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Optimal planning of robot calibration experiments by genetic algorithms
Title
Optimal planning of robot calibration experiments by genetic algorithms.
Creator
Huang, Weizhen., Florida Atlantic University, Wu, Jie
Abstract/Description
In this thesis work, techniques developed in the science of genetic computing is applied to solve the problem of planning a robot calibration experiment. Robot calibration is a process by the robot accuracy is enhanced through modification of its control software. The selection of robot measurement configurations is an important element in successfully completing a robot calibration experiment. A classical genetic algorithm is first customized for a type of robot measurement configuration...
Show moreIn this thesis work, techniques developed in the science of genetic computing is applied to solve the problem of planning a robot calibration experiment. Robot calibration is a process by the robot accuracy is enhanced through modification of its control software. The selection of robot measurement configurations is an important element in successfully completing a robot calibration experiment. A classical genetic algorithm is first customized for a type of robot measurement configuration selection problem in which the robot workspace constraints are defined in terms of robot joint limits. The genetic parameters are tuned in a systematic way to greatly enhance the performance of the algorithm. A recruit-oriented genetic algorithm is then proposed, together with new genetic operators. Examples are also given to illustrate the concepts of this new genetic algorithm. This new algorithm is aimed at solving another type of configuration selection problem, in which not all points in the robot workspace are measurable by an external measuring device. Extensive simulation studies are conducted for both classical and recruit-oriented genetic algorithms, to examine the effectiveness of these algorithms.
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Date Issued
1995
PURL
http://purl.flvc.org/fcla/dt/15186
Subject Headings
Genetic algorithms, Robots--Calibration, Combinatorial optimization
Format
Document (PDF)
novel optimization algorithm and other techniques in medicinal chemistry
Title
A novel optimization algorithm and other techniques in medicinal chemistry.
Creator
Santos, Radleigh G., Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
In this dissertation we will present a stochastic optimization algorithm and use it and other mathematical techniques to tackle problems arising in medicinal chemistry. In Chapter 1, we present some background about stochastic optimization and the Accelerated Random Search (ARS) algorithm. We then present a novel improvement of the ARS algorithm, DIrected Accelerated Random Search (DARS), motivated by some theoretical results, and demonstrate through numerical results that it improves upon...
Show moreIn this dissertation we will present a stochastic optimization algorithm and use it and other mathematical techniques to tackle problems arising in medicinal chemistry. In Chapter 1, we present some background about stochastic optimization and the Accelerated Random Search (ARS) algorithm. We then present a novel improvement of the ARS algorithm, DIrected Accelerated Random Search (DARS), motivated by some theoretical results, and demonstrate through numerical results that it improves upon ARS. In Chapter 2, we use DARS and other methods to address issues arising from the use of mixture-based combinatorial libraries in drug discovery. In particular, we look at models associated with the biological activity of these mixtures and use them to answer questions about sensitivity and robustness, and also present a novel method for determining the integrity of the synthesis. Finally, in Chapter 3 we present an in-depth analysis of some statistical and mathematical techniques in combinatorial chemistry, including a novel probabilistic approach to using structural similarity to predict the activity landscape.
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Date Issued
2012
PURL
http://purl.flvc.org/FAU/3352830
Subject Headings
Drugs, Design, Mathematical models, Combinatorial optimization, Combinatorial chemistry, Genetic algorithms, Mathematical optimization, Stochastic processes
Format
Document (PDF)
General monotonicity, interpolation of operators, and applications
Title
General monotonicity, interpolation of operators, and applications.
Creator
Grigoriev, Stepan M., Sagher, Yoram, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
Assume that {φn} is an orthonormal uniformly bounded (ONB) sequence of complex-valued functions de ned on a measure space (Ω,Σ,µ), and f ∈ L1(Ω,Σ,µ). Let be the Fourier coefficients of f with respect to {φn} . R.E.A.C. Paley proved a theorem connecting the Lp-norm of f with a related norm of the sequence {cn}. Hardy and Littlewood subsequently proved that Paley’s result is best possible within its context. Their results were generalized by Dikarev, Macaev, Askey, Wainger, Sagher, and later by...
Show moreAssume that {φn} is an orthonormal uniformly bounded (ONB) sequence of complex-valued functions de ned on a measure space (Ω,Σ,µ), and f ∈ L1(Ω,Σ,µ). Let be the Fourier coefficients of f with respect to {φn} . R.E.A.C. Paley proved a theorem connecting the Lp-norm of f with a related norm of the sequence {cn}. Hardy and Littlewood subsequently proved that Paley’s result is best possible within its context. Their results were generalized by Dikarev, Macaev, Askey, Wainger, Sagher, and later by Tikhonov, Li yand, Booton and others.The present work continues the generalization of these results.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004290, http://purl.flvc.org/fau/fd/FA00004290
Subject Headings
Combinatorial optimization, Differential dynamical systems, Functions of complex variables, Inequalities (Mathematics), Nonsmooth optimization
Format
Document (PDF)
Construction of combinatorial designs with prescribed automorphism groups
Title
Construction of combinatorial designs with prescribed automorphism groups.
Creator
Kolotoglu, Emre., Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description
In this dissertation, we study some open problems concerning the existence or non-existence of some combinatorial designs. We give the construction or proof of non-existence of some Steiner systems, large sets of designs, and graph designs, with prescribed automorphism groups.
Date Issued
2013
PURL
http://purl.flvc.org/fcla/dt/3360795
Subject Headings
Combinatorial designs and configurations, Finite geometries, Curves, Algebraic, Automorphisms, Mathematical optimization, Steiner systems
Format
Document (PDF)
CBR-based software quality models and quality of data
Title
CBR-based software quality models and quality of data.
Creator
Xiao, Yudong., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The performance accuracy of software quality estimation models is influenced by several factors, including the following two important factors: performance of the prediction algorithm and the quality of data. This dissertation addresses these two factors, and consists of two components: (1) a proposed genetic algorithm (GA) based optimization of software quality models for accuracy enhancement, and (2) a proposed partitioning- and rule-based filter (PRBF) for noise detection toward...
Show moreThe performance accuracy of software quality estimation models is influenced by several factors, including the following two important factors: performance of the prediction algorithm and the quality of data. This dissertation addresses these two factors, and consists of two components: (1) a proposed genetic algorithm (GA) based optimization of software quality models for accuracy enhancement, and (2) a proposed partitioning- and rule-based filter (PRBF) for noise detection toward improvement of data quality. We construct a generalized framework of our embedded GA-optimizer, and instantiate the GA-optimizer for three optimization problems in software quality engineering: parameter optimization for case-based reasoning (CBR) models; module rank optimization for module-order modeling (MOM); and structural optimization for our multi-strategy classification modeling approach, denoted RB2CBL. Empirical case studies using software measurement data from real-world software systems were performed for the optimization problems. The GA-optimization approaches improved software quality prediction accuracy, highlighting the practical benefits of using GA for solving optimization problems in software engineering. The proposed noise detection approach, PRBF, was empirically evaluated using data categorized into two classes. Empirical studies on artificially corrupted datasets and datasets with known (natural) noise demonstrated that PRBF can effectively detect both artificial and natural noise. The proposed filter is a stable and robust technique, and always provided optimal or near-optimal noise detection results. In addition, it is applicable on datasets with nominal and numerical attributes, as well as those with missing values. The PRBF technique supports two methods of noise detection: class noise detection and cost-sensitive noise detection. The former is an easy-to-use method and does not need parameter settings, while the latter is suited for applications where each class has a specific misclassification cost. PRBF can also be used iteratively to investigate the two general types of data noise: attribute and class noise.
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Date Issued
2005
PURL
http://purl.flvc.org/fcla/dt/12141
Subject Headings
Computer software--Quality control, Genetic programming (Computer science), Software engineering, Case-based reasoning, Combinatorial optimization, Computer network architecture
Format
Document (PDF)
An Ant Inspired Dynamic Traffic Assignment for VANETs: Early Notification of Traffic Congestion and Traffic Incidents
Title
An Ant Inspired Dynamic Traffic Assignment for VANETs: Early Notification of Traffic Congestion and Traffic Incidents.
Creator
Arellano, Wilmer, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Vehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road...
Show moreVehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road network and in accordance with their origins and destinations. We present a novel dynamic decentralized and infrastructure-less algorithm to alleviate traffic congestions on road networks and to fill the void left by current algorithms which are either static, centralized, or require infrastructure. The algorithm follows an online approach that seeks stochastic user equilibrium and assigns traffic as it evolves in real time, without prior knowledge of the traffic demand or the schedule of the cars that will enter the road network in the future. The Reverse Online Algorithm for the Dynamic Traffic Assignment inspired by Ant Colony Optimization for VANETs follows a metaheuristic approach that uses reports from other vehicles to update the vehicle’s perceived view of the road network and change route if necessary. To alleviate the broadcast storm spontaneous clusters are created around traffic incidents and a threshold system based on the level of congestion is used to limit the number of incidents to be reported. Simulation results for the algorithm show a great improvement on travel time over routing based on shortest distance. As the VANET transceivers have a limited range, that would limit messages to reach at most 1,000 meters, we present a modified version of this algorithm that uses a rebroadcasting scheme. This rebroadcasting scheme has been successfully tested on roadways with segments of up to 4,000 meters. This is accomplished for the case of traffic flowing in a single direction on the roads. It is anticipated that future simulations will show further improvement when traffic in the other direction is introduced and vehicles travelling in that direction are allowed to use a store carry and forward mechanism.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004566, http://purl.flvc.org/fau/fd/FA00004566
Subject Headings
Vehicular ad hoc networks (Computer networks)--Technological innovations., Routing protocols (Computer network protocols), Artificial intelligence., Intelligent transportation systems., Intelligent control systems., Mobile computing., Computer algorithms., Combinatorial optimization.
Format
Document (PDF)