• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.109-115
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• 2020

In this paper, we propose an artificial intelligence(AI) technology analysis using partial least square(PLS) regression model. AI technology is now affecting most areas of our society. So, it is necessary to understand this technology. To analyze the AI technology, we collect the patent documents related to AI from the patent databases in the world. We extract AI technology keywords from the patent documents by text mining techniques. In addition, we analyze the AI keyword data by PLS regression model. This regression model is based on the technique of partial least squares used in the advanced analyses such as bioinformatics, social science, and engineering. To show the performance of our proposed method, we make experiments using AI patent documents, and we illustrate how our research can be applied to real problems. This paper is applicable not only to AI technology but also to other technological fields. This also contributes to understanding other various technologies by PLS regression analysis.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.39-46
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• 2020

The utilization of artificial intelligence (AI) in the engagement has been a key research topic in the defense field during the last decade. To pursue this utilization, it is imperative to acquire a realistic simulation to train an AI engagement agent with a synthetic, but realistic field. This paper is a case study of training an AI agent to operate with a hardware realism in the air-warfare dog-fighting. Particularly, this paper models the pursuit of an opponent in the dog-fighting setting with a gun-only engagement. In this context, the AI agent requires to make a decision on the pursuit style and intensity. We developed a realistic hardware simulator and trained the agent with a reinforcement learning. Our training shows a success resulting in a lead pursuit with a decreased engagement time and a high reward.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1143-1148
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• 2017

Currently, Artificial Intelligence and Deep Learning are rising as hot social issues, and these technologies are applied to various fields. A good method among the various algorithms in Artificial Intelligence is Convolutional Neural Networks. Convolutional Neural Network is a form that adds Convolution Layers to Multi Layer Neural Network. If you use Convolutional Neural Networks for small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning should take long time when the size of the learning data is large and the structure of layers is complicated. In these cases, GPU-based parallel processing is frequently needed. In this paper, we developed Convolutional Neural Networks using CUDA, and show that its learning is faster and more efficient than learning using some other frameworks or programs.

• Informatization Policy
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• v.24 no.2
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• pp.3-19
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• 2017

This article examines the issues around the coming age of artificial intelligence and the 4th industrial revolution. First, this articles addresses the changes we will encounter with the advance of innovative technologies. Changes in future jobs, education, travel and other lifestyle issues are discussed and responses of a few countries(governments) regarding preparations for such future changes are illustrated. To sum up, three dimensions - sustainable technology development, legal and policy-related establishments, and consensus building among the public - are identified as areas to focus on for the future. Particularly, it is advised that the Korean government apply and utilize new technologies to solve public issues and problems, particularly the newly-emerging "urban renewal" and "smart city" issues.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.77-84
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• 2017

With the entry into the aging society, we are increasingly interested in wellness, and personalized medical services through artificial intelligence are expanding. In order to provide personalized medical services, it is difficult to provide accurate medical analysis services only with the existing hospital system components PM / PA, OCS, EMR, PACS, and LIS. Therefore, it is necessary to present the hospital system model and the construction plan suitable for personalized medical service. Currently, some medical cloud services and artificial intelligence diagnosis services using Watson are being introduced in domestic. However, there are not many examples of systematic hospital system construction. Therefore, this paper proposes a hospital system model suitable for personalized medical service. To do this, we design a model that integrates medical big data construction and AI medical analysis system into the existing hospital system components, and suggest development plan of each module. The proposed model is meaningful as a basic research that provides guidelines for the construction of new hospital system in the future.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.279-285
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• 2018

The purpose of this study is to propose the role of future designers and capabilities to be developed in the age of A.I. Active and preliminary designers should prepare themselves to develop necessary capabilities. As a method of study, we investigated the meaning of design and the changing role of designers from the past to present. Additional research was conducted on generative design, design processes, and A.I based design software. Finally, based on the analysis, we proposed the role of future designers and their capabilities in the age of A.I. In conclusion, the role of future designer should lead social innovation through creativity by coworking with artificial intelligence based on understanding and empathy for users. Based on this research, designers are expected to develop unique humanities skills such as empathy and creativity and work with AI in response to $4^{th}$ industrial revolution.

• Research in Plant Disease
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• v.26 no.3
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• pp.121-133
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• 2020

Climate change, increased extreme weather and climate events, and rapidly changing socio-economic environment threaten agriculture and thus food security of our society. Therefore, it is urgent to shift from conventional farming to smart agriculture using big data and artificial intelligence to secure sustainable growth. In order to efficiently manage plant diseases through smart agriculture, agricultural big data that can be utilized with various advanced technologies must be secured first. In this review, we will first learn about agrometeorological big data consisted of meteorological, environmental, and agricultural data that the plant pathology communities can contribute for smart plant disease management. We will then present each sequential components of the smart plant disease management, which are prediction, monitoring and diagnosis, control, prevention and risk management of plant diseases. This review will give us an appraisal of where we are at the moment, what has been prepared so far, what is lacking, and how to move forward for the preparation of smart plant disease management.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.68-74
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• 2010

This paper suggest a new ACS algorithm to solve SMP which was solved by Gale-Shapley algorithm. The stable marriage problem is an extensively-studied combinatorial problem with many practical applications. The classical Gale-Shapley algorithm produces a marriage that greatly favors the men at the expense of the women, or vice versa. In this paper we apply ACS algorithm to SMP to find 4 kinds of solutions such as stable matching with man-optimal, woman-optimal, egalitarian stable matching, sex-fair stable matching. So this ACS is a novel method to solve Stable Marriage Problem. Our simulation results show the effectiveness of the proposed ACS.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.19-24
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• 2020

Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.

• Journal of KIISE
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• v.44 no.1
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• pp.51-56
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• 2017

The research area for solving question answering (QA) problems using artificial intelligence models is in a methodological transition period, and one such architecture, the dynamic memory network (DMN), is drawing attention for two key attributes: its attention mechanism defined by neural network operations and its modular architecture imitating cognition processes during QA of human. In this paper, we increased accuracy of the inferred answers, by adapting an automatic data augmentation method for lacking amount of training data, and by improving the ability of time perception. The experimental results showed that in the 1K-bAbI tasks, the modified DMN achieves 89.21% accuracy and passes twelve tasks which is 13.58% higher with passing four more tasks, as compared with one implementation of DMN. Additionally, DMN's word embedding vectors form strong clusters after training. Moreover, the number of episodic passes and that of supporting facts shows direct correlation, which affects the performance significantly.