• 산업경영시스템학회지
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• 제22권49호
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• pp.169-182
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• 1999

Nowadays most companies are required to acquire the quality assurance system for their survival. It is essential to improvement company's competitive power that Certification of Quality Assurance System but there are many problems. To analysis the effects of ISO 9000 series/QS-9000 QA systems to the Domestic Company Quality Improvement as well as quality consciousness, data has been surveyed form quality assurance engineers who have work on certification activities. The data is analyzed using relevant tabulations of five scale or paired t test method in order to compare level of quality management of pre-certification with that of post-certification. It is analyzed in two parts which are qualitative effects and quality maturity grid. Also, to analysis for the bottlenecks and problems the data is analyzed using frequency analysis method.

• International journal of advanced smart convergence
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• 제8권3호
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• pp.193-200
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• 2019

Accruals-Quality(AQ) is an important proxy for evaluating the quality of accounting information disclosures. High-quality accounting information will provide high predictability and precision in the disclosure of earnings and will increase the response to stock prices. And high Accruals-Quality, such as mitigating heterogeneity in accounting information interpretation, provides information usefulness in capital markets. The purpose of this study is to suggest how AQ, which represents the quality of accounting information disclosure, is transformed into digitized data in real-time in combination with IT information technology and provided to financial analyst's information environment in real-time. And AQ is a framework for predictive analysis through big data log analysis system. This real-time information from AQ will help financial analysts to increase their activity and reduce information asymmetry. In addition, AQ, which is provided in real time through IT information technology, can be used as an important basis for decision-making by users of capital market information, and is expected to contribute in providing companies with incentives to voluntarily improve the quality of accounting information disclosure.

• Corrosion Science and Technology
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• 제23권2호
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• pp.131-138
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• 2024

This paper introduces Primetals Technologies' Through-Process Optimization (TPO) Services and Through-Process Quality Control (TPQC) System, which integrate domain knowledge, software, and automation expertise to assist steel producers in achieving operational excellence. TPQC collects high-resolution process and product data from the entire production route, providing visualizations and facilitating quality assurance. It also enables the application of artificial intelligence techniques to optimize processes, accelerate steel grade development, and enhance product quality. The main objective of TPO is to grow and digitize operational know-how, increase profitability, and better meet customer needs. The paper describes the contribution of these systems to achieving operational excellence, with a focus on quality assurance. Transparent and traceable production data is used for manual and automatic quality evaluation, resulting in product quality status and guiding the product disposition process. Deviation management is supported by rule-based and AI-based assistants, along with monitoring, alarming, and reporting functions ensuring early recognition of deviations. Embedded root cause proposals and their corrective and compensatory actions facilitate decision support to maintain product quality. Quality indicators and predictive quality models further enhance the efficiency of the quality assurance process. Utilizing the quality assurance software package, TPQC acts as a "one-truth" platform for product quality key players.

• 한국조사연구학회지:조사연구
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• 제11권2호
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• pp.161-176
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• 2010

본 논문은 학위논문에 사용된 여론조사 자료의 품질을 평가하였으며, 또한 학위논문에 사용되는 데이터의 품질평가 모형을 제시하였다. 데이터의 품질평가와 평가모형에 사용한 평가지표는 한국조사연구학회에서 제시한 조사윤리강령 제3조의 16가지 항목을 사용하였다. 연구결과 학위논문에 사용된 여론조사 데이터의 품질은 낮은 수준임을 확인할 수 있었다. 16개 항목별로 석 박사학위에 따른 유의적인 차이가 있는지를 확인하기 위하여 두 집단 간 비율 차이 검정을 실시한 결과 표집오차와 분석방법의 수에서만 차이가 있을 뿐 다른 지표에서는 차이가 나타나지 않았다. 끝으로 학위논문에 사용된 여론조사 자료의 질을 평가하기 위한 품질평가를 위해 9가지 주요 평가지표에 가중치를 부여하는 모형을 제시하였다.

• 한국환경과학회지
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• 제17권12호
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• pp.1353-1361
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• 2008

Quality control of Argo(Array for Real-time Geostrophic Oceanography) data is crucial by reason that salinity measurements are liable to experience some drift and offset due to biofouling, contamination of sensor and wash-out of biocide. The automated Argo real-time quality control has a limit of sorting data quality, so that WJO program is adopted as standardized method of Argo delayed mode quality control (DMQc) in the world that is a precise quality control method. We conducted DMQC on pressure, temperature and salinity measured by Argo floats in the Pacific Ocean including expert evaluation. Particularly, salinity data were corrected using WJO program. 4 salinity profiles of Argo delayed mode were compared with nearby in situ CTD data and other Argo data in deep layer where oceanographic conditions are stable in time and space. The differences of both salinities were lower than target accuracy of Argo. As compared with the difference of salinities before DMQC, those after DMQC decreased by 60-80 percent. Quality of delayed mode salinity data seemed to be improved correcting salinity data suggested by WJO program.

• 산업경영시스템학회지
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• 제40권3호
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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.

• 농업과학연구
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• 제47권4호
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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.

• 한국전자거래학회지
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• 제8권3호
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• pp.117-130
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• 2003

CRM 과 같은 전자상거래응용시스템에서 동일한 데이터의 중복이나 불일치는 종종 일어나며 이는 바람직하지 못하다. 데이터 품질 관리란 데이터들간의 비 일치와 중복을 발견하고 제거함을 목적으로 한다. 통상적인 데이터 품질관리 프로세스는 클리닝, 매칭, 통합의 세 단계를 거친다. 본 논문에서는 일반적인 데이터 품질 관리를 각 단계별로 필요한 연산자들을 정의한다. 특히 실제적 인 시스템 구현에서 필요한 매칭 단계에서 사용하는 거리함수와 매칭 알고리즘을 제안하며, 마지막으로 관련 연구를 제시한다.

• 한국전자통신학회논문지
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• 제16권5호
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• pp.957-968
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• 2021

육상 양식장 수질 모니터링 시스템의 데이터 신뢰도 향상 및 효율적 시스템 운영을 위하여 현재 시험 운영중인 육상 해수 양식장과 해양환경정보망(완도조위관측소)의 수질 데이터를 비교 분석한다. 또한 수질 모니터링 시스템의 데이터 오류를 제거하고, 측정 데이터의 신뢰도를 높이는 방법으로 데이터 유효성 검증, 데이터 범위 필터, 데이터 변위 검사를 적용하여 비교 분석한다.

• 한국태양에너지학회 논문집
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• 제27권1호
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• pp.19-27
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• 2007

The Data quality management system(DQMS) organizes and helps manage and process time sequence data usually collected in monitoring networks and programs. DQMS places particular emphasis on data qualify while maintaining a highly organized and convenient structure for data. It operates with in a flexible and powerful commercial relational data base environment which can readily link to other software platforms from local spreadsheets to network server. The Korea Institute of Energy Research(KIER) has been solar radiation data since May, 1991 for 16 different locations. KIER's new data is expected to be extensively used by designer and researchers of solar systems in lieu of unreliable old ones. Unfortunately, the quality of the data has not always been properly mentioned. The purpose of this study is to systematically identify errors in such data set using DQMS in an effort to rehabilitate error-ridden old data. DET successfully uncovered solar radiation data that had questionable quality.