An alternative objective function for fitting regression trees to functional response variables

2011 ◽  
Vol 55 (9) ◽  
pp. 2557-2567 ◽  
Author(s):  
Stephen E. Lane ◽  
Andrew P. Robinson
Keyword(s):  
Objective Function ◽  
Functional Response ◽  
Regression Trees ◽  
Alternative Objective Function ◽  
Response Variables

scholarly journals Comparison between classical ‘P&O’ algorithm and FLC of MPPT for GPV under partial shading

2021 ◽  
Vol 12 (2) ◽  
pp. 1131
Author(s):  
Youcef Abdelaziz ◽  
Bouanane Abdelkrim ◽  
Merah Abdelkader
Keyword(s):  
Objective Function ◽  
Characteristic P ◽  
Partial Shading ◽  
Classical Algorithm ◽  
Alternative Objective Function ◽  
Low Irradiation ◽  
Parallel Series

<p><span lang="EN-US">When the GPV is under partial shading, several peaks appear in the characteristic P-V, namely a GMP and one or more local maximums. The classical algorithm ‘P&amp;O’ MPPT cannot converge on the GMP for low irradiation values and is trapped by tracking down a LMP so making the algorithm ineffective making the algorithm ineffective, in this case under 200 W/m². An alternative objective function is developed to optimize the performance of the FLC by selecting the appropriate gains using PSO. In this simulation the GPV is composed of one hundred modules grouped parallel series (10x10) and subjected to partial shading. The proposed FLC provides better performance for GMP tracking for the chosen shade configuration selected.</span></p>

A Multi-Variable Approach to Determine the “Best” Gear Design

2000 ◽  
Author(s):  
Donald R. Houser ◽  
Jonny Harianto ◽  
B. Chandrasekaran ◽  
John Josephson ◽  
Naresh Iyer
Keyword(s):  
Objective Function ◽  
Optimization Techniques ◽  
Response Variable ◽  
Gear Design ◽  
Variable Approach ◽  
Design Variables ◽  
Simulation Based ◽  
To Come ◽  
Selection Of ◽  
Response Variables

Abstract Gear design requires the designer to compromise many design variables in order to determine the best performance of a gear set. Unfortunately the designer has a multiplicity of goals including keeping both bending and pitting stresses under an allowable value, minimizing scoring, achieving minimum efficiency and trying to minimize noise. This latter response variable is rarely considered in the initial gear design. In this work, noise is considered to be one of the more important design considerations. One approach to multi-variable gear design that has been tried is design optimization. Usually optimization techniques are limited in the number of variables that can be handled and with so many response variables, it is difficult to come up with an objective function that reflects the considerations of a real gear designer. In this paper we present a simulation-based approach to gear design that allows the designer to essentially “run all of the cases”. The simulation accounts for the true load distribution of the gears when computing response variables. Also, such factors as manufacturing tolerances may be included in the simulation so that truly robust designs may be obtained. Rather than using an objective function approach, designs are analyzed with a dominance filter that assesses each response variable in a manner that results in the “best” design. After these “best” designs are found, an interactive viewer allows the selection of those designs that best meet the designer’s goals with regard to all design variables. Several examples are presented in this paper. In each case, over 65,000 designs are evaluated and the dominance filter results in from 200 to 900 successful designs, depending on the tolerances that are applied. After sorting with the viewer the designer usually ends up with from 5 to 20 designs whose features may vary significantly, but have similar performances.

Broadband Energy Harvesting From Vibrations Using Magnetic Transduction

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Giovanni Caruso
Keyword(s):  
Energy Harvesting ◽  
Objective Function ◽  
Closed Form ◽  
Energy Harvester ◽  
Electric Circuit ◽  
Harmonic Excitation ◽  
Electromagnetic Energy ◽  
Effective Bandwidth ◽  
Harvesting Efficiency ◽  
Alternative Objective Function

In this paper, an adaptive electromagnetic energy harvester is proposed and analyzed. It is composed of an oscillating mass equipped with an electromagnetic transducer, whose pins are connected to a resonant resistive–inductive–capacitive electric circuit in order to increase its effective bandwidth. Closed-form expressions for the optimal circuit parameters are presented, which maximize the power harvested by the device under harmonic excitation. The harvesting efficiency, defined as the ratio between the harvested power and the power absorbed by the oscillating device, is also reported. It is used as an alternative objective function for the optimization of the harvester circuit parameters.

A noise‐optimized objective method for predictive deconvolution

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 552-563
Author(s):  
Scott C. Hornbostel
Keyword(s):  
Linear Operator ◽  
Objective Function ◽  
Input Data ◽  
Random Noise ◽  
Filter Performance ◽  
Predictive Deconvolution ◽  
Noise Energy ◽  
The Impact ◽  
Temporally Correlated ◽  
Alternative Objective Function

Predictive deconvolution filters are designed to remove as much predictable energy as possible from the input data. It is generally understood that temporally correlated geology can cause problems for these filters. It is perhaps less well appreciated that uncorrelated random noise can also severely affect filter performance. The root of these problems is in the objective function being minimized; in addition to minimizing predictable multiple energy, the filter is attempting to simultaneously minimize the temporally correlated geology and the random‐noise energy. Instead of minimizing the input trace energy, an alternative objective function for minimization can be defined that is the result of a linear operator acting on the input data. Ideally this alternative objective function contains only the targeted noise (e.g., multiples). The linear operator that creates this objective function is designated as the “noise‐optimized objective” (NOO) operator. The filter that minimizes this new objective function is the NOO filter. Useful NOO operators for multiple suppression are those that maximize multiple energy and/or minimize primary or random noise energy in the data. Examples of such linear operators include stacking, bandpass filtering, dip filtering, and muting or scaling. Simply scaling down the primary‐containing portion of the objective function can address the problematic removal of correlated geology. Stacking can also be a useful NOO operator. By minimizing the predictable energy on a stacked trace, the prestack filters are less affected by random noise. The NOO stacking method differs from a standard poststack filter design because the filters are designed to be applied prestack. Further, this method differs from a standard prestack prediction filter because it minimizes the predictable energy on the stacked trace. The standard prestack filter has reduced multiple suppression because the filter must compromise between minimizing the multiple energy and minimizing the random noise energy. Minimizing the impact of random noise can be quite important in prediction filtering. At a signal‐to‐random‐noise ratio of one, for example, half the multiple remains after filtering. This random noise‐related degradation might help to explain the common observation that prediction filters tend to leave multiple energy in the data. A time‐varying gap implementation of a stacking NOO filter addresses these random noise effects while also addressing data aperiodicity issues.

Regression trees for multivalued numerical response variables

2017 ◽  
Vol 69 ◽  
pp. 21-28 ◽  
Author(s):  
Antonio D’Ambrosio ◽  
Massimo Aria ◽  
Carmela Iorio ◽  
Roberta Siciliano
Keyword(s):  
Regression Trees ◽  
Numerical Response ◽  
Response Variables

On integer linear programming formulations of a patrol boat scheduling problem with complete coverage requirements

2019 ◽  
pp. 154851291988395 ◽  
Author(s):  
Paul A Chircop ◽  
Timothy J Surendonk
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Column Generation ◽  
Integer Linear Programming ◽  
Minimum Size ◽  
Scheduling Problem ◽  
Complete Coverage ◽  
Continuous Coverage ◽  
Alternative Objective Function ◽  
Theoretical Results

The Patrol Boat Scheduling Problem with Complete Coverage (PBSPCC) is concerned with finding a minimum size patrol boat fleet to provide continuous coverage at a set of maritime patrol regions, ensuring that there is at least one vessel on station in each patrol region at any given time. This requirement is complicated by the necessity for patrol vessels to be replenished on a regular basis in order to carry out patrol operations indefinitely. In this paper, we establish a number of important theoretical results for the PBSPCC. In particular, we establish a set of conditions under which an alternative objective function (minimize the total time not spent on patrol) can be used to derive a minimum size fleet. Preliminary results suggest that the new theoretical insights can be used as part of an acceleration strategy to improve the column generation runtime performance.

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