• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.29 no.1
• /
• pp.273-277
• /
• 2005

In this paper, we study on adaptation of the kalman filter for FDS(fishery detection system) to protect and aquaculture farms. The FDS will detect a robbing vessel with real time and a variance of the position of fishing fields. The kalman filter for tracking system that can be detect and track the approaching object without mounting F-AIS(Fishery Automatic Identification System) is applied. Some simulation results for the acceleration object with white noise is showed and the possibility of adaptation for tracking system is discussed.

• Journal of Advanced Navigation Technology
• /
• v.25 no.6
• /
• pp.441-449
• /
• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

• Journal of the Korea Computer Graphics Society
• /
• v.30 no.3
• /
• pp.171-178
• /
• 2024

Exercises using a Balance Board (BB) are effective in developing balance, strengthening core muscles, and improving physical fitness and concentration. In particular, the Smart Balance Board (SBB), which integrates with various digital content, provides appropriate feedback compared to traditional balance boards, maximizing the effectiveness of the exercise. However, most systems only offer visual and auditory feedback, failing to evaluate the impact on user engagement, interest, and the accuracy of exercise postures. This study proposes an Immersive Smart Balance Board (I-SBB) that utilizes multiple sensors to enable training with various feedback mechanisms and precise postures. The proposed system, based on Arduino, consists of a gyro sensor for measuring the board's posture, a communication module for wired/wireless communication, an infrared sensor to guide the user's foot placement, and a vibration motor for tactile feedback. The board's posture measurements are smoothly corrected using a Kalman Filter, and the multi-sensor data is processed in real-time using FreeRTOS. The proposed I-SBB is shown to be effective in enhancing user concentration and engagement, as well as generating interest, by integrating with diverse content.

• Steel and Composite Structures
• /
• v.52 no.5
• /
• pp.571-581
• /
• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.06a
• /
• pp.1034-1039
• /
• 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
• /
• 2005.06a
• /
• pp.1797-1801
• /
• 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
• /
• v.49 no.4
• /
• pp.407-417
• /
• 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
• /
• v.33 no.4
• /
• pp.291-300
• /
• 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.