• Journal of IKEEE
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• v.14 no.3
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• pp.224-231
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• 2010

Automatic detection of objects in images has been one of core challenges in the areas such as computer vision and pattern analysis. Especially, with the recent deployment of personal mobile devices such as smart phone, such technology is required to be transported to them. Usually, these smart phone users are equipped with devices such as camera, GPS, and gyroscope and provide various services through user-friendly interface. However, the smart phones fail to give excellent performance due to limited system resources. In this paper, we propose a new scheme to improve object recognition performance based on pre-computation and simple local features. In the pre-processing, we first find several representative parts from similar type objects and classify them. In addition, we extract features from each classified part and train them using regression functions. For a given query image, we first find candidate representative parts and compare them with trained information to recognize objects. Through experiments, we have shown that our proposed scheme can achieve resonable performance.

• Journal of Biosystems Engineering
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• v.37 no.6
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• pp.342-351
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• 2012

Purpose: Aerial spraying with an agricultural unmanned helicopter became a new paradigm in the agricultural practice. Laterally tilting behavior of a conventional agricultural helicopter, resulting in the biased down-wash and uneven spray deposit is a physically intrinsic phenomenon while hovering and cruise flights. Authors studied and developed a roll-balanced agricultural helicopter with a raised pylon tail rotor system. In this study, the attitude of the roll-balanced helicopter was determined using the Kalman filter algorithm, and the quality of roll balancing of a bare-airframe helicopter was evaluated. Methods: Instantaneous attitudes were estimated using the advantage of gyroscope, followed by the long term correction and prediction using accelerometer data for the advantage of convergence. The attitudes of the fuselage were calculated by applying the Kalman filter algorithm. The spraying maneuver of the helicopter was performed at a field of 50 m long, and the attitude data were acquired and evaluated. Results: The determination of attitude using the inertial measurement unit(IMU) and Kalman filter was reliable and practical. The intrinsic attitude of the developed helicopter was stable and roll-balanced. The deviation of roll angle was ${\pm}6.3^{\circ}$ with an average of $0^{\circ}$, referring to roll-balanced. Conclusions: Handling quality of the roll attitude determined to be steadily balanced. The balancing behavior of the developed helicopter would result in an even spray pattern during aerial application.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.106-114
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• 2017

In this paper, we propose the method of designing an extended kalman filter in order to accurately measure the position of the spatial-phase system using sensor fusion. We use the quaternion as a state variable in expressing the attitude of an object. Then, the attitude of rigid body can be calculated from the accelerometer and magnetometer by applying the Gauss-Newton method. We estimate the changes of state by using the measurements obtained from the gyroscope, the quaternion, and the vision informations by ARVR_SDK. To increase the accuracy of estimation, we designed and implemented the extended kalman filter, which showed excellent ability to adjust and compensate the sensor error. As a result, we could experimentally demonstrate that the reliability of the attitude estimation value can be significantly increased.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.411-422
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• 2021

The autonomous driving system mounted on the unmanned vehicle recognizes the external environment through several sensors and derives the optimum control value through it. Recently, studies on physical level attacks that maliciously manipulate sensor data by performing signal-injection attacks have been published. signal-injection attacks are performed at the physical level and are difficult to detect at the software level because the sensor measures erroneous data by applying physical manipulations to the surrounding environment. In order to detect a signal-injection attack, it is necessary to verify the dependability of the data measured by the sensor. As so far, various methods have been proposed to attempt physical level attacks against sensors mounted on autonomous driving systems. However, it is still insufficient that methods for defending and detecting the physical level attacks. In this paper, we demonstrate signal-injection attacks targeting MEMS sensors that are widely used in unmanned vehicles, and propose a method to detect the attack. We present a signal-injection detection model to analyze the accuracy of the proposed method, and verify its effectiveness in a laboratory environment.

• PNF and Movement
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• v.20 no.3
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• pp.451-459
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• 2022

Purpose: Temporomandibular disorder (TMD) is a common musculoskeletal problem that causes pain in and disability of masticatory muscles, the temporo-mandibular joint (TMJ), and related structures. The purpose of this study was to compare pressure pain thresholds (PPTs) of masticatory muscles, cervical ranges of motion (ROM), and pelvic mobility during gait of subjects with or without TMD. Methods: In this study, pain thresholds and changes in the mobility of the cervical vertebrae and pelvis were measured in 25 patients with TMD and 25 healthy controls. Using a pressure algometer, the pressure pain thresholds (PPTs) of the masseter and temporalis muscles were measured in both groups. A gyroscope sensor with a mobile application was used to determine cervical ROM in the frontal and sagittal planes. A 3D-motion analysis system was used to evaluate pelvic mobility in the sagittal, frontal, and transverse planes during gait. Results: The TMD group showed significantly decreased PPTs of masseter and temporalis muscles compared with the control group (p < 0.05). Cervical ROM in flexion, extension, and lateral bending were significantly decreased in the TMD group compared with the control group (p < 0.05). In addition, antero-posterior pelvic tilt was significantly decreased in the TMD group (p < 0.05). Conclusion: The results of the current study suggest that there are close anatomical and functional relationships between TMD and muscle chains related to the cervical spine and pelvis. Therefore, more comprehensive body posture assessments, especially of painful areas, should be undertaken when studying TMD patients.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.1
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• pp.36-45
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• 2017

Abstract It is necessary to verify the properties of an inertial measurement unit in the flight environment before applying to military applications. In this paper, we presented a new approach to verify an inertial measurement unit(IMU) in regard to the performance and the robustness in flight environments for the high-dynamics vehicle systems. We proposed two methods for verification of an IMU. We confirmed normal operation of an IMU and properties in flight environment by using direct comparison method. And we proposed real time multi-navigation system to complement the first method. The proposed method made it possible to compare navigation result at the same time. Therefore, it is easy to analyze the performance of an inertial navigation system and robustness during the vehicle flight. To verify the proposed method, we carried out a flight test as well as an experimental test of flight vibration on the ground. As a result of the experiment, we confirmed flight environment properties of an IMU. Therefore, we shows that the proposed method can serve the reliability improvement of IMU.

• Spatial Information Research
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• v.19 no.5
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• pp.87-96
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• 2011

Recently mobile users can access to internet using smart phone at any place and any time, through which they can search and share information. In addition, as the sensors with high-tech functions become cheaper and miniaturized along with the development of MEMS (micro-electo mechanical systems) technology, the extent to utilize smart phone is increasing. Smart phone is equipped with various sensors such as high-resolution camera, GPS, gyroscope and magnetic sensor, which is an appropriate system configuration for remote monitoring research using camera. The remote monitoring system requires camera for video and internet network to send video, for which it has a limitation that it is influenced by the monitoring location. This study is aimed to design and develop the monitoring app. which can be remotely monitored using smart phone technology. The developed monitoring app was designed to take images of ROI (region of interest) within the specified time and to automatically send the images to the server. The developed app. is also possible to be remotely controlled by SMS (short message service). The monitoring proposed in this study can take high-resolution images using CMOS built in the smart phone and send the images and information to the server automatically at any place and any time using 3G and Wi-Fi networks.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.68-77
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• 2021

In this paper, a GPS/MEMS IMU integrated navigation receiver module capable of operating in a high dynamic environment is designed and fabricated, and the results is confirmed. The designed module is composed of RF receiver unit, inertial measurement unit, signal processing unit, correlator, and navigation S/W. The RF receiver performs the functions of low noise amplification, frequency conversion, filtering, and automatic gain control. The inertial measurement unit collects measurement data from a MEMS class IMU applied with a 3-axis gyroscope, accelerometer, and geomagnetic sensor. In addition, it provides an interface to transmit to the navigation S/W. The signal processing unit and the correlator is implemented with FPGA logic to perform filtering and corrrelation value calculation. Navigation S/W is implemented using the internal CPU of the FPGA. The size of the manufactured module is 95.0×85.0×.12.5mm, the weight is 110g, and the navigation accuracy performance within the specification is confirmed in an environment of 1200m/s and acceleration of 10g.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.167-176
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• 2022

We designed a borehole deviation survey tool applicable for steel-cased holes, K-DEV, and developed a prototype for a depth of 500 m aiming to development of own equipment required to secure deep subsurface characterization technologies. K-DEV is equipped with sensors that provide digital output with verified high performance; moreover, it is also compatible with logging winch systems used in Korea. The K-DEV prototype has a nonmagnetic stainless steel housing with an outer diameter of 48.3 mm, which has been tested in the laboratory for water resistance up to 20 MPa and for durability by running into a 1-km deep borehole. We confirmed the operational stability and data repeatability of the prototype by constantly logging up and down to the depth of 600 m. A high-precision micro-electro-mechanical system (MEMS) gyroscope was used for the K-DEV prototype as the gyro sensor, which is crucial for azimuth determination in cased holes. Additionally, we devised an accurate trajectory survey algorithm by employing Unscented Kalman filtering and data fusion for optimization. The borehole test with K-DEV and a commercial logging tool produced sufficiently similar results. Furthermore, the issue of error accumulation due to drift over time of the MEMS gyro was successfully overcome by compensating with stationary measurements for the same attitude at the wellhead before and after logging, as demonstrated by the nearly identical result to the open hole. We believe that the methodology of K-DEV development and operational stability, as well as the data reliability of the prototype, were confirmed through these test applications.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.395-401
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• 2018

Recently, most of mobile devices are equipped with GNSS (Global Navigation Satellite System). When the GNSS signal is available, it is easy to obtain position information. However, GNSS is not suitable solution for indoor localization, since the signals are normally not reachable inside buildings. A wide varieties of technology have been developed as a solution for indoor localization such as Wi-Fi, beacons, and inertial sensor. With the increased sensor combinations in mobile devices, mobile devices also became feasible to provide a solution, which based on PDR (Pedestrian Dead Reckoning) method. In this study, we utilized the combination of three sensors equipped in mobile devices including accelerometer, digital compass, and gyroscope and applied three representative PDR methods. The proposed methods are done in three stages; step detection, step length estimation, and heading determination and the final indoor localization result was evaluated with terrestrial LiDAR (Light Detection And Ranging) data obtained in the same test site. By using terrestrial LiDAR data as reference ground truth for PDR in two differently designed experiments, the inaccuracy of PDR methods that could not be found by existing evaluation method could be revealed. The firstexperiment included extreme direction change and combined with similar pace size. Second experiment included smooth direction change and irregular step length. In using existing evaluation method which only checks traveled distance, The results of two experiments showed the mean percentage error of traveled distance estimation resulted from three different algorithms ranging from 0.028 % to 2.825% in the first experiment and 0.035% to 2.282% in second experiment, which makes it to be seen accurately estimated. However, by using the evaluation method utilizing terrestrial LiDAR data, the performance of PDR methods emerged to be inaccurate. In the firstexperiment, the RMSEs (Root Mean Square Errors) of x direction and y direction were 0.48 m and 0.41 m with combination of the best available algorithm. However, the RMSEs of x direction and y direction were 1.29 m and 3.13 m in the second experiment. The new evaluation result reveals that the PDR methods were not effective enough to find out exact pedestrian position information opposed to the result from existing evaluation method.