• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.403-410
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• 2011

Consistent observation with high temporal and spatial resolution is required for an efficient monitoring of coastal environments. Remote monitoring system installed on the ground is capable of simultaneous observation of wide coastal area and consistent observation with high frequency, which a small number of in-situ measurements cannot manage. This paper studies two typical ground based coastal monitoring system, marine radar and camera system. Marine radar can produce time series of frequency spectrum by integrating wave number spectrum calculated from spatial and temporal variation of waves in the radar image. The time averaged radar images of waves can analyze wave breaking zone, rip currents and location of littoral bars. Camera system can observe temporal variation of foam generation originated from coastal contamination as well as shoreline changes. By extracting the part of foams from rectified images, quantitative analysis of temporal foam variation can be done. By using the two above systems of different characteristics, synergetic benefit can be achieved.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.83-91
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• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.63-74
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• 2024

In agriculture, frost can be devastating, which is why observation and forecasting are so important. According to a recent report analyzing frost observation data from the Korea Meteorological Administration, despite global warming due to climate change, the late frost date in spring has not been accelerated, and the frequency of frost has not decreased. Therefore, it is important to automate and continuously operate frost observation in risk areas to prevent agricultural frost damage. In the existing frost observation using leaf wetness sensors, there is a problem that the reference voltage value fluctuates over a long period of time due to contamination of the observation sensor or changes in the humidity of the surrounding environment. In this study, a datalogger program was implemented to automatically solve these problems. The established frost observation system can stably and automatically accumulate time-resolved observation data over a long period of time. This data can be utilized in the future for the development of frost diagnosis models using machine learning methods and the production of frost occurrence prediction information for surrounding areas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.882-885
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• 2014

The duration and frequency of flooding and not last long, by the time climate change drought. The increased accordingly by reducing stream flow and year variation. This trend is expected to continue, and change towards a comprehensive analysis of such quantity, quality and management of water resources are managed. Flood warning system is called to perform them electronically to the management of water resources such as these to be in the organic water-related basic data acquisition, storage, processing and utilization. Can be divided into hydrological observations and flood warning systems alert system broadcast system. Hydrological observation system is the measurement from the hydrological stations (water level, rainfall, water) that can be observed hydrological status of the dam basin hydrological observation data transmitted to the central office, located at the dam monitoring and control system through a variety of networks including satellite, and the collected defined as the system that sent the K-water head office in 1 minute increments hydrological observation data. Headquartered in support of this decision. Dimensions of the dam are provided in addition to inward. Channeled through various hydrologic analysis and leveraging the data transfer. This paper looks at ways to build out hydrological observation system.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.285-295
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• 2020

In the case of small observation towers located at sea, it is necessary to confirm the change in dynamic characteristics due to the influence of environmental loads. In this study, the dynamic characteristics were analyzed and the numerical analysis model was designed through field dynamic response measurement on the Mangyeong Offshore Observation Tower (Mangyeong Tower) located near the Saemangeum Embankment. As a result of the measurement, the natural frequency was found to increase slowly as the tide level is lowered. In addition, it was confirmed that the same mode has two frequencies, which was judged to be a phenomenon in which the natural frequency was partially increased when the pile and the ground contacted by scouring. For numerical analysis, the upper mass, artificial fixity point, scour depth and fluid influences are reflected in the structural characteristics of the Mangyeong Tower. In addition, the model updating from the estimated natural frequency and pattern search algorithm was performed. From the model updating, it is expected that it can be applied to future studies on stability of Mangyeong Tower.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.6
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• pp.533-539
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• 2008

An high frequency induction hardening technology of vehicle body press-formed of thin sheet steel has been developed to increase the strength of vehicle body parts locally by high frequency induction heating, thereby eliminating the need for reinforcements. And this technique for increasing the tensile strength of sheet steel was practically applied to the front floor cross member and center pillar reinforcement of a passenger car. The side impact behavior has been investigated when induction hardening technology is applied to the conventional low-carbon steel and weight reduction of an automotive body is expected. In this paper, basic experiments were performed for the hat-shaped specimen under high frequency induction heating process. Martensitic transformation was found in the heating zone through microscopic observation which showed higher hardness. In addition, the hardness and strength of the center-pillar specimen made of boron steel increased remarkably by high frequency induction heating.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.949-954
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• 2017

Multiple input, multiple output orthogonal frequency division multiplexing (MIMO OFDM) systems are the candidate for the future wireless communications. However, the main drawback of MIMO OFDM systems is their sensitivity to carrier frequency offset (CFO) similar to the single input, single output OFDM (SISO OFDM) systems. The demodulation of a signal with CFO causes large bit error rate and degrade the performance of a symbol synchronizer. It is important to estimate the frequency offset and minimize or eliminate its impact. In this paper, we propose a technique based on observation training symbols for estimating CFO by employing block-by-block estimation for SISO OFDM systems. The technique of SISO OFDM is extended to the MIMO OFDM systems. Simulation results show that the proposed techniques have a superior performance and better accuracy compared to the conventional techniques in the sense of mean square error.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.2
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• pp.135-145
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• 2020

Wave observation is widely used to direct observation method for observing the water surface elevation using wave buoy or pressure gauge and remote-sensing wave observation method. The wave buoy and pressure gauge can produce high-quality wave data but have disadvantages of the high risk of damage and loss of the instrument, and high maintenance cost in the offshore area. On the other hand, remote observation method such as radar is easy to maintain by installing the equipment on the land, but the accuracy is somewhat lower than the direct observation method. This study investigates the data quality of MIROS Wave and Current Radar (MWR) installed at Dokdo and improve the data quality of remote wave observation data using the wave buoy (CWB) observation data operated by the Korea Meteorological Administration. We applied and developed the three types of wave data quality control; 1) the combined use (Optimal Filter) of the filter designed by MIROS (Reduce Noise Frequency, Phillips Check, Energy Level Check), 2) Spike Test Algorithm (Spike Test) developed by OOI (Ocean Observatories Initiative) and 3) a new filter (H-Ts QC) using the significant wave height-period relationship. As a result, the wave observation data of MWR using three quality control have some reliability about the significant wave height. On the other hand, there are still some errors in the significant wave period, so improvements are required. Also, since the wave observation data of MWR is different somewhat from the CWB data in high waves of over 3 m, further research such as collection and analysis of long-term remote wave observation data and filter development is necessary.

• English Language & Literature Teaching
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• v.17 no.2
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• pp.219-245
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• 2011

This case study aims to investigate how a primary English teacher's professional development was pursued through novice teacher mentoring after the six-month intensive in-service teacher training program(IIETTP). The teacher was involved in mentoring two novice teachers working at the same school. They observed each other's classes and exchanged their views on the classes, focusing on areas to be improved. The observation was done within a framework that consisted of pre-, during- and post-observation sessions. Data was gathered through retrospective entries kept after the post-observation meetings. The entries were categorized according to their saliency, frequency and recurring patterns identified. The findings reveal that learning from the training course could be applied professionally and could serve to bridge the gap between training and teaching. It is also shown that the mentee teachers' professional development was enhanced and the mentor teacher herself benefited from the collaborative learning process involved with working with the novice teachers. Some suggestions are made for the effective implementation of school-based teacher development programs after the IIETTP.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.368-368
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• 2008

Oil spills are a principal factor of the ocean pollution. The complicated problems involved in detecting oil spills are usually due to varying wind and sea surface condition such as ocean wave and current. The Hebei Spirit accident was happened in the west sea ($36^{\circ}$41'04" N, $126^{\circ}$03'12" E) near about 8 km distant from Tae-An, Korea on December 7, 2007. The aim of this work is to improve the detection and classification performance in order to define a more accurate training set and identifying the feature of oil spill region. This paper deals with an optimization technique for the detection and classification scheme using multi-frequency and multi-polarization SAR and optical image data sets of the oil spilled sea. The used image data are the ENVISAT ASAR WS and Radarsat-1 of C-band and ALOS PALSAR of L-band SAR data and KOMPSAT-2 optical images together with meteorological or oceanographic data. Both the theory and the experimental results obtained are discussed.