• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.2
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• pp.60-67
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• 2015

The finite element (FE) model updating is a commonly used approach in civil engineering, enabling damage detection, design verification, and load capacity identification. In the FE model updating, acceleration responses are generally employed to determine modal properties of a structure, which are subsequently used to update the initial FE model. While the acceleration-based model updating has been successful in finding better approximations of the physical systems including material and sectional properties, the boundary conditions have been considered yet to be difficult to accurately estimate as the acceleration responses only correspond to translational degree-of-freedoms (DOF). Recent advancement in the sensor technology has enabled low-cost, high-precision gyroscopes that can be adopted in the FE model updating to provide angular information of a structure. This study proposes a FE model updating strategy based on data fusion of acceleration and angular velocity. The usage of both acceleration and angular velocity gives richer information than the sole use of acceleration, allowing the enhanced performance particularly in determining the boundary conditions. A numerical simulation on a simply supported beam is presented to demonstrate the proposed FE model updating approach.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1523-1535
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• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.99-113
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• 2020

In the future, robots and drones for the convenience of our lives in everyday life will increase. As a method for controlling this, a remote control or a human voice method is most commonly used. However, the remote control needs to be operated by a person and can not ignore ambient noise in the case of voice. In this paper, we propose an economical attitude information acquisition method to accurately acquire the posture information of the arm in real time under the assumption that the surround drones or robots can be controlled wirelessly with the posture information of the arm. For this purpose, the extended Kalman filter was used to eliminate the noise of the arm position information. in order to detect the arm movement, a low cost MEMS type sensor was applied to secure the economical efficiency of the apparatus. To increase the wear ability of the arm, We developed a compact and lightweight attitude information acquisition system by integrating all functions into one chip as much as possible. As a result, the real-time performance of 1 ms was secured and the extended Kalman filter was applied to acquire the accurate attitude information of the arm with noise removed and display the attitude information of the arm in real time. This provides a basis for generating commands using real-time attitude information of the arm.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.441-448
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• 2010

In the aging society, the walking assist robot is a necessary device for being able to help the older and the lower limb disabled people to walk. In order to produce a convenient robot for the older and the lower limb disabled, it is needed for the research to detect the implicit walking intention and to control robot by a user's intention. This study is a previous study to develop the detection model of the walking intention and analyze the user's walking intention while a person is walking with Lofstrand crutches, by the combination of FSR and tilt signals. The FSR sensors attached user's the palm and the soles of foot are sensing force/pressure signals from these areas and are used for detecting the walking intention and states. The tilt sensor acquires roll and pitch signal from area of vertebrae lumbales and reflects the pose of the upper limb. We can recognize the user's walking intention such as 'start walking', 'start of right or left foot forward', and 'stop walking' by the combination of FSR and tilt signals can recognize.

• Journal of Sensor Science and Technology
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• v.7 no.3
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• pp.147-153
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• 1998

The strain characteristics of a fiber optic Fabry-Perot pressure sensor with high sensitivity using a $Si_{3}N_{4}/SiO_{2}/Si_{3}N_{4}$ (N/O/N) diaphragm is experimentally investigated. A 600 nm thick N/O/N diaphragm was fabricated by silicon anisotropic etching technology in 44 wt% KOH solution. An interferometric fiber optic pressure sensor has been manufactured by using a fiber optic Fabry-Perot intereferometer and a N/O/N diaphragm. The 2 cm length fiber optic Fabry-Perot interferometers in the continuous length of single mode fiber were produced with two pieces of single mode fiber coated with $TiO_{2}$ dielectric film utilizing the fusion splicing technique. The one end of the fiber optic Fabry-Perot interferometer was bonded to a N/O/N diaphragm. and the other end was connected to an optical setup through a 3 dB coupler. For the N/O/N diaphragm sized $2{\times}2\;mm^{2}$ and $8{\times}8\;mm^{2}$, the pressure sensitivity was measured 0.11 rad/kPa and 1.57 rad/kPa, respectively, and both of the nonlinearities were less than 0.2% FS.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.49-54
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• 2023

The Global Positioning System (GPS) was developed for military purposes and developed as it is today by opening civilian signals (GPS L1 frequency C/A signals). The current satellite orbits the earth about twice a day to measure the position, and receives more than 3 satellite signals (initially, 4 to calculate even the time error). The three-dimensional position of the ground receiver is determined using the data from the radio wave departure time to the radio wave Time of Arrival(TOA) of the received satellite signal through trilateration. In the case of navigation using GPS in recent years, a location error of 5 to 10 m usually occurs, and quite a lot of areas, such as apartments, indoors, tunnels, factory areas, and mountainous areas, exist as blind spots or neutralized areas outside the error range of GPS. Therefore, in order to acquire one's own location information in an area where GPS satellite signal reception is impossible, another method should be proposed. In this study, IMU(Inertial Measurement Unit) combined with an acceleration and gyro sensor and a geomagnetic sensor were used to design a system to enable location recognition even in terrain where GPS signal reception is impossible. A method to track the current position by calculating the instantaneous velocity value using a 9-DOF IMU and a geomagnetic sensor was studied, and its feasibility was verified through production and experimentation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.76-81
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• 2024

Recently, agricultural methods are changing from experience-based agriculture to data-based agriculture. Changes in agricultural production due to the 4th Industrial Revolution are largely occurring in three areas: smart sensing and monitoring, smart analysis and planning, and smart control. In order to realize open-field smart agriculture, information on the physical and chemical properties of soil is essential. Conventional physicochemical measurements are conducted in a laboratory after collecting samples, which consumes a lot of cost, labor, and time, so they are quickly measured in the field. Measurement technology that can do this is urgently needed. In addition, a soil analysis system that can be carried and moved by the measurer and used in Korea's rice fields, fields, and facility houses is needed. To solve this problem, our goal is to develop and commercialize software that can collect soil samples and analyze the information. In this study, basic soil composition measurement was conducted using soil composition measurement sensors consisting of hardness measurement and electrode sensors. Through future research, we plan to develop a system that applies soil sampling using a CCD camera, ultrasonic sensor, and sampler. Therefore, we implemented a sensor and soil analysis UI that can measure and analyze the soil condition in real time, such as hardness measurement display using a load cell and moisture, PH, and EC measurement display using conductivity.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.833-839
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• 2013

In this study, an experiment was performed to monitor the two-dimensional shape of a cantilever composite structure using fiber Bragg grating (FBG) sensors. To monitor the shape of a composite structure, a deflection equation developed by NASA was applied and a composite beam attached to three FBG sensors was used. In the experiment, the shape of the composite beam was successfully estimated and an error was evaluated by comparing a real deflection. The error increased with real deflection; therefore, it was compensated by using the linear relationship between the error and the real deflection. After compensating the error, the measured deflection shows good agreement with the real deflection. Finally, the experiment shows that the FBG sensor and the deflection equation are suitable for monitoring the deflection curve of the beam structure with compensation of the error.

• Smart Structures and Systems
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• v.15 no.2
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• pp.489-503
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• 2015

Response estimation at unmeasured locations using the limited number of measurements is an attractive topic in the field of structural health monitoring (SHM). Because of increasing complexity and size of civil engineering structures, measuring all structural responses from the entire body is intractable for the SHM purpose; the response estimation can be an effective and practical alternative. This paper investigates a response estimation technique based on the Kalman state estimator to combine multi-sensor data under non-zero mean input excitations. The Kalman state estimator, constructed based on the finite element (FE) model of a structure, can efficiently fuse different types of data of acceleration, strain, and tilt responses, minimizing the intrinsic measurement noise. This study focuses on the effects of (a) FE model error and (b) combinations of multi-sensor data on the estimation accuracy in the case of non-zero mean input excitations. The FE model error is purposefully introduced for more realistic performance evaluation of the response estimation using the Kalman state estimator. In addition, four types of measurement combinations are explored in the response estimation: strain only, acceleration only, acceleration and strain, and acceleration and tilt. The performance of the response estimation approach is verified by numerical and experimental tests on a simply-supported beam, showing that it can successfully estimate strain responses at unmeasured locations with the highest performance in the combination of acceleration and tilt.