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A REAL-LIFE SIMULATION MODEL FOR NPC PERSONALITY WITH GAUSSIAN DISTRIBUTION
Kyungeun Cho,Song Wei,Kyhyun Um 한국멀티미디어학회 2007 한국멀티미디어학회 국제학술대회 Vol.2007 No.-
This paper describes a behavior planning framework in simulation game based on character personality using proposed Gaussian random distribution. Along with the data flowing process in our planning framework, NPC can generate behavior planning autonomously according to the dynamic environment information resulted by human player. Further more, we illuminate applying Gaussian probabilistic function for real-life action simulation in time domain with the expected value estimated by behavior planning during thinking process. To elucidate the mechanism of the framework, we simulated it in a restaurant simulation game.
Simulation framework of ubiquitous network environments for designing diverse network robots
Cho, Seoungjae,Fong, Simon,Park, Yong Woon,Cho, Kyungeun North-Holland 2017 Future generations computer systems Vol.76 No.-
<P><B>Abstract</B></P> <P>Smart homes provide residents with services that offer convenience using sensor networks and a variety of ubiquitous instruments. Network robots based on such networks can perform direct services for these residents. Information from various ubiquitous instruments and sensors located in smart homes is shared with network robots. These robots effectively help residents in their daily routine by accessing this information. However, the development of network robots in an actual environment requires significant time, space, labor, and money. A network robot that has not been fully developed may cause physical damage in unexpected situations. In this paper, we propose a framework that allows the design and simulation of network robot avatars and a variety of smart homes in a virtual environment to address the above problems. This framework activates a network robot avatar based on information obtained from various sensors mounted in the smart home; these sensors identify the daily routine of the human avatar residing in the smart home. Algorithms that include reinforcement learning and action planning are integrated to enable the network robot avatar to serve the human avatar. Further, this paper develops a network robot simulator to verify whether the network robot functions effectively using the framework.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We proposed a framework to simulate a network robot in a virtual smart home. </LI> <LI> A network robot agent identifies daily routines of a resident and executes service. </LI> <LI> The framework shows a network robot could help and reduce tasks of a human agent. </LI> <LI> The simulator verified the framework reduces costs of developing network robots. </LI> </UL> </P>
AN EXTRACTION SCHEME OF DISEASED REGIONS FROM BRAIN MR IMAGES
Kyungeun Cho,Kyhyun Um 한국멀티미디어학회 2006 한국멀티미디어학회 국제학술대회 Vol.2006 No.-
This paper proposes a method of extracting diseased regions from brain MR images. The first stage of the method segments the areas of the brain structure by differentiating the negative image from the original image. After segmenting the white matter from the brain MR image, the remaining area is segmented. Cerebrospinal fluid regions are extracted and diseased areas are segmented according to the thickness of the white-grey matter. Finally, the features of the diseased areas in the medical images are extracted for 3D spatial indexing. This paper has confirmed the effectiveness of this method by showing that various diseased areas can be correctly extracted through experiments with sets of brain MR images
Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud
Cho, Seoungjae,Kim, Jonghyun,Ikram, Warda,Cho, Kyungeun,Jeong, Young-Sik,Um, Kyhyun,Sim, Sungdae Hindawi Publishing Corporation 2014 The Scientific World Journal Vol.2014 No.-
<P>A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.</P>
조경은(Kyungeun Cho),조형제(Hyungje Cho) 한국정보과학회 2001 정보과학회 컴퓨팅의 실제 논문지 Vol.7 No.3
이 논문은 사람의 비언어적 행동을 자동적으로 분석하는 것을 목적으로 60 가지의 기본적인 사람의 윗몸 동작들을 인식하는 방법을 제안한다. 사람 몸동작을 인식하기 위한 방법으로는 확률적 문법추론법을 이용하였으며 모든 관절의 움직임 분석으로 임의의 동작을 인식하는 방법을 사용하였다. 시스템의 입력 데이타로 쓰여지는 각 관절의 실세계 3 차원 좌표들을 일정간격으로 양자화한 후, 각각 xy, zy 평면에 투영하고, 이들을 다시 4 방향 코딩하여 확률적 문법 추론법에 적합한 입력형식으로 변환한다. 또 한 비언어적 행동 분석을 위한 사람의 동작 인식에는 손과 다른 부위와의 관계인 근접 정보가 동작 구분의 중요한 요소가 됨을 감안하여, 확률 문법 추론 방법을 확장하고, 일반적인 확률 문법 추론 방법과 비교하여 인식률이 향상됨을 실험결과를 통해 확인하였다. This paper proposes a human action recognition scheme to recognize nonverbal human communications automatically. Based on the principle that a human body action can be defined as a combination of multiple articulation movements, we use the method of inferencing stochastic grammars to understand each human actions. We measure and quantize each human action in 3D world-coordinate, and make two sets of 4-chain-code for xy and zy projection plane. Based on the fact that the neighboring information among articulations is an essential element to distinguish actions, we designed a new stochastic inference procedure to apply the neighboring information of hands. Our proposed scheme shows better recognition rate than that of other general stochastic inference procedures.
Optimization of Multiple Sensor Data Pipeline for Real-time 3D Terrain Reconstruction
Seoungjae Cho,Seongjo Lee,Kyhyun Um,Kyungeun Cho,Sungdae Sim,Yong Woon Park 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
Remote-control technology is required in an unmanned vehicle such that it can replace humans for executing tasks in various extreme environments. In particular, a remote scene must be reconstructed using 3D meshes for enabling a user to remotely control an unmanned vehicle easily and intuitively. To this end, a large amount of multiple sensor data would be processed using various algorithms in real time. Considering the limited hardware specifications in extreme environments, it is difficult to implement 3D terrain reconstruction in high-quality and real time. This paper proposes the optimization of the architecture of a multiple-sensor-data pipeline. The improved performance resulting from the optimized architecture was analyzed through experimental comparison with a non-optimized system.