• Annual Conference of KIPS
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• 2021.11a
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• pp.1009-1011
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• 2021

게임에서 플레이어에게 시각적으로 제공되는 환경을 View라고 하는데, View의 전환만으로도 전혀 다른 게임의 경험이 가능하다. 본 논문에서는 car racing game에서 View의 전환에 따른 게임의 경험 차이를 비교하였다. 우리는 ARcore 라이브러리를 사용하여 AR car racing game을 구현하였고 virtual joystick을 사용한 Interaction 방법을 구현하였다. Top down view와 first person view의 차이점이 플레이어의 실감에 어떠한 영향을 미치는지 연구하기 위해 두 view을 구현하여 pilot study를 수행하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.601-602
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• 2023

본 논문에서는 인공지능을 결합한 EDR(Endpoint Detection and Response) 시스템을 확인하고, 그 현황을 파악하는 것을 목적으로 한다. 현대에는 점차 보안 위협이 더욱 증가하면서 기존의 방식으로는 대응하기 어려운 상황이 발생하고 있으며, 위협에 대한 예측과 선제적인 대응력을 강화하기 위해 스스로 학습해 감시 및 공격에 대응하는 인공지능 기반의 보안 시스템에 대한 관심이 증가하고 있다. 본 논문은 AI 기반 EDR 시스템과 그 현황에 대해 살펴보고자 한다.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.91-97
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• 2007

Game program had been classed by 3D or on-line game etc, and engine and game programming simply, But, game programmer's kind more classified new, Artifical Intelligence game programmer's role is important. This paper makes game character study and moved by intelligence using reinforcement learning algorithm. Fought with character enemy using developed game, Confirmed whether embodied game character is facile by intelligence, As result of an experiment, we know, studied character defends excellently than randomly moved character.

• Annual Conference of KIPS
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• 2024.10a
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• pp.1060-1061
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• 2024

본 논문은 청각장애인들이 음성 언어 중심의 사회에서 겪는 의사소통 제한을 해소하기 위해 제안되었다. 이를 위해 수어 인식, 입모양 탐지, 음성 인식 모델을 통해 추출된 데이터를 텍스트로 변환하여 접근성을 향상시키는 시스템을 개발하였다. 이 시스템은 청각장애인의 원활한 의사소통을 지원하고 사회적 참여를 증진하는 것을 목표로 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1090-1100
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• 2024

While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2782-2804
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• 2024

The integration of artificial intelligence technology with medicine has rapidly evolved, with increasing demands for quality of life. However, falls remain a significant risk leading to severe injuries and fatalities, especially among the elderly. Therefore, the development and application of computer vision-based fall detection technologies have become increasingly important. In this paper, firstly, the keypoint detection algorithm ViTPose++ is used to obtain the coordinates of human body keypoints from the camera images. Human skeletal feature maps are generated from this keypoint coordinate information. Meanwhile, human dense feature maps are produced based on the DensePose algorithm. Then, these two types of feature maps are confused as dual-channel inputs for the model. The convolutional gated recurrent unit is introduced to extract the frame-to-frame relevance in the process of falling. To further integrate features across three dimensions (spatio-temporal-channel), a dual-channel fall detection algorithm based on video streams is proposed by combining the Convolutional Block Attention Module (CBAM) with the ConvGRU. Finally, experiments on the public UR Fall Detection Dataset demonstrate that the improved ConvGRU-CBAM achieves an F1 score of 92.86% and an AUC of 95.34%.

• Korea Information Processing Society Review
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• v.14 no.4
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• pp.47-52
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• 2007

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.3-14
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• 2002

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.55-78
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• 2004

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.165-181
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• 2001