• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.44-51
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• 1998

Two-step neural network is designed for determining electrical discharge machining parameters in low erosion. The first neural network, which is used as a classification network, checks whether the current conditions are appropriate to electrical discharge machining in low tool erosion. If the conditions are appropriate to EDM in low erosion, suitable EDM parameters are generated by the second neural network. Theoretically known EDM conditions are produced and also utilized for training the second neural network. The trained neural network is tested how well suitable EDM machining conditions are generated under unknown machining situations Experimental result shows that the proposed two-step neural network approach could be effectively used for determining EDM parameters in low tool erosion. The results also have a practical contribution to EDM area in that it could be applied for maintaining low tool wear as well as obtaining maximum machining rates simultaneously.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.784-786
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• 1997

In this Paper, the effective method of determining the water discharge of a pondage hydro-unit for hydro dispatch is proposed. The water discharge of a pondage hydro-unit can be determined subtracting the spillage discharge from summation of the inflow and the pondage according to variation of the water level of the reservoir. Proposed method calculates the inflow using the duration curve, the water level from initial level on January 1, and the spillage discharge from the water discharge.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.5
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• pp.43-49
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• 1998

Adaptive neural network approach is presented for determining Electrical Discharge Machining (EDM) parameters. Electrical Discharge Machining has been widely used with its capability of machining hard metals and tough shapes. In the past few years, EDM has been established in tool-room and large-scale production. However. in spite of it's wide application, an universal selection method of EDM parameters has not been established yet. No attempt has been tried before to suggest a logical method in determining essential machine parameters considering the machining rate and resulting surface roughness integrity. The paper presents a method, which is focusing on determining appropriate machining parameters. Depending on the electrode wear and surface roughness, an adaptive neural network is designed for providing suitable machining guideline.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.7-13
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• 1998

This paper presents a methodology for determining machining conditions in Electric Discharge Wire-Cut. Unification of two phase neural network approach with an automatic generation of machining parameters is designed. The first phase neural network, which is 1 to M backward-mapping neural net, produces approximate machining conditions. Using approximate conditions, all possible conditions are newly created by the proposed automatic generation procedure. The second phase neural net, which is a M to 1 forward-mapping neural net, determines the best one among the generated candidates. Simulation results with ANN are given to verify that the presenting methodology could apply for determining machining parameters in Electric Discharge Wire-Cut.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.279-289
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• 2010

In this study, we propose a simple statistical model for determining the admission or discharge of 668 patients with a chief complaint of dyspnea. For this, we use 11 explanatory variables which are chosen to be important by clinical experts among 55 variables. As a modification process, we determine the discharge interval of each variable by the kernel density functions of the admitted and discharged patients. We then choose the optimal model for determining the discharge of patients based on the number of explanatory variables belonging to the corresponding discharge intervals. Since the numbers of the admitted and discharged patients are not balanced, we use, as the criteria for selecting the optimal model, the arithmetic mean of sensitivity and specificity and the harmonic mean of sensitivity and precision. The selected optimal model predicts the discharge if 7 or more explanatory variables belong to the corresponding discharge intervals.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.125-133
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• 2013

In this paper, penalized binary logistic regression models are employed as statistical models for determining the discharge of 668 patients with a chief complaint of dyspnea based on 11 blood tests results. Specifically, the ridge model based on $L^2$ penalty and the Lasso model based on $L^1$ penalty are considered in this paper. In the comparison of prediction accuracy, our models are compared with the logistic regression models with all 11 explanatory variables and the selected variables by variable selection method. The results show that the prediction accuracy of the ridge logistic regression model is the best among 4 models based on 10-fold cross-validation.

• Korean Journal of Computational Design and Engineering
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• v.4 no.4
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• pp.355-359
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• 1999

This paper presents a method for determining machining parameters in electrical discharge machining process (EDM) based on discharge area. The parameters are the peak value of currents, the pulse-on time, and the pulse-off time, on which the EDM performance depends chiefly. The optimal machining parameters are closely related on discharge area, which can be calculated from a tool electrode and a discharge height. In the paper the discharge area is obtained from NC code for machining the tool electrode instead of its geometric model. The method consists of following three steps. First a Z-Map model is constructed from the NC code. Secondly, the discharge area is obtained from the Z-Map model and a Z-height. Finally, the machining parameters are calculated from the discharge area. An introduced example shows that the machining parameters are calculated by the using a Z-map model obtained from the machining data for a tool electrode.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.27-33
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• 2000

This paper presents a method for determining machining parameters in 3-dimentional electrical discharge machining(EDM). The parameters are the peak value of currents, the pulse-on time, and the pulse-off time. It is known that they influence the performance of EDM more than the other else. The parameters are determined from the discharge area between a tool electrode and a work piece. The discharge area is directly influenced by the geometry of a tool surface and the tool discharge position. The discharge area on a tool discharge position is calculated from intersection curves between the tool surface and a horizontal plane. The grid search method is applied to determine the intersection curves. An example is introduced to show that the machining parameters are obtained from the surface geometry of a tool electrode.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.5
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• pp.58-64
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• 2000

This paper proposes a method for determining optimal EDM parameters based on discharge area from the physical model of a tool electrode. Main parameters, which affect the EDM performance, are peak value of currents, pulse-on time, and pulse-off time. Such parameters are closely dependent on the discharge area in EDM process. In this paper the discharge area is estimated from the CMM scanning data to the tool electrode. The method is very useful when any geometric information to the tool electrode is not provided from tool modeler or producer. The method consists of following four steps. First a triangulation mesh is constructed from the CMM data. Secondly, the z-map is modeled from the triangulated mesh. Thirdly, the discharge area is estimated from intersection between the z-map model and a z-height plane. Finally, the machining parameters are easily calculated by some known EDM equations to the discharge area. An example is introduced to show that the machining parameters are calculated from the CMM data to a tool electrode.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.387-390
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• 2007

The mixing characteristics of heated water discharged from different types of effluents are simulated using a commercial software, Flow-3D model. In order to verify the model, the comparison of numerical results with the experimental data are conducted for each type of effluent, the submerged and surface overfall. It is observed that the numerical results show a reasonable agreement with the experimental data. Based on this study the application to the heated water discharge problem in the field can be expected as a further study and it can be the fundamental data when determining the type of effluent in a powerplant.