• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2107-2112
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• 2016

This paper is designed to develop a Gimbal control stabilizer for drones Gimbal system control for drone for 3D image to make sure clean image in the shaking and wavering environments of drone system. The stabilizer is made of tools which support camera modules and IMU(Inertial Measurement Unit) sensor modules follow exact angles, which can brock vibrations outside of the camera modules. It is difficult for the camera modules to get clean image, because of irregular movements and various vibrations produced by flying drones. Moreover, a general PID controller used for the movements of rolling, pitching and yawing in order to control the various vibrations of various frequencies needs often to readjust PID control parameters. Therefore, this paper aims to conduct the Intelligent-PID controller as well as design the Gimbal control stabilizer to get clean images and to improve irregular movements and various vibrations problems referenced above.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.675-680
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• 2018

In this paper, we propose a hand gesture recognizer using restricted coulomb energy (RCE) neural network, and present hardware implementation results for real-time learning and recognition. Since RCE-NN has a flexible network architecture and real-time learning process with low complexity, it is suitable for hand recognition applications. The 3D number dataset was created using an FPGA-based test platform and the designed hand gesture recognizer showed 98.8% recognition accuracy for the 3D number dataset. The proposed hand gesture recognizer is implemented in Intel-Altera cyclone IV FPGA and confirmed that it can be implemented with 26,702 logic elements and 258Kbit memory. In addition, real-time learning and recognition verification were performed at an operating frequency of 70MHz.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.279-287
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• 2019

In this paper, we attempted to analyze the balance ability of the body by measuring changes in body motion and plantar pressure distribution. So we developed a program that can measure and analyze range of motion and center of body pressure using inertial measurement unit(IMU) and FSR(Force Sensing Resistor) sensor, we also produced a contents that can help improve the balance ability. The quantitative values of range of motion and center of body pressure measured by this program are visualized in real time so that the user can easily recognize the results. In addition, the contents were designed to be adjusted according to the direction of improving the balance ability by adjusting the difficulty level based on the measured balance information. This can be achieved by increasing the concentration and participation will by using visual feedback method that proceeds while watching moving objects according to the user's motion.

• Journal of Drive and Control
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• v.19 no.4
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• pp.19-28
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• 2022

This paper introduces an automatic steering system for straight traveling capable of being mounted on drivable agricultural machinery which user can handle it such as a tractor, a transplant, etc. The modular automatic steering device proposed in the paper is composed of RTK GNSS, IMU, HMI, hydraulic valve, and wheel sensor. The path generation method of the automatic steering system is obtained from two location information(latitude and longitude on each point) measured by GNSS in advance. From HMI, a straight path(AB line) can be created by connecting latitude and longitude on each point and the device makes the machine able to follow the path. During traveling along the reference path, it acquires the real time position data every sample time(0.1s), compares the reference with them and calculates the lateral deviation. The values of deviation are used to control the steering angle of the machine using hydraulic valve mounted on the axle of front wheel. In this paper, Pure Pursuit algorithm is applied used in autonomous vehicles frequently. For the analysis of traveling characteristics, field tests were executed about these conditions: velocity of 2, 3, 4km/h which is applied to general agricultural work and ground surface of solid(asphalt) and weak condition(soil) such as farmland. In the case of weak ground state, two experiments were executed about no-load(without work) and load(with work such as plowing). The maximum average deviations were presented 2.44cm, 7.32cm, and 11.34cm during traveling on three ground conditions : asphalt, soil without load and with load(plowing).

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.433-443
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• 2022

Purpose: In this paper, we propose a study of deep learning algorithms that estimate and predict sarcopenia by exploiting the high penetration rate of smart devices. Method: To utilize deep learning techniques, experimental data were collected by using the inertial sensor embedded in the smart device. We implemented a smart device application for data collection. The data are collected by labeling normal and abnormal gait and five states of running, falling and squat posture. Result: The accuracy was analyzed by comparative analysis of LSTM, CNN, and RNN models, and binary classification accuracy of 99.87% and multiple classification accuracy of 92.30% were obtained using the CNN-LSTM fusion algorithm. Conclusion: A study was conducted using a smart sensoring device, focusing on the fact that gait abnormalities occur for people with sarcopenia. It is expected that this study can contribute to strengthening the safety issues caused by sarcopenia.

• Physical Therapy Korea
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• v.30 no.3
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• pp.174-183
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• 2023

Background: Office workers experience neck or back pain due to poor posture, such as flexed head and forward head posture, during long-term sedentary work. Posture correction is used to reduce pain caused by poor posture and ensures proper alignment of the body. Several assistive devices have been developed to assist in maintaining an ideal posture; however, there are limitations in practical use due to vast size, unproven long-term effects or inconsistency of maintaining posture alignment. We developed a headphone and necklace posture correction system (HANPCS) for posture correction using an inertial measurement unit (IMU) sensor that provides visual or auditory feedback. Objects: To demonstrate the test-retest reliability and concurrent validity of neck and upper trunk flexion measurements using a HANPCS, compared with a three-dimensional motion analysis system (3DMAS). Methods: Twenty-nine participants were included in this study. The HANPCS was applied to each participant. The angle for each action was measured simultaneously using the HANPCS and 3DMAS. The data were analyzed using the intraclass correlation coefficient (ICC) = [3,3] with 95% confidence intervals (CIs). Results: The angular measurements of the HANPCS for neck and upper trunk flexions showed high intra- (ICC = 0.954-0.971) and inter-day (ICC = 0.865-0.937) values, standard error of measurement (SEM) values (1.05°-2.04°), and minimal detectable change (MDC) values (2.92°-5.65°). Also, the angular measurements between the HANPCS and 3DMAS had excellent ICC values (> 0.90) for all sessions, which indicates high concurrent validity. Conclusion: Our study demonstrates that the HANPCS is as accurate in measuring angle as the gold standard, 3DMAS. Therefore, the HANPCS is reliable and valid because of its angular measurement reliability and validity.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.441-449
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• 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 Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.38-46
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• 2011

3D motion analysis system which is currently widely used for walking analysis has limitations due to both necessity of wide space for many cameras for measurement, high cost, and complicated preparation procedure, which results in low accessability in use and application for clinical diagnosis. To resolve this problem, we developed 3-dimensional wireless ambulatory measurement system based on inertial sensor which can be easily applicable for clinical diagnosis for lower extremity deformity and developed system was evaluated by applying for 10 elderly people with diabetes mellitus. Developed system was composed of wireless ambulatory measurement module that consists of inertial measurement unit (IMU) which measures the gait characteristics, microcontroller which collects and precesses the inertial data, bluetooth device which transfers the measured data to PC and Window's application for storing and processing and analyzing received data. This system will utilize not only to measure lower extremity (foot) problem conveniently in clinical medicine but also to analyze 3D motion of human in other areas as sports science, rehabilitation.

• Journal of Ocean Engineering and Technology
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• v.33 no.2
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• pp.168-177
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• 2019

KRISO (Korea Research Institute of Ship & Ocean Engineering) started a project to develop the core algorithms for autonomous intervention using an underwater robot in 2017. This paper introduces the development of the robot platform for the core algorithms, which is an ROV (Remotely Operated Vehicle) type with one 7-function manipulator. Before the detailed design of the robot platform, the 7E-MINI arm of the ECA Group was selected as the manipulator. It is an electrical type, with a weight of 51 kg in air (30 kg in water) and a full reach of 1.4 m. To design a platform with a small size and light weight to fit in a water tank, the medium-size manipulator was placed on the center of platform, and the structural analysis of the body frame was conducted by ABAQUS. The robot had an IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and a depth sensor for measuring the underwater position and attitude. To control the robot motion, eight thrusters were installed, four for vertical and the rest for horizontal motion. The operation system was composed of an on-board control station and operation S/W. The former included devices such as a 300 VDC power supplier, Fiber-Optic (F/O) to Ethernet communication converter, and main control PC. The latter was developed using an ROS (Robot Operation System) based on Linux. The basic performance of the manufactured robot platform was verified through a water tank test, where the robot was manually operated using a joystick, and the robot motion and attitude variation that resulted from the manipulator movement were closely observed.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.451-463
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• 2022

Whenever a mineshaft accidentally collapses, speedy risk assessment is both required and crucial. But onsite safety diagnosis by humans is reportedly difficult considering the additional risk of collapse of the unstable mineshaft. Generally, drones equipped with high-speed lidar sensors can be used for such inspection. However, the drone technology is restrictively applicable at very shallow depth, failing in mineshafts with depths of hundreds of meters because of the limit of wireless communication and turbulence inside the mineshaft. In previous study, a three-dimensional (3D) scanning system with a single channel lidar was fabricated and operated using towed cable in a mineshaft to a depth of 200 m. The rotation and pendulum movement errors of the measuring unit were compensated for by applying the data of inertial measuring unit and comparing the similarity between the scan data of the adjacent depths (Kim et al., 2020). However, the errors grew with scan depth. In this paper, a multi-channel lidar sensor to obtain a continuous cross-sectional image of the mineshaft from a winch system pulled from bottom upward. In this new approach, within overlapped region viewed by the multi-channel lidar, rotation error was compensated for by comparing the similarity between the scan data at the same depth. The fabricated system was applied to scan 0-165 m depth of the mineshaft with 180 m depth. The reconstructed image was depicted in a 3D graph for interpretation.