Transformation of a shoaling undular bore

We consider the propagation of a shallow-water undular bore over a gentle monotonic bottom slope connecting two regions of constant depth, in the framework of the variable-coefficient Korteweg-de Vries equation. We show that, when the undular bore advances in the direction of decreasing depth, its interaction with the slowly varying topography results, apart from an adiabatic deformation of the bore itself, in the generation of a sequence of isolated solitons - an expanding large-amplitude modulated solitary wavetrain propagating ahead of the bore. Using nonlinear modulation theory we construct an asymptotic solution describing the formation and evolution of this solitary wavetrain. Our analytical solution is supported by direct numerical simulations. The presented analysis can be extended to other systems describing the propagation of undular bores (dispersive shock waves) in weakly non-uniform environments

Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993. Therefore, it is crucial to have accurate future electricity consumption forecasting for its future energy management and saving. Even though there are previous works of electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS), but most of their data are multivariate data. In this study, we have only univariate data of UTHM electricity consumption from January 2009 to December 2018 and wish to forecast 2019 consumption. The univariate data was converted to multivariate and ANFIS was chosen as it carries both advantages of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS). ANFIS yields the MAPE between actual and predicted electricity consumption of 0.4002% which is relatively low if compared to previous works of UTHM electricity forecasting using time series model (11.14%), and first-order fuzzy time series (5.74%), and multiple linear regression (10.62%)

Forecasting electricity consumption using the second-order fuzzy time series

There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied by the increasing demand for electricity. Hence, there is a need to forecast UTHM electricity consumption accurately so that UTHM can plan for future energy demand and utility saving decisions. Previous studies on UTHM electricity consumption prediction have been carried out using time series models, multiple linear regression and first-order fuzzy time series (FTS). The first-order FTS yield the best accuracy among these three methods. Previous forecasting problem showed higher order FTS can yield better accuracy. Therefore, in this study, the second-order FTS with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2009 to December 2018 to forecast January to December 2019 monthly electricity consumption. The procedure of the FTS and trapezoidal membership function was described together with January data. The second-order FTS forecast UTHM electricity consumption better than the first-order FTS

Artificial Immune System based on Hybrid and External Memory for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be further improved because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. Thus, a hybrid PSO-AIS and a new external memory CSA based scheme called EMCSA are proposed. In hybrid PSO-AIS, the good features of PSO and AIS are combined in order to reduce any limitation. Alternatively, EMCSA captures all the best antibodies into the memory in order to enhance global searching capability. In this preliminary study, the results show that the performance of hybrid PSO-AIS compares favourably with other algorithms while EMCSA produced moderate results in most of the simulations

Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization

Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hypermutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions

Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for solving optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively,Genetic Algorithms (GAs) and Particle Swarm Optimization(PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solution for each exposure (iteration) namely Single Best Remainder (SBR) CSA. In this study, the results show that the performance of the proposed algorithm (SBR-CSA) compares favourably with other algorithms while Half Best Insertion (HBI) CSA produced moderate results in most of the simulations

Simulation of Internal Undular Bores Propagating over a Slowly Varying Region

Internal undular bores have been observed in many parts of the world. Studies have shown that many marine structures face danger and risk of destruction caused by internal undular bores due to the amount of energy it carries. This paper looks at the transformation of internal undular bore in two-layer fluid flow under the influence of variable topography. Thus, the surface of the bottom is considered to be slowly varying. The appropriate mathematical model is the variable-coefficient extended Korteweg-de Vries equation. We are particularly interested in looking at the transformation of KdV-type and table-top undular bore over the variable topography region. The governing equation is solved numerically using the method of lines, where the spatial derivatives are first discretised using finite difference approximation so that the partial differential equation becomes a system of ordinary differential equations which is then solved by 4th order Runge-Kutta method. Our numerical results show that the evolution of internal undular bore over different types of varying depths regions leads to a number of adiabatic and non-adiabatic effects. When the depth decreases slowly, a solitary wavetrain is observed at the front of the transformed internal undular bore. On the other hand, when the depth increases slowly, we observe the generation of step-like wave and weakly nonlinear trailing wavetrain, the occurrence of multi-phase behaviour, the generation of transformed undular bore of negative polarity and diminishing transformed undular bore depending on the nature of the topography after the variable topography

Stiffness and Damping Properties of a Low Aspect Ratio Shear Wall Building Based on Recorded Earthquake Responses

An investigation into the structural properties and seismic responses of a low aspect ratio shear wall building, which has construction similarity to typical nuclear plant structures, has been performed using actual recorded earthquake motions. This effort used a combination of modal identification to obtain structure modal parameters directly from the recorded motions, and elastic structural analysis using methods and criteria frequently employed by the nuclear industry. Modal parameters determined by modal identification provide excellent fits to the building motions recorded during the 1984 Morgan Hill earthquake. Modal parameters identified for the 1989 Lorna Prieta earthquake are more uncertain. Investigation of building stiffnesses generally confirms the adequacy of bounding estimates currently recommended for nuclear plant structure seismic analysis. Damping values identified for this building supplement the database being compiled to investigate current nuclear plant structure damping criteria

Numerical Simulation of undular bore evolution with chezy friction, 2015

This paper studies and simulates numerically the evolution of undular bore under the effect of damping in the framework of the perturbed extended Korteweg-de Vries equation. Here, we consider Chezy frictional term to be the damping term in the perturbed extended Korteweg-de Vries equation. Numerical simulations show that under the influence of the friction, the undular bore with thick leading wave will transform into KdV-like solitary wave as the leading wave of the undular bore. The amplitude of the "thick" leading wave will remain the same for some time even though there is dissipation effect

Mathematical function optimization using AIS antibody remainder method

Artificial immune system (AIS) is one of the metaheuristics used for solving combinatorial optimization problems. In AIS, clonal selection algorithm (CSA) has good global searching capability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single and multi objective functions