• Journal of Information Processing Systems
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• 제17권4호
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• pp.737-753
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• 2021

As the demand for high-quality rendering for mixed reality, videogame, and simulation has increased, global illumination has been actively researched. Monte Carlo path tracing can realize global illumination and produce photorealistic scenes that include critical effects such as color bleeding, caustics, multiple light, and shadows. If the sampling rate is insufficient, however, the rendered results have a large amount of noise. The most successful approach to eliminating or reducing Monte Carlo noise uses a feature-based filter. It exploits the scene characteristics such as a position within a world coordinate and a shading normal. In general, the techniques are based on the denoised pixel or sample and are computationally expensive. However, the main challenge for all of them is to find the appropriate weights for every feature while preserving the details of the scene. In this paper, we compare the recent algorithms for removing Monte Carlo noise in terms of their performance and quality. We also describe their advantages and disadvantages. As far as we know, this study is the first in the world to compare the artificial intelligence-based denoising methods for Monte Carlo rendering.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.597-602
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• 2011

A robust asset management methodology is essential for effective decision-making of maintenance, repair and rehabilitation of a bridge network. It can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS, and an optimal management philosophy. The maintenance optimization methodology proposed in this paper is developed for a small bridge network with limited structural condition rating records. . The methodology is organized in three major components: (1) bridge health index (BHI); (2) maintenance and budget optimization; and (3) reliable Artificial Intelligence (AI) based bridge deterioration model. The outcomes of the paper will help to identify BMS implementation problems and to provide appropriate solutions for managing small bridge networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.631-653
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• 2020

Sign language is a natural, visually oriented and non-verbal communication channel between people that facilitates communication through facial/bodily expressions, postures and a set of gestures. It is basically used for communication with people who are deaf or hard of hearing. In order to understand such communication quickly and accurately, the design of a successful sign language translation system is considered in this paper. The proposed system includes object detection and classification stages. Firstly, Single Shot Multi Box Detection (SSD) architecture is utilized for hand detection, then a deep learning structure based on the Inception v3 plus Support Vector Machine (SVM) that combines feature extraction and classification stages is proposed to constructively translate the detected hand gestures. A sign language fingerspelling dataset is used for the design of the proposed model. The obtained results and comparative analysis demonstrate the efficiency of using the proposed hybrid structure in sign language translation.

• 한국인공지능학회지
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• 제8권2호
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• pp.11-16
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• 2020

Among the innovative companies leading the era of the 4th industrial revolution, the world's largest Internet company is Google. Google has grown by providing convenient services such as Internet search, Android smartphone operating system, and video. Now, Google is leading the global IT industry by continuing to develop in various new business fields based on open service platforms, artificial intelligence, and big data. In this study, an exploratory discussion was conducted on Google's success factors and future directions. The purpose of the research is to understand the development process of the IT field from the successfactors of Google and to analyze the development direction of the future IT industry. Google's success factors were its open platform policy and successful acquisitions of external companies. In fact, most of the services Google offers come from companies that have acquired and acquired them. In addition, there was a corporate culture that values and supportsthe spirit of challenge and autonomy of members who are not afraid of failure. Based on this study's review of Google's direction analysis, the follow-up study will infer the direction of the IT industry in depth and look at the future technologies that IT majors need to prepare.

• 로봇학회논문지
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• 제17권3호
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.879-884
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• 2004

This paper describes the development of a synthetic multi-agent called KMITL Cadence 2003. KMITL Cadence 2003 is a robotic soccer simulation team consisting of eleven autonomous software agents. Each agent operates in a physical soccer simulation model called Robocup Soccer Server which provides fully distributed and real-time multi-agent system environment. All teammates have to cooperate to achieve the common goal of winning the game. The simulation models many aspects of the football field such as noise in ball movements, noisy sensors, unreliable communication channel between teammates and actuators, limited physical abilities and restricted communication. This paper addresses the algorithm to develop the soccer agents to perform basic actions which are scoring, passing ball and blocking the opponents effectively. The result of this development is satisfactory because the successful scoring attempts is increased from 11.1% to 33.3%, successful passing ball attempts is increased from 22.08% to 63.64%, and also, successful intercepting attempts is increased from 88% to 97.73%.

• Korean Journal of Radiology
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• 제23권9호
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• pp.847-852
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• 2022

• 한국보육지원학회지
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• 제9권2호
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• pp.331-355
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• 2013

본 연구는 유아기 다중지능의 발굴과 지원에 있어서 유아 학부모의 중요한 역할을 인식하고 유아 학부모가 바라보는 다중지능 교육 프로그램에 대한 인식과 실태 및 요구 조사를 통해 유아기 다중지능 교육 프로그램에 활용할 수 있는 기초자료를 제공하는데 연구의 목적이 있다. 연구목적을 달성하기 위해 부산 소재 6개 유치원에 다니고 있는 유아의 학부모 350명을 대상으로 설문지 조사를 하였다. 연구결과 첫째 유아 학부모들의 다중지능교육에 대한 전반적 인식 높게 나타났으며, 둘째 유아 학부모들은 자녀의 강점을 개발하기 위한 방법으로 주로 유아교육기관과 유아용 학습지에 의존하는 경향이 많았다. 셋째 유아 학부모들은 다중지능 교육 시기는 만2세-만7세 이하가 적절하며 유아의 심리와 욕구에 대한 이해가 있는 교사를 선호하였다. 그리고 유아자녀의 다중지능 교육에의 참여와 이와 관련한 부모교육을 희망하였다. 그러나 일부 유아학부모들은 다중지능 교육을 영재교육이나 특수교육의 일환으로 오해하고 있는 부분도 있었다. 이와 같은 결과를 통해 본 연구는 유아 부모의 다중지능 교육에 대한 올바른 이해의 도모와 또한 다중지능 교육을 위한 유아교육기관과 교사 역할의 중요성에 대하여 인지하고 이후 다중지능 교육프로그램 개발 및 관련 부모교육 프로그램을 개발하기 위한 기초자료를 제공하는 데 그 의의가 있다.

• 한국정보통신학회논문지
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• 제26권11호
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• pp.1720-1728
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• 2022

인공지능 기술의 발전으로 산업 4.0시대가 열렸고 축산업에서도 ICT 기술이 접목된 스마트 농장의 구현이 큰 관심을 받고 있다. 그중에서도 컴퓨터 비전 기반 인공지능 기술을 접목한 축산물 및 축산 가공품의 품질 관리 기술은 스마트 축산의 핵심 기술에 해당한다. 그러나 인공지능 모형 훈련을 위한 축산물 이미지 데이터 수의 부족과 특정 범주(class)에 대한 데이터 불균형은 관련 연구 및 기술 개발에 큰 장해물이 되고 있다. 이러한 문제들을 해결하기 위해, 본 연구에서는 오버샘플링과 적대적 사례 생성기법의 활용을 제안한다. 제안되는 방법은 성공적인 불량 탐지 (Defect detection) 관점을 기반으로 하며, 이는 부족한 데이터 레이블을 효과적으로 활용하는데 필요한 방법이다. 최종적으로 실험을 통해 제안된 방법의 타당성을 확인하고 활용 전략을 검토한다.

• 유통과학연구
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• 제15권12호
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• pp.85-94
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• 2017

Purpose - Owing to economic development and rapid globalization, the number of people traveling abroad has increased dramatically in recent years. For instance, according to data from World Tourism Organization, approximately 1,724 million tourists traveled abroad in 2016. This phenomenon has resulted in a change for domestic markets, as they no longer serve only domestic customers but also serve foreign customers as well. Therefore, intercultural service encounters between services providers and customers from diverse cultural backgrounds are becoming more frequent. Especially in the field of retailing, salesperson's customer oriented selling behavior is particularly important for the successful interactions. However, it is hard to find some factors that can improve salesperson's customer oriented selling behavior in intercultural sales encounters. Research design, data, and methodology - A quantitative survey methodology was utilized to collect data on 312 salespeople at duty-free shops located on Jeju Island, Korea. As a tourism-based region, Jeju Island has attracted a large number of foreign tourists since being designated as an international free city in 2002. Owing to this phenomenon, intercultural sales encounters between salespersons and customers from different cultures have become commonplace. Compared to other salespeople, salespeople working in duty-free shops have more frequent intercultural interactions, as over 90% of their total customers are from foreign countries. Additionally, regular professional training programs for salespeople help cultivate cultural intelligence. Data analysis was conducted using SPSS 20. Results - This paper explores the role of empathy and cultural intelligence in intercultural sales encounters using a theoretical model incorporating the causal relationships between empathy(cognitive empathy and emotional empathy) and customer oriented selling behavior, as well as the moderating effects of cultural intelligence in these relationships. Conclusions - This study is almost the first to explore the influence of empathy and cultural intelligence in intercultural sales encounters. Thus, this study provides a meaningful contribution to the application of empathy and cultural intelligence in the retailing field and will draw the attention of personal distribution practicers and researchers to the importance of empathy and cultural intelligence. Additionally, this study has useful managerial implications for employee selection, training, and development in retailing firms engaged in intercultural sales encounters.