• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.297-310
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• 2022

Road facilities such as CCTV poles have potential risk of collision accidents with a car. A collision detection algorithm installed in the facility allows the collision accident to be known remotely. Most collision detection algorithms are operated by simply focusing on whether a collision have occurred, because these methods are used to measure only acceleration data from a 3-axis sensor to detect collision. However, it is difficult to detect other detailed information such as malfunction of the sensor, collision direction and collision strength, because it is not known without witness the accident. Therefore, we proposed enhanced detection algorithm to get the collision direction, and the collision strength from the tilt of the facility after accident using a 9-axis sensor in this paper. In order to confirm the performance of the algorithm, an accuracy evaluation experiment was conducted according to the data measurement cycle and the invocation cycle to an detection algorithm. As a result, the proposed enhanced algorithm confirmed 100% accuracy for 50 weak collisions and 50 strong collisions at the 9-axis data measurement cycle of 10ms and the invocation cycle of 1,000ms. In conclusion, the algorithm proposed is expected to provide more reliable and detailed information than existing algorithm.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.71-75
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• 2019

Sleep apnea is a disease that causes various complications, and the polysomnography is expensive and difficult to measure. The purpose of this study is to develop an unrestricted wearable monitoring system so that patients can be examined in a familiar environment. We used a method to detect sleep apnea events and to determine sleep satisfaction by non-constrained method using SpO2 measurement sensor and 3-axis acceleration sensor. Heart rate and SpO2 were measured at the finger using max30100. After acquiring the SpO2 data of the user in real time, the apnea measurement algorithm was used to transmit the number of apnea events of the user to the mobile phone using Bluetooth (HC-06) on the wrist. Using the three-axis acceleration sensor (mpu6050) attached to the upper body, the number of times of tossing and turning during sleep was measured. Based on this data, this algorithm evaluates the patient's tossing and turning during sleep and transmits the data to the mobile phone via Bluetooth. The power source used 9 volts battery to operate Arduino UNO and sensors for portability and stability, and the data received from each sensor can be used to check the various degree between sleep apnea and sleep tossing and turning on the mobile phone. Through thisstudy, we have developed a wearable sleep apnea measurement system that can be easily used at home for the problem of low sleep efficiency of sleep apnea patients.

• Journal of Biosystems Engineering
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• v.40 no.1
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• pp.28-34
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• 2015

Purpose: This study aimed to install an RTK-GPS (Real Time Kinematic-Global Positioning System) and IMU (Inertial Measurement Unit) on a tractor used in a farm to measure positions, pasture topography, posture angles, and vibration accelerations, translate the information into maps using the GIS, analyze the characteristics of grass harvesting work, and establish new technologies and construction standards for pasture infrastructure improvement based on the analyzed data. Method: Tractor's roll, pitch, and yaw angles and vibration accelerations along the three axes during grass harvesting were measured and a GIS map prepared from the data. A VRS/RTK-GPS (MS750, Trimble, USA) tractor position measuring system and an IMU (JCS-7401A, JAE, JAPAN) tractor vibration acceleration measuring systems were mounted on top of a tractor and below the operator's seat to obtain acceleration in the direction of progression, transverse acceleration, and vertical acceleration at 10Hz. In addition, information on regions with bad workability was obtained from an operator performing grass harvesting and compared with information on changes in tractor posture angles and vibration acceleration. Results: Roll and pitch angles based on the y-axis, the direction of forward movements of tractor coordinate systems, changed by at least $9-13^{\circ}$ and $8-11^{\circ}$ respectively, leading to changes in working postures in the central and northern parts of the pasture that were designated as regions with bad workability during grass harvesting. These changes were larger than those in other regions. The synthesized vectors of the vibration accelerations along the y-axis, the x-axis (transverse direction), and the z-axis (vertical direction) were higher in the central and northwestern parts of the pasture at 3.0-4.5 m/s2 compared with other regions. Conclusions: The GIS map developed using information on posture angles and vibration accelerations by position in the pasture is considered sufficiently utilizable as data for selection of construction locations for pasture infrastructure improvement.

• 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, $9.5pm/^{\circ}C$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1746-1754
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• 2016

In this paper, we provide a development and evaluation method for an archery posture analyzing system, using an inertial sensor. The system was developed using LabVIEW2014 by National Instruments and evaluated using the DTW algorithm. To convert the voltage value of the inertial sensor into a physical value, a coordinate transformation matrix bias was applied. To evaluate the similarity of movement in archery shooting, the DTW distance was calculated and similarity was confirmed based on simple mechanical movement, the same person's shooting movement, shooting movement with another person, and the noise signal. The average similarity comparison results were as follows: simple mechanical movement was 17.05%, the same person's shooting movement was 26.48%, shooting movement with another person was 62.8%, and the noise signal was 328.5%; a smaller value indicates a higher level of similarity. We confirmed the possibility of analyzing the archery posture using 3-axis acceleration of the inertial sensor. We inferred that the proposed method might be important means for assessing shooting skills, evaluation of archer's progress, and finding talented archers in advance.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.190-196
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• 2015

In this paper, we propose multiple location error compensation algorithm for GPS/INS fusion using kalman filter and introduce the way to reduce location error in 9-axis navigation devices for implementing inertial navigation technique. When evaluating location, there is an increase of location error. So navigation systems need robust algorithms to compensate location error in GPS/INS fusion. In order to improve robustness of 9-axis inertial sensor(mpu-9150) over its disturbance, we used tilt compensation method using compensation algorithm of acceleration sensor and Yaw angle compensation to have exact azimuth information of the object. And it shows improved location result using these methods combined with kalman filter.

• 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 Internet Computing and Services
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• v.18 no.3
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• pp.11-17
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• 2017

To the elderly, sudden falls are one of the most frightening accidents. If an accident occurs, a prompt action has to be taken to deal with the situation. Recently, there have been a number of attempts to detect sudden falls using acceleration sensors embedded in the mobile devices, such as smart phones and wrist-bands. However, using the sensor readings only, the detection rate of the falls is around 65%. Ordinary daily activities such as running or jumping could not be well distinguished from the falls. In this paper, we describe our attempts on improving the fall-detection rate. We implemented a wrist-band fall detection module, using a three-axis acceleration sensor. With the pattern matching on the fall signal-strength frequency spectrum, in addition to the conventional signal strength measurement, we could improve the detection rate by 9% point. Furthermore, by applying two wrist-bands in the experiment, we could further improve the detection rate to 82%.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.818-826
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• 2015

This paper presents a novel methodology for estimating vehicle ego-motion, i.e. tri-axis linear velocities and angular velocities by using stereo vision sensor and 2G1Y sensor (longitudinal acceleration, lateral acceleration, and yaw rate). The estimated ego-motion information can be utilized to predict future ego-path and improve the accuracy of 3D coordinate of obstacle by compensating for disturbance from vehicle movement representatively for collision avoidance system. For the purpose of incorporating vehicle dynamic characteristics into ego-motion estimation, the state evolution model of Kalman filter has been augmented with lateral vehicle dynamics and the vanishing point estimation has been also taken into account because the optical flow radiates from a vanishing point which might be varied due to vehicle pitch motion. Experimental results based on real-world data have shown the effectiveness of the proposed methodology in view of accuracy.

• 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.