• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.

• Journal of the Korean Society of Physical Medicine
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• v.19 no.2
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• pp.55-64
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• 2024

PURPOSE: This study evaluated the effects of robot-assisted gait training combined with virtual reality training on balance and gait ability in stroke patients. METHODS: Thirty-one stroke patients were allocated randomly into one of two groups: robot-assisted gait training combined virtual reality training group (RGVR group; n = 16) and control group (n = 15). The RGVR group received 30 minutes of robot-assisted gait training combined with virtual reality training. Robot-assisted gait training was conducted in parallel using a virtual reality device. In the Control group, neurodevelopmental therapy was performed according to the function of chronic stroke patients. Both groups underwent training for 30 minutes, three times per week for eight weeks. The balance assessment system (BioRescue, Marseille, France), BBS, and TUG were used to evaluate the balance ability. The OptoGait (Microgate Srl, Bolzano, Italy) and 10 mWT were measured to evaluate the gait ability. The measurements were performed before and after the eight-week intervention period. RESULTS: Both groups showed significant improvement in their balance and gait ability during the intervention. RGVR showed significant differences in balance and gait ability compared to the control group groups (p < .05). These results showed that RGVR was more effective on balance and gait ability in patients with chronic stroke. CONCLUSION: RGVR can improve balance and gait ability, highlighting the benefits of RGVR. This study provides intervention data for recovering the balance and gait ability of chronic stroke patients.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.176-187
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• 2002

This paper focuses on the study of simulation and evolution of Micro Air Vehicles. Micro Air Vehicles or MAVs are small flying robots that are used for surveillance, search and rescue, and other missions. The simulated robots are designed based on realistic characteristics and the brains (controllers) of the robots are generated using genetic algorithms, i .e., simulated evolution. The objective for the experiments is to investigate the effects of robot team size and topology (simulation environment) on the evolution of simulated robots. The testing of team sizes deals with finding an ideal number of robots to be deployed for a given mission. The goal of the topology experiments is to see if there is an ideal topology (environment) to evolve the robots in order to increase their utility in most environments. We compare the results of the various experiments by evaluating the fitness values of the robots i .e., performance measure. In addition, evolved robot teams are tested in different situation in order to determine if the results can be generalized, and statistical analysis is performed to evaluate the evolved results.

• Fire Science and Engineering
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• v.29 no.6
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• pp.109-118
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• 2015

Recently, three kinds of search robot prototypes were developed to assume the role of fire fighters for search and rescue missions in special disaster areas with high heat, smoke, toxic gases, or radioactivity. To accomplish search missions, these robots should be able to endure heat, overcome various obstacles, suppress fires, and see through dense smoke. This study investigated the heat resistance, practicality, and fire fighting capacity of these robots. The results show that the small and middle-sized robots were resistant to surrounding temperatures of $100{\sim}200^{\circ}C$, and the fire-fighter-riding robot could endure up to $500^{\circ}C$ for half an hour. The fire-fighter-riding robot showed excellent extinguishing performance on an A-10 class fire model, which was extinguished within 3 min. The robots also showed various capacities for overcoming obstacles and are expected to play an active role in various special disaster areas.

• Journal of The Korean Society of Integrative Medicine
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• v.11 no.2
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• pp.221-230
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• 2023

Purpose : This study was performed to evaluate the effects of virtual reality combined robot assist gait training (VRG) on improvement of balance and respiratory function in chronic stroke patients. Methods : A single-blind, randomized controlled trial (RCT) was conducted with 35 chronic stroke patients. They were randomly allocated 2 groups; VRG group (n=18) and conservative treatment group (CG; n=17). The VRG group received 30 minutes robot assisted gait training combined virtual reality training, robot assisted gait training was conducted in parallel using a virtual reality device (2 sessions of 15 minutes in a 3D-recorded walking environment and 15 minutes in a downtown walking environment). In the conservative treatment group, neurodevelopmental therapy and exercise therapy were performed according to the function of stroke patients. Each group performed 30 minutes a day 3 times a week for 8 weeks. The primary outcome balance and respiratory function were measured by a balance measurement system (BioRescue, Marseille, France), Berg balance scale, functional reach test for balance, Spirometry (Cosmed Micro Quark, Cosmed, Italy) for respiratory function Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁), and maximum expiratory volume (PEF) were measured according to the protocol. The measurement were performed before and after the 8 weeks intervention period. Results : Both groups demonstrated significant improvement of outcome in balance and respiratory function during intervention period. VRG revealed significant differences in balance and respiratory function as compared to the CG groups (p<.05). Our results showed that VRG was more effective on balance and respiratory function in patients with chronic stroke. Conclusion : Our findings indicate that VRG can improve balance and respiratory function, highlight the benefits of VRG. This study will be able to be used as an intervention data for recovering balance and respiratory function in chronic stroke patients.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1076-1077
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• 2023

해상 추락사고에 대응하는 해상 구조 로봇 프로젝트는 항만 근로자의 추락사고 감지와 피해자 구조에 초점을 둔다. 센서와 자율주행 기술을 접목하여 정확하고 효율적인 구조 작업을 가능케 하고, 자체 개발한 워터센서를 활용하여 신속한 구조를 지원한다. YOLO를 이용한 피해자 위치 파악, 블루투스 기반 관리자 어플리케이션, 해상 추락 감지 및 센서를 탑재한 구명 조끼, 자동 구조 작업 등의 기능을 통합하여 항만 근로자의 안전을 보장하며, 해수욕장 등 다양한 환경에서도 확장 가능한 창의적인 기술을 제시한다.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1827-1836
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• 2022

Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.

• Wind and Structures
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• v.36 no.6
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• pp.405-421
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• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.