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检索条件"主题词=Optimization Algorithms"

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MRF-based Road Detection with Unsupervised Learning for Autonomous Driving in Changing Environments

MRF-based Road Detection with Unsupervised Learning for Auto...

引用

IEEE Intelligent Vehicles Symposium (IV)

作者： Guo, Chunzhao Mita, Seiichi McAllester, David Toyota Technol Inst Nagoya Aichi 4688511 Japan

ISBN: (纸本)9781424478668

This paper presents a vision-based approach with unsupervised learning for robust, accurate and stable detection of the drivable road to deal with autonomous driving in changing environments. This approach is based on a formulation of stereo with homography as a Maximum A Posteriori (MAP) problem in a Markov Random Field (MRF). Under this formulation, we develop an alternating optimization algorithm that alternates between computing the binary labeling and learning the optimal parameters from the stereo pair itself. The labeling is optimized by minimizing a well-defined energy function that consists of matching energy, smoothness energy and tracking energy. The parameters, including nine homography parameters and four MRF parameters, are learned online by applying a hard Expectation Maximization (EM) algorithm to maximize conditional likelihood. The proposed automatic parameter tuning procedure not only improves the accuracy of road detection but also makes the approach adaptive to changing environments without any a priori knowledge of the road. Experimental results show the optimality as well as adaptability of the proposed approach on a wide variety of challenging roads with changing environments.

关键词： Changing environments Unsupervised learning Road homography optimization algorithms

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A Multi-Objective Genetic Algorithm to Test Data Generation

A Multi-Objective Genetic Algorithm to Test Data Generation

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22nd International Conference on Tools with Artificial Intelligence

作者： Pinto, Gustavo H. L. Vergilio, Silvia R. Univ Fed Parana Dept Comp Sci Ctr Politecn BR-81531970 Jardim Das Amer Curitiba Brazil

ISBN: (纸本)9780769542638

Evolutionary testing has successfully applied search based optimization algorithms to the test data generation problem. The existing works use different techniques and fitness functions. However, the used functions consider only one objective, which is, in general, related to the coverage of a testing criterion. But, in practice, there are many factors that can influence the generation of test data, such as memory consumption, execution time, revealed faults, and etc. Considering this fact, this work explores a multi-objective optimization approach for test data generation. A framework that implements a multi-objective genetic algorithm is described. Two different representations for the population are used, which allows the test of procedural and object-oriented code. Combinations of three objectives are experimentally evaluated: coverage of structural test criteria, ability to reveal faults, and execution time.

关键词： observational data execution time optimization algorithms Coverage Genetic Algorithm (GA) faults health data generation

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DAMAGE IDENTIFICATION THROUGH SPECTRAL ELEMENT MODEL UPDATING

DAMAGE IDENTIFICATION THROUGH SPECTRAL ELEMENT MODEL UPDATIN...

引用

ASME International Design Engineering Technical Conferences/Computers and Information in Engineering Conference

作者： Arafa, Mustafa H. Nassef, Ashraf O. Amer Univ Cairo Dept Mech Engn Cairo Egypt

ISBN: (纸本)9780791848982

The use of vibration-based damage identification techniques has received considerable attention in recent years. These techniques rely on changes in some structural dynamic characteristics in order to establish a damage indicator. While various damage indicators have been reported in the literature, those relying only on changes in the natural frequencies are appealing in many aspects. Nevertheless, the use of natural frequencies in damage identification has been faced with several difficulties, such as insensitivity and uniqueness concerns. In an attempt to overcome these obstacles, this paper addresses the development of a damage identification scheme based on changes in the natural frequencies of beam structures through a numerical model formulated by the Spectral Element Method (SEM) in conjunction with an optimization algorithm in a model updating approach to predict the location and extent of damage. The use of the SEM significantly reduces the number of design variables, which makes the search algorithm in the inverse problem faster and more efficient. Damage is modeled as a localized reduction in the beam thickness. Three spectral elements are employed to model an elastic beam with a defect and three variables are realized to model the location, size and severity of damage. A stochastic genetic algorithm was developed to facilitate the search for damage based on a set of measured natural frequencies. The use of the proposed formulation is supported experimentally on a set of free-free beams provided with various damage scenarios, and is shown to be a viable tool in damage identification.

关键词： algorithms Inverse problems Genetic algorithms Vibration Design Computer simulation Damage optimization algorithms Structural dynamics

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MULTI-OBJECTIVE OPTIMAL DESIGN OF A PASSENGER CAR'S BODY

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10th ASME Biennial Conference on Engineering Systems Design and Analysis

作者： Adl, Amir Hosein Panahi, Masoud Shariat TAM Co BIW Dept Tehran Iran

ISBN: (纸本)9780791849170

The body of a passenger car roughly constitutes 25-30% of its overall weight. Any reduction in the weight of the car's body would not only mean less materials and fuel to be consumed, but also less exhaust emissions to be released and less non-biodegradable materials to be dumped or recycled. However, the automotive industry's desire for an increasing weight reduction of passenger cars is inevitably limited by other design considerations such as mechanical strength, overall stiffness of the body, durability, safety and corrosion resistance. The problem of weight minimization can be expressed in the form of a constrained, multi-objective optimization problem in which the weight of the body and its fatigue life constitute the conflicting cost functions and values of such critical performance parameters as body's natural frequency forms the constraint set. The above optimization problem poses a challenge to the designer, as the weight, fatigue life and natural frequency of the geometrically complex body cannot be readily evaluated and a comprehensive numerical model, such as a Finite-Elements (FE) one, has to be employed. This numerical model would nonetheless be highly time-consuming, especially considering the need for re-assessing the model dozens, and sometimes hundreds, of times per iteration of the optimization algorithm. To avoid this, we use a neural approximation of the FE model to reduce the time and computational cost. Results of a finite number of FE simulations are used to train the Multi-Layer Perceptron (MLP) neural network which will then be used as the evaluation engine of the optimization algorithm. An efficient computer code based on the improved Non-dominated Sorting Genetic algorithms (NSGA II) is used to find the Pareto set of distinct solutions. The designer would then be able to choose from a set of non-dominated, feasible solutions based on economical and/or logistics requirements at an early stage of the design process.

关键词： Emissions Fatigue life Approximation Computer simulation Computers Finite element model Simulation Stiffness Strength (Materials) Logistics Design Trains Artificial neural networks Safety Engines Finite element analysis Biodegradation Fuels Automobiles Exhaust systems optimization algorithms Corrosion resistance Weight (Mass) Genetic algorithms Durability optimization Engineering simulation Multilayer perceptron Pareto optimization

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AIR-FUEL RATIO CONTROL OF SPARK IGNITION ENGINES WITH TWC USING LPV TECHNIQUES

引用

2nd Annual Dynamic Systems and Control Conference (DSCC 2009)/Bath/ASME Symposium on Fluid Power and Motion Control

作者： Zope, Rohit A. Mohammadpour, Javad Grigoriadis, Karolos M. Franchek, Matthew Univ Houston Dept Mech Engn Houston TX 77204 USA

ISBN: (纸本)9780791848920

Precise control of the air-fuel ratio in a spark ignition (SI) engine is important to minimize emissions. The emission reduction strongly depends on the performance of the air-fuel ratio controller for the SI engine in conjunction with the Three Way Catalytic (TWC) converter The TWC converter acts as a buffer to any variations occurring in the air-fuel ratio. It stores oxygen during a lean operation and releases the stored oxygen during a rich transient phase. The stored oxygen must be maintained close to the current storage capacity to yield maximum benefits from the TWC converter. Traditionally this is achieved using a simple PI control or a gain-scheduled PI control to address the variability in the operating conditions of the engine. This, however, does not guarantee closed-loop system stability and/or performance. In this work a model-based linear parameter varying (LPV) approach is used to design an H infinity controller The design goal is to minimize the effect of disturbances on the air-fuel ratio and hence the relative storage level of oxygen in the TWC, over a defined operating range for the SI engine. The design method formulated in terms of Linear Matrix Inequalities (LMIs) leads to a convex optimization problem which can be efficiently solved using existing interior-point optimization algorithms. Simulations performed validate the proposed control design methodology.

关键词： Engines Emissions Closed loop systems Polishing equipment Simulation Fuels optimization Engineering simulation Design Oxygen Ignition Storage Control equipment Linear matrix inequalities optimization algorithms Transients (Dynamics) Stability Design methodology Spark-ignition engine

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Auto-localization in Local Positioning Systems: a closed-form range-only solution

Auto-localization in Local Positioning Systems: a closed-for...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Guevara, J. Jimenez, A. R. Morse, A. S. Fang, J. Prieto, J. C. Seco, F. CSIC Inst Automat Ind Ctra Campo Real Km 0-2 Madrid 28500 Spain Yale Univ Dept Elect Engn New Haven CT 06520 USA

ISBN: (纸本)9781424463916

This paper studies the problem of determining the position of beacon nodes in Local Positioning Systems, for which there is no inter-node distance measurements available. Also, neither the mobile node nor any of the stationary nodes have positioning or odometry information. The common solution is implemented using a mobile node capable of measuring its distance to the stationary beacon nodes within a sensing radius. Many authors had implemented heuristic methods based on optimization algorithms to solve the problem, however such techniques can fail if the range measurements doesn't provide enough information to obtain a unique solution. Equally, the actual methods require a good initial estimation of the node positions in order to find the correct solution. In this paper we use rigidity theory to determine the necessary conditions in which such problem is solvable for Local Positioning Systems. We also a present a new method to calculate the inter-beacon distances based in the linearization of the trilateration equations. This method doesn't require any initial estimation of the nodes position. The simulation results show a good estimation of the beacon nodes position without using any optimization algorithm.

关键词： auto-localization closed-form range-only solution distance measurement estimation theory indoor radio initial estimation inter-beacon distances internode distance measurements linearization local positioning systems mobile node necessary conditions nodes position odometry information optimization algorithms rigidity theory sensing radius stationary beacon nodes stationary nodes trilateration equations

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THE APPLICATION OF BEES ALGORITHM IN FINITE ELEMENT MODEL UPDATING

引用

10th ASME Biennial Conference on Engineering Systems Design and Analysis

作者： Fatahi, Laleh Moradi, Shapour Razi, Pejman Shahid Chamran Univ Ahvaz 61355 Khuzestan Iran

ISBN: (纸本)9780791849187

This research work is aimed to investigate the application of bees algorithm (BA) to the finite element (FE) model updating. BA is an evolutionary optimization algorithm that imitates the natural foraging behavior of the honeybees to find the global optimum of an objective function. Here, the weighted squared sum of the error between the measured modal parameters and the FE model predictions is considered as the objective function. To demonstrate the effectiveness of the proposed method, BA is applied on a piping system to update several physical parameters of its FE model. The results obtained from the numerical model are compared with the experimental ones obtained through the modal testing. The results show that BA successfully updates the FE model. Moreover, the performance of this approach is compared with two popular optimization methods;the genetic algorithm (GA) and the particle swarm optimization (PSO). The comparison shows the advantage of BA over GA and its similarity to PSO in terms of accuracy in the presented case study. However, BA reaches to the optimum solution faster than PSO and GA. Therefore, it can be concluded that BA is a robust and accurate optimization method that could be a good candidate for the FE model updating.

关键词： Finite element analysis algorithms Genetic algorithms optimization Computer simulation Finite element model optimization algorithms Piping systems Particle swarm optimization Testing Errors

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OPTIMALITY ASSESSMENT OF EQUIVALENT CONSUMPTION MINIMIZATION STRATEGY FOR PHEV APPLICATIONS

引用

2nd Annual Dynamic Systems and Control Conference (DSCC 2009)/Bath/ASME Symposium on Fluid Power and Motion Control

作者： Tulpule, Pinak Stockar, Stephanie Marano, Vincenzo Rizzoni, Giorgio Ohio State Univ Ctr Automot Res Columbus OH 43212 USA

ISBN: (纸本)9780791848920

This paper deals with optimization algorithms for energy management of Plug-in Hybrid Electric Vehicles (PHEVs). In order to maximize fuel economy of a PHEV, the battery should attain its lowest admissible state of charge at the end of the driving cycle by following an optimal State of Charge (SOC) profile. Finding this optimal profile is a challenging optimization problem and requires prior knowledge of the entire driving cycle. There are many different optimization methods that can be applied to the energy management of PHEVs and they are usually classified into two main categories according to the optimality of their solutions. In general, in order to obtain the global optimum, the complete knowledge of future driving conditions is needed. This requirement renders unfeasible the on-line implementation of such strategies. On the other hand, simpler algorithms which are on-board implementable, do not provide the optimal solution. In this paper, a global optimal strategy - Dynamic Programming, is considered as a benchmark for evaluating the performance of an onboard implementable strategy - Equivalent Consumption Minimization Strategy with linearly decreasing reference SOC'. The study is conducted on an energy-based model of a parallel hybrid powertrain developed in Matlab/Simulink environment. The model and each powertrain components are validated based on road tests and laboratory data for a Chevrolet Equinox (hybridized at The Ohio State University Center for Automotive Research). The optimality assessment considers two main metrics, namely fuel economy and deviations from the optimal SOC profile. Simulations are carried out by considering different driving scenarios and battery sizes. Results show that for longer distances and bigger batteries, Equivalent Consumption Minimization Strategy and Dynamic Programming provide similar fuel economy and SOC profiles.

关键词： Batteries Matlab algorithms Energy management Simulation optimization Dynamic programming Corporate average fuel economy Engineering simulation Cycles Hybrid electric vehicles optimization algorithms Roads Fuel efficiency

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EFFICIENT SEARCH METHODS AND DEEP BELIEF NETWORKS WITH PARTICLE FILTERING FOR NON-RIGID TRACKING: APPLICATION TO LIP TRACKING

EFFICIENT SEARCH METHODS AND DEEP BELIEF NETWORKS WITH PARTI...

引用

IEEE International Conference on Image Processing

作者： Nascimento, Jacinto C. Carneiro, Gustavo Inst Super Tecn Inst Sistemas & Robot P-1049001 Lisbon Portugal

ISBN: (纸本)9781424479948

Pattern recognition methods have become a powerful tool for segmentation in the sense that they are capable of automatically building a segmentation model from training images. However, they present several difficulties, such as requirement of a large set of training data, robustness to imaging conditions not present in the training set, and complexity of the search process. In this paper we tackle the second problem by using a deep belief network learning architecture, and the third problem by resorting to efficient searching algorithms. As an example, we illustrate the performance of the algorithm in lip segmentation and tracking in video sequences. Quantitative comparison using different strategies for the search process are presented. We also compare our approach to a state-of-the-art segmentation and tracking algorithm. The comparison show that our algorithm produces competitive segmentation results and that efficient search strategies reduce ten times the run-complexity.

关键词： Deep belief Networks lip segmentation optimization algorithms search methods tracking

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A CFD APPROACH TO FLUID DYNAMIC OPTIMUM DESIGN OF STEAM TURBINE STAGES WITH STATOR AND ROTOR BLADES

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ASME Turbo Expo 2010

作者： Yuan, Xin Tanuma, Tadashi Zhu, Xiaofeng Lin, Zhirong Nomura, Daisuke Tsinghua Univ Minist Educ Key Lab Thermal Sci & Power Engn Beijing 100084 Peoples R China

ISBN: (纸本)9780791844021

An advanced aerodynamic design optimization system for steam turbine stages considering rotor blade tip leakage and blade end-wall non-axis-symmetric contouring has been developed. Using this system, fluid dynamic optimizations were carried out for a steam turbine stage with stator and rotor blades. The system includes parametric modeling of blade and end-wall contouring, evaluation system with in-house or package CFD software and optimization strategy module. The designs of rotor blade and hub end-wall surface in a typical large-scale high-pressure steam turbine stage were optimized in order to know this design optimization impact on enhancing the stage efficiency. Results show that: from the current well designed high pressure steam turbine stage, the demonstrated efficiency enhancement with the present optimum design is around 0.2% under consideration of rotor tip leakage. Design cycle could be greatly shortened by parallel optimization algorithm and cluster PC, and especially four days could be sufficient for an optimization with one thousand iterations on 20 CPUs of 2.0G cluster PC.

关键词： Computer software High pressure (Physics) Rotors Steam turbines optimization Modeling Stators Fluids Leakage Blades Design Cycles optimization algorithms Computational fluid dynamics

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