• IE interfaces
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• v.10 no.3
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• pp.125-132
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• 1997

We proposed a 2-step neural network approach for estimating machining parameters of electric discharge wire cut. The first step net, which is described as a backward neuro-estimation, is designed for estimating coarse cutting parameters while the second phase net, as a polishing forward neuro-estimation, is utilized for determining fine parameters. Sequential estimation procedure, based on backward and forward net, is performed using the net's approximation capability which is M to 1 and 1 to M mapping property. Experimental results an given to evaluate the accuracy of the proposed 2-step neuro-estimation.

• 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 Korean Academy of Nursing
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• v.22 no.4
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• pp.589-603
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• 1992

This study investigated a process to estimate the need for nursing staff on the basis of a patient elassification system and the required care needs and activities. The investigation was carried out in the following four steps. Step 1. Patients were classified according to the amount of nursing care need on each shift as class I (mildly ill), class II (mederately ill), class III (acutely ill), and class IV (critically ill). Step 2. Measurement of the direct nursing care hours needed for each patient class, and measurement of indirect nursing care hourse and personal time of the nursing staff. Step 3. Calculation of he total nursing workload in a nursing unit. Step 4. Estimation of the nursing staff needed. The investigation was carried out from July 17th to 30th, during 24hours every other day. The subjects were the patients and the nursing staff on two units of Seoul National University Hospital, Korea. Some of the results from the investigation are as follows : 1) Distribution of patient classification On the neuro surgical (N.S.), the distribution was class I, 22 patient, 3, class II, 27 patients, class III, 26 patients, and class IV, 25 patients, For the orthopedic surgical unit(0.5.), it was class I, 43 patients, class II, 43 patients, class III, patients, and class IV, 3 patients. 2) Direct nursing care hours per day On the N.S. unit, 3.2 hours of direct nursing care were needed for class I, 3.9 hours for class II, 5.1 hours of class III, and 6.2 hours for class IV patients, while 2.0 hours for class I, 2.5 hours for class II, 3.5 hours for class III, 5.0 hours class IV patients were needed on the 0.5. units. 3) Analysis of direct nursing care activities Activities were classified into assessment and observation(47%), medication(38.7%), communiontion(5.1%), exercise(2.4%), elimination and irrigation(1.3%), treatmemt(1.1%), hygiene(0.8%), nutrition(0.8%), and hot and cold compress(0.1%). 4) Average hours of indirect nursing care per day. On the N.S. unit 4.2 hours, and on the O.S. unit, 3.5 hours of RN indirect care was needed. 5) The average personal time used by the of nursing staff was 17 minutes for both RNs and nursing assistants in the N.S. unit, and 32 minutes for both RNs and nursing assistants in the O.S. unit. 6) Estimation of nursing staff needed on two specialized units of a university hospital For the N.S. nursing unit of 43 beds, 31 nursing staff would be indicated. For the 0.5. nursing unit of the same number of beds, 19 nursing staff would be indicated.