• The Korean Nurse
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• v.33 no.3
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• pp.79-91
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• 1994

The purpose of this study was to identify the meaningful nursing diagnosis in maternity unit and to suggest formally the basal data to the nursing service with scientific approach. The subject for this paper were 64 patients who admitted to Chung Ang University Hospital, Located in Seoul, from Mar. 10, to July 21, 1993. The results were as follows: 1. The number of nursing diagnosis from 64 patients were 892 and average number of nursing diagnosis per patient was 13.9. 2. Applying the division of nursing diagnosis to Roy's Adaptation Model, determined nursing diagnosis from the 64 patients were 621 (69.6%) in physiological adaptation mode and (Comfort, altered r/t), (Injury, potential for r/t), (Infection, potential for r/t), (Bowel elimination, altered patterns r/t), (Breathing pattern, ineffective r/t), (Nutrition, altered r/t less than body requirement) in order, and 139 (15.6%) in role function mode, (Self care deficit r/t), (Knowledge deficit r/t), (Mobility, impaired physical r/t) in order, 122 (13.7%) in interdependence adaptation mode, (Anxiety r/t), (Family Process, altered r/t) in order, 10(1.1%) in self concept adaptation mode, (Powerlessness r/t), (Grieving, dysfunctional r/t) in order. 3. Nursing diagnosis in maternity unit by the medical diagnosis, the average hospital dates were 3.8 days in normal delivery and majority of used nursing diagnosis, (Comfort, altered r/t) 64.6%, (Self care deficit r/t) 13.6% in order, and the average hospital dates were 9.6 days in cesarean section delivery and majority of used nursing diagnosis, (Comfort, altered r/t) 51.6%, (Self care deficit r/t) 15.2%, (Infection, potential for r/t) 9.9%, (Injury, potential "for r/t) 8.1%, (Anxiety r/t) 5.0%, (Mobility, impaired physical r/t) 3.3% in order, and the average hospital dates were 15.8days in preterm labor and majority of used nursing diagnosis, (Comfort, altered r/ t), (Anxiety r/t), (Injury, potential for r/t) in order, and the average short-term hospital dates were 2.5days, long-term hospital dates were 11.5days in gynecologic diseases and majority of used nursing diagnosis, (Comfort, altered r/t). (Self care deficit r/t), (Injury, potential for r/t), (Infection, potential for r/t) in order.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.5
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• pp.539-547
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• 1997

This study was carried out to identify how soybean seed protein concentration is influenced by climatic factors. Twelve lines selected for seed protein concentration were studied in 13 environments of North Carolina. Sensitivity of seed protein concentration, total seed protein, and seed yield to climatic variables was investigated using a linear regression model. Best response models were determined using two stepwise selection methods, Maximum R-square and Stepwise Selection. There were wide climatic effects in seed protein concentration, total protein and seed yield. The highest protein concentration environment was characterized by the most high temperature days(HTD) and the smallest variance of average daily temperature range (VADTRg), while the lowest protein concentration environment was distinguished by the fewest HTD and the largest VADTRg. For protein concentration, all lines responded positively to average maximum daily temperature(MxDT), HTD, and average daily temperature range(ADTRg) and negatively to ADRa, while they responded positively or negatively to average daily temperature(ADT), variance of average minimum daily temperature (VMnDT), and VADTRg, indicating that genotypes may greatly differ in degrees of sensitivity to each climatic variable. Eleven lines seemed to have best response models with 2 or 3 variables. Exceptionally, NC106 did not show a significant sensitivity to any climatic variable and thus did not have a best response model. This indicates that it may be considered phenotypically more stable. For total seed protein and seed yield, all the lines responded negatively to both ADTRg and VADRa, suggesting that synthesis of seed components may increase with less daily temperature range and less variation in daily rainfall.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.1-14
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• 1996

We develop a dynamic demand forecasting model compared to regression analysis model and AutoRegressive Integrated Moving Average(ARIMA) model. The dynamic model can apply to the current dynamic data to forecasts through introducing state equation. A multiple regression model and ARIMA model using given data are designed via the model analysis. The forecasting fitness evaluation between the designed models and the dynamic model is compared with the criterion of sum of squared error.

• Asian Journal of Atmospheric Environment
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• v.4 no.3
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• pp.150-156
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• 2010

Inter-comparison of chemical transport models (CTMs) was conducted among four modeling research groups. Model performance of the ensemble approach to $O_3$ and $PM_{2.5}$ simulation was evaluated by using observational data with a time resolution of 1 or 6 hours at four sites in the Kanto area, Japan, in summer 2007. All groups applied the Community Multiscale Air Quality model. The ensemble average of the four CTMs reproduced well the temporal variation of $O_3$ (r=0.65-0.85) and the daily maximum $O_3$ concentration within a factor of 1.3. By contrast, it underestimated $PM_{2.5}$ concentrations by a factor of 1.4-2, and did not reproduce the $PM_{2.5}$ temporal variation at two suburban sites (r=~0.2). The ensemble average improved the simulation of ${SO_4}^{2-}$, ${NO_3}^-$, and ${NH_4}^+$, whose production pathways are well known. In particular, the ensemble approach effectively simulated ${NO_3}^-$, despite the large variability among CTMs (up to a factor of 10). However, the ensemble average did not improve the simulation of organic aerosols (OAs), underestimating their concentrations by a factor of 5. The contribution of OAs to $PM_{2.5}$ (36-39%) was large, so improvement of the OA simulation model is essential to improve the $PM_{2.5}$ simulation.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.03a
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• pp.199-206
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• 2001

Landslides generally occur due to influences of the internal and external factors. Internal factors include ground characteristics, terrain and so on. External factors can also be divided into natural factors such as rainfall, ground water, earthquake and so on, and artificial factors resulting from cutting and embankments. Among these factors, rainfall becomes the most important external factors by means of which landslides occur in Korea. To appropriately deal with tile effects of pore water pressures due to rainfall, the method using the pore water pressure ratio(r$\_$u/) is generally applied in slope stability analysis or the design of slope reinforcements. Since tire value of r,, is in general not constant over the whole cross section, in most slope stability analyses the average values are used with little loss in accuracy. However, determination of the average values of r$\_$u/ to applied in the design is difficult problem. Therefore, in this study, tile average values of r$\_$u/ according to the intensity of rainfall and slope inclination is suggested based on results of the small scaled model tests using the artificial rainfall apparatus. It is found from the model tests that the average values of r$\_$u/ is about 0.07∼0.18(in case of the intensity of rainfall is 50mm/hr.), about 0.10∼0.28(in case of the intensity of rainfall is 100mm/hr.), and about 0.10∼0.33(in case of the intensity of rainfall is 150mm/hr.).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.7
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• pp.1191-1198
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• 2001

MMIC low noise amplifiers for millimeter-wave application using 0.15 $\mu$m pHEMT have been presented in this paper. The design emphasis is on active device model and EM simulation. The deficiency of conventional device models is identified. A distributed device model has been adapted to circumvent the scaling problems and, thus, to predict small signal and noise parameters accurately. Two single-ended low noise amplifier are designed using distributed active device model for Q-band(40 ∼ 44 GHz) and V-band(58 ∼65 GHz) application. The Q-band amplifier achieved a average noise figure of 2.2 dB with 18.3 dB average gain. The V-band amplifier achieved a average noise figure of 2.9 dB with 14.7 dB average gain. The design technique and model employed provides good agreement between measured and predicted results. Compared with the published data, this work also represents state-of-the-art performance in terms of gain and noise figure.

• Journal of Ocean Engineering and Technology
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• v.31 no.4
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• pp.288-298
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• 2017

A study of the damage evolution laws for ductile materials was carried out to predict the ductile fracture behavior of a marine structural steel (EH36). We conducted proportional and non-proportional stress tests in the experiments. The existing 3-D fracture strain surface was newly calibrated using two fracture parameters: the average stress triaxiality and average normalized load angle taken from the proportional tests. Linear and non-linear damage evolution models were taken into account in this study. A damage exponent of 3.0 for the non-linear damage model was determined based on a simple optimization technique, for which proportional and non-proportional stress tests were simultaneously used. We verified the validity of the three fracture models: the newly calibrated fracture strain model, linear damage evolution model, and non-linear damage evolution model for the tensile tests of the asymmetric notch specimens. Because the stress evolution pattern for the verification tests remained at mode I in terms of the linear elastic fracture mechanics, the three models did not show significant differences in their fracture initiation predictions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2233-2252
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• 2018

For the problem of cross-layer joint resource allocation (JRA) in the Long-Term Evolution (LTE)-Advanced standard using carrier aggregation (CA) technology, it is difficult to obtain the optimal resource allocation scheme. This paper proposes a joint resource allocation algorithm based on the weights of user's average quality of experience (JRA-WQOE). In contrast to prevalent algorithms, the proposed method can satisfy the carrier aggregation abilities of different users and consider user fairness. An optimization model is established by considering the user quality of experience (QoE) with the aim of maximizing the total user rate. In this model, user QoE is quantified by the mean opinion score (MOS) model, where the average MOS value of users is defined as the weight factor of the optimization model. The JRA-WQOE algorithm consists of the iteration of two algorithms, a component carrier (CC) and resource block (RB) allocation algorithm called DABC-CCRBA and a subgradient power allocation algorithm called SPA. The former is used to dynamically allocate CC and RB for users with different carrier aggregation capacities, and the latter, which is based on the Lagrangian dual method, is used to optimize the power allocation process. Simulation results showed that the proposed JRA-WQOE algorithm has low computational complexity and fast convergence. Compared with existing algorithms, it affords obvious advantages such as improving the average throughput and fairness to users. With varying numbers of users and signal-to-noise ratios (SNRs), the proposed algorithm achieved higher average QoE values than prevalent algorithms.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1599-1603
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• 2004

Based on the idea that nonlinear PWM controller design can be directly applied to the attitude tracking problem of thruster-controlled spacecraft because it constitutes a sub-class of nonlinear PWM controlled system, nonlinear and output error feedback PWM controlled system is considered to describe the behavior of thruster-controlled spacecraft, and to determine actual thruster on-time which guarantees system stability. A differential geometric approach is utilized to show an asymptotical stability of average PWM system, which finally guarantees the stability of closed loop PWM controlled system. Simulation results show that the motions of PWM controlled system occurs very closely around those of the average model of PWM controlled system.