• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.633-638
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• 2004

Main purpose of existing VOC (the voice of customer) analysis is suppressing the VOC occurrence. But, in real situation, it is impossible to reduce the VOC occurrence to the zero level. Company's strategy to decrease specific customer complaints may cause another customer's complaints increase. Especially, in a financial industry, customer complaints are directly connected with company's profit. That is, the VOC from their profitable customer, product and channel is major concern of companies. In this paper, 1 reveal the relationship between CRM (customer relationship management) activity and VOC analysis and various analysis methods.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.4 no.1
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• pp.39-61
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• 1996

현재 항공기 운항 승무원의 효율적 관리를 위하여 CRM과 LOFT 교육 프로그램이 개발되었다. 이는 Blake & Mouton에 의한 리더십의 유형을 과업과 사람에 대한 관심 정도로 구분하여 5가지 유형으로 나누어 분석하고, 이를 훈련에 적용하고 있다. 그러나 사람의 성격 유형은 보다 복잡하고, 이에 대한 깊은 연구가 요구됨에 따라 본 연구에서 Enneagram에 의한 성격 유형을 고찰하고 민항공사 조종사들의 성격 유형과 성격별 선호 성격 그리고 과업 성과를 높이기 위한 성격간 상호 작용을 규명하여 항공기 운항 승무원 관리에 적용 할 수 있도록 하였다.

• Journal of the Korean Society of Groundwater Environment
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• v.5 no.2
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• pp.116-121
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• 1998

For the application of study on pollution and conservation of environment determination of 33 elemental concetrations in different sediment samples were carried out using instrumental neutron activation analysis (INAA). For verification and evaluation of the analytical method, three standard reference materials (two NIST SRMs and one NRCC CRM) were chosen and the accuracy and precision of the analysis were estimated by comparison to the certified values. Under the optimum condition, the analytical procedure to apply a practical sample was estimated. Neutron irradiation of sample was done at the irradiation facilities (neutron flux, 1-3${\times}$10$\^$13/n/$\textrm{cm}^2$$.$s) of the TRIGA MARK-III and HANARO research reactor in the Korea Atomic Energy Research Institute. In addition, analysis of two IAEA's sediment was performed according to the pre-established analytical method. The analytical results of elements such as Al, As, Co, Cr, Fe, Sb and Zn by INAA were intercompared with those of WD-XRF, ICP-MS and AAS, and are relatively agreed with each other.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• CRM연구
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• v.3 no.1
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• pp.1-17
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• 2010

When the marketer wants to execute the sport sponsorship, he(she) has to consider the numerous intervening variables for the effective results. This study illuminates the relationship between these kind of intervening variables of brand origin, familarity, and consume's ethnocentric delinquencies to the sponsorship attitude. The result shows according to the country origin of brand, the consumer's perception of the attitude to the sponsor brand is changed. That is the more positive of the consumer perceive to the brand origin, the more positive to the sponsoring brand attitude. That means the consumer can judge the pro or con of that kind of sponsorship through the mental accounting. But the consumer believes the global sponsorship helps the sport team. The second result shows that the familiarity toward COO(country of brand origin) affects to the sport sponsorship positively. The subjects respond that when the sponsor COO is not so familiar to them, they can discount the sponsor effect of sponsorship. The third result is that there is litter influence of ethnocentrism of sport sponsorship.

• Journal of Technologic Dentistry
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• v.35 no.1
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• pp.37-42
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• 2013

Purpose: The purpose of this study was to evaluate the repeatability of dental white light scanner. Methods: The impression(Zerosil, Dreve, Germany) were digitized in white light scanner(Identica, Medit, Korea) to create 3-dimensional surface-models. The distribution of the discrepancies between the number of points in the corresponding CRM models and the point clouds in the others were measured by a matching-software(PowerInspect 2012, Delcam Plc, UK). The discriptive statistics were used for statistical analysis(SPSS 20.0). Results: The measurement of repeatablity showed very good reliability. The mean(SD) discrepancy value on the white light scanner digital models was 8.7(0.67) ${\mu}m$, based on SD and absolute mean values. Conclusion: These in vitro studies showed that repeatability of dental white light scanner is high reliability. These results can be confirmed in further clinical studies.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.1-7
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• 2015

Policy Customer Relationship Management(PCRM) is a customer relationship management of public institution. Most of public servants at the 37 government ministries, however, has used PCRM as a simple tool for email service and online survey. This study shows government officials should understand the fact that PCRM is not a tool but a management system for general public; and utilize PCRM in each and every government PR practices. When using the PCRM indispensable to be shared with the PR of public sector, PCRM PR will be activated. The study also furnish various strategies and tactics for government PCRM PR.

• Mass Spectrometry Letters
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• v.3 no.2
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• pp.58-61
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• 2012

A method based on flow injection-isotope dilution-cold vapor-inductively coupled plasma mass spectrometry (FI-IDCV-ICP/MS) has been applied to determine trace level of mercury in fly ash. $^{200}Hg$ isotopic spike was added to 0.25 g of BCR176R fly ash and then decomposed by microwave digestion procedure with acid mixture A (8 mL $HNO_3$ + 2 mL HCl + 2 mL HF) and acid mixture B (8 mL $HNO_3$ + 2 mL $HClO_4$ + 2 mL HF) for applying IDMS. Mercury cold vapor was generated by using reductant solution of 0.2% (w/w) $NaBH_4$ and 0.05% (w/w) NaOH. The measurements of n($^{200}Hg$)/n($^{202}Hg$) isotope ratio was made using a quadrupole ICP/MS system. The accuracy in this method was verified by the analysis of certified reference material (CRM) of fly ash (BCR 176R). The indicative value of Hg in BCR 176R fly ash was $1.60{\pm}0.23$ mg/kg (k = 2). The determined values of Hg in BCR 176R fly ash by the method of FI-CV-ID-ICP/MS described in this paper were $1.60{\pm}0.24$ mg/kg (k = 3.18) and the analysis results were in well agreement with the indicative value within the range of uncertainty.

• CRM연구
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• v.1 no.1
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• pp.49-69
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• 2006

Previous studies, Technology Acceptance Model (TAM) and Post Acceptance Model (PAM) have a little limitation in time series analysis. To solve this limitation, we used system dynamics as research methodology and designed simulation model based on TAM and PAM. Moreover, we designed new simulation model which can analyize time series data in customers' demand change from initial acceptance to post acceptance. This study targeted domestic mobile phone market. The simulation results showed that diffusion graph was similar to real data. That means we validated our simulation model. Since the simulation model offers the graph of customer's demand change by time, so it can be useful as a leaning tool. Therefore, we think this study helps IT companies use the model for forecasting of market demand.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.10-17
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• 2017

This study is the first attempt to raise face recognition ability through machine learning algorithm and apply to CRM's information gathering, analysis and application. In other words, through face recognition of VIP customer in distribution field, we can proceed more prompt and subdivided customized services. The interest in machine learning, which is used to implement artificial intelligence, has increased, and it has become an age to automate it by using machine learning beyond the way that a person directly models an object recognition process. Among them, Deep Learning is evaluated as an advanced technology that shows amazing performance in various fields, and is applied to various fields of image recognition. Face recognition, which is widely used in real life, has been developed to recognize criminals' faces and catch criminals. In this study, two image analysis models, TF-SLIM and Inception-V3, which are likely to be used for criminal face recognition, were selected, analyzed, and implemented. As an evaluation criterion, the image recognition model was evaluated based on the accuracy of the face recognition program which is already being commercialized. In this experiment, it was evaluated that the recognition accuracy was good when the accuracy of the image classification was more than 90%. A limit of our study which is a way to raise face recognition is left as a further research subjects.