• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.407-414
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• 2010

This paper proposes a probability measure for the evaluation of relay performance from two aspects, namely, correct trip and no-unwanted trip. They are developed based on the relationship between relay settings and relay measurements, which follow a Gaussian probability model. The proposed method based on strict mathematical derivation is applied to protection capability evaluation of distance relays under various settings. Considering the specific attributes of each protection zone, the optimal settings are also determined accordingly. The protection capability could demonstrate clearly the relay performance under various settings and the optimal settings could provide good references for engineering applications.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.9
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• pp.1521-1526
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• 2007

This paper presents the application of Adaptive Evolutionary Algorithm (AEA) to search an optimal setting of overcurrent relay coordination to protect ring distribution systems. The AEA takes the merits of both a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner to use the global search capability of GA and the local search capability of ES. The overcurrent relay settings and coordination requirements are formulated into a set of constraint equations and an objective function is developed to manage the overcurrent relay settings by the Time Coordination Method. The domain of overcurrent relays coordination for the ring-fed distribution systems is a non-linear system with a lot of local optimum points and a highly constrained optimization problem. Thus conventional methods fail in searching for the global optimum. AEA is employed to search for the optimum relay settings with maximum satisfaction of coordination constraints. The simulation results show that the proposed method can optimize the overcurrent relay settings, reduce relay mis-coordinated operations, and find better optimal overcurrent relay settings than the present available methods.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.44-50
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• 2001

A genetic algorithm was applied to the arc welding process as to determine the near-optimal settings of welding process parameters that produce the good weld quality. This method searches for optimal settings of welding parameters through the systematic experiments without the need for a model between the input and output variables. It has an advantage of being capable to find the optimal conditions with a fewer number of experiments rather than conventional full factorial designs. A genetic algorithm was applied to the optimization of the weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed. The output variables were the bead height bead width, and penetration. The number of levels for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions,2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions in less than 40 experiments.

• International Journal of Korean Welding Society
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• v.3 no.2
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• pp.15-23
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed, root opening and the output variables were bead height, bead width, penetration and back bead width. The number of level for each input variable is 8, 16, 8 and 3, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 3,072 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 48 experiments.

• Journal of Welding and Joining
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• v.18 no.5
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• pp.63-69
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• 2000

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables was wire feed rate, welding voltage, and welding speed and the output variables were bead height, bead width, and penetration. The number of level for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 40 experiments.

• Proceedings of the KWS Conference
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• 2003.05a
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• pp.172-175
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. According to the conventional full factorial design, in order to find the optimal welding conditions, 16,384 experiments must be performed. The genetic algorithm however, found the near optimal welding conditions from less than 60 experiments.

• Journal of Welding and Joining
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• v.28 no.6
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• pp.76-83
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• 2010

The purpose of this study is to propose a method to decide near-optimal settings of the constant current fuzzy control parameters using a controlled random search. This method tries to find the near-optimal settings of the constant current fuzzy control parameters through experiments. It has an advantage of being able to carry out searches in the search domain which includes some irregular points. The method suggested in this study was used to determine the fuzzy control parameters by which the desired welding current were formed during inverter DC resistance spot welding. The output variable was the ITAE (integral of time multiplied by the absolute error). This output variable was determined according to the input variables, which are the GE, GDE, and GDU. This study described how to obtained near-optimal welding current condition over a wide search space conducting a relatively small number of experiments.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.131-141
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• 2012

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.

• Journal of Biosystems Engineering
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• v.38 no.3
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• pp.180-184
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• 2013

Purpose: This study explored the factors influencing heating value in the process of torrefaction of water hyacinth. Methods: Torrefaction was applied with three temperature settings (200, 300, $400^{\circ}C$) and three time settings (1, 2, 3 h) using small electric heaters (11.3L of holding volume). This study investigated the heating values with the washing process and process factors influenced the torrefaction. In addition, this study compared the heating values in washed and unwashed samples and suggested the optimal conditions for increasing heating value. Results: Torrefaction increased the heating value by 8.18 ~ 30.04%. Comparing heating values of each condition, the optimal temperature for torrefaction was $300^{\circ}C$ and holding time was 1 hour. The washing process increased the heating value by 19 ~ 27%. The heating value of the sample treated at $300^{\circ}C$ for three hours was 4310.80 kcal/kg, which was greater than the first class wood pellet of 4300 kcal/kg. Conclusions: This study proved that the torrefaction and washing process increased the heating value of water hyacinth. Therefore, water hyacinth is expected to be an eco-friendly biomass which substitutes for wood pellet.

• Journal of Ecology and Environment
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• v.41 no.7
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• pp.191-201
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• 2017

Background: Limited availability of corn stover due to the competing uses (organic manure, animal feed, bio-materials, and bioenergy) presents a major concern for its future in the bio-economy. Furthermore, biomass research has exhibited different results due to the differences in the supply of enzymes and dissimilar analytical methods. The effect of the two leading pretreatment techniques (dilute acid and alkaline) on glucose yield from three corn stover fractions (cob, stalk, and leaf) sourced from a single harvest in Uganda were studied at temperatures 100, 120, 140, and $160^{\circ}C$ over reaction times of 5, 10, 30, and 60 min. Results: From this study, the highest glucose concentrations obtained from the dilute acid (DA) pretreated cobs, stalks, and leaves were 18.4 g/L (66.8% glucose yield), 16.2 g/L (64.1% glucose yield), and 11.0 g/L (49.5% glucose yield), respectively. The optimal pretreatment settings needed to obtain these yields from the DA pretreated samples were at a temperature of $160^{\circ}C$ over an incubation time of 30 min. The highest glucose concentrations obtained from the alkaline (AL) pretreated cobs, stalks, and leaves were 24.7 g/L (81.73% glucose yield), 21.3 g/L (81.23% glucose yield), and 15.0 g/L (51.92% glucose yield), respectively. To be able to achieve these yields, the optimal pretreatment settings for the cobs and stalks were $140^{\circ}C$ and for a retention time of 30 min, while the leaves require optimal conditions of $140^{\circ}C$ and for a retention time of 60 min. Conclusions: The study recommends that the leaves could be left on the field during harvesting since the recovery of glucose from the pretreated cobs and stalks is higher.