Automated Shape Optimization of Orienting Devices for Vibratory Bowl Feeders

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Daniel Hofmann ◽  
Hongrong Huang ◽  
Gunther Reinhart
Keyword(s):  
Shape Optimization ◽  
Good Accuracy ◽  
Random Search ◽  
Optimization Technique ◽  
Trial And Error ◽  
Shape Variation ◽  
Search Optimization ◽  
Simulation Based ◽  
Automated Simulation ◽  
Small Parts

Orienting devices for vibratory bowl feeders are still the most widely used system for the automated sorting and feeding of small parts. The design process of these orienting devices has recently been supported by simulation methods. However, this merely shifts the well-known trial-and-error-based adaption of the orienting device's geometry into virtual world. Yet, this does not provide optimal design and, furthermore, requires strong involvement of the developer due to manual shape variation. This paper proposes an optimization algorithm for the automated simulation-based shape optimization of orienting devices for vibratory bowl feeders. First, general formalisms to state the multiobjective optimization problem for arbitrary types of orienting devices and feeding parts are provided. Then, the implementation of the algorithm is described based on Bullet Physics Engine and random search optimization technique. Finally, comparison of simulation results with experimental data point out good accuracy and, thus, great potential of the developed shape optimization software.

scholarly journals Compensating Pose Uncertainties through Appropriate Gripper Finger Cutouts

2018 ◽  
Vol 12 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Adam Wolniakowski ◽  
Andrej Gams ◽  
Lilita Kiforenko ◽  
Aljaž Kramberger ◽  
Dimitrios Chrysostomou ◽  
...  
Keyword(s):  
Vision System ◽  
Geometry Optimization ◽  
Optimization Method ◽  
Trial And Error ◽  
Simulation Environment ◽  
Design And Optimization ◽  
Real World Setting ◽  
Industrial Object ◽  
Simulation Based ◽  
Automated Simulation

Abstract The gripper finger design is a recurring problem in many robotic grasping platforms used in industry. The task of switching the gripper configuration to accommodate for a new batch of objects typically requires engineering expertise, and is a lengthy and costly iterative trial-and-error process. One of the open challenges is the need for the gripper to compensate for uncertainties inherent to the workcell, e.g. due to errors in calibration, inaccurate pose estimation from the vision system, or object deformation. In this paper, we present an analysis of gripper uncertainty compensating capabilities in a sample industrial object grasping scenario for a finger that was designed using an automated simulation-based geometry optimization method (Wolniakowski et al., 2013, 2015). We test the developed gripper with a set of grasps subjected to structured perturbation in a simulation environment and in the real-world setting. We provide a comparison of the data obtained by using both of these approaches. We argue that the strong correspondence observed in results validates the use of dynamic simulation for the gripper finger design and optimization.

scholarly journals A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110254
Author(s):  
Armaghan Mohsin ◽  
Yazan Alsmadi ◽  
Ali Arshad Uppal ◽  
Sardar Muhammad Gulfam
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Fast Convergence ◽  
Good Convergence ◽  
Optimum Point ◽  
Search Optimization ◽  
Higher Dimensional ◽  
Gradient Based ◽  
Improved Accuracy ◽  
Quadratic Cost Function

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of [Formula: see text], that is, [Formula: see text]  = 1 for reflection and [Formula: see text]  = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter [Formula: see text] is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of [Formula: see text] will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

scholarly journals An enhanced krill herd optimization technique used for classification problem

2021 ◽  
Vol 30 (2) ◽  
pp. 354-364
Author(s):  
Firas Al-Mashhadani ◽  
Ibrahim Al-Jadir ◽  
Qusay Alsaffar
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Optimization Technique ◽  
Optimization Method ◽  
Classification Problem ◽  
Test Results ◽  
Internal Evaluation ◽  
Evaluation Measures ◽  
Search Optimization ◽  
Krill Herd

In this paper, this method is intended to improve the optimization of the classification problem in machine learning. The EKH as a global search optimization method, it allocates the best representation of the solution (krill individual) whereas it uses the simulated annealing (SA) to modify the generated krill individuals (each individual represents a set of bits). The test results showed that the KH outperformed other methods using the external and internal evaluation measures.

scholarly journals Hyperparameters optimization for ResNet and Xception in the purpose of diagnosing COVID-19

2021 ◽  
pp. 1-17
Author(s):  
Hania H. Farag ◽  
Lamiaa A. A. Said ◽  
Mohamed R. M. Rizk ◽  
Magdy Abd ElAzim Ahmed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Random Search ◽  
Learning Networks ◽  
Residual Network ◽  
Global Pandemic ◽  
Search Optimization

COVID-19 has been considered as a global pandemic. Recently, researchers are using deep learning networks for medical diseases’ diagnosis. Some of these researches focuses on optimizing deep learning neural networks for enhancing the network accuracy. Optimizing the Convolutional Neural Network includes testing various networks which are obtained through manually configuring their hyperparameters, then the configuration with the highest accuracy is implemented. Each time a different database is used, a different combination of the hyperparameters is required. This paper introduces two COVID-19 diagnosing systems using both Residual Network and Xception Network optimized by random search in the purpose of finding optimal models that give better diagnosis rates for COVID-19. The proposed systems showed that hyperparameters tuning for the ResNet and the Xception Net using random search optimization give more accurate results than other techniques with accuracies 99.27536% and 100 % respectively. We can conclude that hyperparameters tuning using random search optimization for either the tuned Residual Network or the tuned Xception Network gives better accuracies than other techniques diagnosing COVID-19.

Automated simulation-based verification of power requirements for Systems-on-Chips

2010 ◽  
Author(s):  
Christoph Trummer ◽  
Christoph M. Kirchsteiger ◽  
Christian Steger ◽  
Reinhold Weiss ◽  
Markus Pistauer ◽  
...  
Keyword(s):  
Simulation Based ◽  
Automated Simulation

Dynamics of a Vibratory Feeder

1997 ◽  
Author(s):  
Peter Wolfsteiner ◽  
Friedrich Pfeiffer
Keyword(s):  
Coulomb Friction ◽  
Three Dimensional ◽  
Automatic Assembly ◽  
Trial And Error ◽  
Unilateral Constraints ◽  
Multiple Impacts ◽  
Three Dimensional Model ◽  
Transportation Process ◽  
Feeding Process ◽  
Small Parts

Abstract The most common devices used to feed small parts in an automatic assembly framework are vibratory feeders. They are used to store, feed, orientate and isolate the parts. Due to the complex mechanics of the feeding process the design of the feeders is still depending on trial and error. This paper presents a complete dynamical model of the transportation process including unilateral constraints and multiple impacts, both with coulomb friction. Some simulation results, computed with a three dimensional model, explain the practical benefit of the proposed tool.

A Comparison of Machine Learning Methods to Predict Hospital Readmission of Diabetic Patient

2021 ◽  
Vol 7 (2) ◽  
pp. 164-168
Author(s):  
Cuong Le Dinh Phu ◽  
Dong Wang
Keyword(s):  
Machine Learning ◽  
Hospital Readmission ◽  
Random Search ◽  
Learning Model ◽  
The Body ◽  
Hyperparameter Optimization ◽  
Health Records ◽  
Search Optimization ◽  
Machine Learning Model ◽  
Public Dataset

Diabetes is a chronic disease whereby blood glucose is not metabolized in the body. Electronic health records (EHRs) (Yadav, P. et al., 2018). for each individual or a population have become important to standing developing trends of diseases. Machine learning helps provide accurate predictions higher than actual assessments. The main problem that we are trying to apply machine learning model and using EHRs that combines the strength of a machine learning model with various features and hyperparameter optimization or tuning. The hyperparameter optimization (Feurer, M., 2019) uses the random search optimization which minimizes a predefined loss function on given independent data. The evaluation on the method comparisons indicated that machine learning models has increased the ratio of metrics compared to previous models (Accuracy, Recall, F1 and AUC score) on the same public dataset that is reprocessed.

A Quick Presentation of Evolutionary Computation

2010 ◽  
pp. 22-38
Author(s):  
Pierre Collet
Keyword(s):  
Evolutionary Computation ◽  
Random Search ◽  
Optimization Technique ◽  
Parallel Architecture ◽  
Optimization Methods ◽  
General Purpose ◽  
Old Field ◽  
Multiple Data ◽  
Intractable Problems ◽  
The 1960S

Evolutionary computation is an old field of computer science, that started in the 1960s nearly simultaneously in different parts of the world. It is an optimization technique that mimics the principles of Darwinian evolution in order to find good solutions to intractable problems faster than a random search. Artificial Evolution is only one among many stochastic optimization methods, but recently developed hardware (General Purpose Graphic Processing Units or GPGPU) gives it a tremendous edge over all the other algorithms, because its inherently parallel nature can directly benefit from the difficult to use Single Instruction Multiple Data parallel architecture of these cheap, yet very powerful cards.

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