Efficient linear combination method for multi-objective problems with convex polyhedral preference functions

2003 ◽  
Author(s):  
B. Phruksaphanrat ◽  
A. Ohsato
Keyword(s):  
Linear Combination ◽  
Combination Method ◽  
Convex Polyhedral ◽  
Multi Objective ◽  
Preference Functions

scholarly journals Wide range and highly linear signal processed systematic humidity sensor array using Methylene Blue and Graphene composite

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Umair Khan ◽  
Gul Hassan ◽  
Rayyan Ali Shaukat ◽  
Qazi Muhammad Saqib ◽  
Mahesh Y. Chougale ◽  
...  
Keyword(s):  
Methylene Blue ◽  
Linear Combination ◽  
Linear Function ◽  
Humidity Sensor ◽  
Sensor Array ◽  
Combination Method ◽  
Humidity Sensing ◽  
Sensing Applications ◽  
Wide Range ◽  
Graphene Flakes

AbstractThis paper proposes a signal processed systematic 3 × 3 humidity sensor array with all range and highly linear humidity response based on different particles size composite inks and different interspaces of interdigital electrodes (IDEs). The fabricated sensors are patterned through a commercial inkjet printer and the composite of Methylene Blue and Graphene with three different particle sizes of bulk Graphene Flakes (BGF), Graphene Flakes (GF), and Graphene Quantum Dots (GQD), which are employed as an active layer using spin coating technique on three types of IDEs with different interspaces of 300, 200, and 100 µm. All range linear function (0–100% RH) is achieved by applying the linear combination method of nine sensors in the signal processing field, where weights for linear combination are required, which are estimated by the least square solution. The humidity sensing array shows a fast response time (Tres) of 0.2 s and recovery time (Trec) of 0.4 s. From the results, the proposed humidity sensor array opens a new gateway for a wide range of humidity sensing applications with a linear function.

scholarly journals The Application of the BSSD Iono-Free Linear Combination Method in the Processing of Aircraft Positioning

2019 ◽  
Vol 26 (3) ◽  
pp. 15-21
Author(s):  
Janusz Ćwiklak ◽  
Marek Grzegorzewski ◽  
Kamil Krasuski
Keyword(s):  
Linear Combination ◽  
Experimental Test ◽  
Combination Method ◽  
Dual Frequency ◽  
Chi Square ◽  
Internal Reliability ◽  
Navigation Data ◽  
Positioning Method ◽  
Gps System ◽  
Positioning Algorithm

Abstract The article presents the results of research into the use of the differentiation technique of BSSD (Between Satellite Single Difference) observations for the Iono-Free LC combination (Linear Combination) in the GPS system for the needs of aircraft positioning. Within the conducted investigations, a positioning algorithm for the BSSD Iono-Free LC positioning method was presented. In addition, an experimental test was conducted, in which raw observational data and GPS navigation data were exploited in order to recover the aircraft position. The examination was conducted for the Cessna 172 and the on-board dual-frequency receiver Topcon HiperPro. The experimental test presents the results of average errors of determining the position of the Cessna 172 in the XYZ geocentric frame and in the ellipsoidal BLh frame. Furthermore, the article presents the results of DOP (Dilution of Precision) coefficients, the test of the Chi square internal reliability test and the HPL and VPL confidence levels in GNSS precision approach (PA) in air transport. The calculations were performed in the original APS software (APS Aircraft Positioning Software) developed in the Department of Air Navigation of the Faculty of Aeronautics at the Polish Air Force University.

Interactive Multiobjective Optimization Design Strategy for Decision Based Design

1999 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimization Procedure ◽  
Design Strategy ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Original Algorithm ◽  
Multi Objective ◽  
Local Preference ◽  
Preference Functions

Abstract This research focuses on multi-objective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multi-objective optimization algorithm that takes into account the Decision Maker’s (DM’s) preferences during the design process. An Interactive Multi-Objective Optimization Procedure (IMOOP) developed in [12] has been modified in this research to include the DM’s local preference functions in an Iterative Decision Making Strategy (IDMS). This enhanced multiobjective optimization procedure called the interactive MultiObjective Optimization Design Strategy (iMOODS) provides the DM with a formal means for efficient design exploration around a given Pareto point. The use of local preference functions allows the original algorithm [12] to be modified such that the second order Pareto surface approximation is more accurate in the preferred region of the Pareto surface. The iMOODS has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for the objectives and constraints. The second problem is the design and sizing of a high-performance and low-cost ten bar structure that has multiple objectives. The results indicate that the class functions are effective in capturing the local preferences of the DM. The Pareto designs that reflect the DM’s preferences can be efficiently generated within IDMS.

Interactive Multiobjective Optimization Design Strategy for Decision Based Design

1999 ◽  
Vol 123 (2) ◽  
pp. 205-215 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimization Procedure ◽  
Design Strategy ◽  
Test Problems ◽  
Efficient Design ◽  
Surface Approximation ◽  
Multi Objective ◽  
Local Preference ◽  
Preference Functions

This research focuses on multi-objective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multi-objective optimization algorithm that takes into account the Decision Maker’s (DM’s) preferences during the design process. An interactive MultiObjective Optimization Design Strategy (iMOODS) has been developed in this research to include the Pareto sensitivity analysis, Pareto surface approximation and local preference functions to capture the DM’s preferences in an Iterative Decision Making Strategy (IDMS). This new multiobjective optimization procedure provides the DM with a formal means for efficient design exploration around a given Pareto point. The use of local preference functions allows the iMOODS to construct the second order Pareto surface approximation more accurately in the preferred region of the Pareto surface. The iMOODS has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for the objective and constraints. The second problem is the design and sizing of a high-performance and low-cost ten bar structure that has multiple objectives. The results indicate that the class functions are effective in capturing the local preferences of the DM. The Pareto designs that reflect the DM’s preferences can be efficiently generated within IDMS.

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