• 전자공학회논문지
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• 제53권12호
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• pp.169-174
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• 2016

본 연구에서는 실제적인 환자의 모델을 바탕으로 가상현실 기반의 수술환경을 제작하는 방법에 대한 고찰과 함께 치과 임플란트 수술 술기를 배울 수 있는 가상현실을 활용한 수술 모델에 대한 평가를 시행하고자 한다. 환자의 안면 해부학적 모델은 실제 환자의 CT 데이터를 바탕으로 안면부위 골에 대한 삼차원 모델을 먼저 완성한 후 각각의 근육을 표현하고 안면부에 대한 삼차원 스캔 그리고 치아의 3차원 스캔 후 모델링을 통해 각각의 해부학적 구조물을 표현하였다. Unity3D를 활용해 단계별 시나리오를 게임으로 구성하였다. 처음 절개를 시행하는 단계에서부터 환자의 현재 상태에 필요한 상악동 거상술에 대한 술기를 표현하고, 임플란트 식립과정 그리고 골이식술을 하는 과정을 단계별로 포함하였다. 단계별로 중요한 과정의 효과적인 습득을 위해 HMD(Head Mount Display) 및 Leap motion과 연동하여 3차원 모델에 기구를 위치시키고 수술행위를 재현해볼 수 있게하여 몰입감을 높이면서 실제 수술을 하고있는 느낌을 재현하고자 하였다. 총 20명의 수련의를 대상으로 가상현실 시뮬레이션을 수행한 후에 설문조사를 시행하였다. 임플란트 수술 교육 프로그램은 치과 대학생들과 수련의들의 교육 도구로서 수술 술기를 습득하고 평가하는데 사용할 수 있는 가능성을 보여주었다. 통상적으로 수술 술기 교육 방법이 많지 않은 현실에서 HMD 와 Leap motion이라는 비교적 저렴한 기기를 이용하여 몰입감을 높일 수 있는 가상현실 기반의 수술 교육은 장점이 있다고 말할 수 있다.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1217-1222
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• 2004

This paper describes the development of tele-robotic interface for the hot-line maintenance robot system. One of main issues in designing human-robot interface for the hot-line maintenance robot system is to plan the control procedure for each part of the robotic system. Another issue is that the actual degree of freedom (DOF) in the hot-line maintenance robot system is much greater than that of available control devices such as joysticks and gloves in the remote-cabin. For this purpose, a virtual simulator, which includes the virtual hot-line maintenance robot system and the environment, is developed in the 3D environment using CAD data. It is assumed that the control operation is done in the remote cabin and the overall work process is observed using the main-camera with 2 DOFs. For the input device, two joysticks, one pedal, two data gloves, and a Head Mounted Display (HMD) with tracker sensor were used. The interface is developed for each control mode. Designed human-interface system is operated using high-level control commands which are intuitive and easy to understand without any special training.

• 대한기계학회논문집A
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• 제33권11호
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• pp.1326-1331
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• 2009

The sensor data measured by the legged robot are used to recognize the physical environment or information that controls the robot's posture. Therefore, a robot's ambulation can be advanced with the use of such sensing information. For the precise control of a robot, highly accurate sensor data are required, but most sensors are expensive and are exposed to excessive load operation in the field. The seriousness of these problems will be seen if the prototype's practicality and mass productivity, which are closely related to the unit cost of production and maintenance, will be considered. In this paper, the use of a virtual sensor technology was suggested to address the aforementioned problems, and various ways of applying the theory to a walking robot obtained through training with an actual sensor, and of various hardware information, were presented. Finally, the possibility of the replacement of the ground reaction force sensor of legged robot was verified.

• 한국분말재료학회지
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• 제26권5호
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• pp.369-374
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• 2019

The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R2 = 0.95 and Pearson's r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.

• 한국멀티미디어학회논문지
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• 제14권12호
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• pp.1517-1527
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• 2011

This paper presents a motion capture based rehabilitation training system for a lower-limb paretic patient. The system evaluates the rehabilitation status of the patient by using the bend posture of the knees and the weight balance of the body. The posture of both legs is captured with a single camera using the planar mirror. The weight distribution is obtained by the Wii Balance Board. Self-occlusion problem in the tracking of the legs is resolved by using k-nearest neighbor based clustering with body symmetry and local-linearity of the posture data. To do this, we present data normalization and its symmetric property in the normalized vector space.

• 인터넷정보학회논문지
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• 제21권5호
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• pp.49-56
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• 2020

Detecting shadows in images and restoring or removing them was a very challenging task in computer vision. Traditional researches used color information, edges, and thresholds to detect shadows, but there were errors such as not considering the penumbra area of shadow or even detecting a black area that is not a shadow. Deep learning has been successful in various fields of computer vision, and research on applying deep learning has started in the field of shadow detection and removal. However, it was very difficult and time-consuming to collect data for network learning, and there were many limited conditions for shooting. In particular, it was more difficult to obtain shadow data from buildings and satellite images, which hindered the progress of the research. In this paper, we propose a method for generating shadow data from buildings and satellites using Unity3D. In the virtual Unity space, 3D objects existing in the real world were placed, and shadows were generated using lights effects to shoot. Through this, it is possible to get all three types of images (shadow-free, shadow image, shadow mask) necessary for shadow detection and removal when training deep learning networks. The method proposed in this paper contributes to helping the progress of the research by providing big data in the field of building or satellite shadow detection and removal research, which is difficult for learning deep learning networks due to the absence of data. And this can be a suboptimal method. We believe that we have contributed in that we can apply virtual data to test deep learning networks before applying real data.

• 전기전자학회논문지
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• 제27권1호
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• pp.38-44
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• 2023

STAP(space-time adaptive processing) 알고리즘의 성능은 CUT(cell under test) 내의 간섭에 대한 공분산 행렬 추정의 정확도가 결정적 역할을 한다. STAP 데이터는 일반적으로 많은 배열 소자 및 사용된 다수의 송신 펄스에 의해 결정되는 2차원 데이터 구조를 가지고 있다. 그러므로 공분산 행렬 추정의 정확도를 높이기 위해서는 매우 많은 트레이닝 데이터가 요구된다. 본 논문에서는 수신된 적은 개수의 데이터를 공간주파수-도플러 평면으로 변환한 후 가상의 트레이닝 데이터를 생성하는 알고리즘을 제시한다. 클러터 점유 위치를 이론적으로 유도하며 이에 근거해서 가상 트레이닝 데이터 생성 절차를 제시하고 STAP 시뮬레이션을 통해서 제시된 알고리즘이 STAP 성능을 개선할 수 있음을 보인다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2676-2681
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• 2003

The Haptic Exploration Laboratory at The Johns Hopkins University is currently exploring many problems related to haptics (force and tactile information) in human-machine systems. We divide our work into two main areas: virtual environments and robot-assisted manipulation systems. Our interest in virtual environments focuses on reality-based modeling, in which measurements of the static and dynamic properties of actual objects are taken in order to produce realistic virtual environments. Thus, we must develop methods for acquiring data from real objects and populating pre-defined models. We also seek to create systems that can provide active manipulation assistance to the operator through haptic, visual, and audio cues. These systems may be teleoperated systems, which allow human users to operate in environments that would normally be inaccessible due to hazards, distance, or scale. Alternatively, cooperative manipulation systems allow a user and a robot to share a tool, allowing the user to guide or override the robot directly if necessary. Haptics in human-machine systems can have many applications, such as undersea and space operations, training for pilots and surgeons, and manufacturing. We focus much of our work on medical applications.

• 대한간호학회지
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• 제51권4호
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• pp.465-477
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• 2021

Purpose: This study aimed to evaluate the effects of head monted display based home-visits virtual reality simulation (HVRS) program developed for undergraduate nursing students. Methods: A nonequivalent control group with a non-synchronized design was utilized and 84 participants (experimental group, 44; control group, 40) were recruited from August 31, 2020 to November 8, 2020 in Gwangju metropolitan city. The HVRS program consisted of scenarios of three nursing cases, hypertension, diabetes mellitus, and stroke. Data were analyzed SPSS version 25.0 for Windows. Results: At the completion of HVRS, significant differences were found between groups in knowledge of home-visits (t = 4.73, p < .001), self-confidence (t = 6.63, p < .001), self-efficacy (t = 3.13, p = .002), and clinical competency (t = 4.13, p < .001). No significant difference was shown between groups in nursing knowledge about strokes, a subcategory of knowledge pertaining to home visits. Conclusion: The HVRS program developed for undergraduate nursing students is effective in improving knowledge of home-visits, self-confidence, self-efficacy, and clinical competency for nursing students.

• Child Health Nursing Research
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• 제29권1호
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• pp.24-36
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• 2023

Purpose: This systematic literature review and meta-analysis explored extended reality (XR)-based pediatric nursing simulation programs and analyzed their effectiveness. Methods: A literature search was conducted between May 1 and 30, 2022 in the following electronic databases: MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and CINAHL. The search period was from 2000 to 2022. In total, 6,095 articles were reviewed according to the inclusion and exclusion criteria, and 14 articles were selected for the final content analysis and 10 for the meta-analysis. Data analysis was performed using descriptive statistics and the Comprehensive Meta-Analysis program. Results: XR-based pediatric nursing simulation programs have increased since 2019. Studies using virtual reality with manikins or high-fidelity simulators were the most common, with six studies. The total effect size was statistically significant at 0.84 (95% confidence interval=0.50-1.19, z=4.82, p<.001). Conclusion: Based on the findings, we suggest developing standardized guidelines for the operation of virtual pediatric nursing simulation education and practice. Simultaneously, the application of more sophisticated research designs for effect measurement and the combined applications of various virtual simulation methods are needed to validate the most effective simulation methodology.