• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.429-435
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• 2021

In this study, the creation of emotional dialogue was investigated within the process of developing a robot's natural language understanding and emotional dialogue processing. Unlike an English-based dataset, which is the mainstay of natural language processing, the Korean-based dataset has several shortcomings. Therefore, in a situation where the Korean language base is insufficient, the Korean dataset should be dealt with in detail, and in particular, the unique characteristics of the language should be considered. Hence, the first step is to base this study on a specific Korean dataset consisting of conversations on emotional topics. Subsequently, a model was built that learns to extract the continuous dialogue features from a pre-trained language model to generate sentences while maintaining the context of the dialogue. To validate the model, a chatbot system was implemented and meaningful results were obtained by collecting the external subjects and conducting experiments. As a result, the proposed model was influenced by the dataset in which the conversation topic was consultation, to facilitate free and emotional communication with users as if they were consulting with a chatbot. The results were analyzed to identify and explain the advantages and disadvantages of the current model. Finally, as a necessary element to reach the aforementioned ultimate research goal, a discussion is presented on the areas for future studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.75-82
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• 2018

Recently, emerging countries have been paying attention to Korean economic development policy, trying to adopt the Korean regional innovation model. Korea is also interested in exporting its regional innovation model and enhancing economic cooperation with those countries. This paper aims to analyze the capacity-building programs of the Korean regional innovation model for emerging countries and suggests policies for it. For this purpose, the local innovators' participation patterns in the process of collaborative learning/networking/interaction are investigated with a focused group-interview method. From an analysis of the programs supported by Korean organizations, this study finds that the correlation coefficient between the training time of capacity building and the participation rate of local members' collaborative learning is very high (0.975). Since the correlation coefficient between the participation rates of collaborative learning and networking is relatively low (0.667), a policy to link local collaborative learning to networking should be provided. As the correlation coefficient between the participation rates of networking and interaction is high (0.950), networking is a key to regional innovation. This study recommends activity programs to promote networking among local innovators, rather than training and consulting programs. As introduced in the Chungnam Techno Park case, this study suggests that the capacity-building program should include programs to initiate a collaborative learning network, to create a local-demand, regional innovation model, and to operate the regional innovation platform, which should be done by local innovators in the emerging countries.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.99-111
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• 1997

This paper presents an application of an artificial neural net to the implementation of decision class analysis (DCA), together with the generation of a decision model influence diagram. The diagram is well-known as a good tool for knowledge representation of complex decision problems. Generating influence diagram model is known to in practice require much time and effort, and the resulting model can be generally applicable to only a specific decision problem. In order to reduce the burden of modeling decision problems, the concept of DCA is introduced. DCA treats a set of decision problems having some degree of similarityz as a single unit. We propose a method utilizing a feedforward neural net with supervised learning rule to develop DCA based on influence diagram, which method consists of two phases: Phase l is to search for relevant chance and value nodes of an individual influence diagram from given decision and specific situations and Phase II elicits arcs among the nodes in the diagram. We also examine the results of neural net simulation with an example of a class of decision problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.65-76
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• 2022

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• Industry Promotion Research
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• v.3 no.1
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• pp.13-31
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• 2018

The purpose of this study is to investigate the effect of organizational culture on organization and organizational performance, and it was targeting for 350 public officials (local governments, foundation under local governments, public corporation) who research work and consulting were implemented for. When it comes to the response to questionnaires, 313 copies out of 350 were verified on the research hypothesis of the research model by using statistical package programs of SPSS 20.0 and AMOS 20.0. Results of the research hypothesis on research model show that firstly, regarding the research hypothesis 1 that the organizational culture of public organization will have a positive (+) significant effect on learning organization, the organizational culture recognized by the organizational members of public organizations showed a positive influence on the learning organization. In other words, it showed that the organizational culture recognized by the organization members of public organizations is a major factor in building a learning organization. Secondly, regarding the research hypothesis 2, the result of the relationship between organizational culture and organizational performance, that the organizational culture recognized by the organizational members of the public sector showed a positive influence and it implies the importance of recognizing and transforming the organizational culture of public organizations to improve organizational performance of public organizations. Thirdly, regarding the research hypothesis 3, the organizational culture recognized by the organizational members of public organizations showed an influence on organizational performance and also showed apositive(+) influence on organizational performance through learning organization. As the organizational culture recognized by the organization members in the public sectoris influencing the organizational performance through the learning organization, various implementation plans are required to improve organizational culture, improving learning organization, and improving organizational performance in accordance with the characteristics of public organizations.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.337-346
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• 2021

In the fourth industrial revolution, fashion students are expected to work with various technologies to show creativity. This study aimed to conduct project-based learning(PBL) in collaboration with industry experts to design and operate artificial intelligence(AI) in the practice subject of fashion curation through the industrial academic teaching method. We first looked at teaching methods and strategies incorporating PBL in various academic fields. Next, we analyzed fashion projects and fashion curation services applying AI. Then through the question-and-answer method and by consulting with industry experts, we developed a curriculum for AI fashion curation, applying PBL(fashion market and trend analysis; new styles and time, place, and occasion planning; AI machine learning data set production; curation model development; and evaluation) suitable for the university's educational environment, information technology company conditions, and fashion students. As part of a close cooperation system with the industry, we conducted a 15-week Fashion Project II (Capstone Design) course and evaluated the outcomes and student satisfaction with the course. Students were able to develop new style, and time, place, and occasion categories and to utilize strategies for AI fashion curation services reflecting the unique needs of Millennials and Generation Z. Students showed high satisfaction with the curriculum. Further, it was confirmed that the study successfully applied PBL in class using AI technology in fashion education.

• The Korean Journal of Community Living Science
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• v.17 no.4
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• pp.67-80
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• 2006

This study conducted research on the actual state of community-oriented services for elderly rural inhabitants and their desire related to them to develop a local community service network model suitable to the characteristics of rural longevity villages. The research was conducted on 906 elderly people over 65 living in 20 rural longevity villages through questionnaires assessing filming and economy, economic activity, health care, learning and leisure activities as well as asking their wants and needs relative to local community services. As a result, it was found rural elderly people showed a high desire for local community services such as health, transportation and economy activity. In addition, they were mainly cultivating farm products as their economic activity and showed a high demand in the future as well. Most were found to take a walk in the healthcare field and showed a high demand for health examinations, health education, health consulting, hot spring bathing and basking in the woods. Respecting learning, social and leisure activities, they were mostly found to watch TV and do house chores, and showed a high desire for village environment repair, traditional farm music, visiting and tourism. With the above results, it is expected that the desire of rural elderly for such services can be satisfied, and the development of a local community service network model suitable to the characteristic of a local community is recommended.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.99-110
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• 2020

This study deals with the relationship between entrepreneurship of corporate member and creativity at the organizational level by empirically analyzing the relationship between entrepreneurship, organizational learning orientation, individual creativity, and group creativity. In particular, the relationship between entrepreneurship and creativity focuses on analyzing the mediating effects of organizational learning orientation. Based on the literature research, the research model and hypothesis were established by explaining the relationship between entrepreneurship, individual and group creativity, and organizational learning orientation. 308 copies of the questionnaire were distributed and collected for manufacturing workers in Gyeongbuk, and empirical analysis was conducted through structural equations. As a result, it was confirmed that entrepreneurship has an influence on organizational learning orientation without directly affecting individual and group creativity. In addition, the effects of entrepreneurship on organizational learning orientation and organizational learning orientation on individual and group creativity were examined. These findings can provide Directions for organizational management from the cultural perspective by identifying the effects of entrepreneurship and organizational learning orientation at the organizational level.