• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2005년도 춘계학술대회 논문집
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• pp.273-277
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• 2005

본 논문에서는 양식어장을 보호하기 위한 어장 탐지 시스템에 있어서 칼만필터의 적용기법에 대해 논의하였다. 어장탐지시스템(FDS)은 어장에 침입하는 도전선박을 실시간으로 식별하고 어장의 위치변동 등을 파악하고자 하는 것이다. 본 연구에서는 양식어장으로의 접근 물체 중에서 F-AIS를 탑재하지 않은 의심선박에 대해 추적(tracking)을 하기 위해 칼만필터 기법을 적용하고자 한다. 백색잡음을 동반한 가속도계의 대상물에 대하여 위치 및 속도판독을 위한 시뮬레이션을 행하고 트랙킹 시스템으로의 적용 가능성에 대해 살펴본다.

• 한국항행학회논문지
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• 제25권6호
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• pp.441-449
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• 2021

지능형 이동체 시스템 발달에 따라서, 보다 정확한 위치 정보 추정 기술에 대한 요구가 증가하고 있다. 특히, 실내에서 사용되는 이동 로봇에게 주어진 일을 정해진 위치에서 수행할 때에는 보다 정확한 위치 추정에 대한 성능을 필요로 한다. 따라서, 이 논문에서는 고정형 또는 이동형 사물에 적용 가능한 진보된 위치 추정 방법을 제안한다. 제안 방법은 미리 설치된 블루투스 비콘 신호로부터 위치 추정 결과를 칼만 필터의 관찰 신호로 사용한다. 또한, 센서의 위치와 각도에 따라서 결정되는 각 방향의 중력 가속도를 추정하기 위해서, 롤(roll)과 피치(pitch) 각도를 먼저 계산하고, 이 결과를 자기장 센서 출력과 결합하여 요(Yaw) 각도를 추정함으로써,이동체의 진행 방향을 정확히 추정한다. 이를 기반으로 이동체의 제어 입력이 되는 가속도 신호를 정확히 계산함으로써, 칼만 필터의 성능을 향상시키는 방법을 제안한다. 제안 방법의 성능은 고정 상태와 이동 상태로 나누어 평균 위치 오차를 계산하여 기존의 칼만 필터와 비교시 위치 오차를 크게 향상시킴을 확인하였다.

• 한국컴퓨터그래픽스학회논문지
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• 제30권3호
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• pp.171-178
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• 2024

밸런스 보드 (Balance Board, BB)를 활용한 운동은 균형 감각 발달, 코어 근육 강화 등 신체 운동 능력 향상과 집중력 증진에 효과적이다. 특히, 다양한 디지털 콘텐츠와 연동되는 스마트 밸런스 보드 (Smart Balance Board, SBB)는 기존 밸런스 보드에 비해 적절한 피드백을 제공하여 운동 효과를 극대화한다. 그러나 대부분의 시스템들은 시/청각적인 피드백만 제공하여 사용자의 운동 몰입도 및 흥미 그리고 운동 자세의 정확성에 미치는 영향을 평가하지 못한다. 본 연구에서는 멀티 센서를 활용하여 다양한 피드백과 정확한 자세로 훈련이 가능한 몰입형 스마트 밸런스 보드 (Imemersive-SBB, I-SBB)를 제안한다. 제안된 시스템은 아두이노 기반으로 보드의 자세을 측정하는 자이로 센서, 유/무선 통신을 위한 통신 모듈, 사용자의 정확한 발 위치를 유도하는 적외선 센서, 촉각 피드백을 위한 진동 모터로 구성되어 있다. 측정된 보드의 자세는 칼만 필터 (Kalman Filter)를 이용하여 부드럽게 보정되고, 멀티 센서 데이터는 FreeRTOS를 활용해 실시간으로 병렬처리된다. 제안된 I-SBB는 다양한 콘텐츠와 연동하여 사용자의 집중도 및 몰입도 향상과 흥미 유발에 효과적임을 보인다.

• Steel and Composite Structures
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• 제52권5호
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• pp.571-581
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• 2024

After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. This work studies the tracking control problem of a class of strict-feedback nonlinear systems with input saturation nonlinearity. Under the framework of dynamic surface control design, RBF neural networks are introduced to approximate the unknown nonlinear dynamics. In order to address the impact of input saturation nonlinearity in the system, an auxiliary control system is constructed, and by introducing a class of first-order low-pass filters, the problems of large computation and computational explosion caused by repeated differentiation are effectively solved. In response to unknown parameters, corresponding adaptive updating control laws are designed. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1034-1039
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• 2005

SALM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important tasks in mobile robot research. Until now expensive sensors such as a laser sensor have been used for mobile robot localization. Currently, the proliferation of RFID technology is advancing rapidly, while RFID reader devices, antennas and tags are becoming increasingly smaller and cheaper. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying location of the mobile robot in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, the localization error results from the sensing area of the RFID reader, because the reader just knows whether the tag is in the sensing range of the sensor and, until now, there is no study to estimate the heading of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. The Markov localization method is used to reduce the location(X,Y) error and the Kalman Filter method is used to estimate the heading($\theta$) of mobile robot. The algorithms which are based on Markov localization require high computing power, so we suggest fast Markov localization algorithm. Finally we applied these algorithms our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors such as odometers and RFID tags for mobile robot localization in the smart floor

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1797-1801
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• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.

• Steel and Composite Structures
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• 제49권4호
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• pp.407-417
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• 2023

After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. Many domain schemes based on the measured vibration computations, including least squares estimation and neural fuzzy logic control, have been studied and found to be effective for online/offline monitoring of structural damage. Traditional strategies require all external stimulus data (input data) which have been measured available, but this may not be the generalized for all structures. In this article, a new method with unknown inputs (excitations) is provided to identify structural matrix such as stiffness, mass, damping and other nonlinear parts, unknown disturbances for example. An analytical solution is thus constructed and presented because the solution in the existing literature has not been available. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Smart Structures and Systems
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• 제33권4호
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.