• 품질경영학회지
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• 제51권4호
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• pp.551-571
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• 2023

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.

• 품질경영학회지
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• 제51권1호
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• pp.37-54
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• 2023

Purpose: A main aims are to check the level of satisfaction of service quality and derive service quality factors in the field of defense quality assurance activities that need improvement. Furthermore, the paper presents a basic data for identifying future development directions. Methods: Classify the level of service perceived by customers and calculate the customer satisfaction coefficient and PCSI index. In addition, the direction of future research is established by empirically comparing and analyzing the data of this study and the past data. Results: The paper derive the service quality factors to be provided to small businesses in the current state. Moreover, It shows the increasing trend of new companies using comparative analysis with past data Conclusion: Since the new company consists of small businesses with less than 50 employees, further research on small businesses is needed in the future.

• 한국농림기상학회지
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• 제22권3호
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• pp.194-204
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• 2020

작물 모형의 품종에 따른 특성을 나타내는 품종 모수를 추정하기 위해서는 많은 양의 생육 관측 자료가 요구되며, 이를 확보하기 위해서는 많은 비용과 노력이 요구된다. 고품질 자료는 아니더라도 공개되어 있는 작물 생육 자료를 활용하여 모수 추정에 사용할 수 있으나, 이러한 자료의 품질에 대한 평가가 선행되어야 한다. 본 연구에서는 농업자료에 대한 정량적 평가 도구인 DatasetRanker를 사용하여 벼에 대한 지역적응시험 자료를 평가하였다. 또한, 결과를 바탕으로 자료의 품질을 개선하기 위한 관측체계의 개선방안을 제시하고자 하였다. 평가 결과 각각의 품종들은 모두 네 등급 중 세 번째로 높은 은 등급으로 평가되었으며, 더 상위의 등급을 얻지 못한 것은 대체로 생육 및 생육환경에 대한 관측자료의 부족에 기인하였다. 결과를 개선하기 위해서는 추가적인 관측자료가 요구되며, 일부 재배관리 등의 기본적인 조건들에 대한 정보를 추가하는 것만으로도 품질에 대한 평가 점수가 약 10%정도 상승할 것으로 예상되었다. 또한, 정확한 위치정보가 공개될 경우 이를 기준으로 수집되는 토양 정보와 기상 정보의 불확실성을 감소시킬 수 있을 것이다. 생육기간 중 시계열적인 관측자료가 수집된다면 품질이 상당히 개선될 것으로 예상되었으며, 이를 위한 연구가 지속적으로 이루어져야 할 것이다.

• 환경영향평가
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• 제20권5호
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• 보건의료산업학회지
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• 제10권3호
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• pp.63-74
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• 2016

Objectives : There is a need to develop a data quality management algorithm to improve the quality of healthcare data using a data quality management system. In this study, we developed a data quality control algorithms associated with diseases related to hypertension in patients with diabetes mellitus. Methods : To make a data quality algorithm, we extracted the 2011 and 2012 discharge damage survey data from diabetes mellitus patients. Derived variables were created using the primary diagnosis, diagnostic unit, primary surgery and treatment, minor surgery and treatment items. Results : Significant factors in diabetes mellitus patients with hypertension were sex, age, ischemic heart disease, and diagnostic ultrasound of the heart. Depending on the decision tree results, we found four groups with extreme values for diabetes accompanying hypertension patients. Conclusions : There is a need to check the actual data contained in the Outlier (extreme value) groups to improve the quality of the data.

• 대한산업공학회지
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• 제42권6호
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• pp.370-376
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• 2016

Manufacturing data analysis and its applications are getting a huge popularity in various industries. In spite of the fast advancement in the big data analysis technology, however, the manufacturing quality data monitored from the automated inspection system sometimes is not reliable enough due to the complex patterns of product quality. In this study, thus, we aim to define the level of trusty of an automated quality inspection system and improve the reliability of the quality inspection data. By correlation analysis and feature selection, this paper presents a method of improving the inspection accuracy and efficiency in an SVM-based automatic product quality inspection system using thermal image data in an auto part manufacturing case. The proposed method is implemented in the sealer dispensing process of the automobile manufacturing and verified by the analysis of the optimal feature selection from the quality analysis results.

• 한국융합학회논문지
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• 제5권4호
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• pp.21-25
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• 2014

현재 대부분의 기업들이 정보기술을 기반으로 정보 시스템을 이용한 데이터 분석 기법을 활용하고 있다. 이러한 데이터 분석은 기업의 다양한 의사결정에 영향을 미치는 데이터의 품질 평가에 주목하고 있다. 이는 데이터 품질 평가가 기업의 효과적인 운영뿐만 아니라 여러 부분에서 중요한 역할을 하기 때문이다. 본 연구에서는 현재 다양하게 연구되고 있는 데이터 품질 평가 모델에 관하여 기술하고, 이를 기반으로 데이터 품질 분석에 활용되고 있는 융합적이며, 적응적 모델인 DQnA 모델에 관하여 서술하고, 이를 활용한 품질 분석 방법에 관하여 논의하고자 한다.

• 한국정보전자통신기술학회논문지
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• 제14권4호
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• pp.292-299
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• 2021

IT기술의 발달로 제조 기반의 IIoT환경을 기반으로 한 데이터 마이닝 형태의 소프트웨어들이 점차 늘어나고 있다. 그러나 빅데이터 및 데이터마이닝을 진행해야 하는 대량의 데이터를 가지는 제조 기업의 소프트웨어 특성상 일반 소프트웨어와 동일한 형태로 소프트웨어 품질을 평가하기 힘든 실정이다. 또한 이기종간의 장비 및 소프트웨어가 혼재된 제조 기반의 환경에서 특히 기존의 품질 특성을 적용하여 사용되는 소프트웨어에 대한 품질 판단을 진행하기 어렵다. 본 논문에서는 제조 기반의 특성을 조사하고 이에 맞는 소프트웨어 품질 평가 모델을 개발하여 평가를 실시하고자 한다.

• 대기
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• 제26권3호
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• pp.461-471
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• 2016

A quality check algorithm for the Weather Information Service Engine pulsed Doppler wind lidar is developed from a view point of spatial and temporal consistencies of observed wind speed. Threshold values for quality check are determined by statistical analysis on the standard deviation of 3-component of wind speed obtained by a wind lidar, and the vertical gradient of horizontal wind speed obtained by a radiosonde system. The algorithm includes carrier-to-noise ratio (CNR) check, data availability check, and vertical gradient of horizontal wind speed check. That is, data sets whose CNR is less than -29 dB, data availability is less than 90%, or vertical gradient of horizontal wind speed is less than $-0.028s^{-1}$ or larger than $0.032s^{-1}$ are classified as 'doubtful', and flagged. The developed quality check algorithm is applied to data obtained at Bucheon station for the period from 1 to 30 September 2015. It is found that the number of 'doubtful' data shows maxima around 2000 m high, but the ratio of 'doubtful' to height-total data increases with increasing height due to atmospheric boundary height, cloud, or rainfall, etc. It is also found that the quality check by data availability is more effective than those by carrier to noise ratio or vertical gradient of horizontal wind speed to remove an erroneous noise data.

• 품질경영학회지
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• 제30권2호
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• pp.72-85
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• 2002

Advances in computer and sensor technology have made it possible to obtain superlarge manufacturing process data in real time, letting us extract meaningful information from these superlarge data sets. We propose a systematic data analysis procedure which field engineers can apply easily to manufacture quality products. The procedure consists of data cleaning and data analysis stages. Data cleaning stage is to construct a database suitable for statistical analysis from the original superlarge manufacturing process data. In the data analysis stage, we suggest a graphical easy-to-implement approach to extract practical information from the cleaned database. This study will help manufacturing companies to achieve six sigma quality.