Discrete Deformation Models for Real-Time Computation of Compliant Mechanisms

2013 ◽  
Author(s):  
Jingjing Ji ◽  
Kok-Meng Lee
Keyword(s):  
Real Time ◽  
Force Feedback ◽  
Compliant Mechanisms ◽  
Simulated Data ◽  
Operating Conditions ◽  
Practical Implementation ◽  
Computation Method ◽  
Beam Model ◽  
Path Lengths ◽  
External Forces

This paper presents the formulation of a reduced-order linear discrete–path approximation in state space and its solution as a function of path lengths for a 3D curvature-based beam model (CBM). Solutions to both forward and inverse problems are discussed; the former is essential for real-time deformed shape visualization whereas the latter is much needed for haptic force feedback. The method is illustrated with an application example where a 2D beam is characterized by a 6th order CBM. Practical implementation shows that when external forces as system input are expressed in global coordinates, the CBM can be decoupled into two 2nd order systems enabling parallel computing of the deformed shape and the orientation and moment, and effectively reducing the table size for storing the operating conditions. The proposed real-time computation method has been validated by verifying results against published experimental and MSM simulated data.

In Situ Measurement and Modeling of Human Cadaveric Soft Tissue Mechanical Properties for Use in Real Time Surgical Simulation

2008 ◽  
Author(s):  
Yi-Je Lim ◽  
Dhannanjay Deo ◽  
Suvranu De
Keyword(s):  
Mechanical Properties ◽  
Real Time ◽  
Soft Tissues ◽  
Force Feedback ◽  
Mechanical Response ◽  
Surgical Simulation ◽  
Physical Models ◽  
Nonlinear Viscoelastic ◽  
External Forces ◽  
Surgery Simulator

Development of a realistic surgery simulator that delivers high fidelity visual and haptic (force) feedback, based on the physical models of soft tissues, requires the use of empirical data on the mechanical behavior of intra-abdominal organs under the action of external forces. Measurement of mechanical properties of soft tissues on live human patients presents significant risks, making the use of cadavers a logical alternative. In this paper we present techniques of measuring and modeling the mechanical response of human cadaveric tissue for the purpose of developing a “virtual cadaver” model. The major contribution of this paper is the development of physics-based models of soft tissues that range from linear elastic models to nonlinear viscoelastic models which are efficient for application within the framework of a real time surgery simulator.

Power Test System Development and Dynamic Performance State Estimation Based on Hub Motor Vehicle

2020 ◽  
Vol 13 (2) ◽  
pp. 126-140
Author(s):  
Jing Gan ◽  
Xiaobin Fan ◽  
Zeng Song ◽  
Mingyue Zhang ◽  
Bin Zhao
Keyword(s):  
Real Time ◽  
Dynamic Performance ◽  
Test System ◽  
Operating Conditions ◽  
Motor Vehicles ◽  
Maximum Speed ◽  
Performance Parameters ◽  
Power Performance ◽  
Power Test ◽  
The Real

Background: The power performance of an electric vehicle is the basic parameter. Traditional test equipment, such as the expensive chassis dynamometer, not only increases the cost of testing but also makes it impossible to measure all the performance parameters of an electric vehicle. Objective: A set of convenient, efficient and sensitive power measurement system for electric vehicles is developed to obtain the real-time power changes of hub-motor vehicles under various operating conditions, and the dynamic performance parameters of hub-motor vehicles are obtained through the system. Methods: Firstly, a set of on-board power test system is developed by using virtual instrument (Lab- VIEW). This test system can obtain the power changes of hub-motor vehicles under various operating conditions in real-time and save data in real-time. Then, the driving resistance of hub-motor vehicles is analyzed, and the power performance of hub-motor vehicles is studied in depth. The power testing system is proposed to test the input power of both ends of the driving motor, and the chassis dynamometer is combined to test so that the output efficiency of the driving motor can be easily obtained without disassembly. Finally, this method is used to carry out the road test and obtain the vehicle dynamic performance parameters. Results: The real-time current, voltage and power, maximum power, acceleration time and maximum speed of the vehicle can be obtained accurately by using the power test system in the real road experiment. Conclusion: The maximum power required by the two motors reaches about 9KW, and it takes about 20 seconds to reach the maximum speed. The total power required to maintain the maximum speed is about 7.8kw, and the maximum speed is 62km/h. In this article, various patents have been discussed.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

scholarly journals Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 20
Author(s):  
Reynaldo Villarreal-González ◽  
Antonio J. Acosta-Hoyos ◽  
Jaime A. Garzon-Ochoa ◽  
Nataly J. Galán-Freyle ◽  
Paola Amar-Sepúlveda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Real Time ◽  
Binary Classification ◽  
Simulated Data ◽  
Classification Model ◽  
Rt Pcr ◽  
Simon Bolivar ◽  
Polymerase Chain ◽  
Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

scholarly journals Development and Testing of a Methodology for the Assessment of Acceptability of LKA Systems

Machines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 47 ◽  
Author(s):  
Luca Salvati ◽  
Matteo d’Amore ◽  
Anita Fiorentino ◽  
Arcangelo Pellegrino ◽  
Pasquale Sena ◽  
...  
Keyword(s):  
Force Feedback ◽  
Feedback System ◽  
Operating Conditions ◽  
Experimental Methodology ◽  
Driving Safety ◽  
Steering Wheel ◽  
Human In The Loop ◽  
Automation Systems ◽  
Driving Stability ◽  
Lane Keeping

In recent years, driving simulators have been widely used by automotive manufacturers and researchers in human-in-the-loop experiments, because they can reduce time and prototyping costs, and provide unlimited parametrization, more safety, and higher repeatability. Simulators play an important role in studies about driver behavior in operating conditions or with unstable vehicles. The aim of the research is to study the effects that the force feedback (f.f.b.), provided to steering wheel by a lane-keeping-assist (LKA) system, has on a driver’s response in simulators. The steering’s force feedback system is tested by reproducing the conditions of criticality of the LKA system in order to minimize the distance required to recover the driving stability as a function of set f.f.b. intensity and speed. The results, obtained in three specific criticality conditions, show that the behaviour of the LKA system, reproduced in the simulator, is not immediately understood by the driver and, sometimes, it is in opposition with the interventions performed by the driver to ensure driving safety. The results also compare the performance of the subjects, either overall and classified into subgroups, with reference to the perception of the LKA system, evaluated by means of a questionnaire. The proposed experimental methodology is to be regarded as a contribution for the integration of acceptance tests in the evaluation of automation systems.

GPU-Based Haptic Simulator for Dental Bone Drilling

2011 ◽  
Author(s):  
Fei Zheng ◽  
WenFeng Lu ◽  
Yoke San Wong ◽  
Kelvin Weng Chiong Foong
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Force Feedback ◽  
Mandibular Canal ◽  
Structure Design ◽  
Patient Specific ◽  
Force Model ◽  
Bone Drilling ◽  
The Real ◽  
Drill Bits

Dental bone drilling is an inexact and often a blind art. Dentist risks damaging the invisible tooth roots, nerves and critical dental structures like mandibular canal and maxillary sinus. This paper presents a haptics-based jawbone drilling simulator for novice surgeons. Through the real-time training of tactile sensations based on patient-specific data, improved outcomes and faster procedures can be provided. Previously developed drilling simulators usually adopt penalty-based contact force models and often consider only spherical-shaped drill bits for simplicity and computational efficiency. In contrast, our simulator is equipped with a more precise force model, adapted from the Voxmap-PointShell (VPS) method to capture the essential features of the drilling procedure. In addition, the proposed force model can accommodate various shapes of drill bits. To achieve better anatomical accuracy, our oral model has been reconstructed from Cone Beam CT, using voxel-based method. To enhance the real-time response, the parallel computing power of Graphics Processing Units is exploited through extra efforts for data structure design, algorithms parallelization, and graphic memory utilization. Preliminary results show that the developed system can produce appropriate force feedback at different tissue layers.

scholarly journals DeepFogSim: A Toolbox for Execution and Performance Evaluation of the Inference Phase of Conditional Deep Neural Networks with Early Exits Atop Distributed Fog Platforms

2021 ◽  
Vol 11 (1) ◽  
pp. 377
Author(s):  
Michele Scarpiniti ◽  
Enzo Baccarelli ◽  
Alireza Momenzadeh ◽  
Sima Sarv Ahrabi
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Future Internet ◽  
Operating Conditions ◽  
Graphic User Interface ◽  
Energy Aware ◽  
Internet Applications ◽  
The Real ◽  
Delay Performance ◽  
Hard Constraints

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox.

Development of Active Rehabilitation Device of Hand Joint

2012 ◽  
Vol 591-593 ◽  
pp. 245-250 ◽  
Author(s):  
Hsi Chuan Huang ◽  
W.H. Kao ◽  
T.S. Wei ◽  
S.Y. Liu ◽  
Y.S. Syu ◽  
...  
Keyword(s):  
Control System ◽  
Embedded System ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Operating Conditions ◽  
Control Technology ◽  
Rehabilitation Process ◽  
Processing Control ◽  
Active Rehabilitation ◽  
External Forces

If the hand joints patients have not been taking autonomic or external force rehabilitation, they might become disabled, even leading the cause ofirreversible disability eventually. Generally speaking, the medical treatment of rehabilitation has been doing by physical therapist by providing patients with external forces rehabilitation assistance as far as we know currently. For the purpose of both of reducing the workload of physical therapists and providing the quantitative data obtained during the rehabilitation process so as for physical therapist’s reference. This research will build an automatic finger stretch and grip control system by using the Human- Machine Interface for operation control. It can be done by physical therapist or physical doctor to set the operating conditions so as to help the patients with their finger joints motion so as to achieve the rehabilitation effect of their fingers. This research is trying to integrate the mechanism and control technology mainly. With regards to the control technology, it uses a micro chip so as to lead the motions of the stretch & grip for finger rehabilitation by a signal processing control servo motor. And its operation interface is using an embedded system along with Visual Studio compiling software so as to touch input the operating conditions. The system operation functions are including single finger joint motion orsynchronized actuation of all fingers and the setting of the cycle numbers. Furthermore, in order to soften the finger tendons so as to promote the rehabilitation effect during the rehabilitation process, the system adds external steam in so as to control the internal temperature & humidity of the rehabilitation box and apparently, the whole finger automatic stretch & grip control system will be better owing to it.

The Motion of Floating Systems: Nonlinear Dynamics in Periodic and Random Waves

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Katrin Ellermann
Keyword(s):  
Dynamical Behavior ◽  
Nonlinear Behavior ◽  
Operating Conditions ◽  
Random Waves ◽  
Multiple Components ◽  
Random Disturbances ◽  
Restoring Forces ◽  
Steady State Responses ◽  
External Forces ◽  
Floating Systems

Floating systems, such as ships, barges, or semisubmersibles, show a dynamical behavior, which is determined by their internal structure and the operating conditions caused by external forces e.g., due to waves and wind. Due to the complexity of the system, which commonly includes coupling of multiple components or nonlinear restoring forces, the response can exhibit inherently nonlinear characteristics. In this paper different models of floating systems are considered. For the idealized case of purely harmonic forcing they all show nonlinear behavior such as subharmonic motion or different steady-state responses at constant operating conditions. The introduction of random disturbances leads to deviations from the idealized case, which may change the overall response significantly. Advantages and limitations of the different mathematical models and the applied numerical techniques are discussed.

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