• Journal of Technology Innovation
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• v.23 no.3
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• pp.397-432
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• 2015

This paper investigates the effects of technology collaboration between the main supplier and buyer on buyer's new product development under dynamic environment. Based on 428 Korean manufacturing firms, we conducted regression analysis. The technology collaboration between the main supplier and buyer is adopted as a independent variable and quality, cost and lead time performance of new product development projects are used as dependents variables. Environment dynamic is also used as a moderate variables. We found that the in general, technology collaboration is positively associated with the performance of buyers' new product development, but in the high degree of dynamic environment, technology collaboration is negatively associated with the performance of buyers' new product development unlike our expectation. Thus, we divide our sample into two groups; shipbuilding industry with the low degree of environment dynamic and electronic and IT device industry with the high degree of environment dynamic and conducted a post hoc analysis. As a result, in ship building industry, the technology collaboration is significant to improve NPD projects performance, while in electronic and IT device industry, the technology collaboration with a main supplier is not significant as well as coefficient is negative. In that, under the highly dynamic condition with the fast change of technology and products obsolescence the NPD collaboration with the main supplier does not works unlike a stable environment. This implies that the NPD attributes of buyer are different by their environmental factor and the fit between given environmental feature and the collaboration synergy is critical factor for improving the effect of NPD collaboration between supplier and buyer.

• Journal of agricultural medicine and community health
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• v.31 no.1
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• pp.35-46
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• 2006

Objectives: In recently. the management and protection on individual information in patient's medical & nursing records have been very important, and that need a guideline. The purpose of this study was to investigate the status of using the patient's nursing records of nursing students in clinical practice, to find and discuss the patient's informed consent, and status of education and management concerned to patient's nursing records. Methods: This study used a mailing survey. data collected from September 24th to October 31th in 2002. The subject were 333 professors who are major in adult nursing, pediatric nursing, psychological nursing of 111 university of nursing department and nursing college. And then we received the survey mail from 103 professors that respondent rate was 30.9%. Results: The characteristics of study subjects showed 49.0% of university. 51.0% of college of nursing. 50.0% of the subjects practiced point the patient by oral approval in clinical practice. But when the decision of the patient was very difficult, 21.6% of the subjects take to informed consent from his or her families. During the clinical practice, 49.0% of the subjects were explain to patient about clinical practice and contents of the nursing student, only 7.8% of the subjects were explain to patient with nursing records. 52.0% of the subjects were took out records from the hospital, only 17.6% of the subjects had standard of the patient's informed consent and standard of handling practice records. 17.6%-92.2% of the subjects that educate and manage concern to patient's nursing records.

• Korea journal of population studies
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• v.30 no.2
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• pp.71-95
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• 2007

This paper attempts to identify the principle of family succession and family patterns of yangban in the late Chosun period through an analysis of male adaptation cases found in family registration records. The primary source of analysis is the family registration documents of Uiseong Kim's from the late 17th century to the early 20th century. As a result, it is found that there is a substantial change in the patterns of family from the early and mid Chosun period to the late Chosun period. The change is the strengthening of the principle of patriarchy succession through male adoption. Looking at the data as a whole, the average number of household members is increased and the membership of kinship also expanded. In contrast to the family patterns of the early Chosun period, not only the patterns of Uiseong Kim's family are predominately immediate family or collateral family but also the majority is extended family in the 18th and 19th centuries. The male adoption cases recorded in Uiseong Kim's family registration documents take up 33.8% of the male adoption cases in the entire family registration documents. This goes to show that the strengthening of the principle of primogeniture succession at a time when child mortality rate is very high resulted in the increase of male adoption. In conclusion, the late Chosun society was a society where the seat of primogeniture was much more important than immediate hereditary members in the family succession.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.11
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• pp.1552-1563
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• 2016

To examine the cognitive function of onion (Allium cepa L.) beverages (odourless and fortified), we analyzed in vitro neuronal cell protection against $H_2O_2$-induced cytotoxicity and performed in vivo tests on amyloid beta ($A{\beta}$)-induced cognitive dysfunction. Cellular oxidative stress and cell viability were evaluated by DCF-DA assay and MTT assay. These results show that fortified beverage resulted in better neuronal cell protection than odourless beverage at lower concentration ($0{\sim}100{\mu}g/mL$). Fortified beverage also showed more excellent acetylcholinesterase (AChE) inhibitory activity ($IC_{50}$: 4.20 mg/mL) than odourless beverage. The cognitive functions of odourless beverage and fortified beverage in $A{\beta}$-induced neurotoxicity were assessed by Y-maze, passive avoidance, and Morris water maze tests. The results show improved cognitive function in both groups treated with beverages. After in vivo tests, cholinergic activities were determined based on AChE inhibition and acetylcholine levels, and antioxidant activities were measured as SOD, oxidized glutathione (GSH)/total GSH ratio, and MDA levels in mouse brain tissue. In a Q-TOF UPLC/MS system, main compounds were analyzed as follows: odourless beverage (five types of sugars and three types of phenolics) and fortified beverages (six types of phenolics and two types of steroidal saponins).

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.863-874
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• 2018

The purpose of this study is to evaluate the future climate change impact on stream aquatic ecology health of Han River watershed ($34,148km^2$) using SWAT (Soil and Water Assessment Tool) and random forest. The 8 years (2008~2015) spring (April to June) Aquatic ecology Health Indices (AHI) such as Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI) and Fish Assessment Index (FAI) scored (0~100) and graded (A~E) by NIER (National Institute of Environmental Research) were used. The 8 years NIER indices with the water quality (T-N, $NH_4$, $NO_3$, T-P, $PO_4$) showed that the deviation of AHI score is large when the concentration of water quality is low, and AHI score had negative correlation when the concentration is high. By using random forest, one of the Machine Learning techniques for classification analysis, the classification results for the 3 indices grade showed that all of precision, recall, and f1-score were above 0.81. The future SWAT hydrology and water quality results under HadGEM3-RA RCP 4.5 and 8.5 scenarios of Korea Meteorological Administration (KMA) showed that the future nitrogen-related water quality in watershed average increased up to 43.2% by the baseflow increase effect and the phosphorus-related water quality decreased up to 18.9% by the surface runoff decrease effect. The future FAI and BMI showed a little better Index grade while the future TDI showed a little worse index grade. We can infer that the future TDI is more sensitive to nitrogen-related water quality and the future FAI and BMI are responded to phosphorus-related water quality.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• 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.

• Journal of Christian Education in Korea
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• v.64
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• pp.51-79
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• 2020

This study was conducted to pursue Christian education with the socially underprivileged in the era of the Corona-19 pandemic. Corona-19 is a disaster which is caused, destroyed and exploited by human being. At the time of the indiscriminately spreading global pendemic, we must work together to overcome our selfish self-centeredness and make an attempt for everyone in need. It is a study on how humans can help each other survive in the era of Corona-19 and its post-corona. The problem is that there is too much discrimination between the state, race, and economic capacity, and in the end, the extreme discrimination of capitalism is appeared in society and across the country. There is no significant difference in the confirmation rate when Corona-19 infiltrates, but there is a big difference between those with and less in mortality. As a result, today's reality is that people who have a hard time living because they have less usually are far more vulnerable to blocking and defeating virus attacks. Unfortunately, this is the current situation. From the standpoint of a large discourse, attention is paid to climate change and ecological environment, and as a micro discourse, a number of societies who live with tremendous discrimination according to the gap between the rich and the poor (it is gender, race, disabled, nationality) that exist in almost all countries on the planet. We need attention to the weak. To this end, discourses on vaccine inequality, discourses on the needs of the disabled, discourses on different racial damages, discourses on polarization and dystopia, and discourses on educational inequality were treated as the reality faced by the socially underprivileged in the Corona 19 pandemic. To explore Christian education with the socially underprivileged, to explore ways of sharing, giving, and solidarity for win-win, discourse on inter-dependence and mutual responsibility of mankind, direct counter-measures for the socially underprivileged, and critical literacy education. He proposed a discourse on Korea, a discourse on Homo sapiens, which must return to being a part of creation, and finally a theology of friendship with the weak. Christian education based on Bible words must go forward in the era of the Corona 19 pandemic, hungry, naked, nowhere to go, sick, but dying because of being unable to get a remedy. He emphasized the need to establish a caring theology of friendship and pursue a life in which thought and practice harmonize. Thus, the paper proposed the spirit of Christian education not only doing something for the socially weak, but with the socially weak in the daily life.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.111-129
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• 2022

Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.