• Korean Journal of Adult Nursing
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• v.28 no.6
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• pp.619-627
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• 2016

Purpose: This study was to test a structural model of spirituality and the quality of life of stroke survivors' caregivers in order to provide guidelines for the development of intervention and strategies to improve their quality of life. Methods: Data were collected from 133 family caregivers of stroke patients who were hospitalized in C university hospital located in Seoul. Data collection using survey questionnaires was done from May, 2013 to February, 2014. Results: Fitness of the hypothetical model was appropriate. Physical component of quality of life of family caregivers is directly affected by two variables (51.5%), burden and depression. Mental component of quality of life of family caregivers is directly affected by three variables (77.6%), depression, burden, and functional dependence of patients. Depression as well as burden were directly affected by spirituality and functional dependence of patients respectively. Thus, spirituality directly affected depression and burden and indirectly affected the quality of life of family caregivers. Conclusion: Therefore, spiritual intervention to improve the stroke caregivers' quality of life might be necessary to support and strengthen their spirituality as a mediating variable that can contribute to decreasing their depression and burden.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.271-277
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• 2013

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.18-26
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• 2017

Defense industries which require high reliability need an optimized quality management system with well-planned implementation. And the government should examine the overall status of defense industries, then establish practical policies with a proper support plan in required areas to upgrade the quality management level of manufacturers. Thus, DTaQ developed the model for 2 years from 2014, which specialized in quality management level analysis for defense industries. And a survey has been undertaken with that model by DTaQ and Korea Research Center in 2016. The surveyed companies randomly sampled among those which have more than 30 employees and delivery history over past 3 years, and finally 106 defense industries were selected. This paper present survey method and indexes for survey of defense industry quality management level. The survey was conducted in the order of planning, data collection and data processing, and the validity and reliability of the data were verified to increase objectivity of survey results. The survey contents mainly consist of system quality and management quality. System quality includes Product Development Management, Production Operation Management, supply chain quality management, Safety & Environment Management and Reliability Management, on the other hand, management quality includes Strategic Leadership, Human Resource Management, Customer Market Management and Information & Knowledge Management. Thus this proposes the current overall quality management status of the 106 defense industries and shows level differences by company sizes and manufacturing sectors based on the result of survey. Specifically, this paper enables to track the areas which need prompt government support with the policy directions to make quality management level higher. Therefore, it is expected that this can be used as reference data in establishing quality policies for military supplies in the future.

• Journal of Korean Society on Water Environment
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• v.36 no.2
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• pp.98-108
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• 2020

The purpose of this study was to evaluate water quality in Hapcheon dam via using the Hydrological Simulation Program-Fortran (HSPF) model and applied livestock reduction scenarios. Hapcheon dam watershed input data for the HSPF model were established using the stream, land use, digital elevation map and meteorological data and others. The HSPF model was calibrated and validated using the observed water quality data from 2000 to 2016. For water quality simulation, we calculated the generated and discharge loads of the population, livestock, industry and land use following the guideline provided by the Ministry of Environment. The pollutant data were obtained from National Institute of Environmental Research (NIER). The monthly discharge load were estimated by applying the delivery rate. The calibration and validation results showed that the annual mean BOD had a difference of 0.22 mg/L and an error of ±13 %, T-N had a difference of 0.66 mg/L and an error of ±16 % and T-P had a difference of 0.027 mg/L and an error of ±13 %. In order to evaluate the nonpoint pollutants management effects, we applied livestock reduction scenarios because livestock consists of the largest portion of pollutants. As a result of the 20 % of livestock reduction, BOD, T-N and T-P decreased by 3 %, 1 % and 3 %, respectively. When 40 % of livestock reduction was applied, BOD, T-N and T-P decreased by 5 %, 3 % and 4 %, respectively. Based on the results of this study, effective pollutant management methods can be applied to improve the water quality and achieve the target water quality of Hapcheon dam watershed.

• Journal of Information Technology Applications and Management
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• v.30 no.2
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• pp.1-18
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• 2023

Recently, as technology development has accelerated and product life cycles have been shortened, it is necessary to derive key product features from customers in the R&D planning and evaluation stage. More companies want differentiated competitiveness by providing consumer-tailored products based on big data and artificial intelligence technology. To achieve this, the need to correctly grasp the required quality, which is a requirement of consumers, is increasing. However, the existing methods are centered on suppliers or domain experts, so there is a gap from the actual perspective of consumers. In other words, product attributes were defined by suppliers or field experts, but this may not consider consumers' actual perspective. Accordingly, the demand for deriving the product's main attributes through reviews containing consumers' perspectives has recently increased. Therefore, we propose a review data analysis-based required quality methodology containing customer requirements. Specifically, a pre-training language model with a good understanding of Korean reviews was established, consumer intent was correctly identified, and key contents were extracted from the review through a combination of KeyBERT and topic modeling to derive the required quality for each product. RevBERT, a Korean review domain-specific pre-training language model, was established through further pre-training. By comparing the existing pre-training language model KcBERT, we confirmed that RevBERT had a deeper understanding of customer reviews. In addition, all processes other than that of selecting the required quality were linked to the automation process, resulting in the automation of deriving the required quality based on data.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of Environmental Health Sciences
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• v.28 no.3
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• pp.19-25
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• 2002

In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)$_{12}$, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

• Korean Management Science Review
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• v.12 no.2
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• pp.129-146
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• 1995

As society's relience on computerized information systems to support a wide range of activities proliferates, the long recognized importance for adequate data quality becomes imperative. Furthermore, current trends in information systems such as dispersal of the data resource together with its management have increased the difficulty of maintaining suitable levels of data integrity. Especially, the importance of adequate accounting (transaction) data quality has been long recognized and many procedures (extensive and often elaborate checks and controls) to prevent errors in accounting systems have been introduced and developed. Nevertheless, over time, even in the best maintained systems, deficiencies in stored data will develop. In order to maintain the accuracy and reliability of accounting data at certain level, periodic internal checks and error corrections (internal audits) are required as a part of internal control system. In this paper we develop a general data quality degradation (error accumulation ) and cost model for an account in which we have both error occurrences and error amounts and provide a closed form of optimal audit timing in terms of the number of transactions that should occur before an internal audit should be initiated. This paper also considers the cost- effectiveness of various audit types and different error prevention efforts and suggests how to select the most economical audit type and error prevention method.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.533-541
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• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.

• Korean Journal of Adult Nursing
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• v.19 no.4
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• pp.547-555
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• 2007

Purpose: The purpose of this study was to test a hypothetical model of health-related quality of life in patients with heart failure. The hypothetical model was derived from the Wilson and Cleary's model, the Rector's model, and published research findings. Methods: Data from 103 patients with heart failure were analyzed to determine the best multivariate health-related quality of life model given variables derived from the prior studies. The statistics programs SPSS 12.0 and LISREL 8.7 program were used for descriptive statistics and covariance structure analysis respectively. Results: The overall fitness of the path final model was good(GFI=.97, AGFI=.95, NNFI=1.06, NFI=.96, p=.96). Symptoms were directly affected by gender. HYHA Class was directly affected by only gender. Physical functioning limitation was directly affected by exercise. Health perception was directly affected by economics, symptom, and physical functioning limitation. Depression was directly affected by exercise and health perception. Heath-related quality of life was directly affected by physical functioning limitation and depression, indirectly affected by gender, economics, exercise, symptoms, NYHA Class, and health perception. This path analysis model explained 51% of health-related quality of life in patients with heart failure. Conclusion: To improve of health-related quality of life with heart failure patients, it is necessary to make nursing interventions for physical functioning and depression.