Chu Kiong Loo

A novel approach for unit commitment problem via an effective hybrid particle swarm optimization

This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation, and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods.

Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization

This paper presents a Hybrid Particle Swarm Optimization (HPSO) to solve the Unit Commitment (UC) problem. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented. Results shown are acceptable at this early stage.

Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models

In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e., (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min–Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM–RF ensemble (or FMM–RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM–RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The signals are processed to produce a set of harmonic-based features for classification using the FMM–RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM–RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM–RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments.

Hybrid particle swarm optimization algorithm with fine tuning operators

This paper introduces a new approach called hybrid particle swarm optimisation like algorithm (hybrid PSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimisation like algorithm (ePSO). In order to accelerate the PSO algorithms to obtain the global optimal solution, three fine tuning operators, namely mutation, cross-over and root mean square variants are introduced. The effectiveness of the fine tuning elements with various PSO algorithms is tested through three benchmark functions along with a few recently developed state-of-the-art methods and the results are compared with those obtained without the fine tuning elements. From several comparative analyses, it is clearly seen that the performance of all the three PSO algorithms (pf-PSO, ePSO, and hybrid PSO) is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms.

Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP

In this paper, an accurate and effective probabilistic plurality voting method to combine outputs from multiple simplified fuzzy ARTMAP (SFAM) classifiers is presented. Five ELENA benchmark problems and five medical benchmark data sets have been used to evaluate the applicability and performance of the proposed probabilistic ensemble simplified fuzzy ARTMAP (PESFAM) network. Among the five benchmark problems in ELENA project, PESFAM outperforms the SFAM and multi-layer perceptron (MLP) classifier. In addition, the effectiveness of the proposed PESFAM is delineated in medical diagnosis applications. For the medical diagnosis and classification problems, PESFAM achieves 100 percent in accuracy, specificity, and sensitivity based on the 10-fold crossvalidation and these results are superior to those from other classification algorithms. In addition, a posteri probability of the predicted class can be used to measure the prediction reliability of PESFAM. The experiments demonstrate the potential of the proposed multiple SFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent medical diagnosis tool.

Quantum-implementable selective reconstruction of high-resolution images

This paper, written for interdisciplinary audience, presents computational image reconstruction implementable by quantum optics. The input-triggered selection of a high-resolution image among many stored ones, and its reconstruction if the input is occluded or noisy, has been successfully simulated. The original algorithm, based on the Hopfield associative neural net, was transformed in order to enable its quantum-wave implementation based on holography. The main limitations of the classical Hopfield net are much reduced with the simulated new quantum-optical implementation.

Application of Active Force Control and Iterative Learning in a 5-Link Biped Robot

This paper investigates the efficacy of the implementation of the conventional Proportional-Derivative (PD) controller and different Active Force Control (AFC) strategies to a 5-link biped robot through a series of simulation studies. The performance of the biped system is evaluated by making the biped walk on a horizontal flat surface, in which the locomotion is constrained within the sagittal plane. Initially, a classical PD controller has been used to control the biped robot. Then, a disturbance elimination method called Active Force Control (AFC) schemes has been incorporated. The effectiveness and robustness of the AFC as “disturbance rejecter” has been examined when a conventional crude approximation (AFCCA), and an intelligent active force control scheme, which is known as Active Force Control and Iterative Learning (AFCAIL) are employed. It is found that for both of the AFC control schemes proposed, the system is robust and stable even under the influence of disturbances. An attractive feature of the AFCAIL scheme is that inertia matrix tuning becomes much easier and automatic without any degradation in the performance.

An Effective Surveillance System Using Thermal Camera

Thermography, or thermal visualization is a type of infrared visualization. Thermographic cameras are used in many heavy factories like metal recycling factories, wafer production factories and etc for monitoring the temperature conditions of the machines. Besides, thermographic camera can be used to detect trespassers in environment with poor lighting condition, whereby, the conventional digital cameras are less applicable in. In this paper, we proposed two simple and fast detection algorithms into a cost effective thermal imaging surveillance system. This surveillance system not only used in monitoring the functioning of different machinery and electrical equipments in a factory site, it can also used for detecting the trespassers in poor lighting condition. Experimental results show that the proposed surveillance system achieves high accuracy in monitoring machines conditions and detecting trespassers.

On the optimal control of the steel annealing processes as a two-stage hybrid systems via PSO algorithms

The computation of optimal control variables for a two-stage steel annealing process which comprises of one or more furnaces is proposed in this paper. The heating and soaking furnaces of the steel annealing line form the two-stage hybrid systems. Three algorithms including particle swarm optimisation (PSO) with globally and locally tuned parameters (GLBest PSO), a parameter free PSO algorithm (pf-PSO) and a PSO-like algorithm via extrapolated PSO (ePSO) are considered to solve this optimal control problem for the two-stage steel annealing processes (SAP). The optimal solutions including optimal line speed, optimal cost and job completion time obtained through these three methods are compared with one another and those obtained via conventional PSO (cPSO) with time varying inertia weight (TVIW) and time varying acceleration coefficient (TVAC). From the results obtained through the five algorithms considered, the efficacy and validity of each algorithm are analysed.

A new class of operators to accelerate particle swarm optimization

We present some experiments with a new class of variations of mutation to accelerate the convergence of PSO. These robust mutation variations are tested on benchmark problems and the results show a significant improvement as compared to the original particle swarm optimization algorithm.