• Korean Management Science Review
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• v.15 no.2
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• pp.143-152
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• 1998

This paper focuses on the determining the optimal replacement interval and the corresponding minimum cost of replacement for the renewal T-53 engine. It is assumed that sample failure data of T-53 engine are drawn from the mixed population, and then parameters of the failure distributions are estimated. On the basis of the above situation, the Multi-step Weibull distributions are estimated and then the optimal replacement time of T-53 engine is determined. This paper shows that if the replacement time is reduced to 2000 hours, the 2,217won of the replacement cost per unit time is only saved but also reliability of the T-53 engine is increased.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.227-233
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• 2010

In this study, a genetic algorithm was utilized to optimize the stacking sequence of a composite plate subjected to a high velocity impact. The aim is to minimize the maximum backplane displacement of the plate. In the finite element model, we idealized the impactor using solid elements and modeled the composite plate by shell elements to reduce the analysis time. Various tests were carried out to investigate the effect of parameters in the genetic algorithm such as the type of variables, population size, number of discrete variables, and mutation probability.

• Journal of Environmental Science International
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• v.8 no.4
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• pp.443-449
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• 1999

This paper describe the working of algal culture system under batch and continuous feeding effluents in biological treatment process. The main objective of this study was the determination of fundamental opeating parameters such as dilution rates, light intensity, biomass concentration, nutrients contents, which engender an effective nutrient and organic waste removal process. The results of this research indicate that the algae system will remove effectively nutrient and organic waste. In batch cultures, 91.8% dissolved orthophosphate and 83.3% ammonia nitrogen were removed from the sewage in ten days. In continuous flow systems, a detention time of 2.5 days was found adequate to remove 91% T-P, 87% T-N and 95% $NH_3-N$. At 22-28$^{\circ}C$, 60 rpm, with an intensity of 3500 Lux, the specific growth rate, k was 0.59/day in batch experiments. The optimal growth temperature and nutrients rate (N/P) were respectively $25^{\circ}C$ and 3~5. With an abundant supply of untrients, it was possible to sustain substantial population densities in the temperature range of 22~28$^{\circ}C$.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.215-235
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• 2017

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1815-1821
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• 2015

Rapid increase in population growth has made the mankind to delve in appropriate identification of individuals through biometrics. Foot Print Recognition System is a new challenging area involved in the Personal recognition that is easy to capture and distinctive. Foot Print has its own dimensions, different in many ways and can be distinguished from one another. The main objective is to provide a novel efficient automated system Segmentation using Foot Print based on structural relations among the features in order to overcome the existing manual method. This system comprises of various statistical computations of various foot print parameters for identifying the factors like Instep-Foot Index, Ball-Foot Index, Heel- Index, Toe- Index etc. The input is naked footprint and the output result to an efficient segmentation system thereby leading to time complexity.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.17 no.1
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• pp.13-22
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• 2014

Esophageal pH monitoring is considered the gold standard for the diagnosis of gastroesophageal reflux disease because of the normal ranges across the pediatric age range. However, this method can only detect acid reflux. Multichannel intraluminal impedance-pH (MII-pH) monitoring has recently been used for the detection of bolus reflux in infants and children. This method allows for the detection of liquid, gas or mixed reflux in addition to acid, weakly acidic or weakly alkaline reflux. MII-pH monitoring can record the direction of flow and the height of reflux, which are useful parameters to identify an association between symptoms and reflux. However, the technique is limited by its high cost and the lack of normative data of MII-pH in the pediatric population. Despite certain limitations, MII-pH monitoring will become more common and gradually replace pH monitoring in the future, because pH monitoring is part of MII-pH.

• Journal of Microbiology and Biotechnology
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• v.14 no.6
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• pp.1163-1169
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• 2004

An unstructured mathematical model is presented for lactic acid fermentation based on the energy balance. The proposed model reflects the energy metabolic state and then predicts the cell growth, lactic acid production, and glucose consumption rates by relating the above rates with the energy metabolic rate. Fermentation experiments were conducted under various initial lactic acid concentrations of 0, 30, 50, 70, and 90 g/l. Also, a genetic algorithm was used for further optimization of the model parameters and included the operations of coding, initialization, hybridization, mutation, decoding, fitness calculation, selection, and reproduction exerted on individuals (or chromosomes) in a population. The simulation results showed a good fit between the model prediction and the experimental data. The genetic algorithm proved to be useful for model parameter optimization, suggesting wider applications in the field of biological engineering.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.125-129
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• 2008

A new genetic algorithm for the solution of a multi-tasking problem is presented in this paper. The approach introduces innovative genetic operation that guides the genetic algorithm more directly towards better quality of the population. A wide variety of standard genetic parameters are explored, and results allow the comparison of performance for cases both with and without the new operator. The proposed algorithm improves the convergence speed by reducing the number of generations required to identify a near-optimal solution, significantly reducing the convergence time in each instance.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.119-124
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• 2007

The most difficult and time-intensive issue in the successful implementation of genetic algorithms is to find good parameter setting, one of the most popular subjects of current research in genetic algorithms. In this study, we present a new efficient experimental design method for parameter optimization in a genetic algorithm for general job shop scheduling problem using the Taguchi method. Four genetic parameters including the population size, the crossover rate, the mutation rate, and the stopping condition are treated as design factors. For the performance characteristic, makespan is adopted. The number of jobs, the number of operations required to be processed in each job, and the number of machines are considered as noise factors in generating various job shop environments. A robust design experiment with inner and outer orthogonal arrays is conducted by computer simulation, and the optimal parameter setting is presented which consists of a combination of the level of each design factor. The validity of the optimal parameter setting is investigated by comparing its SN ratios with those obtained by an experiment with full factorial designs.

• Management Science and Financial Engineering
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• v.11 no.2
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• pp.45-60
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• 2005

This paper investigates the application of the computer method COMSOAL (Computer Method of Sequencing Operations for Assembly Lines) to the process routing (PR) problem with multiple objectives. In any computer aided process planning (CAPP) system, one of the most critical activities for manufacturing a part could be to generate the sequence that optimizes production time, production cost, machine utilization or with multiple these criteria. The COMSOAL has been adopted to find the optimum sequence of operations that optimizes two major conflicting criteria : production cost and production quality. The COMSOAL is here slightly modified to simultaneously generate and evaluate a set of possible solutions (called as population) instead of processing a solution stepwise in each iteration. The significant features of the COMSOAL include : no parameters settings needed, and a guarantee of feasible solutions. Experimental results show that COMSOAL is a simple but powerful method to quickly generate multiple feasible solutions which are as good as the ones obtained from several other well-known process routing algorithms.