• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.139-145
• /
• 2020

In the era of the 4th Industrial Revolution, the use of information based on AI APIs has a great influence on industry and life. In particular, the use of artificial intelligence data in the medical field will have many changes and effects on society. This paper is to study the necessary components to implement the "Cognitive Health Advisor model (CHA model)" and to implement the "CHA model using chatbot" based on this. It uses the open Cognitive chatbot to analyze and analyze the health status of users changing in their daily lives. The user's health information analyzed by the biometric sensor and chatbot consultation delivers the information to the user through the chatbot. And it implements a cognitive health advisor model that provides educational information for users' health promotion. Through this implementation, it intends to confirm the possibility of future use and to suggest research directions.

• Ecology and Resilient Infrastructure
• /
• v.1 no.2
• /
• pp.94-101
• /
• 2014

This is the urban design master plan for the Public Administration Town. The project proposes a newly configured city, where environmental and democratic principles are expressed in the shape of the urban fabric. To achieve the goal, the concepts of 'Flat City, Link City, and Zero City' were introduced. These concept show "Space fabric arrange, connection and material circulation and flow from ecological landscape". 'Flat City' shaped the government buildings into an iconic plane, and democratic society. The iconic plane's surface extends across the whole city, creating an expansive public park, which is easily accessible, and open to nature. 'Link City' connects governmental agencies to enhance their function and interactions. Government facilities, parks and green spaces, cultural facilities, commercial zones, and residential districts areas create an interconnecting network. 'Zero City' has integrated infrastructure systems to reuse waste, reduce pollution, and provide essential city functions. It creates a new wildlife habitat, making 'Zero City' a good neighborhood. This proposal was made to integrate historical, regional, nature experiences with various approaches in architecture, city, and landscape architecture.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.34 no.1 s.114
• /
• pp.69-80
• /
• 2006

The purposes of this study are to analyze the meanings of urban spaces which are the background of the Korean films, and to capture the interactions of ordinary culture and urban spaces. By reading urban spaces through films, it is possible to understand the variety of experiences that are hardly captured with direct eyes, specific and vivid urban images, and various events formed by the interactions of spaces and people. The scope of the study is the urban spaces revealed in Korean films portraying cities after the 1960's as their settings, and the total of 18 films was selected with every $4{\sim}5$ films for each time stage. With the selected films, analytical meanings were developed with considering three aspects; 1) phylogenetic meaning that simply reflects social-cultural and historical background, 2) the outer strum meaning that concerns the situation of special background and film scene, and 3) metaphorical and metanymic meaning on films. According to the appearance frequency, spatial backgrounds of film scenes are mainly alleys, main streets, railroad, loft, and riverside. And then the connection between spaces and meaning clusters was grasped, and reflected meanings were derived for every spaces. And the meaning of urban space in films was analyzed based on the meaning of developments and outer stratum. The fundamental characteristics and feelings of people in media such as films are more emphasized than in real world. Urban space is not considered as a simple visible shell, but is recognized as 'a real situation' created by people. The intension of this study was to open the possibility of the various views of urban spaces. The construction of the urban space should be approached from a perspective of creating new places at where the space and human beings interact with considerations of stories of various human lives. I hope new vistas can be opened up for the research subjects and methodologies about the hereafter study of urban spaces through the mutual communications with various adjacent regions including films.

• Journal of Plant Biotechnology
• /
• v.45 no.4
• /
• pp.347-356
• /
• 2018

Chionanthus retusus is a small deciduous tree that is widely used in landscaping due to its beautiful white spring flowers and ornamental value. Conventional propagation through seeds requires one to two years of breaking dormancy. The objective of this study was to determine the conditions of in vitro germination in C. retusus. In vitro embryo culture was carried out to investigate the effects of six factors: basal media (McCown Woody Plant Medium (WPM) and Murashige and Skoog (MS)); plant growth regulators (different combinations and concentrations of naphthaleneacetic acid (NAA), 6-Benzylaminopurine (BA), and gibberellic acid ($GA_3$)); embryo age (collected weekly beginning 36 days after fruit setting); low temperature pretreatment (storing $4^{\circ}C$ for 1, 2, 3, and 4 weeks); coconut additives (100, 200, and $300ml{\cdot}L^{-1}$); and genotype (grouping plants depending on their flowering nature). The basal medium used in this study was WPM with $2mg{\cdot}L^{-1-1}\;GA_3$, $20g{\cdot}L^{-1}$ sucrose, and $6g{\cdot}L^{-1}$ Agar. WPM medium mixed with $GA_3$, resulted in higher germination rate as compared to when using a combination of auxin and cytokinin. $GA_3$ at $2mg{\cdot}L^{-1}$ was the most effective of all combinations and concentrations of PGRs. WPM medium with $2mg{\cdot}L^{-1}GA_3$ resulted in better and faster germination (75.93%). Embryos collected at 57 days after fruit setting had the highest percent of germinated seeds (87.04%) while low-temperature pretreatment of fruits at $4^{\circ}C$ for two weeks produced the highest germination (95.37%). These results of this study could be an open ground for development of an efficient protocol for commercial production of the ornamental tree.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.2
• /
• pp.85-92
• /
• 2021

Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.4
• /
• pp.183-194
• /
• 2020

Recently, the number of hip dysplasia (DDH) that occurs during infant and child growth has been increasing. DDH should be detected and treated as early as possible because it hinders infant growth and causes many other side effects In this study, two modelling techniques were used for multiple training techniques. Based on the results after the first transformation, the training was designed to be possible even with a small amount of data. The vertical flip, rotation, width and height shift functions were used to improve the efficiency of the model. Adam optimization was applied for parameter learning with the learning parameter initially set at 2.0 x 10e-4. Training was stopped when the validation loss was at the minimum. respectively A novel image overlay system using 3D laser scanner and a non-rigid registration method is implemented and its accuracy is evaluated. By using the proposed system, we successfully related the preoperative images with an open organ in the operating room

• Journal of Advanced Navigation Technology
• /
• v.26 no.2
• /
• pp.72-77
• /
• 2022

Among the techniques of reinforcement learning, Q-Learning means learning optimal policies by learning Q functions that perform actionsin a given state and predict future efficient expectations. Q-Learning is widely used as a basic algorithm for reinforcement learning. In this paper, we studied the effectiveness of selecting and learning efficient paths by designing policies and rewards based on Q-Learning. In addition, the results of the existing algorithm and punishment compensation policy and the proposed punishment reinforcement policy were compared by applying the same number of times of learning to the 8x8 grid environment of the Frozen Lake game. Through this comparison, it was analyzed that the Q-Learning punishment reinforcement policy proposed in this paper can significantly increase the learning speed compared to the application of conventional algorithms.

• Journal of Practical Engineering Education
• /
• v.14 no.1
• /
• pp.219-224
• /
• 2022

Q-Learning is a technique widely used as a basic algorithm for reinforcement learning. Q-Learning trains the agent in the direction of maximizing the reward through the greedy action that selects the largest value among the rewards of the actions that can be taken in the current state. In this paper, we studied a policy that can speed up agent training using Q-Learning in Frozen Lake 8×8 grid environment. In addition, the training results of the existing algorithm of Q-learning and the algorithm that gave the attribute 'direction' to agent movement were compared. As a result, it was analyzed that the Q-Learning policy proposed in this paper can significantly increase both the accuracy and training speed compared to the general algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.2
• /
• pp.201-211
• /
• 2022

According to the recent change in the cybersecurity paradigm, research on anomaly detection methods using machine learning and deep learning techniques, which are AI implementation technologies, is increasing. In this study, a comparative study on data preprocessing techniques that can improve the anomaly detection performance of a GRU (Gated Recurrent Unit) neural network-based intrusion detection model using NGIDS-DS (Next Generation IDS Dataset), an open dataset, was conducted. In addition, in order to solve the class imbalance problem according to the ratio of normal data and attack data, the detection performance according to the oversampling ratio was compared and analyzed using the oversampling technique applied with DCGAN (Deep Convolutional Generative Adversarial Networks). As a result of the experiment, the method preprocessed using the Doc2Vec algorithm for system call feature and process execution path feature showed good performance, and in the case of oversampling performance, when DCGAN was used, improved detection performance was shown.

• Journal of the Korean Society for information Management
• /
• v.40 no.3
• /
• pp.77-98
• /
• 2023

Large Language Models (LLMs) are becoming the major trend in the natural language processing field. These models were built based on research data, but information such as types, limitations, and risks of using research data are unknown. This research would present how to analyze and evaluate the LLMs that were built with research data: LLaMA or LLaMA base models such as Alpaca of Stanford, Vicuna of the large model systems organization, and ChatGPT from OpenAI from the perspective of research data. This quality evaluation focuses on the validity, functionality, and reliability of Data Quality Management (DQM). Furthermore, we adopted the Holistic Evaluation of Language Models (HELM) to understand its evaluation criteria and then discussed its limitations. This study presents quality evaluation criteria for LLMs using research data and future development directions.