• Journal of Korean Society for Quality Management
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• v.28 no.3
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• pp.68-81
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• 2000

In this paper, we studied the four stages of quality related education and training and identified alignment factors that have influence on successful TQM education and training. Based on extensive literature reviews the four stages are extracted such as quality concepts training, quality tools training, special topics training, and leadership training. Also we determine the alignment factors. A framewok of research model including above factors is developed and tested statistically. The perceived data are collected from managers of quality departments of 140 Korean firms through survey. The results show that alignment factors which achieve success in Quality related education training are using relevant examples and implementing training at the top in quality concepts training, providing time and opportunity to master skills in quality tools training, organizing courses into a logical curriculum in special topics training, and providing ongoing feedback in leadership training. We also offered numerous suggestions that can help organizations develop effective training programs to meet their objectives.

• Journal of Korean Society for Quality Management
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• v.31 no.4
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• pp.176-183
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• 2003

The concept of cost of quality has become important economic measures of quality issues for several decades. Most of the research to date has focused on the problem and solution under static business conditions. However, some researchers have found that prevention cost of COQ is one of the expenses of investment and the payback will be shown by the improvement of product quality. These approaches have considered cost of quality issues based on a time factor. It means that there is a trade­off between prevention/appraisal costs and failure costs. Based on a capital budgeting approach, this research reviews the economics of quality improvement activities and proposes the decision criteria to justify quality improvement activities.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.4 s.121
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• pp.298-309
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• 2007

There are various sounds in the car as much as cars have many mechanical parts. These sounds make various psychological development. The international competition in car markets has continuously required the research about the sound quality of a car. The domestic car makers have also invested a lot of money for the research and development of sound quality. Car axle plays an important role in a vehicle and its NVH development is also important. By this time, NVH development of car axle is mainly based on the reduction of sound pressure level (dBA), which cannot gives, the satisfaction to the customers in view of the sound quality of a vehicle. Therefore, in this paper, a sound quality index evaluating the sound quality of axle noise based on human sensibility is developed.

• Journal of Korean Society for Quality Management
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• v.45 no.4
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• pp.761-768
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• 2017

Purpose: Quality management has significantly contributed to the productivity revolution in the past century. However, the argument that quality can be an impediment to innovation in the age of industry 4.0 has intensified recently. Possible solutions to this controversial issue are discussed. Methods: The research papers and magazine articles which described quality as an impediment to innovation are reviewed, and the industries in which this particular problem arises are examined. Results: Conventional quality management is as effective as ever when technology changes quite slowly. However, strong 'quality first' culture serves as an impediment to innovation when technology changes quickly as in the ICT(information and communication technology) industry and platform business. Conclusion: When the technology changes rapidly, faster time-to-market is much more important than variation control and continuous improvement. Moving from a productivity focus to a creativity focus is also required in quality management.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.16 no.2
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• pp.225-233
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• 1998

It is necessary to check the water quality of the lake on a continuous basis to determine the appearance of water pollution; however, it not only takes much time and expenses but it is considerably difficult to investigate the wide range of the area. If we use the remote sensing technique through the use of satellites, the status of water quality can be checked covering many wide areas simultaneously; and because the same area can be measured on a periodic basis, it is extremely effective in investigating the water quality. Furthermore, as some of the Landsat sensors carry characteristics which sense objects according to wave length, the distribution of water quality can be checked relatively accurately within a short period of time, while its image can be displayed in color. Hence, this research has attempted to extract water quality elements, such as transparency, water depth, and surface water temperature by utilizing the satellite data, and has prepared the water quality distribution image map of the Lake Hwajinpo by presenting the related empirical formula of the water quality elements. If the water quality distribution image map is prepared after extracting the water quality elements from the DN of the Landsat TM image and then carrying out TIN analysis through the use of GIS, relatively more accurate pattern can be learned covering a wide rage of area than the pattern presented based on the value obtained from actual observation.

• 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 the Korea Society for Simulation
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• v.17 no.4
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• pp.89-97
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• 2008

In wireless personal area networks (WPANs), the successful design of channel time allocation algorithm is a key factor in guaranteeing the various quality of service (QoS) requirements for the stringent real-time constraints of multimedia services. In this paper we propose a channel time allocation algorithm for achieving a high quality transmission and high channel error tolerance of MPEG stream in the IEEE 802.15.3 high-rate wireless PANs. Our algorithm exploits the characteristics of MPEG stream. When a new MPEG stream arrives, a DEV models it by the traffic envelope and delivers the traffic envelope to the piconet coordinator (PNC) along with the channel time request. The PNC performs channel time allocation according to the envelope. Performance of the proposed scheme is investigated by simulation and analysis. Our results show that compared to conventional scheme, the proposed scheme is very effective and provides a good performance under typical channel error conditions.

• The Korean Journal of Food And Nutrition
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• v.37 no.1
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• pp.40-47
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• 2024

Gastrodia elata has been used in traditional Chinese medicine for treating headaches, dizziness, and convulsive illness for centuries. G. elata has traditionally been processed by steaming or blanching to increase the content and quality of its main ingredients. This study aimed to identify changes in physicochemical properties and active ingredients of G. elata depending on the steaming time. Data of this study could be used to develop traditional medicine and health foods. No steaming was used as a control. Steaming time was 5, 10, 20, 30, 60, or 120 min. The drying yield according to the steaming time ranged from 20.2% to 22.9%, with the lowest drying yield at 120 min. As the steaming time increased, gastrodin content increased more than that in fresh G. elatadue to inhibition of β-glucosidase enzyme activity, 4-hydroxybenzyl alcohol condensation, and parishin decomposition. Steamed G. elatadid not show higher total polyphenols, total flavonoids, or ABTS radical scavenging activities than fresh G. elata even with an increase of steaming time. The steaming time to improve the quality of G. elata may varied depending on the size of G. elata. Thus, it is important to set the steaming time taking these characteristics into consideration.

• Journal of Environmental Health Sciences
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• v.44 no.3
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• pp.250-258
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• 2018

Objectives: The study examined indoor air quality during daytime and commute times in underground parking lots in five apartments located in northern Gyeonggi province. Methods: This study examined the temporal characteristics of $PM_{10}$, $CO_2$, CO, HCHO and VOCs in the indoor air quality of the underground parking lots in five apartments. Results: $PM_{10}$ concentration in daytime were ranged from 37.4 to $69.9{\mu}g/m^3$ which complied with the indoor air quality maintenance standard ($200{\mu}g/m^3$) and Gyeonggi province ordinance standard ($180{\mu}g/m^3$). However $PM_{10}$ concentrations in commuting time were in range of $447.3{\sim}944.0{\mu}g/m^3$ that exceeded for both criteria. The $CO_2$, CO and HCHO concentrations complied with the standard. The VOCs indicated high tendency in the order of toluene, xylene, ethylbenzene and benzene. This tendency was more apparent in commuting time than daytime. Conclusion: All indoor parking lots of five apartments were equipped with ventilators. However the most apartment residents did not operate blowers to save electricity and the lack of interest for indoor air quality. In order to improve the indoor air quality of underground parking lots in apartment, operating ventilation system during commuting time is recommended.