• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2329-2331
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• 2001

In this paper, were developed the 3 axes attitude control sensor system to control and monitoring the moving object. The proposed sensor system has been studied in Japan, America for a year ago. But it is high expensive and has a difficulty in application. To overcome these problems, proposed the 3 axes attitude control sensor system is low cost and easily applied. Proposed sensor system is equipped with the 3 gyro sensors, 2 tilt sensors and 3 MR sensors using 80C51 microprocessor for signal processing. It's output value transmit at long distance using RS232 serial communication protocol. We expect this system shall have a good performances in many applications of control and monitoring the moving object.

• Journal of Advanced Navigation Technology
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• v.11 no.2
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• pp.209-216
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• 2007

In this paper, we embodied posture-stabilization and dynamic gait in a walking robot. 10 RC servo motors are used to operate joints. And the joints have enough moving ranges suitable in any walking pattern. Each joint trajectory is generated by cubic spline interpolation method and the stability of the trajectory is verified by using Zero Moment Point from the robot modeling. To avoid complex structure and expression, Zero Moment Point of the biped robot used angular acceleration is suggested. To measure the stability of the biped robot, Tilt sensor and gyro sensor are used. Finally, Personal Computer is used computer monitoring and data processing. Most of computation, such as 10 RC servo motor control, joint trajectory generating, ZMP compensation, sense measuring, etc, was used Digital Signal Processor.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.213-220
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• 2023

Falling occurs unexpectedly during daily activities, causing many difficulties in life. The purpose of this study was to establish a system for fall detection of high-risk occupations and to verify their effectiveness by collecting data and applying it to predictive models. To this end, a wearable device was configured to detect fall by calculating acceleration signals and azimuths through acceleration sensors and gyro sensors. In addition, the study participants wore the device on their abdomen and measured necessary data from falls-related movements in the process of performing predetermined activities and transmitted it to the computer through a Bluetooth device present in the device. The collected data was processed through filtering, applied to fall detection prediction models based on deep learning algorithms which are 1D CNN, LSTM and CNN-LSTM, and evaluate the results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.311-317
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• 2014

Buoy has different motion characteristics depends on the sea weather situations. The motion characteristics has an impact on antenna, solar power generation system and etc. installed within a buoy. Therefore, it is important to analyse motion characteristics for management and analyse the buoy conditions. This paper's Buoy motion monitoring system uses gyro sensor to detect motions of a light buoy, and the measured data transfers to the PC on the shore using signal processing algorithm. The aim of this research is to develop monitoring and management mechanism of a buoy by applying motion monitoring system. In this paper, the operation characteristic of movement monitoring system is verified through experiment. Further, in this paper, it can apply such as real-time visibility into the status of the buoy or many ocean facility's motion estimation of the future.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.20-29
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• 2021

This study developed a smartwatch and a wind speed system in accordance with the necessity of a device that provides the information required in marine leisure. Based on a marine leisure smartwatch with a multi-sensor, a gyro box, and a wind speed system, external data such as GPS, motion, humidity, temperature, air pressure, and heart rate can be collected. In addition, the collected external environment data can be managed through an application on a smartphone, which is an Android-based mobile device. The developed smartwatch and wind speed system are expected to contribute to increasing accessibility and revitalization of the marine leisure industry. In addition, in terms of safety and education, the need for a device that provides marine information is large, so it is expected to increase the possibility of entering the high value-added market and improve the product localization rate.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.234-240
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• 2022

This paper proposes a customized AI exercise recommendation service for balancing the relative amount of exercise according to the working environment by each occupation. WISDM database is collected by using acceleration and gyro sensors, and is a dataset that classifies physical activities into 18 categories. Our system recommends a adaptive exercise using the analyzed activity type after classifying 18 physical activities into 3 physical activities types such as whole body, upper body and lower body. 1 Dimensional convolutional neural network is used for classifying a physical activity in this paper. Proposed model is composed of a convolution blocks in which 1D convolution layers with a various sized kernel are connected in parallel. Convolution blocks can extract a detailed local features of input pattern effectively that can be extracted from deep neural network models, as applying multi 1D convolution layers to input pattern. To evaluate performance of the proposed neural network model, as a result of comparing the previous recurrent neural network, our method showed a remarkable 98.4% accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.82-89
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• 2023

Due to the characteristics of ship structure, the compartment structure is complicated and narrow, so safety accidents frequently occur during the work process. The main causes of accidents include structural collisions, falling objects, toxic substance leaks, fires, explosions, asphyxiation, and more. Understanding the on-site conditions of workers during accidents is crucial for mitigating damages. In order to ensure safety, the on-site situation is monitored using CCTV in the ship, but it is difficult to prevent accidents with the existing method. To address this issue, a smart safety helmet equipped with location identification and voice/video communication capabilities is being developed as a safety technology. Additionally, the smart safety helmet incorporates environmental sensors for temperature, humidity, vibration, noise, tilt (gyro sensor), and gas detection within the work area. These sensors can notify workers wearing the smart safety helmet of hazardous situations. By utilizing the smart safety helmet and environmental sensors, the safety of workers aboard ships can be enhanced.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.8
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• pp.211-218
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• 2018

In this study, we propose an algorithm to detect eating activity and estimation mealtime using 6-axis inertial sensor. The eating activity is classified into three types: food picking, food eating, and lowering. The feature points of the gyro signal are selected for each gesture, and the eating activity is detected when each feature point appears in the sequence. Morphology technique is used to post-process to detect meal time. The proposed algorithm achieves the accuracy of 94.3% and accuracy of 84.1%.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.21-28
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• 2020

In this paper, a new type of electric wheelbarrow is proposed and developed. The developed electric wheelbarrow is equipped with an attitude reference system(ARS) sensor, which consists of 3-axis acceleration sensor and 2-axis Gyro sensor so that it can estimate pitch angle and roll angle. When an operator tilts the wheelbarrow up and down, the pitch angle is detected. The sign of the pitch angle is interpreted as the operator's intention for moving the wheelbarrow forward or backward and the controller drives the wheel of the wheelbarrow with the velocity according to the magnitude of the detected pitch angle. A cargo box of the wheelbarrow is designed to rotate and is controlled to maintain level always, so an operator can handle the electric wheelbarrow easily and safely. The wheelbarrow consists of an in-wheel motor, a DC motor, motor drives, an ARS sensor considering economical use in industrial field. Three experiments are performed to verify the feasibility and stability of the electric wheelbarrow.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.6
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• pp.271-280
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• 2017

In this paper, we propose a new safety system composed of wearable devices, driver's seat belt, and integrating controllers. The wearable device and driver's seat belt capture driver's biological information, while the integrating controller analyzes captured signal to alarm the driver or directly control the car appropriately according to the status of the driver. Previous studies regarding driver's safety from driver's seat, steering wheel, or facial camera to capture driver's physiological signal and facial information had difficulties in gathering accurate and continuous signals because the sensors required the upright posture of the driver. Utilizing wearable sensors, however, our proposed system can obtain continuous and highly accurate signals compared to the previous researches. Our advanced wearable apparatus features a sensor that measures the heart rate, skin conductivity, and skin temperature and applies filters to eliminate the noise generated by the automobile. Moreover, the acceleration sensor and the gyro sensor in our wearable device enable the reduction of the measurement errors. Based on the collected bio-signals, the criteria for identifying the driver's condition were presented. The accredited certification body has verified that the devices has the accuracy of the level of medical care. The laboratory test and the real automobile test demonstrate that our proposed system is good for the measurement of the driver's condition.