• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6234-6241
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• 2014

The purpose of this study was to examine the status of medical staff stress and accommodating manners on the death of patients in a hospital setting for serving the basic information to develop a death education program of medical personnel from April 1 to April 30, 2014. A survey was performed on 353 medical personnel at K university hospital, located in Daejeon metropolitan city. Frequency analysis, chi-square test, and independent t-test were used to analyze the data. The results showed that 'to understand the value of the time and preparedness of a meaningful future' were the most important perspectives on the contents of death education (p<0.05), 'in order to change perceptions and attitudes toward death positively' was the most important reason why they required death education'(p<0.05), 'case-based teaching and problem-based learning' was the most effective way of death education (p<0.05), 'negative or hostile response of a patient's guardian to medical personnel' was the largest stress that medical personnel confront upon witnessing a death'(p<0.05). An understanding of the death of patients by medical personnel and an awareness of the need for death education will help improve the understanding of the patient, their guardian, and medical personnel themselves. The main findings will contribute to the development of a specific death education program on the medical personnel in a hospital setting.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Journal of Science and Technology Studies
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• v.19 no.2
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• pp.169-206
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• 2019

This article seeks to explore new relationships and ethics of human and technology by analyzing a cultural imaginary produced by artificial intelligence. Drawing on theoretical reflections of the Feminist Scientific and Technological Studies which understand science and technology as the matter of care(Puig de la Bellacas, 2011), this paper focuses on the fact that artificial intelligence and robots materialize cultural imaginary such as autonomy. This autonomy, defined as the capacity to adapt to a new environment through self-learning, is accepted as a way to conceptualize an authentic human or an ideal subject. However, this article argues that artificial intelligence is mediated by and dependent on invisible human labor and complex material devices, suggesting that such autonomy is close to fiction. The recent growth of the so-called 'assistant technology' shows that it is differentially visualizing the care work of both machines and humans. Technology and its cultural imaginary hide the care work of human workers and actively visualize the one of the machine. And they make autonomy and agency ideal humanness, leaving disabled bodies and dependency as unworthy. Artificial intelligence and its cultural imaginary negate the value of disabled bodies while idealizing abled-bodies, and result in eliminating the real relationship between man and technology as mutually dependent beings. In conclusion, the author argues that the technology we need is not the one to exclude the non-typical bodies and care work of others, but the one to include them as they are. This technology responsibly empathizes marginalized beings and encourages solidarity between fragile beings. Inspired by an art performance of artist Sue Austin, the author finally comes up with and suggests 'artificial intelligence in wheelchair' as an alternative figuration for the currently dominant 'autonomous artificial intelligence'.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.5
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• pp.14-27
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• 2019

This study presents a plan for a memorial park that respects the characteristics based on the historical facts for the concept of space of the Gwangju Hospital of Korea Army and the location of the 505 Security Forces, which were designated as historic sites after the 5-18 Democratization Movement. The Gwangju Metropolitan City as it is the location of the 5-18 historic sites, is taking part in the 5-18 Memorial Project, and plans to establish a city park recognizing the historic site of the 5-18 Democratization Movement, which has been preserved only as a memory space to this point. The park is promoting a phased development plan. This study suggests that the 5-18 historic sites can be modernized and that social consensus can establish the framework of the step-by-step planning and composition process to ensure the plans for the space heals wounds while preserving the history. In this paper, we propose a solution to a problem. We solve the approach for space utilization through an analysis of precedent research and planning cases related to park planning at historical sites. In addition to exploring the value of the site, we also describe the space utilization strategy that covers the historical characteristics and facts while maintaining the concept of park planning. As a result of the research, the historic site of the Gwangju Hospital of Korea Army is planned as a park of historical memory and healing in order to solve the problems left behind by the 5-18 Democratization Movement. The historic site of the 505 Security Forces was selected as an area for historical experiences and a place for learning that can be sympathized with by future generations of children and adolescents in terms of expanding and sustaining the memory of the 5-18 Democratization Movement. In the planning stage, the historical sites suggested the direction of space utilization for representation as did the social consensus of citizens, related groups, and specialists. Through this study, we will contribute to construction of a memorial park containing historical values in from 5-18 historic sites. It is meaningful to suggest a direction that can revitalize the life of the city as well as its citizen and can share with the history with future generations beyond being a place to heal wounds and keep alive the memory of the past.

• Journal of the Daesoon Academy of Sciences
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• v.39
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• pp.69-97
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• 2021

'Déconstruction' is a system of thought that induces the emergent property that characterizes contemporary philosophy. The tradition of ancient Greek philosophy evolved over and over again, giving rise to the Renaissance and Enlightenment. It seemed to have reached its end under the historical perspective of modernity. However, contemporary philosophy wanted to see more possibilities through the deconstruction of modern philosophy. If modern philosophy dreams of a strange cohabitation between God and man with the humanistic completion of Plato's philosophy, modern philosophy rejects even that through deconstruction. Although Plato's classical metaphysics is a stable system centered around the absolute, it is ultimately based on God and religion. Under that system, human autonomy is only the autonomy bestowed by God. Contemporary philosophy is one of the results of efforts that try to begin philosophy from the original human voice through deconstruction. Instead of epistemology dependent on metaphysics, they wanted to establish epistemology from human existence and realize the best good that would set humans free through deconstruction. As such, it is no mistake to say that deconstruction is also an extension of the modern topic of human freedom. Deconstruction and human freedom act as one body in that the two cannot be separated from each other. Oddly enough, Daesoon Thought, which seems to have religious faith and traditional conservatism as main characteristics, has an emergent property that encompasses modern and contemporary times. The period of Korea, when Kang Jeungsan was active and founded Daesoon Thought, has an important meaning for those who have a keen view of history. Such individuals likely think that they have found a valuable treasure. This is because that period was a time when ideological activities were conducted due to an intense desire to discover the meaning of human freedom and envision a new world without copying the ways of the West. Instead they looked to face internal problems and raise people's awareness through subjectivity. In other words, the subtle ideas created by Korea's self-sustaining liberalism often take the form of what is commonly called new religions in modern times. Among these new religions, Daesoon Thought, as a Chamdonghak (true Eastern Learning), aims to spread a particular modern value beyond modern times through the concept of Haewon (the resolution of grievances) that was proclaimed by Jeungsan. The Haewon espoused in Daesoon Thought is in line with the disbandment of modern philosophy in that it contains modernity beyond modern times. First, Haewon means to resolve the fundamental resentment of human existence, which arose from Danju's grievance. Secondly, Haewon in Daesoon Thought encompasses the Haewon of the Three Realms of Heaven, Earth, and Humanity centers on a Haewon-esque style of existence called Injon (Human Nobility). Haewon in Daesoon Thought can be understood in the same context as Derrida's philosophy of Deconstruction. Modern deconstruction attempts to expose the invisible structures and bonds within human society and attempt to destroy them. In a similar way, Haewon endeavors to resolve the conflicts among the Three Realms by releasing the bonds of fundamental oppression that hinder the Three Realms of Heaven, Earth, and Humanity.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.

• Korean Journal of School Psychology
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• v.16 no.3
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• pp.231-263
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• 2019

The purpose of this study was to verify the effects of growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships in the group coaching program for the promotion of growth orientation for university students based on the model of the social-cognitive approach to motivation. The program consisted of eight topics: growth orientation, growth mindset and brain plasticity, self-directed goal setting, talent which is a product of ongoing effort, failure attitude and perspective change, positive emotion, thinking and behavior, value of growth orientation and self-coaching, respectively. The program comprised a total of eight sessions, 120 minutes each, and the final program was completed through a preliminary experiment with three university students. In order to verify the effectiveness of the program, 48 university students were divided into 16 in the experimental group, 16 in the comparative group, and 16 in the control group. The experimental group participated in the group coaching program to enhance the growth orientation based on the model of the social-cognitive approach to motivation developed in this study, the comparative group participated in a learning goal orientation improvement program based on an incremental implicit theory, and the control group did not carry out any program. Three groups were tested in pre, post, follow-up1(after 1 month) and follow-up2(after 3 months) in order to growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships. We performed analysis to confirm the homogeneity to the data of the three groups and to verify the interaction effects between times and groups. As a result, it was confirmed that the group coaching program to promote growth orientation, life satisfaction, perceived stress, positive psychological capital and interpersonal relationships had statistically significant effect and was more effective than the comparative program due to the larger effective size. Also, we confirmed that the coaching effect was sustained after the program was finished and more effectively maintained than the comparative program. Based on the results of this study, this study has academic implications because it verify the effectiveness of the group coaching for the promotion of the growth orientation by scient ic method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.203-223
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• 2023

Recently, the platform industry is rapidly growing in the global market, and competition is intensifying at the same time. Therefore, in order for domestic platform companies to have global competitiveness in the platform market, it is necessary to study the platform business ecosystem and success factors. However, most of the recent platform-related studies have been theoretical studies on the characteristics of platform business status analysis, platform economy, and indirect network externalities of platforms. Therefore, this study comprehensively analyzed the success factors of Silicon Valley's business ecosystem proposed in previous studies, and at the same time analyzed the success factors extracted from stakeholders in the actual Silicon Valley platform business ecosystem. And based on these factors, an IPA analysis was conducted as a way to propose a success plan to stakeholders in the platform business ecosystem. As a result of the analysis, among the success factors collected through previous studies, manpower, capital, and challenge culture were identified as factors that are relatively well maintained in both importance and satisfaction in Silicon Valley. In the end, it can be seen that the creation of an environment and culture in which Silicon Valley can use it to challenge itself based on excellent human resources and abundant capital contributes the most to the success of Silicon Valley's platform business. On the other hand, although it is of high importance to Silicon Valley's platform corporate ecosystem, the factors that show relatively low satisfaction among stakeholders are 'learning and benchmarking among active companies' and 'strong ties and cooperation between members', and it is analyzed that interest and effort are needed to strengthen these factors in the future. Finally, the systems and policies necessary for market autonomous competition, 'business support service industry', 'name value', and 'spin-off start-up' were important factors in literature research, but the importance and satisfaction of these factors were lowered due to changes in the times and environment. This study has academic implications in that it comprehensively analyzes the success factors of Silicon Valley's business ecosystem proposed in previous studies, and at the same time analyzes the success factors extracted from stakeholders in the actual Silicon Valley platform business ecosystem. In addition, there is another academic implications that importance and satisfaction were simultaneously examined through IPA analysis based on these various extracted factors. As for academic implications, it is meaningful in that it contributed to the formation of the domestic platform ecosystem by providing the government and companies with concrete information on the success factors of the platform business ecosystem and the theoretical grounds for the growth of domestic platform businesses.