A constrained optimization technique for estimating environmental parameters from CZMIL hyperspectral and lidar data

2010 ◽  
Author(s):  
Minsu Kim ◽  
Joong Yong Park ◽  
Grady Tuell
Keyword(s):  
Constrained Optimization ◽  
Optimization Technique ◽  
Environmental Parameters ◽  
Lidar Data

scholarly journals Correction of failure in linear antenna arrays with greedy sparseness constrained optimization technique

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189240 ◽  
Author(s):  
Shafqat Ullah Khan ◽  
M. K. A. Rahim ◽  
Murtala Aminu-Baba ◽  
N. A. Murad
Keyword(s):  
Constrained Optimization ◽  
Antenna Arrays ◽  
Optimization Technique ◽  
Linear Antenna ◽  
Linear Antenna Arrays

scholarly journals REDUCTION OF STRIPING NOISE IN OVERLAPPING LIDAR INTENSITY DATA BY RADIOMETRIC NORMALIZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 151-156
Author(s):  
Wai Yeung Yan ◽  
Ahmed Shaker
Keyword(s):  
Land Cover ◽  
Environmental Parameters ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Intensity Data ◽  
Joint Histogram ◽  
Before And After ◽  
Range Equation ◽  
Parallel Strips ◽  
Airborne Lidar Data

To serve seamless mapping, airborne LiDAR data are usually collected with multiple parallel strips with one or two cross strip(s). Nevertheless, the overlapping regions of LiDAR data strips are usually found with unbalanced intensity values, resulting in the appearance of stripping noise. Despite that physical intensity correction methods are recently proposed, some of the system and environmental parameters are assumed as constant or not disclosed, leading to such an intensity discrepancy. This paper presents a new normalization technique to adjust the radiometric misalignment found in the overlapping LiDAR data strips. The normalization technique is built upon a second-order polynomial function fitted on the joint histogram plot, which is generated with a set of pairwise closest data points identified within the overlapping region. The method was tested on Teledyne Optech’s Gemini dataset (at 1064 nm wavelength), where the LiDAR intensity data were first radiometrically corrected based on the radar (range) equation. Five land cover features were selected to evaluate the coefficient of variation (<i>cv</i>) of the intensity values before and after implementing the proposed method. Reduction of <i>cv</i> was found by 19% to 59% in the Gemini dataset, where the striping noise was significantly reduced in the radiometrically corrected and normalized intensity data. The Gemini dataset was also used to conduct land cover classification, and the overall accuracy yielded a notable improvement of 9% to 18%. As a result, LiDAR intensity data should be pre-processed with radiometric correction and normalization prior to any data manipulation.

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

2012 ◽  
pp. 643-662
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Objective Function ◽  
Penalty Function ◽  
Manufacturing Systems ◽  
Optimization Technique ◽  
Hybrid Genetic Algorithm ◽  
Decision Variable ◽  
Exponential Penalty Function ◽  
Simulation Scenario

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

2011 ◽  
pp. 212-231 ◽  
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Objective Function ◽  
Penalty Function ◽  
Manufacturing Systems ◽  
Optimization Technique ◽  
Hybrid Genetic Algorithm ◽  
Decision Variable ◽  
Exponential Penalty Function ◽  
Simulation Scenario

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

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