An efficient recursive rotational-coordinate-based formulation of a planar Euler–Bernoulli beam

Multibody System Dynamics - A recursive rotational-coordinate-based formulation of a planar Euler–Bernoulli beam is developed, where large displacements, deformations, and rotations are...     

Automated fault detection in power distribution networks using a hybrid fuzzy–genetic algorithm approach

This paper describes the development of an intelligent technique based on artificial intelligence for automatically detecting incidents on power distribution networks. A hybrid combination of fuzzy logic and genetic algorithms (GAs) has been applied to detect faults in these networks. The robust nature of a fuzzy controller allows it to model functions of arbitrary complexity, while the maximising capabilities of GAs allow optimisation of the fuzzy design parameters to achieve optimal performance. The hybrid approach used in this paper builds on these individual strengths and seeks to blend fuzzy set and GAs techniques to compensate for their inadequacies. The technique for fault detection is described and verified with experiments on a 33 kV test system containing 12 busbars, eight transformers and eight line sections. The results obtained from the test data file of 500 test cases contain only one undetected case (0.2%), 458 correctly detected cases (91.6%) of actual faults and 41 cases (8.2%) where the protection system components either had not operated or had malfunctioned but were correctly identified by the incident detection system.     

On profile reconstruction of Euler–Bernoulli beams by means of an energy based genetic algorithm

Engineering with Computers - This paper studies the inverse problem related to the identification of the flexural stiffness of an Euler Bernoulli beam to reconstruct its profile starting from...     

Optimization of oval–round pass design using genetic algorithm

The primary purpose of this paper is to propose a computer aided optimal design system to support a generalized oval–round pass design, which is widely used as both intermediate and final passes in the process of rod rolling. This system, which is based on a hybrid model and the genetic algorithm, is developed to improve the efficiency, to reduce the manufacturing errors, as well as to extend the useful life of rolls through uniform wear design. Generalized parametric equations are established for geometrical modeling, graphic plotting of oval–round passes, as well as calculation of the cross section area, contact area and the lengths of contact arcs along the cross section of round groove in the MATLAB programming environment. Moreover, these equations can also realize the parametric transformation between roll profile and mathematical models for the oval–round pass design and optimization. The genetic algorithm is employed for the optimal design of oval–round passes in this paper. The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs. To reduce the complexity and computational burden of the system, some reliable empirical formulas for the calculations of contact area and contact arc length are applied. Finally, the proposed approach is applied to an oval–round pass design. Through simulation and comparison of results against experimental data acquired from literature, it is found that this system is reliable, effective and easier to use.     

Rapid stabilisation of an Euler–Bernoulli beam with the internal delay control

ABSTRACT

In this paper, we are concerned with rapid stabilisation of an Euler--Bernoulli beam with internal delayed control. Herein we introduce a new approach of the feedback control design from the system equivalence point of view. The design approach can be divided into several steps. First, we construct a target system of the desired stability. Second, we select a suitable integral transform that transforms the present system to the target system. In this procedure, one can get a corresponding feedback control. Third, we find a transform that transforms the target system to the present system, which provides the equivalence of both systems. Finally, we prove that the two transforms are bounded linear operators in an appropriate Hilbert space.     

Feedback stabilisation of an Euler–Bernoulli beam with the boundary time-delay disturbance

In this paper, we consider the feedback stabilisation of an Euler–Bernoulli beam with the boundary time-delay disturbance. Due to unknown time-delay coefficient, the system might be exponentially increasing at the lack of control. We design the feedback control law based on Lyapunov function method. Different from usual use of Lyapunov function method, our approach is to combine the construction of Lyapunov functionals with the controller design, which will guarantee the system energy function decays exponentially. In this procedure, we deduce the inequality equations satisfied by the system parameters. We prove the well-posedness of the corresponding closed-loop system by using semigroup theory and the inequality equations are solvable. Moreover, the exponential decay rate of the system is estimated. In addition, some numerical simulations are also presented to support the obtained results.     

Assessment of composite beam performance using GWO–ELM metaheuristic algorithm

Composite beams (CBs) include concrete slabs jointed to the steel parts by the shear connectors, which highly popular in modern structures such as high rise buildings and bridges. This study has investigated the structural behavior of simply supported CBs in which a concrete slab is jointed to a steel beam by headed stud shear connector. Determining the behavior of CB through empirical study except its costly process can also lead to inaccurate results. In this case, AI models as metaheuristic algorithms could be effectively used for solving difficult optimization problems, such as Genetic algorithm, Differential evolution, Firefly algorithm, Cuckoo search algorithm, etc. This research has used hybrid Extreme machine learning (ELM)–Grey wolf optimizer (GWO) to determine the general behavior of CB. Two models (ELM and GWO) and a hybrid algorithm (GWO–ELM) were developed and the results were compared through the regression parameters of determination coefficient (R²) and root mean square (RMSE). In testing phase, GWO with the RMSE value of 2.5057 and R² value of 1.2510, ELM with the RMSE value of 4.52 and R² value of 1.927, and GWO–ELM with the RMSE value of 0.9340 and R² value of 0.9504 have demonstrated that the hybrid of GWO–ELM could indicate better performance compared to solo ELM and GWO models. In this case, GWO–ELM could determine the general behavior of CB faster, more accurate and with the least error percentages, so the hybrid of GWO–ELM is more reliable model than ELM and GWO in this study.

     

Exponential stability of a network of serially connected Euler–Bernoulli beams

The aim is to prove the exponential stability of a system modelling the vibrations of a network of N Euler–Bernoulli beams serially connected. Using a result given by K. Ammari and M. Tucsnak, the problem is reduced to the estimate of a transfer function and the obtention of an observability inequality. The solution is then expressed in terms of Fourier series so that one of the sufficient conditions for both the estimate of the transfer function and the observability inequality is that the distance between two consecutive large eigenvalues of the spatial operator involved in this evolution problem is superior to a minimal fixed value. This property called spectral gap holds. It is proved using the exterior matrix method due to W. H. Paulsen. Two more asymptotic estimates involving the eigenfunctions are required. They are established using an adequate basis.     

Experimental application and enhancement of the XFEM–GA algorithm for the detection of flaws in structures

The extended finite element formulation (XFEM) combined with genetic algorithms (GAs) have previously been shown to be very effective in the detection of flaws in structures. By this approach, the XFEM is used to model the forward problem and a GA is used as the optimization scheme, converging to the true flaw. The convergence is obtained by minimizing the error between sensor measurements and data obtained by solving the forward problem.The current study proposes several advances of this XFEM–GA algorithm, more specifically: (i) a novel genetic algorithm that accelerates the convergence of the scheme and alleviates entrapment in local optima, (ii) a generic XFEM formulation of an elliptical hole which is utilized to detect any type of flaw (cracks or holes) of any shape, and (iii) experimental verification of the approach for an arbitrary crack in a 2D plate.Convergence studies on various benchmark problems including the experimental verification clearly show the potential of this approach to detection of arbitrary flaws.     

Application of permutation genetic algorithm for sequential model building–model validation design of experiments

The work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient design of experiments (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to enable an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of model building–model validation (MB–MV) sequences, by generating optimal infill sets of test points as OLH DoEs that preserve good space-filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB–MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back function, as well as the Gasoline Direct Injection engine steady-state engine mapping problem that motivated this research. The case studies show that the algorithm is effective in delivering quasi-orthogonal space-filling DoEs with good properties even after several MB–MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, is that significant reduction in the effort and cost of testing can be achieved.     

Gait modification and optimization using neural network–genetic algorithm approach: Application to knee rehabilitation

Gait modification strategies play an important role in the overall success of total knee arthroplasty. There are a number of studies based on multi-body dynamic (MBD) analysis that have minimized knee adduction moment to offload knee joint. Reducing the knee adduction moment, without consideration of the actual contact pressure, has its own limitations. Moreover, MBD-based framework that mainly relies on iterative trial-and-error analysis, is fairly time consuming. This study embedded a time-delay neural network (TDNN) in a genetic algorithm (GA) as a cost effective computational framework to minimize contact pressure. Multi-body dynamic and finite element analyses were performed to calculate gait kinematics/kinetics and the resultant contact pressure for a number of experimental gait trials. A TDNN was trained to learn the nonlinear relation between gait parameters (inputs) and contact pressures (output). The trained network was then served as a real-time cost function in a GA-based global optimization to calculate contact pressure associated with each potential gait pattern. Two optimization problems were solved: first, knee flexion angle was bounded within the normal patterns and second, knee flexion angle was allowed to be increased beyond the normal walking. Designed gait patterns were evaluated through multi-body dynamic and finite element analyses.The TDNN-GA resulted in realistic gait patterns, compared to literature, which could effectively reduce contact pressure at the medial tibiofemoral knee joint. The first optimized gait pattern reduced the knee contact pressure by up to 21% through modifying the adjacent joint kinematics whilst knee flexion was preserved within normal walking. The second optimized gait pattern achieved a more effective pressure reduction (25%) through a slight increase in the knee flexion at the cost of considerable increase in the ankle joint forces. The proposed approach is a cost-effective computational technique that can be used to design a variety of rehabilitation strategies for different joint replacement with multiple objectives.     

Detection of idea plagiarism using syntax–Semantic concept extractions with genetic algorithm

Plagiarism is increasingly becoming a major issue in the academic and educational domains. Automated and effective plagiarism detection systems are direly required to curtail this information breach, especially in tackling idea plagiarism. The proposed approach is aimed to detect such plagiarism cases, where the idea of a third party is adopted and presented intelligently so that at the surface level, plagiarism cannot be unmasked. The reported work aims to explore syntax-semantic concept extractions with genetic algorithm in detecting cases of idea plagiarism. The work mainly focuses on idea plagiarism where the source ideas are plagiarized and represented in a summarized form. Plagiarism detection is employed at both the document and passage levels by exploiting the document concepts at various structural levels. Initially, the idea embedded within the given source document is captured using sentence level concept extraction with genetic algorithm. Document level detection is facilitated with word-level concepts where syntactic information is extracted and the non-plagiarized documents are pruned. A combined similarity metric that utilizes the semantic level concept extraction is then employed for passage level detection. The proposed approach is tested on PAN13-14¹plagiarism corpus for summary obfuscation data, which represents a challenging case of idea plagiarism. The performance of the current approach and its variations are evaluated both at the document and passage levels, using information retrieval and PAN plagiarism measures respectively. The results are also compared against six top ranked plagiarism detection systems submitted as a part of PAN13-14 competition. The results obtained are found to exhibit significant improvement over the compared systems and hence reflects the potency of the proposed syntax-semantic based concept extractions in detecting idea plagiarism.     

Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran

This study compares the daily potato crop evapotranspiration (ET_C) estimated by artificial neural network (ANN), neural network–genetic algorithm (NNGA) and multivariate nonlinear regression (MNLR) methods. Using a 6-year (2000–2005) daily meteorological data recorded at Tabriz synoptic station and the Penman–Monteith FAO 56 standard approach (PMF-56), the daily ET_C was determined during the growing season (April–September). Air temperature, wind speed at 2 m height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of ANN models. In this study, the genetic algorithm was applied for optimization of the parameters used in ANN approach. It was found that the optimization of the ANN parameters did not improve the performance of ANN method. The results indicated that MNLR, ANN and NNGA methods were able to predict potato ET_C at desirable level of accuracy. However, the MNLR method with highest coefficient of determination (R ² > 0.96, P value < 0.05) and minimum errors provided superior performance among the other methods.     

Numerical study and optimization of thermohydraulic characteristics of a graphene–platinum nanofluid in finned annulus using genetic algorithm combined with decision-making technique

Engineering with Computers - The heat transfer and flow attributes of a cylindrical microchannel heat sink (CMCHS) operated with a hybrid nanofluid containing the graphene nanoplatelets and...     

Modular design of a hybrid genetic algorithm for a flexible job–shop scheduling problem

The aim of the job–shop scheduling problem is to optimize the task planning in an industrial plant satisfying time and technological constraints. The existing algorithmic and mathematical methods for solving this problem usually have high computational complexities making them intractable. Flexible job–shop scheduling becomes even more complex, since it allows one to assign each operation to a resource from a set of suitable ones. Alternative heuristic methods are only able to satisfy part of the constraints applicable to the problem. Moreover, these solutions usually offer little flexibility to adapt them to new requirements. This paper describes research within heuristic methods that combines genetic algorithms with repair heuristics. Firstly, it uses a genetic algorithm to provide a non-optimal solution for the problem, which does not satisfy all its constraints. Then, it applies repair heuristics to refine this solution. There are different types of heuristics, which correspond to the different types of constraints. A heuristic is intended to evaluate and slightly modify a solution that violates a constraint in a way that avoids or mitigates such violation. This approach improves the adaptability of the solution to a problem, as some changes can be addressed just modifying the considered chromosome or heuristics. The proposed solution has been tested in order to analyse its level of constraint satisfaction and its makespan, which are two of the main parameters considered in these types of problems. The paper discusses this experimentation showing the improvements over existing methods.     

Development of a new over‐water Advanced Very High Resolution Radiometer dust detection algorithm

A new over–water dust detection algorithm is developed and applied to the 5–channel Advanced Very High Resolution Radiometer (AVHRR) imager onboard the National Oceanic and Atmospheric Administration series of polar orbiting satellites. The algorithm has been developed to improve the distinction between dust and meteorological clouds for very optically thick dust storms that would have previously been flagged as cloud under the Clouds from AVHRR Extended cloud mask algorithm. The algorithm has been assessed by making daily comparisons with data from the Aerosol Robotic Network and by making a climatological comparison with METEOSAT and Total Ozone Mapping Spectrometer data over a portion of the North Atlantic. The new algorithm improves the separation of clouds and airborne dust. Application of the new product to the 5–channel AVHRR historic data sets can provide information on the global dust signal, especially on an interannual time scale.     

Economic-statistical design of the X¯ chart used to control a wandering process mean using genetic algorithm

This research considers the process mean wanders according to a first-order autoregressive model. During the in-control period the process mean wanders around its target value, and after the assignable cause occurrence, around an off-target value. The cost model proposed by Duncan was used to select the X bar chart’s parameters and the genetic algorithm to meet their optimum values. The wandering movement required a Markov chain to obtain the properties of the control chart. The autocorrelation among mean values increases the monitoring costs and reduces significantly the chart’s efficiency.     

Stackelberg–Nash equilibrium of pricing and inventory decisions in duopoly supply chains using a nested evolutionary algorithm

Pricing and inventory control in a competing environment, as separate entities, have attracted much attention from academics and practitioners. However, integrating these decisions in a competitive setting has not been significantly analyzed by academics, but is of great significance to practitioners. In this study, the joint decision on price and inventory control of a deterioration product is investigated in a duopoly setting. We consider two competing supply chains, each consisting of one manufacturer and one retailer. Each manufacturer, as the leader of their supply chain determines the wholesale price to maximize their profit, while the retailer as the follower should determine the retail price and inventory cycle to maximize his or her profit. Using a game theoretic approach, we formulate in-chain, and chain-to-chain competition as a bi-level programming problem, and analyze Stackelberg–Nash equilibrium of the problem. Furthermore, two versions of a nested algorithm are proposed to obtain the equilibrium. Both versions employ a modified threshold-accepting (TA) algorithm to solve the first level of the problem. However, while the first version utilizes the modified TA algorithm to deal with the second level of the problem, the second version applies a differential evolution (DE) approach. Eventually, a numerical study is carried out not only to compare two developed versions of the algorithm, but also to implement the sensitivity analysis of main parameters. Based on numerical experiments, although the accuracy of both versions of algorithm are alike, using TA is more computationally efficient than using DE. Furthermore, despite the permissibility of partial backlogging, it has never occurred in equilibrium points due to in-chain and chain-to-chain competition.