B-spline curve fitting with invasive weed optimization

Uyar, Kübra; Ülker, Erkan

doi:10.1016/j.apm.2017.07.047

Cited by 31 publications

(17 citation statements)

References 33 publications

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“…An invasive weed optimization algorithm is selected because it demonstrated its higher search capacity in solving diverse and sophisticated engineering problems such as optimal resource operation ( 66 ), optimization of energy supply systems ( 67 ), and prediction of the compression index of limited-treated expansive clays ( 68 ). In addition, the invasive weed optimization algorithm outperformed a set of common and efficient meta-heuristics including the non-dominated sorting genetic algorithm II, the particle swarm optimization algorithm, the artificial immune system, and the artificial bee colony ( 69 – 71 ).…”

Section: Proposed Methodsmentioning

confidence: 99%

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Abdelkader

Moselhi

Marzouk

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

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Section: Proposed Methodsmentioning

confidence: 99%

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Abdelkader

Moselhi

Marzouk

et al. 2020

Transportation Research Record: Journal of the Transportation R

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“…Natural Cubic Spline algorithm (19) has been applied due to its simplicity. Spline is a composite curve that consists of many third-degree (fourth order) polynomials.…”

Section: Methodsmentioning

confidence: 99%

Modeling of Energy Efficiency of a Turboprop Engine using Ant Colony Optimisation

Piskin¹,

Baklacıoğlu²,

Turan

et al. 2019

Aeronaut. j.

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Exergy efficiency can be used as an objective function in order to improve systems efficiency. Thus, the most efficient regions for the operation parameters can be searched easily. Exergy efficiency data of a turboprop engine’s components that have been calculated using basic engine parameters in the previous studies are modeled using cubic spline curve fitting methodology. Spline curves are on the two dimensional plane, where x axis is the input parameter and y axis is the exergy efficiency of the component. A spline curve is defined by the points subject to arbitrary selection of number and position. Initially positions of the points are located with two different methods and then in order to obtain better accuracy point positions are improved by ‘Ant colony’ and ‘Goldsection’ optimisation methods. Sum of Squares of the errors between the fitted value and data value was used as the fitness function. Least square error of 5 × 10−9 is assumed as acceptable accuracy which yields to a minimum R = 0.9998 linear correlation coefficient. In the optimisation step, independent engine variable versus calculated engine performance parameters were checked against spline fitted values. Improvement of the fitness function is observed as the number of fitting points is increased. Ant colony optimisation in engine exergy efficiency parametric modeling is a new approach in authors’ point of view.

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“…Mathematical methods are utilized to fit the curve of the rotation angle relationship between the planet carrier and the seedling pick-up arm. 18 To make the derivative of the beginning of the curve equals to the end, extending the five groups of data to three cycles, and select angle curve of the middle one cycle which is shown in Figure 7.…”

Section: Optimization Design Of Gear Pitch Curves and Parameters Of Tmentioning

confidence: 99%

Research on design method of non-circular planetary gear train transplanting mechanism based on precise poses and trajectory optimization

Zhao

Chu

et al. 2018

Advances in Mechanical Engineering

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Among all kinds of transplanting mechanisms which are the important parts of transplanters, the planetary gear train transplanting mechanism is widely used for its excellent transmission performance, but it is difficult to design the gear pitch curves and structural parameters according to the trajectory and pose requirements of transplanting. The current design method of the non-circular planetary gear train transplanting mechanism cannot ensure the precise position and posture in the crucial points of transplanting trajectory, nor take both the anticipated trajectory and the smoothness of the gear pitch curves into account, it will only get one solution which is available for mechanism design. In order to solve those problems, the non-circular planetary gear train transplanting mechanism is analyzed as a combination of the bargroup and gear train system. According to the transplanting requirements, three points with precise position and posture which called pose points in the trajectory are given to obtain the solution domain of the parameters of bar-group, and then trajectory shape control points are inlet to design the desired shape of the transplanting trajectory and obtain the transmission ratio curve which can be used to get the gear pitch curve. The most suitable parameters of bar-group in solution domain are selected based on the smoothness of the gear pitch curves. This article establishes threedimensional model of the mechanism and utilizes the ADAMS to carry out a motion simulation; all simulation results are consistent with theoretical design results, which confirm that the design method based on prescribed pose points and trajectory control points is adequate for the transplanting mechanism. With the proposed method, the selectivity of the mechanism's parameters solution is increased, and transmission performance benefits from the integrated design of trajectory control and the smoothness of the pitch curves.

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B-spline curve fitting with invasive weed optimization

Cited by 31 publications

References 33 publications

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Modeling of Energy Efficiency of a Turboprop Engine using Ant Colony Optimisation

Research on design method of non-circular planetary gear train transplanting mechanism based on precise poses and trajectory optimization

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