• 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 Soil Science and Fertilizer
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• v.39 no.6
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• pp.329-333
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• 2006

Most of volcanic ash soils in South Korea are distributed in Jeju province which is an island placed on southern part of Korea and has steep slope mountain area. There are many soils containing high contents of organic matter (OM) derived from volcanic ash in Jejudo, also. Therefore, irrigation and drainage in volcanic ash soil different with general soil which has low OM content have to be applied with another management way, but studies searching appropriate methods for them are set on insufficient situation because the area of volcanic ash soil in South Korea is only 1.3% (130,000ha). This study was conducted for analysis of soil water content and irrigation quantity appropriate for crops cultivated in volcanic ash soil with high OM content. Although soils with different soil color have the same soil texture, soil water characteristics curve by soil color showed the difference of water retention capability by OM content. But, this characteristics classified with soil color could be unified by scaling technique with similitude analysis method which get dimensionless water content using a present water content, a residual water content and saturated water content (or water content at 10kPa). A relation of gravimetric soil water content (GSWC) and dimensionless water content by the results showed a form of power function. The dimensionless water content (DWC) express a relative saturation degree of present water content. This was also expressed by van Genuchten model which describe the relation between relative saturation degrees and matric potentials. These results on soil water characteristics curve (SWCC) of volcanic ash soil will be the basic of irrigation plan in area having high organic contents into soil.

• Journal of Korea Port Economic Association
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• v.40 no.2
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• pp.65-78
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• 2024

This study investigates the correlation between financial constraints and investment activities in Korean logistics firms. A sample of 340 companies engaged in the transportation sector, as per the 2021 KSIC, was selected for analysis. Financial data obtained from the DART were used to compile a panel dataset spanning from 1996 to 2021, totaling 6,155 observations. The research model was validated, and tests for heteroscedasticity and autocorrelation in the error terms were conducted considering the panel data structure. The relationship between investment activities in the previous period and current investment activities was analyzed using panel Generalized Method of Moments(GMM). The validation results of the research indicate that Korean logistics firms tend to increase investment activities as their level of financial constraints improves. Specifically, a positive relationship between the level of financial constraints and investment activities was consistently observed across all models. These findings suggest that investment decision-making varies based on the financial constraints faced by companies, aligning with previous research indicating that investment activities of constrained firms are subdued. Moreover, while the results from the model examining whether investment activities in the previous period affect current investment activities indicated an influence of investment activities from the previous period on current investment activities, the investment activities from two periods ago did not show a significant relationship with current investment activities. Among the control variables, firm size and cash flow variables exhibited positive relationships, while debt size and asset diversification variables showed negative relationships. Thus, larger firm size and smoother cash flows were associated with more proactive investment activities, while high debt levels and extensive asset diversification appeared to constrain investment activities in logistics companies. These results interpret that under financial constraints, internal funding sources such as cash flows exhibit positive relationships, whereas external capital sources such as debt demonstrate negative relationships, consistent with empirical findings from previous research.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.31-54
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• 2007

The exchange between buyers and sellers in the industrial market is changing from short-term to long-term relationships. Long-term relationships are governed mainly by formal contracts or informal agreements, but many scholars are now asserting that controlling relationship by using formal contracts under environmental dynamism is inappropriate. In this case, partners will depend on each other's flexibility or interdependence. The former, flexibility, provides a general frame of reference, order, and standards against which to guide and assess appropriate behavior in dynamic and ambiguous situations, thus motivating the value-oriented performance goals shared between partners. It is based on social sacrifices, which can potentially minimize any opportunistic behaviors. The later, interdependence, means that each firm possesses a high level of dependence in an dynamic channel relationship. When interdependence is high in magnitude and symmetric, each firm enjoys a high level of power and the bonds between the firms should be reasonably strong. Strong shared power is likely to promote commitment because of the common interests, attention, and support found in such channel relationships. This study deals with environmental dynamism in high-tech industry. Firms in the high-tech industry regard it as a key success factor to successfully cope with environmental changes. However, due to the lack of studies dealing with environmental dynamism and supply chain commitment in the high-tech industry, it is very difficult to find effective strategies to cope with them. This paper presents the results of an empirical study on the relationship between environmental dynamism and supply chain commitment in the high-tech industry. We examined the effects of consumer, competitor, and technological dynamism on supply chain commitment. Additionally, we examined the moderating effects of flexibility and dependence of supply chains. This study was confined to the type of high-tech industry which has the characteristics of rapid technology change and short product lifecycle. Flexibility among the firms of this industry, having the characteristic of hard and fast growth, is more important here than among any other industry. Thus, a variety of environmental dynamism can affect a supply chain relationship. The industries targeted industries were electronic parts, metal product, computer, electric machine, automobile, and medical precision manufacturing industries. Data was collected as follows. During the survey, the researchers managed to obtain the list of parts suppliers of 2 companies, N and L, with an international competitiveness in the mobile phone manufacturing industry; and of the suppliers in a business relationship with S company, a semiconductor manufacturing company. They were asked to respond to the survey via telephone and e-mail. During the two month period of February-April 2006, we were able to collect data from 44 companies. The respondents were restricted to direct dealing authorities and subcontractor company (the supplier) staff with at least three months of dealing experience with a manufacture (an industrial material buyer). The measurement validation procedures included scale reliability; discriminant and convergent validity were used to validate measures. Also, the reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than.70. A series of exploratory factor analyses was conducted. We conducted confirmatory factor analyses to assess the validity of our measurements. A series of chi-square difference tests were conducted so that the discriminant validity could be ensured. For each pair, we estimated two models-an unconstrained model and a constrained model-and compared the two model fits. All these tests supported discriminant validity. Also, all items loaded significantly on their respective constructs, providing support for convergent validity. We then examined composite reliability and average variance extracted (AVE). The composite reliability of each construct was greater than.70. The AVE of each construct was greater than.50. According to the multiple regression analysis, customer dynamism had a negative effect and competitor dynamism had a positive effect on a supplier's commitment. In addition, flexibility and dependence had significant moderating effects on customer and competitor dynamism. On the other hand, all hypotheses about technological dynamism had no significant effects on commitment. In other words, technological dynamism had no direct effect on supplier's commitment and was not moderated by the flexibility and dependence of the supply chain. This study makes its contribution in the point of view that this is a rare study on environmental dynamism and supply chain commitment in the field of high-tech industry. Especially, this study verified the effects of three sectors of environmental dynamism on supplier's commitment. Also, it empirically tested how the effects were moderated by flexibility and dependence. The results showed that flexibility and interdependence had a role to strengthen supplier's commitment under environmental dynamism in high-tech industry. Thus relationship managers in high-tech industry should make supply chain relationship flexible and interdependent. The limitations of the study are as follows; First, about the research setting, the study was conducted with high-tech industry, in which the direction of the change in the power balance of supply chain dyads is usually determined by manufacturers. So we have a difficulty with generalization. We need to control the power structure between partners in a future study. Secondly, about flexibility, we treated it throughout the paper as positive, but it can also be negative, i.e. violating an agreement or moving, but in the wrong direction, etc. Therefore we need to investigate the multi-dimensionality of flexibility in future research.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of Korean Academy of Nursing Administration
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• v.6 no.1
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• pp.135-146
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• 2000

The purpose of this study was to develope, based on the Nursing Intervention Classification (NIC) system. a set of standardized nursing interventions which had been validated. and their associated activities. for use with nursing diagnoses related to home health care for women who have had a caesarian delivery and for their newborn babies. This descriptive study for instrument development had three phases: first. selection of nursing diagnoses. second, validation of the preliminary home health care interventions. and third, application of the home care interventions. In the first phases, diagnoses from 30 nursing records of clients of the home health care agency at P. medical center who were seen between April 21 and July 30. 1998. and from 5 textbooks were examined. Ten nursing diagnoses were selected through a comparison with the NANDA (North American Nursing Diagnosis Association) classification In the second phase. using the selected diagnoses. the nursing interventions were defined from the diagnoses-intervention linkage lists along with associated activities for each intervention list in NIC. To develope the preliminary interventions five-rounds of expertise tests were done. During the first four rounds. 5 experts in clinical nursing participated. and for the final content validity test of the preliminary interventions. 13 experts participated using the Fehring's Delphi technique. The expert group evaluated and defined the set of preliminary nursing interventions. In the third phases, clinical tests were held at in a home health care setting with two home health care nurses using the preliminary intervention list as a questionnaire. Thirty clients referred to the home health care agency at P. medical center between October 1998 and March 1999 were the subjects for this phase. Each of the activities were tested using dichotomous question method. The results of the study are as follows: 1. For the ten nursing diagnoses. 63 appropriate interventions were selected from 369 diagnoses interventions links in NlC., and from 1.465 associated nursing activities. From the 63 interventions. the nurses expert group developed 18 interventions and 258 activities as the preliminary intervention list through a five-round validity test 2. For the fifth content validity test using Fehring's model for determining lCV (Intervention Content Validity), a five point Likert scale was used with values converted to weights as follows: 1=0.0. 2=0.25. 3=0.50. 4=0.75. 5=1.0. Activities of less than O.50 were to be deleted. The range of ICV scores for the nursing diagnoses was 0.95-0.66. for the nursing interventions. 0.98-0.77 and for the nursing activities, 0.95-0.85. By Fehring's method. all of these were included in the preliminary intervention list. 3. Using a questionnaire format for the preliminary intervention list. clinical application tests were done. To define nursing diagnoses. home health care nurses applied each nursing diagnoses to every client. and it was found that 13 were most frequently used of 400 times diagnoses were used. Therefore. 13 nursing diagnoses were defined as validated nursing diagnoses. Ten were the same as from the nursing records and textbooks and three were new from the clinical application. The final list included 'Anxiety', 'Aspiration. risk for'. 'Infant behavior, potential for enhanced, organized'. 'Infant feeding pattern. ineffective'. 'Infection'. 'Knowledge deficit'. 'Nutrition, less than body requirements. altered', 'Pain'. 'Parenting'. 'Skin integrity. risk for. impared' and 'Risk for activity intolerance'. 'Self-esteem disturbance', 'Sleep pattern disturbance' 4. In all. there were 19 interventions. 18 preliminary nursing interventions and one more intervention added from the clinical setting. 'Body image enhancement'. For 265 associated nursing activities. clinical application tests were also done. The intervention rate of 19 interventions was from 81.6% to 100%, so all 19 interventions were in c1uded in the validated intervention set. From the 265 nursing activities. 261(98.5%) were accepted and four activities were deleted. those with an implimentation rate of less than 50%. 5. In conclusion. 13 diagnoses. 19 interventions and 261 activities were validated for the final validated nursing intervention set.

• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.177-190
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• 2016

The purpose of this study is to validate learning progressions for Earth's motion and solar system from two different perspectives of validity. One is construct validity, that is whether a hypothetical pathway derived from our study of LPs is supported by empirical evidence of children's substantive development. The other is consequential validity, which refers to the impact of LP-based adaptive instruction on children's improved learning outcomes. For this purpose, 373 fifth-grade students and 17 teachers from six elementary schools in Seoul, Kangwon province, and Gwangju participated. We designed LP-based adaptive instruction modules delving into the unit of 'Solar system and stars.' We also employed 13 ordered multiple-choice items and analyzed the transitions of children's achievement levels based on the results of pre-test and post-test. For testing construct validity, 64 % of children in the experimental group showed improvement according to the hypothetical pathways. Rasch analysis also supports this results. For testing consequential validity, the analysis of covariance between experimental and control groups revealed that the improvement of experimental group is significantly higher than the control group (F=30.819, p=0.000), and positive transitions of children's achievement level in the experimental group are more dominant than in the control group. In addition, the findings of applying Rasch model reveal that the improvement of students' ability in the experimental group is significantly higher than that of the control group (F=11.632, p=0.001).

• Progress in Medical Physics
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• v.25 no.3
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• pp.128-138
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• 2014

In gated radiation therapy (gRT), due to residual motion, beam delivery is intended to irradiate not only the true extent of disease, but also neighboring normal tissues. It is desired that the delivery covers the true extent (i.e. clinical target volume or CTV) as a minimum, although target moves under dose delivery. The objectives of our study are to validate if the intended dose is surely delivered to the true target in gRT and to quantitatively understand the trend of dose delivery on it and neighboring normal tissues when gating window (GW), motion amplitude (MA), and CTV size changes. To fulfill the objectives, experimental and computational studies have been designed and performed. A custom-made phantom with rectangle- and pyramid-shaped targets (CTVs) on a moving platform was scanned for four-dimensional imaging. Various GWs were selected and image integration was performed to generate targets (internal target volume or ITV) for planning that included the CTVs and internal margins (IM). The planning was done conventionally for the rectangle target and IMRT optimization was done for the pyramid target. Dose evaluation was then performed on a diode array aligned perpendicularly to the gated beams through measurements and computational modeling of dose delivery under motion. This study has quantitatively demonstrated and analytically interpreted the impact of residual motion including penumbral broadening for both targets, perturbed but secured dose coverage on the CTV, and significant doses delivered in the neighboring normal tissues. Dose volume histogram analyses also demonstrated and interpreted the trend of dose coverage: for ITV, it increased as GW or MA decreased or CTV size increased; for IM, it increased as GW or MA decreased; for the neighboring normal tissue, opposite trend to that of IM was observed. This study has provided a clear understanding on the impact of the residual motion and proved that if breathing is reproducible gRT is secure despite discontinuous delivery and target motion. The procedures and computational model can be used for commissioning, routine quality assurance, and patient-specific validation of gRT. More work needs to be done for patient-specific dose reconstruction on CT images.