• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.35-46
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• 2019

Artificial intelligence (AI) technology has been evolving to recognize and learn the languages, voice tones, and facial expressions of users so that they can respond to users' emotions in various contexts. Many AI-based services of particular importance in communications with users provide emotional interaction. However, research on nonverbal interaction as a means of expressing emotion in the AI system is still insufficient. We studied the effect of lighting on users' emotional interaction with an AI device, focusing on color and flickering motion. The AI device used in this study expresses emotions with six colors of light (red, yellow, green, blue, purple, and white) and with a three-level flickering effect (high, middle, and low velocity). We studied the responses of 50 men and women in their 20s and 30s to the emotions expressed by the light colors and flickering effects of the AI device. We found that each light color represented an emotion that was largely similar to the user's emotional image shown in a previous color-sensibility study. The rate of flickering of the lights produced changes in emotional arousal and balance. The change in arousal patterns produced similar intensities of all colors. On the other hand, changes in balance patterns were somewhat related to the emotional image in the previous color-sensibility study, but the colors were different. As AI systems and devices are becoming more diverse, our findings are expected to contribute to designing the users emotional with AI devices through lighting.

• Informatization Policy
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• v.29 no.2
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• pp.3-36
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• 2022

Most research in artificial intelligence (AI) has focused on the development of new algorithms. But as artificial intelligence has been spreading over many applications and gaining more attention from managers in the organization, academia has begun to understand the necessity of developing new artificial intelligence theories related to AI management. We reviewed recent studies in the field from 2015, and further analysis has been done for 785 studies chosen based on citation numbers of over 20. The results show that most studies have still been in the prototyping application phase of artificial intelligence across different industries. We conclude our study by calling for more research in the application of artificial intelligence in terms of organizational structures and project and risk management.

• Journal of the Korean Geosynthetics Society
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• v.14 no.3
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• pp.21-29
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• 2015

An artificial reef is a human-made underwater structure to improve marine environment and to provide a habitat for fish and other ocean wildlife. An artificial reef is placed on the ocean ground. In soft ground like most of the seabed soil, the ground has been settled due to weight of artificial reef. This study investigated the bearing capacity and settlement reduction effect of geosynthetics which were reinforced on the ground in a large size tank. Penetration tests and large soil tank laboratory tests were performed to investigate settlement reduction effect and bearing capacity on artificial reef with different spreading area of geogrid. Laboratory test results indicate that the spreaded geogrid under artificial reef reduce the settlement of ground and increase bearing capacity of ground.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.484-484
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• 2023

지진해일은 해저지진, 화산활동, 해저 산사태 등에 의해 발생되는 장주기 파랑이다. 지진해일은 발생빈도가 낮지만, 한번 발생하면 많은 에너지가 연안으로 유입되어 인명 및 재산피해를 야기 시킬 수 있다. 따라서, 과거 수십년동안 지진해일에 대한 연구는 지진해일의 역학관계를 이해하고, 이를 바탕으로 한 수치모델 개발에 초점을 두어 연구가 진행되어 왔다. 더욱이, 지진해일 실험적 연구는 많은 경제적 비용을 지불해야 하기에 수치모델개발 연구가 더욱 중점적으로 수행되어 왔다. 지리학적으로 우리나라는 지진해일에 안전하지 못하다. 하나의 예로, 1983년 5월 26일, 일본 서해안에서 발생한 지진해일은 동해로 전파되어 동해안 지역에 커다란 피해를 야기시켰다. 이 당시, 강원도삼척시 원덕읍에 위치한 임원항에서는 2명의 사상자와 2명의 부상자가 발생하였고, 당시 금액으로 약3억원의 재산피해가 발생하였다. 이 연구는 인공지능 기법 중 하나인 인공신경망을 이용하여 인명과 재산피해가 발생한 임원항에서 최대지진해일고를 예측하고자 하였다. 지진해일 수치모델은 뛰어난 정확도를 나타내는 반면, 결과를 산출하는데 상당한 시간을 필요로 한다. 이에 반해, 인공신경망은 수치모델과 유사한 정확도 및 결과를 신속하게 제공할 수 있다는 장점을 가지고 있다. 지진해일 인공신경망 모델 개발은 지진의 단층파라미터를 바탕으로 작성된 지진해일의 시나리오를 토대로 연구가 진행되었고, 우리나라 동해에 위치한 외해 관측 지점의 지진해일고 자료를 통해, 임원항에서의 최대 지진해일고가 예측되도록 개발되었다. 이를 위하여, 인공신경망의 학습 및 검증 과정을 수행하였고, 향후 발생 가능한 다양한 지진해일에 대해 평가함으로써, 인공신경망 모델의 예측성능을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.766-772
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• 2009

Generally, wind industry has been oriented to large power systems which require large windy areas and often need to overcome environment restrictions. However, small-scale wind turbines are closer to the consumers and have a large market potential, and much more efforts are required to become economically attractive. In this paper, a prototype of a small-scale urban wind generation system for battery charging application is described and a neural network based MPPT(Maximum Power Point Tracking) algorithm which can be effectively applied to urban wind turbine system is proposed. Through Matlab based simulation studies and actual implementation of the proposed algorithm, the feasibility of the proposed scheme is verified.

• Journal of the Korean Geotechnical Society
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• v.17 no.2
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• pp.73-83
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• 2001

본 연구에서는 전국 4개 지구의 화강암 풍화토를 연구대상으로 현장 및 실내시험을 수행하고 토량 변화율을 노상과 노체에 대하여 결정하였다. 그리고, 본 연구에서는 인공 신경망 중 오류 역전파 학습 알고리즘을 도입하여 토량 변화율 C 값을 추정하고 신경망의 적용성에 대한 검증을 수행하였다. 화강암 풍화토에 대한 실내 및 현장시험 결과에서 얻어진 토량 변화율 C 값은 노상과 노체 구분 없이 최소 0.7에서 최대 1.2정도의 넓은 범위로 나타났다. 토지공사에서 제안하는 C값의 산정식과 본 연구 결과를 비교한 결과, 토지공사의 산정식에 의한 결과가 과대 평가될 가능성이 큰 것으로 나타났다. 비중, 자연 함수비, 자연상태의 습윤단위중량, #200 통과율 그리고 균등계수의 입력변수를 갖는 $I_{5-1}$$H_{30-30}$$O_1$의 신경망에서 다른 신경망 구조들보다 잦은 지역 최소점에 수렴하는 결과를 보였다. 본 연구에서 사용한 모든 신경망 구조에서 시험결과와 신경망 결과의 상관계수는 0.9이상으로 나타나 높은 상관성을 나타내었다. 특히, 인공 신경망에 의한 예측결과는 다양한 영향인자들 중에서 비중, 자연 함수비, 자연상태의 습윤단위중량 그리고 #200 통과율의 4개 변수만으로도 C값을 예측할 수 있었으며, 상관계수는 0.96으로 나타났다.다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.197-209
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• 2024

This study aimed to analyze recent research trends in Artificial Intelligence (AI) education within South Korea with the overarching objective of exploring the future direction of AI education. For this purpose, an analysis of 697 papers related to AI education published in Research Information Sharing Service (RISS) from 2016 to November 2023 were analyzed using word cloud and Latent Dirichlet Allocation (LDA) topic modeling technique. As a result of the analysis, six major topics were identified: generative AI utilization education, AI ethics education, AI convergence education, teacher perceptions and roles in AI utilization, AI literacy development in university education, and AI-based education and research directions. Based on these findings, I proposed several suggestions, (1) including expanding the use of generative AI in various subjects, (2) establishing ethical guidelines for AI use, (3) evaluating the long-term impact of AI education, (4) enhancing teachers' ability to use AI in higher education, (5) diversifying the curriculum of AI education in universities, (6) analyzing the trend of AI research, and developing an educational platform.

• Proceedings of the Korean Magnestics Society Conference
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• 2014.11a
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• pp.163-164
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• 2014

• Korean Journal of Environmental Agriculture
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• v.17 no.4
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• pp.362-367
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• 1998

To investigate the effects of simulated acid rain(SAR) on the downward movement of mineral nutrients, SARs of different pH were applied to the soil. SAR of pH 2.0 decreased the soil pH greatly, while SAR of pH 4.0 and 6.0 did not change the soil pH to compare to that of SAR of pH 2.0. Decrease in soil pH was in the order of sandy loam > loam > clay loam. The amoumt of leached exchangeable and soluble bases from the soil due to the penetration of SAR was in the order of Ca >Mg > K. After application of 1200mm SAR of pH 2.0 in to the soil downward mean movements of the exchangeable and soluble bases was in the order of Mg > Ca > K in sandy loam and loam soil and Ca > Mg > K in clay loam soil. Downward movements of the those bases under pH 4.0 into the soil was in the order of Mg > K > Ca in sandy loam and clay loam, and K > Mg > Ca in loam soil. Available phosphorus moved slightly downward with increasing acidity of the SAR.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.143-149
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• 2013

Many researchers and developers take a greater interest on the LOD AI techniques as users demand more elaborate and sophisticated game AI in recent years. However, contrary to the traditional geometry LOD, existing LOD AI techniques can be used only to a limited extent. Therefore, in this paper, I propose an LOD AI technique, which uses HSM(Hierarchical State Machine) and the Lua script language as the method to control game objects. Using the proposed approach, we can easily produce multilevel AI models for LOD and design various objects without hard-coding state machines. Moreover, in order to show the effectiveness of the presented technique, this paper exemplifies the results of the efficiency test through the prototype engine.