• 한국정보처리학회논문지
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• 제6권3호
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• pp.773-780
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• 1999

In this paper, we explore a behavioral animation of artificial birds that have lived by doing an aggregate motion. We first model individual birds and then propose a behavioral model for an aggregate motion of a flock of birds. In order to represent realistically collision avoidance and flock centering among birds, which are necessary properties in a flock of birds, we consider motive of a flock of birds, and role, velocity, momentum, banking and internal characteristics of each bird. The paper presents the simulation result of the proposed model for a flock of 100 birds.

• 한국컴퓨터정보학회논문지
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• 제21권10호
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• pp.21-28
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• 2016

The aim of this research is to analyze Perceived Usefulness(PU) and Perceived Ease of Use(PEOU) based on Technology Acceptance Model in , and how viewing experiences and knowledge of motion graphics have an impact on attitude toward using and behavioral intention to use. Both usability for learning and usability for appreciation in terms of PU have significant correlations with the degree of satisfaction and immersion, and behavioral intention to use. On the other hand, PEOU has an influence on degree of exhibition satisfaction and immersion, and onto behavioral intention to use with the exception of intention to revisiting . Unlike PU or PEOU, previous viewing experiences do not have correlation with attitude toward using and behavioral intention to use. Only previous knowledge of motion graphics has a correlation with degree of satisfaction and immersion, and behavioral intention to use. As the influence on PU and PEOU's attitude toward using and and behavioral intention to use has been verified, our findings show that two variables of TAM enable the prediction of user's technology acceptance on digital exhibitions and as a result prove the suitability for TAM as an evaluation model for digital exhibition of remediating the originals. This study offers a fresh understanding of the importance of motion graphic effects which influence attitude toward using and behavioral intention to use from the perspective of curating methodology.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제16권4호
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• pp.477-481
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• 2010

본 연구에서는 무선 센서 네트워크를 기반으로 침대 주변에서 사람의 행동유형을 분류하고자 한다. 침대 주변에서 사람의 다섯가지 행동유형과 세가지 상태들을 정의하고, 이들을 상태기계로 표현하였다. 움직임 감지 및 진동센서들을 통해 행동유형 관련 데이터들을 수집하고 이로 부터 특정벡터를 추출하였다. 행동유형별 특징벡터와 상태기계를 기초로 행동유형 모델을 정립하였고, 정립된 모델의 유효성 검증을 위해 실험을 실시한 후 행동유형 모델을 보정하였다. 이러한 실험결과들은 침대 주변에서 사람들이 행하는 행동유형들이 잘 분류될 수 있음을 보여준다.

• 통합자연과학논문집
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• 제9권4호
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• pp.281-291
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• 2016

This study examines the causal relationship between adolescent impulsivity, academic procrastination and school adaptability. The main purpose of this study is to confirm the degree of influence among these variables and analyze the causes of school adaptability. In this model, impulsivity and academic procrastination were set as independent variables and school adaptability was set as a dependent variable. Impulsivity of adolescents affects academic procrastination and school adaptability, and academic procrastination will affect school adaptability. As a result of the research, first, it can be seen that adolescents 'cognition impulsivity, motion impulsivity, and unplanned impulsivity have a significant influence on behavioral in adolescents'. Second, adolescents' cognition impulsivity, motion impulsivity, and unplanned impulsivity a significant influence on the cognitive in the adolescents'. Third, adolescents' behavioral, cognitive, and emotional have a significant influence on school adaptability in adolescents' school adaptability. In conclusion, we proposed a policy proposal on school adaptability by deriving meaning to improve adolescents' school adaptability.

• 로봇학회논문지
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• 제18권2호
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• pp.143-154
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• 2023

In this study, a fast motion planning method for the swing motion of a 6x6 wheel-legged robot to traverse large obstacles and gaps is proposed. The motion planning method presented in the previous paper, which was based on trajectory optimization, took up to tens of seconds and was limited to two-dimensional, structured vertical obstacles and trenches. A deep neural network based on one-dimensional Convolutional Neural Network (CNN) is introduced to generate keyframes, which are then used to represent smooth reference commands for the six leg angles along the robot's path. The network is initially trained using the behavioral cloning method with a dataset gathered from previous simulation results of the trajectory optimization. Its performance is then improved through reinforcement learning, using a one-step REINFORCE algorithm. The trained model has increased the speed of motion planning by up to 820 times and improved the success rates of obstacle crossing under harsh conditions, such as low friction and high roughness.

• Journal of Ubiquitous Convergence Technology
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• 제1권1호
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• pp.53-59
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• 2007

This paper proposes a new group mobility model for wireless communication. The mobility model considers the psychological and sociological behavior of each node and the perception of other nodes for describing interactions among a set of nodes. The model assumes no permanent membership of a group, capable of capturing natural behaviors as fork and join. It emulates a cooperative movement pattern observed in mobile ad hoc networks of military operation and campus, in which a set of mobile stations accomplish a cooperative motion affected by the individual behavior as well as a group behavior. The model also employs a physic model to avoid a sudden stopping and a sharping turning.

• 대한의용생체공학회:의공학회지
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• 제20권4호
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• pp.485-493
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• 1999

Human can generate various posture and motion with nearly 350 muscle pairs. From the viewpoint of mechanisms, the human skeleton mechanism represents great kinematic and dynamical complexity. Physical and behavioral fidelity of human motion requires dynamically accurate modeling and controling. This paper describes a mathematical modeling, and dynamic simulation of human body. The human dynamic model is simplified as a rigid body consisting of 18 actuated degrees of freedom for the real time computation. Complex kinematic chain of human body is partitioned as 6 serial kinematic chains that is, left arm, right arm, support leg, free leg, body, and head. Modeling is developed based on Newton-Euler formulation. The validity of proposed dynamic model, which represents mathematically high order differential equation, is verified through the dynamic simulation.

• 대한조선학회논문집
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• 제40권2호
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• pp.41-48
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• 2003

The human behavior is very important in development of simulation system for evacuation analysis. The walking speed of passenger is especially affected by dynamic effect and list due to damage and ship motion in wave. There are various methods to get an useful data for evacuation simulation. The onboard experimental approach is one of the most strong method. In this paper, the onboard experiment is performed to obtain human behavioral data. To realize ship trim and heel due to maritime casuality, the passage model for experiment is made. The experiment is carried out at dynamic and static condition respectively using the ship with passage model. The result is evaluated and it will be reflected in evacuation simulation tool.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.176-180
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• 1998

We may gaze at some peculiar scenes of flocking of birds and fishes. This paper demonstrates that multiple agent mobile robots show complex behaviors from efficient and strategic rules. The simulated flock are realized by a distributed behavioral model and each mobile robot decides its own motion as an individual which moves constantly by sensing the dynamic environment.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권5호
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• pp.187-194
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• 2021

사람의 행동인식(Action Recognition)은 사람의 관절 움직임에 따라 어떤 행동을 하는지 인식하는 것이다. 이를 위해서 영상처리에 활용되는 컴퓨터 비전 태스크를 활용하였다. 사람의 행동인식은 딥러닝과 CCTV를 결합한 안전사고 대응서비스로서 안전관리 현장 내에서도 적용될 수 있다. 기존연구는 딥러닝을 활용하여 사람의 관절 키포인트 추출을 통한 행동인식 연구가 상대적으로 부족한 상태이다. 또한 안전관리 현장에서 작업자를 지속적이고 체계적으로 관리하기 어려운 문제점도 있었다. 본 논문에서는 이러한 문제점들을 해결하기 위해 관절 키포인트와 관절 움직임 정보만을 이용하여 위험 행동을 인식하는 방법을 제안하고자 한다. 자세추정방법(Pose Estimation)의 하나인 AlphaPose를 활용하여 신체 부위의 관절 키포인트를 추출하였다. 추출된 관절 키포인트를 LSTM(Long Short-Term Memory) 모델에 순차적으로 입력하여 연속적인 데이터로 학습을 하였다. 행동인식 정확률을 확인한 결과 "누워있기(Lying Down)" 행동인식 결과의 정확도가 높음을 확인할 수 있었다.