• Journal of Internet Computing and Services
• /
• v.15 no.1
• /
• pp.63-71
• /
• 2014

Currently many non-traditional application areas such as artificial intelligence and web databases require advanced modeling power than the existing relational data model. In those application areas, object-oriented database (OODB) is better data model since an OODB can providemodeling power as grouping similar objects into class, and organizing all classes into a hierarchy where a subclass inherits all definitions from its superclasses. The purpose of this paper is to develop an OODB concurrency control scheme dealing with multiple inheritance. The proposed scheme, called Multiple Inheritance Implicit Locking (MIIL), is based on so-called implicit locking. In the proposed scheme, we eliminate redundant locks that are necessary in the existing implicit locking scheme. Intention locks are required as the existing implicit locking scheme. In this paper, it is shown that MIIL has less locking overhead than implicit locking does. We use only OODB inheritance hierarchies, single inheritance and multiple inheritance so that no additional overhead is necessary for reducing locking overhead.

• Journal of Korea Water Resources Association
• /
• v.47 no.2
• /
• pp.207-221
• /
• 2014

Recently, due to global warming, climate change has affected short time concentrated local rain and unexpected heavy rain which is increasingly causing life and property damage. Therefore, this paper studies the characteristic of localized heavy rain and flash flood in Nakdong basin study area by applying Data Mining method to predict flood and constructing water level predicting model. For the verification neural network from Data Mining method and hydraulic flood routing was used for flood from July 1989 to September 1999 in Nakdong point and Iseon point was used to compare flood level change between observed water level and SAM (Slope Area Method). In this research, the study area was divided into three cases in which each point's flood discharge, water level was considered to construct the model for hydraulic flood routing and neural network based on artificial intelligence which can be made from simple input data used for comparison analysis and comparison evaluation according to actual water level and from the model.

• Journal of Information Technology Applications and Management
• /
• v.26 no.3
• /
• pp.121-134
• /
• 2019

The Fourth Industrial Revolution has differentiated technologies such as artificial intelligence, IoT(Internet of things), big data, and mobile. As the civilization develops more and more, humanity enjoy the cultural activities more than economic activity for the food and shelter. The platform structure based on the advanced information technology of the present will expand the cultural contents area in a variety of ways. Cultural contents respond sensitively to changes in consumer and will be useful experiences of human activities. Therefore, it should be noted again that the contents industry should not be limited to the discussion of the application of the fourth technology, but should be produced with emphasis on useful experiences of human being. In other words, the discussion of human activities around cultural contents should be focused on how to apply beyond the use of fourth industrial technology. Therefore, it is necessary to analyze the basis of the successful storytelling of the planning stage to connect the fourth industrial technology and human useful experience as a method for developing cultural contents, and to build and propose a model as a strategic method. This study analyzes domestic and foreign cases made by using big data among the visual contents which show continuous increase of consumption among culture industry field, and draws success factors and limit points. Next, we extract what is the successful matching factor that influenced consumer 's consciousness, and find out that the structure of culture prototype has been applied in the long history of mankind, and presents it as a storytelling model. Through the above research, this study aims to present a new interpretation and creative activity of cultural contents by presenting a storytelling model as a methodology for connecting creative knowledge, away from the general interpretation of social phenomenon applied with big data.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.3
• /
• pp.25-30
• /
• 2009

Recent days intellectual systems using reinforcement learning are being researched at various fields of game and web searching applications. A good training models are called to be fitted with trainning data and also classified with new records accurately. A overfitted model with training data may possibly bring the unfavored fallacy of hasty generalization. But it would be unavoidable in actual world. The entropy and mutation model are suggested to reduce the overfitting problems on this paper. It explains variation of entropy and artificial development of entropy in datamining, which can tell development of mutation to survive in nature world. Periodical generation of maximum entropy are introduced in this paper to reduce overfitting. Maximum entropy model can be considered as a periodical generalization in intensified process of intellectual web searching.

• Journal of the Korea Institute of Building Construction
• /
• v.16 no.4
• /
• pp.331-339
• /
• 2016

According to high-rise, complexation, and enlargement of buildings, various construction methods are being developed, and the significance of construction method selection about main work types has emerged as a major interest. However, it has been pointed out that hand-on workers cannot consider project characteristics carefully, and they lack an objective standard or reference for main construction method selection. Hence, the selection is being made depending on hand-on workers' experience and intuition. To solve this problem, various studies have proceeded for construction method selection of main work types using Artificial Intelligence like Fuzzy, AHP and Case-based reasoning. It is difficult to apply many different kinds of construction method selection to every main work type with consideration for characteristics of work types and condition of a construction site when selecting construction method in the field. Accordingly, this study proposed the decision-making model which can apply to fields easily. Using matrix analysis and liner transformation, this study verified consistency of study models applied in the process of soil retaining selection with a case study.

• Journal of the Korea Convergence Society
• /
• v.12 no.10
• /
• pp.45-53
• /
• 2021

In our technology-driven world, various methods for teaching in an educational venue or in a simulated environment have been suggested especially for computer and coding education. In particular, IoT related education has been made possible owing to the industrial developments that have occurred in various fields since the Fourth Industrial Revolution. The proposed model allows various IoT systems to be indirectly built; it provides an inexpensive learning method by applying a simulation system in a 3D environment. The model is implemented on Virtual Remote IO based on the Arduino platform, thereby reducing the cost of building an education system. In addition various education-related content can be provided to learners through such an indirectly developed system. Test code was written to check the consistency of an operation between the real system and the virtual system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.11
• /
• pp.5568-5587
• /
• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.1
• /
• pp.93-109
• /
• 2022

Today, digital transformation in providing service value to companies that combine service and technology is becoming a necessity. In the transition period of digital transformation, various factors such as data, artificial intelligence technology, and partnerships can become competitive factors. In particular, digital transformation, which combines information and services with customers, creates a new business model that changes the entire industry and is presented as core competitiveness that creates customer value. From these aspects, the purpose of this case study is to derive competitive advantages on digital transformation using the case of company S. First, the study analyzes the same type of industry based on the case of app service. Second, this study presents preference factors in the operational process to enhance competitiveness by expanding user participation in accommodation reservation services. In addition, the customer service value model provides through the analysis of the five competitive factors in the operational process. This study elaborates the implications of the customer service value creation model in terms of new opportunities and challenges in digital transformation as a new customer service strategy.

• Journal of Creative Information Culture
• /
• v.7 no.4
• /
• pp.197-206
• /
• 2021

This study is a case of convergence education using the AI model of entry in elementary schools. The subject is English, and the class was conducted based on the image learning model among the convergence activities with the art department drawing and the AI model of the entry. In order to effectively achieve the learning goals of speaking and writing in English education. The class was designed by combining art and SW. Students experienced communication using AI, improved confidence, and were able to improve creativity and communication skills by expressing not only listening and speaking but also expressing through various media such as pictures and photos. In addition, in order to find out the effectiveness of the class, a survey was conducted on students and the results were analyzed. As a result of the analysis, it was found that it had a positive effect on students' participation rate, degree of understanding AI after class, interest in AI, satisfaction with AI classes.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.35 no.2
• /
• pp.134-142
• /
• 2022

As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material's features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.