• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.4
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• pp.444-452
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• 2012

Purpose: To explore the usefulness of previous fall history as a triage variable for inpatients. Methods: Medical records of 21,382 patients, admitted to medical units of one tertiary hospital, were analyzed retrospectively. Inpatient falls were identified from the hospital's self-report system. Non-falls in 1,125 patients were selected by a stratified matching sampling with 125 patients with falls (0.59%). A comparative and predictive accuracy analysis was conducted to describe differences between the two groups with and without a history of falls. Logistic regression was used to measure the effect size of the fall history. Results: The fall history group showed higher prevalence by 9 fold than the non-fall history group. The relationships between falls and relevant variables which were significant in the non-fall history group, were not significant for the fall history group. Falls in the fall history group were 25 times more likely than in the non-fall group. Predictive accuracy of the risk assessment tool showed almost zero specificity in the fall history group. Conclusion: The presence of fall history, the fall prevalence, variables relevant to falls, and the accuracy of the risk tool were different, which support the usefulness of the fall history as a triage variable.

• KIEAE Journal
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• v.15 no.4
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• pp.105-110
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• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.8
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• pp.3443-3446
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• 2014

The aim of this study was to determine predictive factors for neutropenia after docetaxel-based systemic chemotherapy in patients with castration-resistant prostate cancer (CRPC). The study included 40 Korean CRPC patients who were treated with several cycles of docetaxel plus prednisolone from May 2005 to May 2012. Patients were evaluated for neutropenia risk factors and for the incidence of neutropenia. In this study, nine out of forty patients (22.5%) developed neutropenia during the first cycle of docetaxel-based systemic chemotherapy. Four experienced grade 2, three grade 3, and one grade 4 neutropenia. Multivariate analysis showed that pretreatment white blood cell (WBC) count (p=0.042), pretreatment neutrophil count (p=0.015), pretreatment serum creatinine level (p=0.027), and pretreatment serum albumin level (p=0.017) were significant predictive factors for neutropenia. In conclusion, pretreatment WBC counts, neutrophil counts, serum creatinine levels, and serum albumin levels proved to be significant independent risk factors for the development of neutropenia induced by docetaxel-based systemic chemotherapy in patients with CRPC.

• Clinical and Experimental Reproductive Medicine
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• v.20 no.1
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• pp.1-7
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• 1993

The present study was designed to test the validity of the semen analysis(S/A) and the sperm penetration assay(SPA) as a prognostic indicator of male fertility in 123 patients undergoing in vitro fertilization(IVF). We attempted to correlate the traditional semen parameters or the extent of sperm penetration in SPA with the results of human IVF rate or cleavage rate. Poor correlation was found between the results of S/A and human IVF rate(sensitivity, 80.6% ;specificity, 46.7%; positive predictive value, 91.6%;negative predictive value, 25%). Conversely, good correlation was found between the results of SPA and human IVF rate(sensitivity, 100% ; specificity, 80% ;positive predictive value, 97.3% ;negative predictive value, 100%). Our results corroborate the conclusion that SPA can be a valuable tool as a prognostic indicator of male fertilizing ability.

• Journal of Sasang Constitutional Medicine
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• v.32 no.2
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• pp.10-21
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• 2020

Objectives Genome-wide association studies(GWAS) is a useful method to identify genetic associations for various phenotypes. The purpose of this study was to develop predictive models for Sasang constitution types using genetic factors. Methods The genotypes of the 1,999 subjects was performed using Axiom Precision Medicine Research Array (PMRA) by Life Technologies. All participants were prescribed Sasang Constitution-specific herbal remedies for the treatment, and showed improvement of original symptoms as confirmed by Korean medicine doctor. The genotypes were imputed by using the IMPUTE program. Association analysis was conducted using a logistic regression model to discover Single Nucleotide Polymorphism (SNP), adjusting for age, sex, and BMI. Results & Conclusions We developed models to predict Korean medicine constitution types using identified genectic factors and sex, age, BMI using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN). Each maximum Area Under the Curve (AUC) of Teaeum, Soeum, Soyang is 0.894, 0.868, 0.767, respectively. Each AUC of the models increased by 6~17% more than that of models except for genetic factors. By developing the predictive models, we confirmed usefulness of genetic factors related with types. It demonstrates a mechanism for more accurate prediction through genetic factors related with type.

• Journal of Power Electronics
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• v.15 no.1
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• pp.202-215
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• 2015

This paper investigates the stability and performance of model predictive controlled active-front-end (AFE) rectifiers for energy storage systems, which has been increasingly applied in power distribution sectors and in renewable energy sources to ensure an uninterruptable power supply. The model predictive control (MPC) algorithm utilizes the discrete behavior of power converters to determine appropriate switching states by defining a cost function. The stability of the MPC algorithm is analyzed with the discrete z-domain response and the nonlinear simulation model. The results confirms that the control method of the active-front-end (AFE) rectifier is stable, and that is operates with an infinite gain margin and a very fast dynamic response. Moreover, the performance of the MPC controlled AFE rectifier is verified with a 3.0 kW experimental system. This shows that the MPC controlled AFE rectifier operates with a unity power factor, an acceptable THD (4.0 %) level for the input current and a very low DC voltage ripple. Finally, an efficiency comparison is performed between the MPC and the VOC-based PWM controllers for AFE rectifiers. This comparison demonstrates the effectiveness of the MPC controller.

• Journal of the FoodService Safety
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• v.2 no.2
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• pp.84-90
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• 2021

This study was performed to assess the microbiological quality and safety of microgreen sampled from harvesting farms and food processing plant in Korea. The samples were analyzed for total viable counts, coliforms, Enterobacteriaceae, Escherichia coli, Salmonella spp., Listeria monocytogenes, Vibrio parahaemolyticus, Bacillus cereus, and Staphylococcus aureus. Total viable counts were highly contaminated in samples collected from farms (7.7~8.2 log CFU/g) and the final products (5.8~7.8 log CFU/g), respectively. B. cereus was detected less than 100 CFU/g, which was satisfied with Korean standards (<1,000 CFU/g) of fresh-cut produce. A predictive model was developed for the changes of total viable counts in microgreens during storage at 5~35℃. The predictive models were developed using the Baranyi model for the primary model and the square root model for the secondary model. The results obtained in this study can be useful to develop the safety management options along the food chain, including fresh-cut produce storage and distribution.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.57-58
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• 2022

Concrete mix design is used as essential data for the quality of concrete, analysis of structures, and stable use of sustainable structures. However, since most of the formulation design is established based on the experience of experts, there is a lack of data to base it on. are suffering Accordingly, in this study, the purpose of this study is to build a predictive model to use the concrete mixing factor as basic data for calculation using the DNN technique. As for the data set for DNN model learning, OPC and ternary concrete data were collected according to the presence or absence of admixture, respectively, and the model was separated for OPC and ternary concrete, and training was carried out. In addition, by varying the number of hidden layers of the DNN model, the prediction performance was evaluated according to the model structure. The higher the number of hidden layers in the model, the higher the predictive performance for the prediction of the mixing elements except for the compressive strength factor set as the output value, and the ternary concrete model showed higher performance than the OPC. This is expected because the data set used when training the model also affected the training.

• Proceedings of the KWS Conference
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• 2004.05a
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• pp.234-236
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• 2004

The purpose of this study is to establish the predictive equation of welding distortion at the thick AA5083 alloy weldment. In order to do it, the extensive FE analysis was peformed to identify the principal factor controlling welding distortion. Based on the results, the predictive equations of transverse shrinkage and angular distortion at the thick AA5083 alloy weldment were formulated as the function of heat intensity (Q), in-plane(Di) and bending(Db) rigidity.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.60-66
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• 1996

Model Based Predictive Control(MBPC) has been widely used in predictive control since 80's. GPC[1] which is the superset of many MBPC strategies a popular method, but GPC has some weakness, such as insufficient stability analysis, non-applicability to internally unstable systems. However, CRHPC[2] proposed in 1991 overcomes the above limitations. So we chose RHPC based on CRHPC for electric furnace control. An electric furnace which has nonlinear properties and large time delay is difficult to control by linear controller because it needs nearly perfect modelling and optimal gain in case of PID. As a result, those controls are very time-consuming. In this paper, we applied RHPC with equality constraint to electric furnace. The reults of experiments also include the case of RHPC with monotonic weighting improving the transient response and including unmodelled dynamics. So, This paper proved the practical aspect of RHPC for real processes.