Fast Mapping of Obstacles Into Configuration Space

1998 ◽  
Vol 120 (2) ◽  
pp. 298-304 ◽  
Author(s):  
Wei Li ◽  
Chenyu Ma ◽  
Zushun Chen ◽  
Qi Cao ◽  
Jingnan Ye
Keyword(s):  
Configuration Space ◽  
Critical Points ◽  
Computational Time ◽  
Global Information ◽  
Cad Model ◽  
Two Dimensional ◽  
Fast Mapping ◽  
Mapping Procedure ◽  
Industrial Manipulators ◽  
Time Required

This paper presents an approach to fast mapping of obstacles from a workspace into a configuration space, based on defining some specific points in the workspace as fundamental obstacles. In order to achieve this objective, we propose efficient algorithms for mapping complex Cartesian obstacles by determining their critical points. The computational time required for mapping a two-dimensional obstacle is approximately 5 ms with a 33 Mhz 80486 CPU. Obstacle mapping in this paper is based on a CAD model of an environment that provide global information. The proposed mapping procedure and algorithms are suitable for industrial manipulators with a symmetric workspace occupied by polygonal obstacles.

Nonsimilar Solution of the Laminar Boundary Layer in an Oscillatory Flow by an Integral Matrix Method

1971 ◽  
Vol 93 (4) ◽  
pp. 543-549
Author(s):  
B. A. Gastrock ◽  
J. A. Miller
Keyword(s):  
Oscillatory Flow ◽  
Numerical Technique ◽  
Computational Time ◽  
Two Dimensional ◽  
Integral Matrix ◽  
Oscillation Cycle ◽  
Laminar Boundary Layers ◽  
Time Required ◽  
Core Storage ◽  
Good Agreement

The development of a numerical technique for the treatment of two-dimensional non-similar, unsteady, laminar boundary layers is presented. The method is an extension to nonsteady flows of the integral matrix procedure of Kendall. Solutions of example problems are presented demonstrating good agreement with known classical results. Core storage requirements of 130K bytes allow consideration of as many as 1250 field points and 50 time increments per oscillation cycle. Solution of oscillating Blasius flow for 8 nodal points and 16 time increments in 13.49 seconds demonstrates the practicality of the computational time required, while agreement with both the analysis and experiment of Nickerson for this flow is excellent.

An Image Segmentation Method Based on Two-Dimensional Entropy and Chaotic Lightning Attachment Procedure Optimization Algorithm

2020 ◽  
Vol 34 (11) ◽  
pp. 2054030 ◽  
Author(s):  
Wei Liu ◽  
Shuai Yang ◽  
Zhiwei Ye ◽  
Qian Huang ◽  
Yongkun Huang
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Computational Time ◽  
Two Dimensional ◽  
Threshold Segmentation ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Competitive Algorithms

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently.

Two-dimensional critical points and critical isotherms

1985 ◽  
Vol 32 (7) ◽  
pp. 4760-4762 ◽  
Author(s):  
Jeffrey J. Hamilton
Keyword(s):  
Critical Points ◽  
Two Dimensional

Equilibrium and nonequilibrium models on solomon networks with two square lattices

2017 ◽  
Vol 28 (08) ◽  
pp. 1750099
Author(s):  
F. W. S. Lima
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Critical Exponent ◽  
Critical Points ◽  
Majority Vote ◽  
Universality Class ◽  
2D Systems ◽  
Two Dimensional ◽  
Critical Properties ◽  
Regular Lattices

We investigate the critical properties of the equilibrium and nonequilibrium two-dimensional (2D) systems on Solomon networks with both nearest and random neighbors. The equilibrium and nonequilibrium 2D systems studied here by Monte Carlo simulations are the Ising and Majority-vote 2D models, respectively. We calculate the critical points as well as the critical exponent ratios [Formula: see text], [Formula: see text], and [Formula: see text]. We find that numerically both systems present the same exponents on Solomon networks (2D) and are of different universality class than the regular 2D ferromagnetic model. Our results are in agreement with the Grinstein criterion for models with up and down symmetry on regular lattices.

Developing and Comparing Alternative Design Optimization Formulations for a Vibration Absorber Example

2017 ◽  
Author(s):  
Siyao Luan ◽  
Deborah L. Thurston ◽  
Madhav Arora ◽  
James T. Allison
Keyword(s):  
Design Problem ◽  
Mathematical Optimization ◽  
Computational Time ◽  
Vibration Absorber ◽  
Solution Quality ◽  
Design Representation ◽  
Core Elements ◽  
Improved Design ◽  
Time Required ◽  
Level Of Effort

In some cases, the level of effort required to formulate and solve an engineering design problem as a mathematical optimization problem is significant, and the potential improved design performance may not be worth the excessive effort. In this article we address the tradeoffs associated with formulation and modeling effort. Here we define three core elements (dimensions) of design formulations: design representation, comparison metrics, and predictive model. Each formulation dimension offers opportunities for the design engineer to balance the expected quality of the solution with the level of effort and time required to reach that solution. This paper demonstrates how using guidelines can be used to help create alternative formulations for the same underlying design problem, and then how the resulting solutions can be evaluated and compared. Using a vibration absorber design example, the guidelines are enumerated, explained, and used to compose six alternative optimization formulations, featuring different objective functions, decision variables, and constraints. The six alternative optimization formulations are subsequently solved, and their scores reflecting their complexity, computational time, and solution quality are quantified and compared. The results illustrate the unavoidable tradeoffs among these three attributes. The best formulation depends on the set of tradeoffs that are best in that situation.

scholarly journals A Simple Guide to Lawn Mowing

2021 ◽  
Vol 8 (14) ◽  
pp. 73-90
Author(s):  
Perry Y.C. Lee ◽  
Joshua B. Lee
Keyword(s):  
Quantitative Information ◽  
Two Dimensional ◽  
Rectangular Region ◽  
Time Required

Abstract This paper presents the total time required to mow a two-dimensional rectangular region of grass using a push mower. In deriving the total time, each of the three ‘well known’ (or intuitive) mowing patterns to cut the entire rectangular grass area is used. Using basic mathematics, analytical and empirical time results for each of the three patterns taken to completely cover this rectangular region are presented, and examples are used to determine which pattern provides an optimal total time to cut a planar rectangular region. This paper provides quantitative information to aid in deciding which mowing pattern to use when cutting one’s lawn.

Evaluation of a Multi-Goal Solver for Use in a Blackboard Architecture

2014 ◽  
Vol 6 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Jeremy Straub
Keyword(s):  
Multiple Solutions ◽  
Computational Time ◽  
Problem Analysis ◽  
Robotic Control ◽  
Operating Environment ◽  
Blackboard Architecture ◽  
Rule Based ◽  
Multiple Levels ◽  
Time Required ◽  
Naive Approach

This article presents a multi-goal solver for problems that can be modeled using a Blackboard Architecture. The Blackboard Architecture can be used for data fusion, robotic control and other applications. It combines the rule-based problem analysis of an expert system with a mechanism for interacting with its operating environment. In this context, numerous control or domain (system-subject) problems may exist which can be solved through reaching one of multiple outcomes. For these problems which have multiple solutions, any of which constitutes an end-goal, a solving mechanism which is solution-choice-agnostic and finds the lowest-cost path to the lowest-cost solution is required. Such a solver mechanism is presented and characterized herein. The performance of the solver (including both the computational time required to ascertain a solution and execute it) is compared to the naïve Blackboard approach. This performance characterization is performed across multiple levels of rule counts and rule connectivity. The naïve approach is shown to generate a solution faster, but the solutions generated by this approach, in most cases, are inferior to those generated by the solver.

scholarly journals Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

2019 ◽  
Vol 11 (12) ◽  
pp. 1405 ◽  
Author(s):  
Razika Bazine ◽  
Huayi Wu ◽  
Kamel Boukhechba
Keyword(s):  
Hyperspectral Imagery ◽  
Wiener Filter ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Computational Time ◽  
Support Vector ◽  
Two Dimensional ◽  
Significant Information ◽  
Dct Domain ◽  
Frequency Components

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.

scholarly journals Comparison of Genetic Algorithm and Harmony Search Method for 2D Geometry Optimization.

2018 ◽  
Vol 159 ◽  
pp. 01009 ◽  
Author(s):  
Mohammad Ghozi ◽  
Anik Budiati
Keyword(s):  
Genetic Algorithm ◽  
Harmony Search ◽  
Geometry Optimization ◽  
Steel Structure ◽  
Search Method ◽  
Geometric Optimization ◽  
Computational Time ◽  
Time Required ◽  
Average Computational Time ◽  
Better Than

There are many applications of Genetic Algorithm (GA) and Harmony Search (HS) Method for solving problems in civil engineering design. The question is, still, which method is better for geometry optimization of a steel structure. The purpose of this paper is to compare GA and HS performance for geometric optimization of a steel structure. This problem is solved by optimizing a steel structure using GA and HS and then comparing the structure’s weight as well as the time required for the calculation. In this study, GA produced a structural weight of 2308.00 kg to 2387.00 kg and HS scored 2193.12 kg to 2239.48 kg. The average computational time required by GA is 607 seconds and HS needed 278 seconds. It concludes that HS is faster and better than GA for geometry optimization of a steel structure.

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