• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.222-235
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• 2022

The Government provides a financial assistance to stimulate firm R&D and innovation activities. Previous papers on the impact of public subsidies on firm R&D investments mainly had a focus on an individual policy tool regardless of potential impacts of other policy instruments. This study addresses this gap by examining the effects of policy mix regarding a subsidy and a tax credit. The empirical analyses from fixed effect model using Survey on Technology of SMEs 2015-2017 revealed valuable points. First, policy mix induces more R&D investment of SMEs, which in turn, shows a complementary relationship between two instruments. Second, even if impact of tax credit controlled, subsidy is positively associated with SMEs R&D investment. These findings justify policy mix interventions to promote SME R&D activity. Moreover, grants can be applied as a more useful policy tool for SMEs that are constrained by resources and capabilities.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.132-140
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• 2022

This study was conducted to identify the optimal lotion manufacturing conditions with decursin and decursinol angelate of Angelica gigas Nakai extraction. Lotion was confirmed that it had viscosity (5,208±112 cPs), assay (99.71±1.01%), and pH (5.62) for 3 months. The optimization of manufacturing conditions of mixing 4 for lotion formulation were made by 22+3 full factorial design. Mixing temperature (40-80℃) and mixing time (10-30 min) were used as independent variables with three responses(assay, pH, and weight variation) as critical quality attributes (CQAs). The model for assay and weight variation identified a proper fit having a determination coefficient of the regression equation (about 0.9) and a p-value less than 0.05. Estimated conditions for the optimal manufacturing process of lotion were 61.93℃ in mixing temperature and 15.85 min in mixing time. Predicted values at the mixing temperature (60℃) and mixing time (20 min) were 100.69% of assay, 5.57 of pH, and 98.07% of weight variation. In the verification of the actual measurement the obtained values showed 100.29±0.98% of assay, 5.57±0.02 of pH, and 98.27±0.89% of weight variation, respectively, in good agreement with predicted values.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.1-16
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• 2022

Social network service (SNS) using mobile or web is growing rapidly, and the emergence of various platform services is causing innovative changes in social network service (SNS). This study is to identify the target relation factors of social network users and to empirically study the causal relationship of how much these factors affect user satisfaction through pleasure and self-efficacy. To present an effective and efficient development plan in. In order to empirically verify the research model of this study, a survey was conducted with the general public who had experience using social network services (SNS). Path analysis was performed. As a result, it was possible to verify the correlation of the object relational factors on user satisfaction through pleasure and self-efficacy.First, non-excluded had a significant effect on pleasure, but did not significantly affect self-efficacy. Second, stability attachment did not significantly affect both enjoyment and self-efficacy. Third, social ability did not significantly affect both enjoyment and self-efficacy. Fourth, self-centeredness did not have a significant effect on both enjoyment and self-efficacy. Fifth, pleasure had a significant effect on both self-efficacy and user satisfaction. Sixth, self-efficacy had a significant effect on user satisfaction.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.81-91
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• 2024

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

• Journal of Radiation Protection and Research
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• v.10 no.1
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• pp.50-63
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• 1985

A derivation of new release limit, named Derived Release Limit(DRL), into the atomsphere from a reference nuclear power plant has been performed on the basis of the new system of dose limitation recommended by the ICRP, instead of the (MPC)a limit which has been currently used until now as a general standard for radioactive effluents in Korea. In DRL Calculation, a Concentration Factor Method was applied, in which the concentrations of long-term routinely released radionuclides were in equilibrium with dose in environment under the steady state condition. The analytical model used in the exposure pathway analysis was the one which has been suggested by the USNRC and the exposure limits applied in this analysis were those recommended by the USEPA lately. In the exposure pathway analysis, all of the pathways are not considered and some may be excluded either because they are not applicable or their contribution to the exposure is insignificant compared with other pathways. In case, the environmental model developed in this study was applied to the Kori nuclear power plant as the reference power plant, the highest DRL value was calculated to be as $9.10{\times}10^6Ci/yr$ for Kr-85 in external whole body exposure from the semi-infinite radioactive cloud, while the lowest DRL value was observed 3.64Ci/yr for Co-60 in external whole body exposure from the contaminated ground, by the radioactive particulates. The most critical exposure pathway to an individual in the unrestricted area of interest (Kilchun-Ri, 1.3 km to the north of the release point) seems to be the exposure pathway from the contaminated ground and the most critical radionuclide in all pathways appears to be Co-60 in the same pathway. When comparing the actual release rate from KNU-l in 1982 with the DRL's obtained here the release of radionuclides from KNU-1 were much lower than the DRL's and it could be conclued that the exposure to an individual had been kept below the exposure limits recommended by the USEPA.

• Journal of Korea Water Resources Association
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• v.40 no.11
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• pp.887-898
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• 2007

A physically based semi-distributed model, SWAT was applied to the Chungju Dam upstream watershed in order to investigate the spatial and temporal characteristics of watershed sediment yields. For this, general features of the SWAT and sediment simulation algorithm within the model were described briefly, and watershed sediment modeling system was constructed after calibration and validation of parameters related to the runoff and sediment. With this modeling system, temporal and spatial variation of soil loss and sediment yields according to watershed scales, land uses, and reaches was analyzed. Sediment yield rates with drainage areas resulted in $0.5{\sim}0.6ton/ha/yr$ excluding some upstream sub-watersheds and showed around 0.51 ton/ha/yr above the areas of $1,000km^2$. Annual average soil loss according to land use represented the higher values in upland areas, but relatively lower in paddy and forest areas which were similar to the previous results from other researchers. Among the upstream reaches, Pyeongchanggang and Jucheongang showed higher sediment yields which was thought to be caused by larger area and higher fraction of upland than other upstream sub-areas. Monthly sediment yields at the main outlet showed same trend with seasonal rainfall distribution, that is, approximately 62% of annual yield was generated during July to August and the amount was about 208 ton/yr. From the results, we could obtain the uniform value of sediment yield rate and could roughly evaluate the effect of soil loss with land uses, and also could analyze the temporal and spatial characteristics of sediment yields from each reach and monthly variation for the Chungju Dam upstream watershed.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.2
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• pp.101-109
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• 2022

The mobility and transport of radioactive cesium are crucial factors to consider for the safety assessment of high-level radioactive waste disposal sites in granite. The retardation of radionuclides in the fractured crystalline rock is mainly controlled by the hydrochemical condition of groundwater and surface reactions with minerals present in the fractures. This paper reports the experimental results of cesium sorption to the Wonju Granite, a typical Mesozoic granite in Korea, performed in an anaerobic chamber that mimics the anoxic environment of a deep disposal site. We measured the rates and amounts of cesium (¹³³Cs) removed by crushed granite samples in different electrolyte (NaCl, KCl, and CaCl₂) solutions and a synthetic groundwater solution, with variations in the initial cesium concentration (10^-5, 5×10^-6, 10^-6, 5×10^-7 M). The cesium sorption kinetic and isotherm data were successfully simulated by the pseudo-second-order kinetic model (r²= 0.99) and the Freundlich isotherm model (r²= 0.99), respectively. The sorption distribution coefficient of granite increased almost linearly with increasing biotite content in granite samples, indicating that biotite is an effective cesium scavenger. The cesium removal was minimal in KCl solution compared to that in NaCl or CaCl₂ solution, regardless of the ionic strength and initial cesium concentration that we examined, showing that K⁺ is the most competitive ion against cesium in sorption to granite. Because it is the main source mineral of K⁺ in fracture fluids, biotite may also hinder the sorption of cesium, which warrants further research.

• Journal of Distribution Science
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• v.8 no.3
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• pp.37-47
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• 2010

CRM refers to the operating activities that always maintain and promote good relationship with customers to ultimately maximize the company's profits by understanding the value of customers to meet their demands, establishing a strategy which may maximize the Life Time Value and successfully operating the business by integrating the customer management processes. In our country, many big businesses are introducing CRM initiatively to use it in marketing strategy however, most medium and small sized companies do not understand CRM clearly or they feel difficult to introduce it due to huge investment needed. This study is intended to present CRM promotion strategy and activities plan fit for the medium and small sized companies by analyzing the success factors of the leading companies those have already executed CRM by surveying the precedents to make the distributors out of the industries have close relation with consumers to overcome their weakness in scale and strengthen their competitiveness in such a rapidly changing and fiercely competing market. There are 5 stages to build CRM such as the recognition of the needs of CRM establishment, the establishment of CRM integrated database, the establishment of customer analysis and marketing strategy through data mining, the practical use of customer analysis through data mining and the implementation of response analysis and close loop process. Through the case study of leading companies, CRM is needed in types of businesses where the companies constantly contact their customers. To meet their needs, they assertively analyze their customer information. Through this, they develop their own CRM programs personalized for their customers to provide high quality service products. For customers helping them make profits, the VIP marketing strategy is conducted to keep the customers from breaking their relationships with the companies. Through continuous management, CRM should be executed. In other words, through customer segmentation, the profitability for the customers should be maximized. The maximization of the profitability for the customers is the key to CRM. These are the success factors of the CRM of the distributors in Korea. Firstly, the top management's will power for CS management is needed. Secondly, the culture across the company should be made to respect the customers. Thirdly, specialized customer management and CRM workers should be trained. Fourthly, CRM behaviors should be developed for the whole staff members. Fifthly, CRM should be carried out through systematic cooperation between related departments. To make use of the case study for CRM, the company should understand the customer and establish customer management programs to set the optimal CRM strategy and continuously pursue it according to a long-term plan. For this, according to collected information and customer data, customers should be segmented and the responsive customer system should be designed according to the differentiated strategy according to the class of the customers. In terms of the future CRM, integrated CRM is essential where the customer information gathers together in one place. As the degree of customers' expectation increases a lot, the effective way to meet the customers' expectation should be pursued. As the IT technology improved rapidly, RFID (Radio Frequency Identification) appears. On a real-time basis, information about products and customers is obtained massively in a very short time. A strategy for successful CRM promotion should be improving the organizations in charge of contacting customers, re-planning the customer management processes and establishing the integrated system with the marketing strategy to keep good relation with the customers according to a long-term plan and a proper method suitable to the market conditions and run a company-wide program. In addition, a CRM program should be continuously improved and complemented to meet the company's characteristics. Especially, a strategy for successful CRM for the medium and small sized distributors should be as follows. First, they should change their existing recognition in CRM and keep in-depth care for the customers. Second, they should benchmark the techniques of CRM from the leading companies and find out success points to use. Third, they should seek some methods best suited for their particular conditions by achieving the ideas combining their own strong points with marketing. Fourth, a CRM model should be developed that will promote relationship with individual customers just like the precedents of small sized businesses in Switzerland through small but noticeable events.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.