A Parameter Identification Study of Kinematic Errors in Planar Mechanisms

1975 ◽  
Vol 97 (2) ◽  
pp. 635-642 ◽  
Author(s):  
S. Dubowsky ◽  
J. Maatuk ◽  
N. D. Perreira
Keyword(s):  
Poor Performance ◽  
Optimization Techniques ◽  
System Response ◽  
Thermal Gradients ◽  
Trial And Error ◽  
Mathematical Methods ◽  
Planar Mechanisms ◽  
Manufacturing Errors ◽  
Computer Optimization ◽  
Kinematic Errors

The performance of machines and mechanical systems based on an evaluation of their theoretical design is often less than is optimally expected due to manufacturing errors, clearance, play in the machanism connections, wear, thermal gradients and unstable material properties. Yet the problem of identifying the sources of the poor performance based on available measured data has been treated essentially by trial and error methods. This study applies mathematical methods drawn from recently developed computer optimization techniques to identify the sources of this poor performance based on an examination of the system response.

Harmonic Synthesis Design Methodology for Dynamic Spring Balancing

1996 ◽  
Vol 118 (4) ◽  
pp. 733-740 ◽  
Author(s):  
Eungsoo Shin ◽  
D. A. Streit
Keyword(s):  
Dynamic System ◽  
Initial Conditions ◽  
Phase 1 ◽  
System Optimization ◽  
Optimization Method ◽  
Optimization Techniques ◽  
System Response ◽  
Two Phase ◽  
Walking Machine ◽  
Synthesis Design

A new spring balancing technique, called a two-phase optimization method, is presented. Phase 1 uses harmonic synthesis to provide a system configuration which achieves an approximation to a desired dynamic system response. Phase 2 uses results of harmonic synthesis as initial conditions for dynamic system optimization. Optimization techniques compensate for nonlinearities in machine dynamics. Example applications to robot manipulators and to walking machine legs are presented and discussed.

scholarly journals Performance Analysis and Optimization Techniques for Oracle Relational Databases

2019 ◽  
Vol 19 (2) ◽  
pp. 117-132
Author(s):  
Fernando Almeida ◽  
Pedro Silva ◽  
Fernando Araújo
Keyword(s):  
Execution Time ◽  
Relational Databases ◽  
Database System ◽  
Optimization Techniques ◽  
Management Systems ◽  
System Response ◽  
Database Access ◽  
Data Manipulation ◽  
System Response Time ◽  
Analyze Data

Abstract Databases provide an efficient way to store, retrieve and analyze data. Oracle relational database is one of the most popular database management systems that is widely used in a different variety of industries and businesses. Therefore, it is important to guarantee that the database access and data manipulation is optimized for reducing database system response time. This paper intends to analyze the performance and the main optimization techniques (Forall, Returning, and Bulk Collect) that can be adopted for Oracle Relational Databases. The results have shown that the adoption of Forall and Bulk Collect approaches bring significant benefits in terms of execution time. Furthermore, the growth rate of the average execution time is lower for Bulk Collect than Forall. However, adoption of Returning approach doesn’t bring significant statistical benefits.

Integrated Optimal Design of Planar Mechanisms Using Fuzzy Theories

1989 ◽  
Author(s):  
A. K. Dhingra ◽  
S. S. Rao
Keyword(s):  
Nonlinear Programming ◽  
High Speed ◽  
Integrated Approach ◽  
Problem Formulation ◽  
Optimization Techniques ◽  
Motion Path ◽  
Programming Formulation ◽  
Planar Mechanisms ◽  
Dynamic Synthesis ◽  
Motion Characteristics

Abstract A new integrated approach to the design of high speed planar mechanisms is presented. The resulting nonlinear programming formulation combines both the kinematic and dynamic synthesis aspects of mechanism design. The multiobjective optimization techniques presented in this work facilitate the design of a linkage to meet several kinematic and dynamic design criteria. The method can be used for motion, path, and function generation problems. The nonlinear programming formulation also permits the imposition of constraints to eliminate solutions which possess undesirable kinematic and motion characteristics. To model the vague and imprecise information in the problem formulation, the tools of fuzzy set theory have been used. A method of solving the resulting fuzzy multiobjective problem using mathematical programming techniques is presented. The outlined procedure is expected to be useful in situations where doubt arises about the exactness of permissible values, degree of credibility, and correctness of statements and judgements.

Comparative Study of Evolutionary Algorithms for Parameter Identification of an Impact Oscillator

2014 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu Mahfouz
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Optimization Techniques ◽  
System Response ◽  
Impact Oscillator ◽  
Swarm Optimization ◽  
System Parameters ◽  
Highly Nonlinear ◽  
The Impact

The use of non-classical evolutionary optimization techniques such as genetic algorithms, differential evolution, swarm optimization and genetic programming to solve the inverse problem of parameter identification of dynamical systems leading to chaotic states has been gaining popularity in recent years. In this paper, three popular evolutionary algorithms — differential evolution, particle swarm optimization and the firefly algorithm are used for parameter identification of a clearance-coupled-impact oscillator system. The behavior of impacting systems is highly nonlinear exhibiting a myriad of harmonic, low order and high order sub-harmonic resonances, as well as chaotic vibrations. The time-history simulations of the single-degree-of-freedom impact oscillator were obtained by the Neumark-β numerical integration algorithm. The results are illustrated by bifurcation graphs, state space portraits and Poincare’ maps which gives valuable insights on the dynamics of the impact system. The parameter identification problem relates to finding one set of system parameters given a chaotic or periodic system response as a set of Poincaré points and a different but known set of system parameters. The three evolutionary algorithms are compared over a set of parameter identification problems. The algorithms are compared based on solution quality to evaluate the efficacy of using one algorithm over another.

scholarly journals The Effects of Feature Optimization on High-Dimensional Essay Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Bong-Jun Yi ◽  
Do-Gil Lee ◽  
Hae-Chang Rim
Keyword(s):  
Poor Performance ◽  
Feature Space ◽  
Optimization Techniques ◽  
Training Data ◽  
High Dimensional ◽  
Automated Essay Scoring ◽  
Training Time ◽  
Training And Performance Improvement ◽  
Feature Optimization ◽  
And Performance

Current machine learning (ML) based automated essay scoring (AES) systems have employed various and vast numbers of features, which have been proven to be useful, in improving the performance of the AES. However, the high-dimensional feature space is not properly represented, due to the large volume of features extracted from the limited training data. As a result, this problem gives rise to poor performance and increased training time for the system. In this paper, we experiment and analyze the effects of feature optimization, including normalization, discretization, and feature selection techniques for different ML algorithms, while taking into consideration the size of the feature space and the performance of the AES. Accordingly, we show that the appropriate feature optimization techniques can reduce the dimensions of features, thus, contributing to the efficient training and performance improvement of AES.

The Emergence of Mathematical Needs in an Industrial Center

1953 ◽  
Vol 46 (1) ◽  
pp. 1-2
Author(s):  
Arvid W. Jacobson
Keyword(s):  
Mathematical Model ◽  
Modern Science ◽  
Order Effects ◽  
Entire System ◽  
Trial And Error ◽  
Mathematical Methods ◽  
Economic Process ◽  
Operating Unit ◽  
Industrial Center

The foremost feature in modern science and technology is the expanding role of mathematics. In industry and business the increasing complexity of problems and the ever-present search for better products and services, lead to the use of mathematical methods. Trial and error methods can not alone yield the information of the behavior of a physical system or a business procedure or an economic process necessary if improvement in design or function is to be achieved. To understand and evaluate the effects of small components on the behavior of a system, it must be considered as a single operating unit. The functional dependance of the entire system must be expressed in terms of all of the components, large and small. Thus a mathematical model emerges which is an abstraction of the quantitative and logical relationships of the system. Often, as further improvements are sought, the effect of a larger number of these smaller components need to be understood and weighed. It is thus the proper evaluation of the small effects or “second order effects” that determines progress.

Computer Optimization Techniques.

1982 ◽  
Vol 77 (377) ◽  
pp. 223 ◽  
Author(s):  
John J. Bartholdi ◽  
William Conley
Keyword(s):  
Optimization Techniques ◽  
Computer Optimization

Optimization Techniques in Numerical Simulation of Casting Process

2013 ◽  
Vol 457-458 ◽  
pp. 463-466 ◽  
Author(s):  
Guo Hong Zhang ◽  
Rong De Li ◽  
Chang Tian ◽  
Ke Qiang Qiu ◽  
Jun Xian Ma
Keyword(s):  
Numerical Simulation ◽  
Casting Process ◽  
Optimization Techniques ◽  
Human Intervention ◽  
Trial And Error ◽  
Design Work

This paper presents an overview and example using optimization techniques in casting numerical simulation. Most of the design work can fulfill with the software without human intervention. It really frees the engineer from the amount of trial-and-error that is necessary in traditional modeling.

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