• Journal of Environmental Impact Assessment
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• v.8 no.1
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• pp.17-27
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• 1999

Applicability of LCA in establishing the integrated solid waste management were examined. Data were collected from current waste management system. LCA method was then applied to find environmentally sustainable waste management. The process comprises five steps : (1) collecting data, comparing the data with comprehensive urban planning as well, (2) analyzing the current waste treatment procedure leading to the database establishment, (3) LCA for the waste management system, (4) finding alternative scenarios based on the former steps, (5) establishing the optimum method in Ecopolis, best fitted to the local situation. The results and suggestions in this study are expected to yield comprehensive analysis as to current practices in waste management. More importantly it will be a valuable data in minimizing environmental burdens in connecting with living environments of Ecopolis. Information regarding the urban system and total environmental quality are expected from this study.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.47-52
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• 2005

In this paper we describe SLA metrics and software architecture for newly upcoming IP TV service. Firstly we show the general description for IP TV. This consists of brief description of IP TV, the general figure of configuration and elements of IP TV network and communication protocol stack of IP TV data transmission for deriving communication quality parameter. And we propose SLA metrics depending on quality measurement point. According to the measurement point the measurable SLA metrics are different. The total view of the IP TV SLA measurement and management architecture is shown. When the quality measurement point is the routers that consist of internet node, the IP media NMS is the quality collection system. And when the quality measurement point is the IP TV STB(Set-Top Box), the IP media collector and Quality Collection Server are the quality collection system. And we show the software block diagram of IP TV SLA processing system and the other related network management systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.170-178
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• 2004

As quality becomes a primary leading factor of organizational success, various management strategies have been introduced to improve quality competitiveness. Quality competitiveness, however, is difficult to measure and numerous organizations are struggling to set realistic improvement objectives. The primary purpose of this research is to propose a systematic approach to help the practitioners develop an improvement plan for their organizational quality competitiveness. This approach employs DEA(Data Envelopment Analysis) to evaluate relative efficiency among companies which make efforts to improve their quality competitiveness. It presents an integer programming model to elicit an optimal improvement plan for meeting a target level. A decision support system is also developed for the managers to plan a sequential improvement plan based on both DEA model and the integer programming model.

• 시스템엔지니어링워크숍
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• s.4
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• pp.90-94
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• 2004

System engineering(SE) management techniques applied for space launch system development are introduced to assess the current status and address the effwctiveness of these techniques. Management plans and guides are prepared for the work breakdown structure , data, comfiguration, interface control, Quality assurance, procurement, reliability, risk and verification/validation . Further improvement is required for the system engineering management plan(SEMP) to merge the international cooperation into current engineering managment system.

• International Journal of Quality Innovation
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• v.5 no.2
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• pp.1-16
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• 2004

Credit scoring is widely used to make credit decisions, to reduce the cost of credit analysis and enable faster decisions. However, traditional credit scoring models do not account for the influence of noises. This study proposes a robust credit scoring system based on Mahalanobis-Taguchi System (MTS). The MTS, primary proposed by Taguchi, is a diagnostic and forecasting method using multivariate data. The proposed approach's effectiveness is demonstrated by using real case data from a large Taiwanese bank. The results reveal that the robust credit scoring system can be successfully implemented using MTS technique.

• The Journal of Industrial Distribution & Business
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• v.6 no.3
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• pp.9-17
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• 2015

Purpose - The study's aim is to assess performance quality delivered through a performance management system. The case study first measures the effectiveness of the present performance management system of Bhushan Steel Ltd. (BSL), and then secondarily analyzes the employee training impact. Research design, data, and methodology - A case study research approach using a survey was followed. The final sample size was 50, with a simple random sampling technique used. Primary data came from the company itself and the secondary from books, the Internet, journals, etc. Results - BSL employee appraisals are used for many developmental aspects such as competence in the present job, improvements, performance development, and training needs. The results indicate that individual performance can improve if employees perceive the system as more transparent (i.e., not so confidential). Conclusions - Overall, the BSL employees think that the company appraisal system is useful; however, to make it better the company has to improve many aspects including appraisals based on supervisor observations. The company could use a self-appraisal and a 360-appraisal approach to improve such assessments.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.47-56
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• 1999

In this paper, we analyzed two methodologies to evaluate the total quality management activities comparatively. One of them is the traditional scoring system (TSS) by analytic hierarchy process (AHP) and the other is the efficiency measuring system (EMS) by data envelop analysis (DEA). It is demonstrated that the index by TSS and the efficiency index by EMS are not correlated. Also, the efficiency index by EMS in terms of company characteristics such as the size of company and ISO certifications is different from the index by TSS. The result implies that to evaluate the quality activities by EMS is needed as a supplementary methodology.

• International Journal of Quality Innovation
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• v.2 no.2
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• pp.24-33
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• 2001

As we know, the quality of processes is technically depicted by variation, a product or process with the best quality must naturally require the variation as less as possible. The variation is usually reduced with many ways, say, by adjusting parameters settings under robust design with many turns expensive experiments. So ones are trying to reach the robustness by detecting cheap and simple methods. In this paper, a both practical and simple technique for quality improvement, namely reducing the variation, by data classification is studied. First, all possible system factors are included, which may dominate the variation law. And then we make use of the past observations and their classification as well as boxplot charts to find out the internal rule between the variation and the system factor. Next, adjust the location of the system factor according to the rule so that the variation could, to some extent, be lessened. Finally, two typical quality improvement cases based on data classification are presented.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.13-20
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• 2009

The most of navigation services these days, are designed in order to just provide a shortest path from current position to destination for a user. Several navigation services provides not only the path but some fragmentary information about its point, but, the data tends to be highly restricted because it's quality and quantity totally depends on service provider's providing policy. In this paper, we describe the folksonomy POI(Point of interest) recommender system using mashup in order to provide the information that is more useful to the user. The POI recommender system mashes-up the user's folksonomy data that stacked by user with using external folksonomy service(like Flickr) with others' in order to provide more useful information for the user. POI recommender system recommends others' tag data that is evaluated with the user folksonomy similarity. Using folksonomy mahup makes the services can provide more information that is applied the users' karma. By this, we show how to deal with the data's restrictions of quality and quantity.