• Journal of Welding and Joining
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• v.23 no.4
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• pp.41-47
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• 2005

Generally, the use of robots in manufacturing industry has been increased during the past decade. GMA(Gas Metal Arc) welding process is an actively Vowing area, and many new procedures have been developed for use with high strength alloys. One of the basic requirement for the automatic welding applications is to investigate relationships between process parameters and bead geometry. The objective of this paper is to develop a new approach involving the use of neural network and multiple regression methods in the prediction of bead geometry for GMA welding process and to develop an intelligent system that visualize bead geometry in order to employ the robotic GMA welding processes. Examples of the simulation for GMA welding process are supplied to demonstrate and verify the proposed system developed using MATLAB. The developed system could be effectively implemented not oかy for estimating bead geometry, but also employed to monitor and control the bead geometry in real time.

• Journal of Welding and Joining
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• v.29 no.4
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• pp.41-47
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• 2011

GMA welding process is a production process to improve productivity for the provision of higher welding quality of material. These includes numerous process variables that could affect welding quality, productivity and cost savings. Recently, the welding part of construction equipment had frequent failure of major components in the welding part of each subsidiary material due to shock which is very poor according to the welding part. Therefore, the implementation of sound welding procedure is the most decisive factor for the reliability of construction machinery. The data generated through experiments conducted in this study has validated its effectiveness for the optimization of bead geometry and process variables is presented. The criteria to control the process parameters, to achieve a good bead geometry. This study has developed mathematical models and algorithms to predict or control the bead geometry in GMA fillet welding process.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.3
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• pp.427-436
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• 2015

Developing two process models to simulate wastewater treatment process is needed to draw a comparison between measured BOD data and estimated process model data: a mathematical model based on the process mass-balance and an ANN (artificial neural network) model. Those two types of simulator can fit well in terms of effluent BOD data, which models are formulated based on the distinctive five parameters: influent flow rate, effluent flow rate, influent BOD concentration, biomass concentration, and returned sludge percentage. The structuralized mass-balance model and ANN modeI with seasonal periods can estimate data set more precisely, and changing optimization algorithm for the penalty could be a useful option to tune up the process behavior estimations. An complex model such as ANN model coupled with mass-balance equation will be required to simulate process dynamics more accurately.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2315-2326
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• 2016

This paper discusses the elimination of DC voltage deviation and the enhancement of load current sharing accuracy in multi-terminal high voltage direct current (MT-HVDC) systems. In order to minimize the power line losses in different parallel network topologies and to insure the stable operation of systems, a decentralized control method based on a modified droop control is presented in this paper. Averaging the DC output voltage and averaging the output current of two neighboring converters are employed to reduce the congestion of the communication network in a control system, and the decentralized control method is implemented. By minimizing the power loss of the cable, the optimal load current sharing proportion is derived in order to achieve rational current sharing among different converters. The validity of the proposed method using a low bandwidth communication (LBC) network for different topologies is verified. The influence of the parameters of the power cable on the control system stability is analyzed in detail. Finally, transient response simulations and experiments are performed to demonstrate the feasibility of the proposed control strategy for a MT-HVDC system.

• Environmental Engineering Research
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• v.24 no.4
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• pp.711-717
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• 2019

The application of advanced oxidation for the treatment of oil refinery wastewater under UV radiation by using nanoparticles of titanium dioxide was investigated. Synthetic wastewater prepared from phenol crystals; Power Glide SAE40 motor vehicle oil and water was used. Response surface methodology (RSM) based on the Box-Behnken design was employed to design the experimental runs, optimize and study the interaction effects of the operating parameters including catalyst concentration, run time and airflow rate to maximize the degradation of oil (SOG) and phenol. The analysis of variance and the response models developed were used to evaluate the data obtained at a 95% confidence level. The use of the RSM demonstrated the graphical relationship that exists between individual factors and their interactive effects on the response, as compared to the one factor at time approach. The obtained optimum conditions of photocatalytic degradation are the catalyst concentration of 2 g/L, the run time of 30 min and the airflow rate of 1.04 L/min. Under the optimum conditions, a 68% desirability performance was obtained, representing 81% and 66% of SOG and phenol degradability, respectively. Thus, the hydrocarbon oils were readily degradable, while the phenols were more resistant to photocatalytic degradation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.10
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• pp.870-878
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• 2003

The maintenance of thermal equilibrium between the human body and its environment is one of the primary requirements for health, wellbeing and comfort. For the effective control of indoor thermal environment, thermostat or humidistat is used. But, it is not sufficient to control the indoor thermal environment using only one or two parameters as human response for the indoor comfortable environment. So an environmental thermal index is required for the control of indoor thermal environment effectively. In this study, a PMV sensor has been developed which has integrated from various kinds of individual sensors for temperature, humidity, air velocity, radiant temperature. After applying the PMV and PPD equation, it is possible to monitor the indoor thermal environment with the sensor system, which is adopted to the circuit for optimization according to the human response with the metabolic rate and activities. The measurement was carried out to verify the performance of the integrated sensor system in comparison with existing measurement system, the PMV meter. As a result, the possibility of applying the PMV sensor to control the indoor thermal environment simultaneously was examined.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.11
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• pp.754-761
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• 2011

The present study numerically investigates two-dimensional flow and heat transfer in the multiple confined impinging slot jet. Numerical simulations are performed for the different Reynolds numbers(Re=100 and 200) in the range of nozzles from 1 to 9 and height ratios(H/D) from 2 to 5, where H/D is the ratio of the channel height to the slot width. The vector plots of velocity profile, stagnation and averaged Nusselt number distributions are presented in this paper. The dependency of thermal fields on the Reynolds number, nozzle number and height ratio can be clarified by observing the Nusselt number as heat transfer characteristic at the stagnation point and impingement surface. The Nusselt number at the stagnation point of the central slot shows unsteadiness at H/D=3 and Re=200. The value of Nusselt number at the stagnation point of the central slot decreases with higher Reynolds number and number of nozzle although overall area averaged Nusselt number increases. Hence careful selection of geometrical parameters and number of nozzle are necessary for optimization of the heat transfer performance of multiple slot impinging jet.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.231-238
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• 2014

Optimum lot size calculation for real world manufacturing environment has been focused since last few decades. Several extensions have been made to the basic economic order and production order quantity models to realize the possible practical situations in industry. However, focus on work-in-process inventory has been ignored relatively. This paper provides a comprehensive review of the models developed for group technology based manufacturing environment focusing on work-in-process inventory. Models have been extended from a perfect manufacturing conditions to an imperfect manufacturing situation considering rework, rejection and inspection. Optimum lot size has been evaluated using a simple algebraic optimization approach. Significant parameters are highlighted using sensitivity analysis for the developed models. Numerical example is used to illustrate the utilization of such models in day-to-day production setups and the impact of significant factors' variation on total cost and optimum lot size.

• Bulletin of the Korean Chemical Society
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• v.25 no.8
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• pp.1125-1129
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• 2004

This work assesses the potential of natural Analcime Zeolite as an adsorbent for preconcentration of lead (II) traces. Lead is quantitatively retained on 2-(5-bromo-2-pyridylazo)-5-diethylaminophenol by column method with Analcime in the pH range of 5-6.5 and 2 mL $min^{?1}$ flow rate. Lead was removed from the column with 10.0 mL of 4 M hydrochloric acid and was determined by anodic stripping differential pulse voltammetry. 0.5ppb detection limit was obtained and linear dynamic range was 3 to $1.2{\times}10^5$ ppb in final solution with correlation coefficient of 0.999 and relative standard deviation of ${\pm}$ 1.2% (for eight replicate determination of 2.5 ${\mu}g\;mL^{?1}$ of lead). Various parameters such as the effect of pH, flow rate, instrumental conditions and interferences of some ions on the determination of lead have been studied in detail for optimization of conditions. The method was successfully applied for determination of lead in various samples.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.