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A Novel Optimal Layout Method of Airborne Distributed POS Based on MAC and GMC Hybrid optimization Criterion

IEEE SENSORS JOURNAL 2021年第12期21卷 13638-13648页

作者： Gong, Xiaolin Sun, Lujin Peng, Zhiwei Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100191 Peoples R China

The layout of the airborne distributed position and orientation system (POS) directly affects the overall accuracy of the motion parameters of all load points obtained by the distributed POS. However, there are restrictions of space, weight and cost of the distributed POS, it is unrealistic to install an inertial measurement unit (IMU) at the location of each load. To solve this problem, based on modal analysis of wing structure, considering the two factors of the modal spatial intersection angle and the vibration amplitude, a novel fitness function model based on modal assurance criterion (MAC) and Gramian matrix criterion (GMC) is proposed to evaluate the population in thea* optimization algorithm. The hybrid adaptive particle swarm optimization and genetic algorithm (HAPSOGA) based on this proposed model is applied to the multiple system layout of airborne distributed POS to obtain the optimal layout scheme. The results of semi-physical simulation and ground verification experiment show that compared with the optimal method based on MAC, the optimal layout scheme determined by the proposed method can use less IMU and obtain higher overall accuracy of motion parameters for the remote sensing loads.

关键词： Layout optimization Strain Atmospheric modeling Vibrations Sensors Remote sensing Distributed position and orientation system SINS GPS integration layout optimization optimization algorithm optimization criterion

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Developing aquila optimization-based fuzzy system to predict the mechanical properties of the improved HPC

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 7859-7873页

作者： Ji, YingZhou Niuo, Qiang Guizhou Univ Sch Civil Engn Guiyang Guizhou Peoples R China Zhengzhou Univ Sci & Technol Fac Civil & Architectural Engn Zhengzhou Peoples R China

High-performance concrete performs better than normal concrete because of using additional components than usual concrete components. Several artificially based analytics were used to evaluate the compressive strength (CS) of high-performance concrete (HPC) made with fly ash and blast furnace slag. In the present research, the Aquila optimizer (AO) was used to find the best values for the determinants of the adaptive neuro-fuzzy inference system (ANFIS), and radial basis function neural network (RBFNN) that may be changed to enhance performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The results of the outperformed model were then contrasted with those found in the existing scientific literature. Calculation results point to the potential benefit of combining AO-RBFNN and AO-ANFIS study. The AO-ANFIS demonstrated significantly higher R-2 (Train: 0.9862, Test: 0.9922) and lower error metrics (such as: RMSE at 2.1434 (train) and 1.2763 (Test)) when compared to the AO-RBFNN and previously published articles. In summation, the proposed method for determining the CS of HPC supplemented with blast furnace slag and fly ash may be established using the suggested AO-ANFIS analysis.

关键词： High-performance concrete estimation artificial intelligence ANFIS optimization algorithm

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Multi-objective pareto concurrent subspace optimization for multidisciplinary design

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AIAA JOURNAL 2007年第8期45卷 1894-1906页

作者： Galuski, J. Bloebaum, C. L. Ind Technol Res Inst Mech & Syst Res Labs Hsinchu 310 Taiwan SUNY Buffalo Dept Mech & Aerosp Engn Buffalo NY 14260 USA

Most real-world design problems are complex and multidisciplinary, with almost always more than one objective (cost) function to be extremized simultaneously. The primary goal of this research is to develop a framework to enable multi-objective optimization of multidisciplinary design applications, wherein each discipline is able to retain substantial autonomous control during the process. To achieve this end, we have extended the capability of the concurrent subspace optimization method to handle multi-objective optimization problems in a multidisciplinary design optimization context. Although the conventional concurrent subspace optimization approach is easily able to deal with multi-objective optimization problems by applying the weighted sum approach, the main disadvantage is that the weighted sum cannot capture Pareto points on any nonconvex part of the Pareto frontier. Moreover, an aggregate objective function simply cannot reflect the true spirit of the concurrent subspace optimization method, which was developed to allow groups of specialists to independently have more control over their own design criteria and goals, even while maintaining system level coordination. In this paper, the multi-objective Pareto concurrent subspace optimization method is proposed in which each discipline has substantial control over its own objective function during the design process, while still ensuring responsibility for constraint satisfaction in coupled subspaces. The proposed approach is particularly useful given the realities of geographical distribution, computational platform variation, and dependence upon legacy codes within individual disciplines that so predominates the design of large-scale products such as aircraft and automobiles. As part of the multi-objective Pareto concurrent subspace optimization method developed here, it is demonstrated that the endpoints of the Pareto frontier can be easily identified, together with an ability to generate Pareto points with

关键词： Pareto Optimality MDO Concurrent Subspace optimization Method Multi Objective Genetic algorithm Sensitivity Analysis optimization algorithm Lagrange Multipliers Aircraft Design NASA Langley Research Center Evolutionary algorithm

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Modeling, optimization, and Operation of Large-Scale Air Traffic Flow Management on Spark

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2017年第9期14卷 504-516页

作者： Chen, Jun Cao, Yi Sun, Dengfeng Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47906 USA

The nationwide air traffic flow management problem often encounters computational difficulty because it is generally modeled as an integer programming problem that requires computationally expensivea* optimization algorithms. This paper introduces a customized Spark-based optimization architecture for such large-scale integer programming problems to further speed up the modeling and optimization process, where Spark is a big data cluster-computing platform. First, a novel layered aggregate model is developed for handling flexible rerouting problem, which is not well handled in a previous link transmission model. As an aggregate linear model, the layered aggregate model has the nice features of computational efficiency and scalability, which make it suitable for Apache Spark. By applying a dual decomposition method, the original large-scale problem is decomposed into a number of small subproblems. The optimization proceeds by iteratively solving subproblems and updating Lagrange multipliers. This paper encapsulated the process into the Spark-based data processing model such that the optimization is automatically scheduled to run in parallel. Spark gains efficiency by means of in-memory computing and dynamic schedule allocation. This is demonstrated in the experimental results that are compared to an earlier Hadoop MapReduce-based model, where Hadoop MapReduce is a basic cloud computing framework;the Spark-based model is solved twice as fast as the Hadoop MapReduce-based model.

关键词： Enhanced Traffic Management System Basic Cloud Computing Framework Lagrange Multipliers optimization algorithm Data Processing Air Route Traffic Control Center Special Use Airspace Current Flight Plan Flight Data Air Transportation System

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Resulted word counts optimization - A new approach for better automatic image annotation

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PATTERN RECOGNITION 2008年第12期41卷 3562-3571页

作者： Kwasnicka, Halina Paradowski, Mariusz Wroclaw Univ Technol Inst Appl Informat PL-50370 Wroclaw Poland

One of major problems in image auto-annotation is the difference between the expected word counts vector and the resulted word counts vector. This paper presents a new approach to automatic image annotation-an algorithm called resulted word counts optimizer which is an extension to existing methods. An ideal annotator is defined in terms of recall quality measure. On the basis of the ideal annotator an optimization criterion is defined. it allows to reduce the difference between resulted and expected word counts vectors. The proposed algorithm can be used with various image auto-annotation algorithms because its generic nature. Additionally, it does not increase the computational complexity of the original annotation method processing phase. It changes output word probabilities according to a pre-calculated vector of correction coefficients. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： automatic image annotation ideal image annotator recall quality measure optimization algorithm

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High-Dimensional Bayesian optimization Using Both Random and Supervised Embeddings

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AIAA JOURNAL 2024年第1期63卷 162-173页

作者： Priem, Remy Diouane, Youssef Bartoli, Nathalie Dubreuil, Sylvain Saves, Paul Tech Unit DGA Maitrise Informat Aeronaut Syst F-35170 Bruz France Polytech Montreal Math & Ind Engn Dept Montreal PQ H3T 1N8 Canada GERAD Math & Ind Engn Dept Montreal PQ H3T 1N8 Canada Univ Toulouse DTIS ONERA F-31000 Toulouse France Univ Toulouse Informat Proc & Syst Dept Federat ENAC ISAE SUPAERO ONERA F-31000 Toulouse France

Bayesian optimization (BO) is one of the most powerful strategies to solve computationally expensive-to-evaluate blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, a high-dimensionnal optimization method incorporating linear embedding subspaces of small dimension is proposed to efficiently perform the optimization. An adaptive learning strategy for these linear embeddings is carried out in conjunction with the optimization. The resulting BO method, named efficient global optimization coupled with random and supervised embedding (EGORSE), combines in an adaptive way both random and supervised linear embeddings. EGORSE has been compared to state-of-the-art algorithms and tested on academic examples with a number of design variables ranging from 10 to 600. The obtained results show the high potential of EGORSE to solve high-dimensional blackbox optimization problems, in terms of both CPU time and the limited number of calls to the expensive blackbox simulation.

关键词： Bayesian optimization Cumulative Distribution Function Multidisciplinary Design Analysis and optimization Computer Programming and Language Numerical Analysis Robots optimization algorithm Aircraft Design Robot Kinematics Flight Recorder

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Cognitive radar ambiguity function optimization for unimodular sequence

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2016年第1期2016卷 1页

作者： Zhang, Jindong Qiu, Xiaoyan Shi, Changli Wu, Yue Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Jiangsu Peoples R China

An important characteristic of a cognitive radar is the capability to adjust its transmitted waveform to adapt to the radar environment. The adaptation of the transmit waveform requires an effective framework to synthesize waveforms sharing a desired ambiguity function (AF). With the volume-invariant property of AF, the integrated sidelobe level (ISL) can only be minimized in a certain area on the time delay and Doppler frequency shift plane. In this paper, we propose a new algorithm for unimodular sequence to minimize the ISL of an AF in a certain area based on the phase-only conjugate gradient and phase-only Newton's method. For improving detection performance of a moving target detecting (MTD) radar system, slow-time ambiguity function (STAF) is defined, and the proposed algorithm is presented to optimize the range-Doppler response. We also devise a cognitive approach for a MTD radar by adaptively altering its sidelobe distribution of STAF. At the simulation stage, the performance of the proposed algorithm is assessed to show their capability to properly shape the AF and STAF of the transmitted waveform.

关键词： Cognitive radar Unimodular sequence synthesis optimization algorithm Ambiguity function Slow-time ambiguity function

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Comparison of optimization Strategies for High-Lift Design

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JOURNAL OF AIRCRAFT 2017年第2期54卷 642-658页

作者： Iannelli, Pierluigi Moens, Frederic Minervino, Mauro Ponza, Rita Benini, Ernesto Ctr Italiano Ric Aerosp Fluid Mech Dept Aerodynam Technol & Icing Lab I-81043 Capua Italy Off Natl Etud & Rech Aerosp Appl Aerodynam Dept F-92190 Meudon France Piaggio Aero Ind Res & Dev Dept I-17038 Villanova Dalbenga Italy Ctr Italiano Ric Aerosp Fluid Mech Dept Multidisciplinary Anal & Design Lab I-81043 Capua Italy Univ Padua Dept Ind Engn I-35131 Padua Italy

The design of high-lift systems represents a challenging task within the aerospace community, being a multidisciplinary, multi-objective, and multipoint problem. The Design, Simulation and Flight Reynolds Number Testing for Advanced High-Lift Solution project, funded by the European Commission under the Seventh Framework Programme, aimed at improving the aerodynamics of high-lift systems by developing, in a coordinated approach, both efficient numerical design strategies and measurement techniques for cryogenic conditions. Within the Design, Simulation and Flight Reynolds Number Testing for Advanced High-Lift Solution project, different partners used several numerical automatic optimization strategies for high-lift system design purposes. A realistic multi-objective and multipoint optimization problem was defined and solved by adopting different flow model dimensionality, meshing techniques, geometry parameterization, and optimization strategies. Special attention was devoted to perform a fair comparison of the results, and useful information was obtained about trends, pros, and cons of the approaches used. The outcome of these activities is that an efficient design process can be set up through decoupling of the original multi-objective problem into several, sequential suboptimization processes. Nevertheless, several decoupling possibilities exist, and the most efficient one can be identified only on the bases of preanalysis or preknowledge of the specific problem. Second, the exercise carried out demonstrated the maturity and feasibility of a full three-dimensional automatic high-lift design.

关键词： Aerodynamic Force Coefficients CFD Simulation Aerodynamic Efficiency Flap Deflection Reynolds Averaged Navier Stokes Multi Objective Evolutionary algorithms Multi Objective Genetic algorithm Two Dimensional Flow optimization algorithm Navier Stokes Solver

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optimization of a Sparse Array Antenna for 3D Imaging in Near Range

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IEICE TRANSACTIONS ON ELECTRONICS 2019年第1期E102C卷 46-50页

作者： Lyulyakin, Andrey Chernyak, Iakov Sato, Motoyuki Tohoku Univ Grad Sch Environm Studies Sendai Miyagi 9808579 Japan Tohoku Univ Ctr Northeast Asian Studies Sendai Miyagi 9808576 Japan

In order to improve an imaging performance of a sparse array radar system we propose an optimization method to find a new antenna array layout. The method searches for a minimum of the cost function based on a 3D point spread function of the array. We found a solution for the simulated problem in a form of the new layout for the antenna array with more sparse middle-point distribution comparing with initial one.

关键词： sparse array nearfield imaging array optimization optimization algorithm

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An adaptable broadband MEMS vibratory gyroscope by simultaneous optimization of robustness and sensitivity parameters

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SENSORS AND ACTUATORS A-PHYSICAL 2014年 206卷 132-137页

作者： Esmaeili, Ali Kupaei, Mehrnaz Aghanouri Faghihian, Hamed Mirdamadi, Hamid Reza Isfahan Univ Technol Dept Mech Engn Esfahan *** Iran Isfahan Math House Esfahan *** Iran

This article presents a novel method to optimize the sensitivity and robustness of MEMS vibratory gyroscopes with the help of a 3-DOF MEMS vibratory gyroscope with a 2-DOF-sense mode, which enables adjusting an adaptive trade-off between the precision and robustness for their different desirable importance ratios and a broadband range of frequency response. In this approach, a nonlinear optimization problem is defined in order to maximize both sensitivity and robustness. The design variables are spring constants and the operating frequency, the objective function is obtained regarding both sensitivity and robustness with weighting factors, and both equality and inequality constraint are defined based on the problem. To solve the problem, the Sequential Quadratic Programming (SQP) is applied and the optimized values of design variables, sensitivity and robustness are derived. Changing the weighting factors for the sensitivity and robustness, results in the adjustable values of the vibratory system parameters for different bands of frequency response. A comparison is made among a fully coupled gyroscope with no adjustment capability, an adjustable and fully coupled gyroscope, and an adjustable and broadband gyroscope. Then, they are simulated with different weighting factors. Comparing the results shows the superiority of the proposed adjustable as well as broadband gyroscope. (C) 2013 Elsevier B.V. All rights reserved.

关键词： MEMS gyroscope Adaptive gyroscope Broadband vibratory gyroscope Robustness Sensitivity optimization algorithm

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