• Journal of Korean Association for Spatial Structures
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• v.16 no.1
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• pp.85-94
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• 2016

In this study, an acceleration sensor that has optical fibers to measure the inclination and acceleration of a structure through contradictory changes in two-component FBG sensors was examined. The proposed method was to ensure precise measurement through the unification of the deformation rate sensor and the angular displacement sensor. A high sensitivity three-axis accelerometer was designed and prepared using this method. To verify the accuracy of the accelerometer, the change in wavelength according to temperature and tension was tested. Then, the change in wavelength of the prepared accelerometer according to the sensor angle, and that of the sensor according to the change in ambient temperature were measured. According to the test results on the FBG-based vibration sensor that was developed using a high-speed vibrator, the range in measurement was 0.7 g or more, wavelength sensitivity, 2150 pm/g or more, and the change in wavelength change, $9.5pm/^{\circ}C$.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1779-1785
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• 2018

In this paper, we propose an error correction method to improve the accuracy of human activity recognition using sensor event data obtained by smart devices such as wearable and smartphone. In the context awareness through the smart device, errors inevitably occur in sensing the necessary context information due to the characteristics of the device, which degrades the prediction performance. In order to solve this problem, we apply Kalman filter's error correction algorithm to compensate the signal values obtained from 3-axis acceleration sensor of smart device. As a result, it was possible to effectively eliminate the error generated in the process of the data which is detected and reported by the 3-axis acceleration sensor constituting the time series data through the Kalman filter. It is expected that this research will improve the performance of the real-time context-aware system to be developed in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1224-1230
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• 2020

Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.914-921
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• 2014

In this paper, an algorithm is suggested to detect an offset angle of the absolute rotor position sensor after the initial assembly of a PMSG. Unlike previous studies in a stationary state, this one is not designed to detect an electrical angle but rather the absolute position of the rotor is detected while operating the generator. Also,a position sensor, current sensors and voltage sensor were used to ensure reliability. This technique completes the detection of the sensor offset in two steps. In the first step, a zero-crossing of the EMF is measured using a voltage sensor to detect the electrical angle offset when the alternator is actuated by the engine. In the second step, a high frequency current is injected along the d-axis on-line during the control of the generation, eventually to obtain the inductance using a DFT (Discrete Fourier Transform), and then to ultimately extract the final electrical angle offset through the comparison of the inductance magnitude. The suggested algorithm was validated with PSIM simulation and, furthermore, was tested with actual experiments on a dynamometer.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.7
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• pp.659-666
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• 2016

Magnetic disturbance is one of the main factors that deteriorate the accuracy of orientation estimation methods based on inertial/magnetic sensor signals. This paper proposes a parallel Kalman filter(KF) that explicitly detects magnetic disturbances and thus can accurately estimate 3D orientation in magnetically disturbed environments. Due to the parallel nature of the proposed KF, even severe magnetic disturbances only affect yaw estimation, while roll and pitch values remain accurate. Consequently, the proposed KF can be effectively used in various applications that involve magnetically inhomogeneous environments, such as robots, ships, and planes.

• Journal of Platform Technology
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• v.6 no.2
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• pp.3-9
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• 2018

Recently VR environment is used in many areas including mobile learning, smart factory. However HMD(head-mounted display) is required to a dedicated and expensive system with high-end specification. When designing a VR system, it is needed to handle performance, mobility and usability. Many VR applications need to handle diverse sensors and user inputs continuously in a streaming manner. In this paper we design a VR mobile platform and implement a low-cost mobile VR HMD running on the platform. The VR HMD supports 3D contents delivery in a mobile manner. It is used to detect the motion detection based on angle value of a VR player from accelerator and gyro sensor. The MPU-6050, 6-axis sensor, is used to get a sensory value and the sensory value is taken as an input to a VR rendering server on a Unity game engine that is generated 3D images.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.4
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• pp.9-15
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• 2012

This paper presents application of a new tactile slippage sensation algorithm in robot hand control system. The optical three-axis tactile sensor is a type of tactile sensor capable of defining normal and shear forces simultaneously. The tactile sensor is mounted on fingertip of robotic hand. Shear force distribution is used to define slippage sensation in the robot hand system. Based on tactile slippage analysis, a new control algorithm was proposed. To improve performance during object handling motions, analysis of slippage direction is conducted. The control algorithm is classified into two phases: grasp-move-release and grasp-twist motions. Detailed explanations of the control algorithm based on the existing robot arm control system are presented. The experiment is conducted using a bottle cap, and the results reveal good performance of the proposed control algorithm to accomplish the proposed object handling motions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.3-9
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• 2006

Among many fabrication methods of composite materials, filament winding is the most effective method for fabricating axis-symmetric structures such as pressure tanks and pipes. Filament wound pressure tanks are under high internal pressure during the operation and it has the complexity in damage mechanisms and failure modes. Fiber optic sensors, especially FBG sensors can be easily embedded into the composite structures contrary to conventional electric strain gages (ESGs). In addition, many FBG sensors can be multiplexed in single optical fiber using wavelength division multiplexing (WDM) techniques. In this paper, we fabricated several filament wound pressure tanks with embedded FBG sensors and conducted some kinds of experiments such as an impact test, a bending test, and a thermal cycling test. From the experimental results, it was successfully demonstrated that FBG sensors are very appropriate to composite structures fabricated by filament winding process even though they are embedded into composites by multiplexing.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.285-286
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• 2014

세계적으로 지진, 해일, 홍수 등의 자연 재해와 화재, 테러 등의 인위적 재해가 빈번하게 발생하고 있다. 이러한 다양하고 예측 불가능한 재해로 인해 다수의 사상자가 발생하고 있다. 재해 발생 시 빠른 시간에 재해 발생 여부를 파악하여 재해 발생 위치와 적절한 피난 경로 등의 정보를 피해자에게 제공한다면 신속히 안전한 장소로 대피가 가능하여 피해를 최소화 할 수 있다. 본 논문에서는 재해 발생 시 피난 지원을 목적으로 연구하는 긴급피난지원시스템(ERESS)을 효과적으로 지원할 수 있도록 비상 시 긴급피난지원시스템을 위한 9축 센서 기반 행동 분석 시스템을 구현하였다.