• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.9-10
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• 2012

This paper presents an observer to measure the lengths of cylinders of an underwater robot for harbour construction using a pressure sensor. In harbour constructing, we place heavy armour stones weighing over 2~3 tons on the surface of the bank to protect it from storming wave. This work typically done by a diver is difficult and dangerous so that we have developed Stone Diver which is the underwater robot for harbour construction. The robot needs a position sensor to control the hydraulic cylinder. The position sensors mounted outside the cylinders cause poor durability in construction site where shock and dust usually occur. However, the pressure sensor mounted inside a waterproof box improves the durability. Based on the dynamic parameters and the pressures in the cylinder, the observer measures the cylinder's position. This paper presents the positional accuracy of the pressure based observer and the performance of the underwater robot to assemble the armour stones.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.551-556
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• 2018

In recent years, drones have been used to measure aircraft flights data and weather information. Related applications include the measure for low-altitude atmospheric data, the measure for atmospheric fine dust, and the measure for air pollution. However, the mounting position of the atmospheric measurement sensor should be mounted by considering the effects of propeller flow, the EMI effects, and the changes in the weight of the drone. Among these, the upper flow of the propeller affects the wind speed and direction, so the optimal position should be selected. This study deals with the proper height of the atmospheric data measurement sensor. Through the flow analysis, we study the flow characteristics of around a drone and suggest the proper sensor mounting height.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.71-78
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• 2021

There are regularly planned overhaul periods in thermal power plants, which involve the maintenance of the boiler of the power plants. However, thermal power plants workers are always exposed to risk during overhaul periods owing to the narrow space and significant dust inside the boiler. Therefore, it is essential to develop a safety monitoring system that is suitable for operating in this type of environment. In this study, we developed not only a worker three-dimensional (3D)-location monitoring system that can monitor and record the entry/exit of workers, their 3D-location, and fall accidents but also a method to secure the working environment and operation efficiency. This system comprises of a worker tag, which was equipped with an inertial measurement unit, a barometric pressure sensor, and a Bluetooth low energy (BLE), and the tags were given to each worker. In addition, the location of workers inside the boiler was measured using a pedestrian dead reckoning (PDR) method and BLE beacons. The location data of the workers tag were transmitted to the integrated database (DB) server through a gateway, and to the administrator monitoring system. The performance of the system was demonstrated inside an actual thermal power plant boiler, and the accuracy and reliability of the system were verified through a number of repeated tests. These results provide insights on designing a new system for monitoring enclosed spaces.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.161-166
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• 2014

This Electromagnetic metal piece detection sensor, having high sensitivity even under high humidity and dust density, using LVDT was investigated. Metal detection characteristics using phase detection method, for 3 frequencies covering ELF and LF band, were experimentally measured. It was found that the sensitivity for metal and animal food, in which metal piece was included, was exponentially increased as frequency increased. Reducing cutoff frequency of LPF after PD was found to be effective to proportionally increase sensitivity. Also the sensitivity of metal piece detection was enhanced by optimizing BPF bandwidth and SNR. Metal piece detection limit using available ferrous test samples was found to be 0.7mm diameter from the experiment using 50kHz, in ELF band, which was known to have better selectivity to animal food. is an example of ABSTRACT format.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.865-871
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• 2012

This paper presents an observer to estimate the displacement of hydraulic cylinders of an underwater robot for harbour construction using a pressure sensor. In harbour constructing, we place heavy armour stones weighing over 2~3 tons on the surface of the bank to protect it from storming wave. This work typically done by a diver is difficult and dangerous so that we have developed Stone Diver which is the underwater robot for harbour construction. The robot needs a displacement sensors to control the position of hydraulic cylinders. The position sensors mounted outside the cylinders cause poor durability in construction site where shock and dust usually occur. However, the pressure sensor mounted inside a waterproof box improves the durability. Based on the dynamic parameters and the pressures in the cylinder, the observer estimates the cylinder's position. This paper presents the positional accuracy of the pressure based observer and the performance of the underwater robot to assemble the armour stones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1708-1721
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• 2011

Recent outbreak of cattle diseases such as foot-and-mouth disease(FMD) requires constant monitoring of burial sites of mass cull of cattles. However, current monitoring system takes environmental samples from burial sites with period of between one and two weeks, which makes it impossible for non-stop management of hazardous bio-waste. Therefore, in this study, we suggest an improved real-time environmental monitoring system for such bio-hazardous sites based on wireless sensor networks, which makes constant surveillance of the FMD burial sites possible. The system consists mainly several wireless environmental monitoring sensors(i.e dust, Co2, VOC, NH3, H2S, temperature, humidity) nodes and GPS location tracking nodes. Through analysis of the relayed of the environmental monitoring data via gateway, the system makes it possible for constant monitoring and quick response for emergency situation of the burial sites. In order to test the effectiveness of the system, we have installed a set of sensor to gas outlets of the burial sites, then collected and analyzed measured bio-sensing data. We have conducted simulated emergency test runs and was able to detect and monitor the foul smell constantly. With our study, we confirm that the preventive measures and quick response of bio environmental accident are possible with the help of a real-time environmental monitoring system.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.435-442
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• 2010

The efficiency of the car's wiper blade has a great contribution to the guarantee of security. To guarantee the wiper blade's ability of getting rid of dust sticking on the glass surface, the qualities of lubricant, durability, heat resistant, low temperature, ozone resistant, chemical resistant must be good as well as it being noiseless. Like this, in order to improve the wiper's skills, it is essential to have a system that is able to assess and analyse the properties of the wiper. In this paper, to create a system that measures the car's wiper pressure, an analog/digital converter (ADC) that receives signals generated from the pressure sensor and transmits it to a personal computer is proposed. The designed ADC is one of the pipeline ADCs that can obtain fast movement rate and also a structure that can optimize the entire system's area as well as the consumption of strength.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.2
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• pp.159-171
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• 2012

Aerosol direct radiative forcing (ADRF) retrieval method was developed by combining data from passive and active satellite sensors. Aerosol optical thickness (AOT) retrieved form the Moderate Resolution Imaging Spectroradiometer (MODIS) as a passive visible sensor and aerosol vertical profile from to the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) as an active laser sensor were investigated an application possibility. Especially, space-born Light Detection and Ranging (Lidar) observation provides a specific knowledge of the optical properties of atmospheric aerosols with spatial, temporal, vertical, and spectral resolutions. On the basis of extensive radiative transfer modeling, it is demonstrated that the use of the aerosol vertical profiles is sensitive to the estimation of ADRF. Throughout the investigation of relationship between aerosol height and ADRF, mean change rates of ADRF per increasing of 1 km aerosol height are smaller at surface than top-of-atmosphere (TOA). As a case study, satellite data for the Asian dust day of March 31, 2007 were used to estimate ADRF. Resulting ADRF values were compared with those retrieved independently from MODIS only data. The absolute difference values are 1.27% at surface level and 4.73% at top of atmosphere (TOA).

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.75-82
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• 2018

Efforts to reduce damages from micro dust and harmful gases in life have been led by national or local governments, and information on air quality has been provided along with real-time weather forecast through TV and internet. It is not enough to provide information on the individual indoor space consumed. So in this paper, we propose a IoT-based Real-Time Air Quality Sensing Board Corresponding Fine Particle for Air Quality Management in Buildings. Proposed board is easy to install and can be placed in the right place. In the proposed board, the air quality (level of pollution level) in the indoor space (inside the building) is easy and it is possible to recognize the changed indoor air pollution situation and provide countermeasures. According to the advantages of proposed system, it is possible to provide useful information by linking information about the overall indoor space where at least one representative point is located. In this paper, we compare the performance of the proposed board with the existing air quality measurement equipment.