• Journal of Sensor Science and Technology
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• v.8 no.2
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• pp.139-147
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• 1999

In this paper, we present the new angular velocity pick-off method for DTG (dynamically tuned gyroscope) which is widely used in various inertial navigation systems and motion control systems. In case of the external angular velocity input, the proposed scheme can make a smaller tilt-angle rather than that of conventional PI method in the transient and steady state because it has an additional inner rebalance loop with a mathematical model of the real gyroscope. So, without any mechanical redesign of the DTG, its dynamic range can be enlarged by the proposed method. The theoretical analysis and simulation model of DTG with the proposed scheme are given. Finally, the proposed scheme is verified.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.117-125
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• 2015

In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this study, we collected users' postures and judged whether they are normal or abnormal. To obtain a user's posture, we propose a four-joint motion capture system that uses inertial sensors. The system collects the subject's postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints' rotation angles and positions; the normal posture judgment reached a success rate of 99.79%. This result suggests that the features of the four joints can be used to judge and help correct a user's posture through application to a spinal disease prevention system in the future.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.255-260
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• 2006

Safety-critical navigation systems have to provide 'reliable' position solutions, i.e., they must detect and exclude measurement or system faults and estimate the uncertainty of the solution. To obtain more accurate and reliable navigation systems, various filtering methods have been employed to reduce measurement noise level, or integrate sensors, such as global navigation satellite system/inertial navigation system (GNSS/INS) integration. Recently, particle filters have attracted attention, because they can deal with nonlinear/non-Gaussian systems. In most GNSS applications, the GNSS measurement noise is assumed to follow a Gaussian distribution, but this is not true. Therefore, we have proposed a fault detection and exclusion method using particle filters assuming non-Gaussian measurement noise. The performance of our method was contrasted with that of conventional Kalman filter methods with an assumed Gaussian noise. Since the Kalman filters presume that measurement noise follows a Gaussian distribution, they used an overbounded standard deviation to represent the measurement noise distribution, and since the overbound standard deviations were too conservative compared to the actual distributions, this degraded the integrity-monitoring performance of the filters. A simulation was performed to show the improvement in performance of our proposed particle filter method by not using the sigma overbounding. The results show that our method could detect smaller measurement biases and reduced the protection level by 30% versus the Kalman filter method based on an overbound sigma, which motivates us to use an actual noise model instead of the overbounding or improve the overbounding methods.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.15-21
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• 2015

In this paper, a behavior notification system of the harness of a guide dog is implemented for a blind person to get helps for environmental and situational awareness while walking with the guide dog. IMU modules is attached on the guide dog's harness saddle and the acceleration sensor belt is mounted on its thigh. Gait estimation and behavior judgement are performed by recording and analyzing the outputs of the sensors. Performance analysis for seven different kinds of behaviors has been done. The seven different behaviors, which the guide dog recognizes, are descending stairs, climbing stairs, uphill, downhill, stop, flat road, and selective disobedience. Results for the performance analysis show that the average success rate of the behavior rule estimation of harness of the guide dog is 92.78% and the behavior notification system can be effectively used in real situations.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.4
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• pp.8-16
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• 2009

An experimental study was carried out to investigate aerodynamic characteristics on reduced frequency of flapping wings. The half span of the wing is 28cm, and the mean chord length of wing is 10cm. In flight, the Reynolds Number range of birds is about $10^4$, and the reduced frequency during a level flight is 0.25. The experimental variables of present study were set to have similar conditions with the bird flight's one. The freestream velocities in a wind tunnel were 2.50, 3.75 and $5.00^m/s$, and the corresponding Reynolds numbers were $1.7{\times}10^4$, $2.5{\times}10^4$ and $3.3{\times}10^4$, respectively. The wing beat frequencies of an experimental model were 2, 3 and 4Hz, and the corresponding reduced frequency was decided between 0.1 and 0.5. Aerodynamic forces of an experimental flapping model were measured by using 2 axis load-cell. Inertial forces measured in a vacuum chamber were removed from measuring forces in the wind tunnel in order to acquire pure aerodynamic forces. Hall sensors and laser trigger were used to make sure the exact position of wings during the flapping motion. Results show that the ratio of downstroke in a wing beat cycle is increased as a wing beat frequency increases. The instantaneous lift coefficient is the maximum value at the end of downstroke of flapping wing model. It is found that a critical reduced frequency with large lift coefficient is existed near k=0.25.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.153-164
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• 2008

According to the economic growth, many highways are constructed for increasing need of better life style. Especially roads and roadside facilities are used for accident prevention and offering mobility for drivers. For these purpose, roads and roadside facilities should be well maintained and managed. Now, many roads and roadside facilities are constructed in many areas. Because of traditional surveying method requires much time and surveying efforts, we designed and developed mobile mapping system for highway maintenance and management purpose using multi sensors. We tested our mobile mapping system and data management process. Using developed database, road managers can easily check the information of facility conditions, positions, and attributes. We are expecting low cost and efficient road maintenance process by using our system.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1349-1359
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• 2021

Acquisition of precise position and velocity information of GNSS-INS (Global Navigation Satellite System; Inertial Navigation System) sensors in obtaining SAR SLC (Single Look Complex) images from raw data using BPA (Backprojection Algorithm) was regarded decisive. Several studies on BPA were accompanied by Kalman Filter for sensor noise oppression, but often implemented once where insufficient information was given to determine whether the filtering was effectively applied. Multiple operation of Kalman Filter on GNSS-INS sensor was presented in order to assess the effective order of sensor noise calibration. FMCW (Frequency Modulated Continuous Wave)-SAR raw data was collected from twice airborne experiments whose GNSS-INS information was practically and repeatedly filtered via Kalman Filter. It was driven that the FMCW-SAR raw data with diverse path information could derive different order of Kalman Filter with optimum operation of BPA image restoration.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.1
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• pp.13-21
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• 2018

AGVS (Automated Guided Vehicle System) is a core technology of logistics automation which automatically moves specific objects or goods within a certain work space. Conventional AGVS generally requires the in-door localization system and each AGV equips expensive sensors such as laser, magnetic, inertial sensors for the route recognition and automatic navigation. thus the high installation cost is inevitable and there are many restrictions on route(path) modification or expansion. To address this issue, in this paper, we propose a cost-effective and scalable AGV based on a light-weight pattern recognition technique. The proposed pattern recognition technology not only enables autonomous driving by recognizing the route(path), but also provides a technique for figuring out the loc ation of AGV itself by recognizing the simple patterns(bar-code like) installed on the route. This significantly reduces the cost of implementing AGVS as well as benefiting from route modification and expansion. In order to verify the effectiveness of the proposed technique, we first implement a pattern recognition algorithm on a light-weight MCU(Micro Control Unit), and then verify the results by implementing an MCU_controlled AGV prototype.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.365-372
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• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

• Science of Emotion and Sensibility
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• v.27 no.1
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• pp.59-68
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• 2024

This study aimed to develop a soft fabric-based elbow-bending angle sensor that can replace conventional hard-type inertial sensors and a system for estimating bending angles using it. To enhance comfort during exercise, this study treated four fabrics (Bergamo, E-band, span cushion, and polyester) by single-walled carbon nanotube dip coating to create conductive textiles. Subsequently, one fabric was selected based on performance evaluations, and an elbow flexion angle sensor was fabricated. Gauge factor, hysteresis, and sensing range were employed as performance evaluation metrics. The data obtained using the fabricated sensor showed different trends in sensor values for the changes in the angle during bending and extending movements. Because of this divergence, the two movements were separated, and this constituted the one-step process. In the two-step process, multilayer perceptron (MLP) was employed to handle the complex nonlinear relationships and achieve high data accuracy. Based on the results of this study, we anticipate effective utilization in various smart wearable and healthcare domains. Consequently, a soft- fabric bending angle sensor was developed, and using MLP, nonlinear relationships can be addressed, enabling angle estimation. Based on the results of this study, we anticipate the effective utilization of the developed system in smart wearables and healthcare.