetrahedral rolling robots. It is also an attempt to use the improved rolling locomotion making this kind of mobile robot more practical. Meanwhile, the reasonable engineering structure of the robot provides feasibility for load carrying.

Optical Analysis of Solar Concentrators Using Photogrammetry Process

2017 ◽  
pp. 175-201
Author(s):  
Safa Skouri ◽  
Salwa Bouadila
Keyword(s):  
Measurement Technique ◽  
Measurement Techniques ◽  
High Impact ◽  
Geometric Errors ◽  
Solar Concentrator ◽  
Optical Analysis ◽  
Optical Efficiency ◽  
Solar Concentrators ◽  
Displacement Error ◽  
Slope Error

As the optical efficiency of solar concentrators has a high impact on its thermal performance. However a qualification method determining the geometrical accuracy of a solar concentrator system is necessary. The purpose of this chapter is to gives an optical analysis of solar concentrator with an imaging process in order to improve the thermal efficiency of the solar concentrator. In this order measurement techniques used to determine geometric errors of the solar concentrating system have been described. Intercept factor, slope error and displacement error have been identified and analyzed. Examples of the intercept factor for concentrator reflector along with optical efficiency has been developed and determined related to the experimental results given by photogrammetry measurement technique.

scholarly journals A novel accuracy assessment model for video stabilization approaches based on background motion

2019 ◽  
pp. 1260-1275
Author(s):  
MD ALAMGIR HOSSAIN ◽  
TIEN-DUNG NGUYEN ◽  
EUI NAM HUH
Keyword(s):  
Euclidean Distance ◽  
Accuracy Assessment ◽  
Measurement Model ◽  
Assessment Model ◽  
Video Stabilization ◽  
Accuracy Measurement ◽  
Displacement Error ◽  
Two Dimensional Flow ◽  
Flow Vectors ◽  
Background Motion

In this paper, we propose a new accuracy measurement model for the video stabilization method based on background motion that can accurately measure the performance of the video stabilization algorithm. Undesired residual motion present in the video can quantitatively be measured by the pixel by pixel background motion displacement between two consecutive background frames. First of all, foregrounds are removed from a stabilized video, and then we find the two-dimensional flow vectors for each pixel separately between two consecutive background frames. After that, we calculate a Euclidean distance between these two flow vectors for each pixel one by one, which is regarded as a displacement of each pixel. Then a total Euclidean distance of each frame is averaged to get a mean displacement for each pixel, which is called mean displacement error, and finally we calculate the average mean displacement error. Our experimental results show the effectiveness of our proposed method.

scholarly journals Vehicle trajectory prediction and generation using LSTM models and GANs

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253868
Author(s):  
Luca Rossi ◽  
Andrea Ajmar ◽  
Marina Paolanti ◽  
Roberto Pierdicca
Keyword(s):  
Traffic Congestion ◽  
Short Term Memory ◽  
Autonomous Driving ◽  
Generative Models ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Displacement Error ◽  
Adversarial Network ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory

Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.

scholarly journals Mesh Free Radial Point Interpolation Based Displacement Recovery Techniques for Elastic Finite Element Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1900
Author(s):  
Mohd. Ahmed ◽  
Devinder Singh ◽  
Saeed AlQadhi ◽  
Majed A. Alrefae
Keyword(s):  
Finite Element ◽  
Error Recovery ◽  
Basis Functions ◽  
Least Square ◽  
Element Solution ◽  
Displacement Error ◽  
Mesh Free ◽  
Radial Point Interpolation ◽  
Point Interpolation ◽  
Recovery Technique

The study develops the displacement error recovery method in a mesh free environment for the finite element solution employing the radial point interpolation (RPI) technique. The RPI technique uses the radial basis functions (RBF), along with polynomials basis functions to interpolate the displacement fields in a node patch and recovers the error in displacement field. The global and local errors are quantified in both energy and L2 norms from the post-processed displacement field. The RPI technique considers multi-quadrics/gaussian/thin plate splint RBF in combination with linear basis function for displacement error recovery analysis. The elastic plate examples are analyzed to demonstrate the error convergence and effectivity of the RPI displacement recovery procedures employing mesh free and mesh dependent patches. The performance of a RPI-based error estimators is also compared with the mesh dependent least square based error estimator. The triangular and quadrilateral elements are used for the discretization of plates domains. It is verified that RBF with their shape parameters, choice of elements, and errors norms influence considerably on the RPI-based displacement error recovery of finite element solution. The numerical results show that the mesh free RPI-based displacement recovery technique is more effective and achieve target accuracy in adaptive analysis with the smaller number of elements as compared to mesh dependent RPI and mesh dependent least square. It is also concluded that proposed mesh free recovery technique may prove to be most suitable for error recovery and adaptive analysis of problems dealing with large domain changes and domain discontinuities.

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