scholarly journals ADATABLE INDUSTRIAL 4.0 : AN IMPROVEMENT DETECTED OBSOLETE BUSHING CONDITION ON RUBBER CURING MACHINE INDUSTRY

2019 ◽  
Vol 1 (1) ◽  
pp. 1-4
Author(s):  
Agus Purwanto ◽  
Edi Sofyan
Keyword(s):  
Simulated Data ◽  
Simulation Software ◽  
Data Detection ◽  
Accelerometer Sensor ◽  
Closing Force ◽  
Machine Industry ◽  
Left And Right ◽  
Rubber Product ◽  
Dial Gauge ◽  
Sound Vibration

Bushing is a part of rubber curing machine that have function as cushion pin joint , generally make from a bronze material. Pin joint have a function to connected with 1 paired linkage arm with rotated crank gear of each via pin joint to get closing force at rubber curing machine when mould closed. When bushing condition at pin joint on rubber curing machine was not good caused by friction. It will make closing force linkage arm left and right ( 1 paired ) on rubber curing machine unbalanced. So it will make closing force to force closed rubber mould undistributed evenly, so it caused mould rubber closed with gap. This condition will make defect rubber product open mould after cured finished. In this case is important to find new methode detection and monitored bushing condition. This research was done by measured vibration around pin joint at linkage arm by taking data of sound and acceleration on the real object research and used 2D software simulator to measured bushing condition good or not. Acceleration measurement was done by measured at axis Y and Z by used accelerometer sensor, and accelero sensor on cell phone via sains journal android application. And collected the data stress value on linkage arm by used nidle dial gauge millimeter scale. By simulation software are measured acceleration at axis X and Y with good bushing condition or vise versa.The actual data that was collected : acceleration, sound vibration and stress value and simulated data by software to be compare the amplitude and choose the biggest amplitude as new methode measurement of vibration to detect and monitor bushing condition.From the result data, detection of vibration around pin joint by acceleration value by used accelerometer sensor measured at axis Y and Z more prefer with the other measurement methode.

scholarly journals Gesture Controlled Robot using Arduino

2021 ◽  
Vol 9 (VI) ◽  
pp. 2758-2765
Author(s):  
Biyyala Srijith
Keyword(s):  
Remote Control ◽  
Steering Wheel ◽  
Human Hand ◽  
Robot Arm ◽  
Radio Waves ◽  
Robotic Control ◽  
Accelerometer Sensor ◽  
Left And Right ◽  
Human Touch ◽  
The Right

A Gesture Controlled Car is a robot that can be controlled with a simple human touch. The user only needs to wear a touch device where the sensor is installed. The sensor will record the movement of the hand in a certain direction that will lead to the movement of the robot in the right places. The robot and the touch device are connected wirelessly with radio waves. The user can communicate with the robot in a very friendly way due to wireless communication. We can control the car using accelerometer sensors that are connected to our hand glove. Sensors are designed to replace the remote control commonly used to drive a car. It will allow the user to control the forward, backward, left and right, while using the same accelerometer sensor to control the car's steering wheel. The movement of the car is controlled by the separation method. The machine involves rotating both front and rear wheels on the left or right side to move the non-clockwise side and another pair around the clock causing the car to rotate with its axis without going forward or backward. The main advantage of this machine is that the car with this method can take sharp turns without difficulty. The design and use of a robotic control arm using a flex sensor is suggested. The robot arm is designed to consist of four moving fingers, each with three connectors, an opposing thumb, a round wrist, and an elbow. The robot arm is designed to mimic the movements of a human hand using a hand glove.

Validating a Single-Phase Flow Pipeline Simulation Model Against Real Data From a Vapor Collapse Event

2018 ◽  
Author(s):  
Ulli Pietsch ◽  
Hanna Sieben
Keyword(s):  
Data Acquisition ◽  
Simulated Data ◽  
Large Change ◽  
Measured Data ◽  
Simulation Software ◽  
Operating Conditions ◽  
Pressure Data ◽  
Hydraulic Conditions ◽  
Pipeline Segment ◽  
Pressure Surge

Transient hydraulic conditions during a shutdown and subsequent start-up of a segment of a pipeline that runs through a mountainous region were simulated using commercially available hydraulic simulation software and a model of the relevant portion of the pipeline facilities. The segment of interest is located in an area where the pipeline is normally operated with vapor present (slack line flow conditions) due to the large change in elevation. Pressure data that was recorded by the pipeline’s data acquisition system indicated a pressure surge occurred when the line was restarted. The suspected cause of this pressure surge was the collapse of the vapor in this pipeline segment. Beginning with an estimate of the flow, pressure and temperature data for the pipeline segment at steady state conditions prior to the shutdown, the simulation was tuned to reasonably match the measured data. The resulting simulated data closely replicated the surge event. Examination of the simulated data provides insights into the hydraulic conditions in the pipeline at locations where pressure data is not measured, as well as during the time intervals between data acquisition scans. It also reveals impact of the timing of the mainline valve opening sequence. Further, since the simulated data does accurately replicate the actual measured data, the model can be used to evaluate how changes to facilities or operating conditions impact the formation and the collapse of vapor in this pipeline segment.

scholarly journals Estimativa do campo de vento em alta resolução pelo Modelo Wind Ninja para estimativa em Energia Eólica

2020 ◽  
Vol 42 ◽  
pp. e33
Author(s):  
César Magno Leite de Oliveira Júnior ◽  
Nivea Maria Barreto Nunes Oleques ◽  
Fabricio Pereira Harter ◽  
Jonas Da Costa Carvalho ◽  
Marcelo Romero de Moraes
Keyword(s):  
Wind Farm ◽  
Weather Forecasting ◽  
Wrf Model ◽  
Simulated Data ◽  
Momentum Conservation ◽  
Simulation Software ◽  
Observation Data ◽  
Wind Fields ◽  
Numerical Weather Forecasting ◽  
Conservation Model

In this work it was proposed to predict high resolution wind fields in order to simulate the wind speed and direction variables in the Cerro Chato wind complex located in the city of Santana do Livramento / RS. For this, numerical weather forecasting techniques were used, using the \ textit {Weather Research and Forecasting} (WRF) model and a microscale mass and momentum conservation model, known as Windninja. In this study, the simulated data was compared with the observation data collected by the towers of the wind farm in question, using statistical procedures. The results presented by the Windninja simulation software were efficient, but not very effective.

scholarly journals Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations

10.2196/12641 ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. e12641 ◽  
Author(s):  
Bethany Percha ◽  
Edward B Baskerville ◽  
Matthew Johnson ◽  
Joel T Dudley ◽  
Noah Zimmerman
Keyword(s):  
Precision Medicine ◽  
Simulated Data ◽  
Patient Characteristics ◽  
Simulation Software ◽  
Single Individual ◽  
Digital Medicine ◽  
Number Of Treatment ◽  
Study Designs ◽  
Stochastic Time Series ◽  
Stochastic Time

Background Recent advances in molecular biology, sensors, and digital medicine have led to an explosion of products and services for high-resolution monitoring of individual health. The N-of-1 study has emerged as an important methodological tool for harnessing these new data sources, enabling researchers to compare the effectiveness of health interventions at the level of a single individual. Objective N-of-1 studies are susceptible to several design flaws. We developed a model that generates realistic data for N-of-1 studies to enable researchers to optimize study designs in advance. Methods Our stochastic time-series model simulates an N-of-1 study, incorporating all study-relevant effects, such as carryover and wash-in effects, as well as various sources of noise. The model can be used to produce realistic simulated data for a near-infinite number of N-of-1 study designs, treatment profiles, and patient characteristics. Results Using simulation, we demonstrate how the number of treatment blocks, ordering of treatments within blocks, duration of each treatment, and sampling frequency affect our ability to detect true differences in treatment efficacy. We provide a set of recommendations for study designs on the basis of treatment, outcomes, and instrument parameters, and make our simulation software publicly available for use by the precision medicine community. Conclusions Simulation can facilitate rapid optimization of N-of-1 study designs and increase the likelihood of study success while minimizing participant burden.

scholarly journals MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

2021 ◽  
Author(s):  
Susitra Dhanarajalu
Keyword(s):  
Regression Analysis ◽  
Fuzzy Inference ◽  
Simulated Data ◽  
Switched Reluctance Motor ◽  
Simulation Software ◽  
Accurate Estimation ◽  
Computationally Efficient ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Non Linear

This chapter aims in presenting the methods for the accurate estimation of highly non linear phase inductance profile of a switched reluctance motor (SRM). The magnetization characteristics of a test SRM is derived from the SRDaS (Switched Reluctance Design and Simulation) simulation software. Statistical interpolation based regression analysis and Artificial Intelligence (AI) techniques are used for developing the computationally efficient inductance model. Multi Variate Non linear Regression (MVNLR) from the class of regression analysis and Adaptive Neuro Fuzzy Inference System (ANFIS) under the class of AI are implemented and tested on the simulated data. Non linear Inductance profile L(I,θ) of SRM is successfully estimated for the complete working range of phase currents (Iph). At each Iph, L(I,θ) values are estimated and presented for one cycle of rotor position (θ). Estimated inductance profile based on the two proposed methods is observed to be in excellent correlation with the true value of data.

Preliminary Validation of Urban Building Integrated PV System Simulation Software

Solar Energy ◽  
2006 ◽  
Author(s):  
Kalyan Rapolu ◽  
Kaylan Sites ◽  
Mandeep Guragain ◽  
Pritpal Singh
Keyword(s):  
Simulated Data ◽  
Software Tool ◽  
Simulation Software ◽  
Test Facility ◽  
Pv System ◽  
Solar Module ◽  
Preliminary Validation ◽  
Current Voltage ◽  
Glass Panels ◽  
Module Performance

The objective of the present project is to develop a software simulation tool to accurately estimate the power generated from solar energy absorbed by photovoltaic panels mounted on the fac¸ade of a building in an urban environment, taking into account shading and reflection from neighboring buildings. The software tool has been designed and modeled in AutoCAD, with the help of AccuRender/Ecotect. We have previously presented the modeling approach [1]. We have recently established a test facility in which a 50W BP Solar module surrounded by several glass panels in wood frames has been used to simulate a BIPV system with neighboring buildings. Current-voltage characteristics at different times of day for different months are in the process of being recorded together with solar insolation data. These data will be used to perform preliminary validation of the simulation software. A preliminary comparison of the collected experimental solar module performance data with the computer simulated data will be presented.

scholarly journals Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations (Preprint)

2018 ◽  
Author(s):  
Bethany Percha ◽  
Edward B Baskerville ◽  
Matthew Johnson ◽  
Joel T Dudley ◽  
Noah Zimmerman
Keyword(s):  
Precision Medicine ◽  
Simulated Data ◽  
Patient Characteristics ◽  
Simulation Software ◽  
Single Individual ◽  
Digital Medicine ◽  
Number Of Treatment ◽  
Study Designs ◽  
Stochastic Time Series ◽  
Stochastic Time

BACKGROUND Recent advances in molecular biology, sensors, and digital medicine have led to an explosion of products and services for high-resolution monitoring of individual health. The N-of-1 study has emerged as an important methodological tool for harnessing these new data sources, enabling researchers to compare the effectiveness of health interventions at the level of a single individual. OBJECTIVE N-of-1 studies are susceptible to several design flaws. We developed a model that generates realistic data for N-of-1 studies to enable researchers to optimize study designs in advance. METHODS Our stochastic time-series model simulates an N-of-1 study, incorporating all study-relevant effects, such as carryover and wash-in effects, as well as various sources of noise. The model can be used to produce realistic simulated data for a near-infinite number of N-of-1 study designs, treatment profiles, and patient characteristics. RESULTS Using simulation, we demonstrate how the number of treatment blocks, ordering of treatments within blocks, duration of each treatment, and sampling frequency affect our ability to detect true differences in treatment efficacy. We provide a set of recommendations for study designs on the basis of treatment, outcomes, and instrument parameters, and make our simulation software publicly available for use by the precision medicine community. CONCLUSIONS Simulation can facilitate rapid optimization of N-of-1 study designs and increase the likelihood of study success while minimizing participant burden.

scholarly journals Automatic Inference of Demographic Parameters Using Generative Adversarial Networks

2020 ◽  
Author(s):  
Zhanpeng Wang ◽  
Jiaping Wang ◽  
Michael Kourakos ◽  
Nhung Hoang ◽  
Hyong Hark Lee ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Simulated Data ◽  
Real Data ◽  
Simulation Software ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Isolation With Migration ◽  
Adversarial Network ◽  
Novel Approach ◽  
Input Parameters

AbstractPopulation genetics relies heavily on simulated data for validation, inference, and intuition. In particular, since real data is always limited, simulated data is crucial for training machine learning methods. Simulation software can accurately model evolutionary processes, but requires many hand-selected input parameters. As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it. In this work, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project, and show that we can accurately recapitulate the features of real data.

Tolerance Simulation of Compliant Sheet Metal Assemblies Using Automatic Node-Based Contact Detection

2008 ◽  
Author(s):  
Kristina Wa¨rmefjord ◽  
Lars Lindkvist ◽  
Rikard So¨derberg
Keyword(s):  
Simulated Data ◽  
Simulation Software ◽  
Contact Detection ◽  
Contact Modeling ◽  
Industrial Case Study ◽  
Critical Dimensions ◽  
Finite Element Software ◽  
Inspection Data ◽  
Variation Simulation

Tolerance simulation is a crucial tool for predicting the outcome in critical dimensions, and is used during early phases of product development in automotive industry. In order to increase the accuracy and the agreement with reality of the predictions even further, variation simulation software offer in some cases the possibility to perform compliant analysis, i.e. the parts are not restricted to be rigid. In compliant analysis contact modeling is an important feature. In this paper a simplified method for automatic contact detection, well suited for tolerance simulations, is suggested. Traditionally, those kinds of non-rigid simulations are very time consuming, but by using this kind of simplified contact modeling in the Monte Carlo simulations, the simulation times can be kept down. The method is tested on an industrial case study. The analyses are done with and without contact modeling and those results are compared to real inspection data. The contact modeling turns out to be an important feature; the correlation between the results with contact modeling and inspection data is much stronger than the correlation for simulations without contact modeling. When using the new contact modeling algorithm the correspondence between simulated data and inspection data is very satisfying and the algorithm seems to be faster than traditional finite element software.

scholarly journals Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda

2021 ◽  
Author(s):  
J. J. Johannes Hjorth ◽  
Jeanette Hellgren Kotaleski ◽  
Alexander Kozlov
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Rapid Development ◽  
Dorsal Striatum ◽  
Simulated Data ◽  
Cellular Level ◽  
Simulation Software ◽  
Important Approach ◽  
Set Up ◽  
Large Scale Networks

AbstractSimulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further ‘curate’ data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.

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