• The Journal of Engineering Geology
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• v.10 no.2
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• pp.79-97
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• 2000

Trend of earthquake occurrence of Korea represents that the term from 1978 to 1982 may be called as "active period", the term from 1983 to 1991 is rather tranquil, and from 1992, the occurrence number is increasing greatly. Instrumental earthquake observation of Korea started in 1905 by Japan. It continued until 1943 and ceased then through social disorders such as the independence from Japan Empire and the civil war. After that the observation restarted in 1963 by the establishment the World Wide Standard Seismograph Network. And the fundamental earthquake observation period started in 1978 with the Hongsung earthquake event. KMA(Korea Meteorological Administration) has constructed and operated the 24-hour earthquake and tsunami monitoring system and it propels new construction project of the national seismographic network system. In the result of this project, KMA operates 27 seismic networks, 42 accelerator networks, seismic analyzing system, and sea level monitoring system now. It has the plan to enlarge these systems that 31 seismic networks and 75 accelerator networks until 2001.

• Atmosphere
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• v.33 no.3
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• pp.319-330
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• 2023

As the need for PM type observation increases, Surface Particulate Matter Network (SPARTAN), PM samplers analyzes aerosol samples for PM mass concentration and chemical composition, were recently installed at two sites: Yonsei University at Seoul and Ulsan Institute of Science and Technology (UNIST) at Ulsan. These SPARTAN filter samplers and nephelometers provide the PM_2.5 mass concentration and chemical speciation data with aerosol type information. We introduced the overall information and installation of SPARTAN at the field site in this study. After installation and observation, both Seoul and Ulsan sites showed a similar time series pattern with the daily PM_2.5 mass concentration of SPARTAN and the data of Airkorea. In particular, in the case of high concentrations of fine particles, daily average value of PM_2.5 was relatively well-matched. During the Yonsei University observation period, high concentrations were displayed in the order of sulfate, black carbon (BC), ammonium, and calcium ions on most measurement days. The case in which the concentration of nitrate ions showed significant value was confirmed as the period during which the fine dust alert was issued. From the data analysis, SPARTAN data can be analyzed in conjunction with the existing urban monitoring network, and it is expected to have a synergetic effect in the research field. Additionally, the possibility of being analyzed with optical data such as AERONET is presented. In addition, the method of installing and operating SPARTAN has been described in detail, which is expected to help set the stage for the observation system in the future.

• Journal of Astronomy and Space Sciences
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• v.32 no.4
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• pp.357-366
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• 2015

By using the Optical Wide-field Patrol (OWL) network developed by the Korea Astronomy and Space Science Institute (KASI) we generated the right ascension and declination angle data from optical observation of Low Earth Orbit (LEO) satellites. We performed an analysis to verify the optimum number of observations needed per arc for successful estimation of orbit. The currently functioning OWL observatories are located in Daejeon (South Korea), Songino (Mongolia), and Oukaïmeden (Morocco). The Daejeon Observatory is functioning as a test bed. In this study, the observed targets were Gravity Probe B, COSMOS 1455, COSMOS 1726, COSMOS 2428, SEASAT 1, ATV-5, and CryoSat-2 (all in LEO). These satellites were observed from the test bed and the Songino Observatory of the OWL network during 21 nights in 2014 and 2015. After we estimated the orbit from systematically selected sets of observation points (20, 50, 100, and 150) for each pass, we compared the difference between the orbit estimates for each case, and the Two Line Element set (TLE) from the Joint Space Operation Center (JSpOC). Then, we determined the average of the difference and selected the optimal observation points by comparing the average values.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.183-192
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• 2012

Although underground water resource has relatively less pollution rate compared with surface water, its recovery faces many difficulties due to poor management. Our country monitors underground water to manage it effectively through auxiliary observation network for underground water. In this paper, we suggest water-well auto measure system based on Geosensor for business efficiency increase of water-well management and realtime monitering. In this system is consist of user GUI(Graphic User Interface) composed with water-well information and movement sub-observation network prototype system composed with GPS(Global Positioning System) and wireless sensor node such as water temperature, water level, electrical conductivity. In this system is using the light of the sun for self-power, variety water-well information collected wireless sensor node was a wireless transmitting/receiving a using CDMA(Code Division Multiple Access) module. Also, for promote with user ease, user GUI express that water-well collected in GIS(Geographic Information System) map. For performance evaluation of the proposed system, we perform experiment using sensing information through designed sub-observation network. And we was proved superiority of the proposed system through qualitative evaluation with other paper.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.117-127
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• 2020

The Early Warning System for agrometeorological hazard of the Rural Development Administration (Korea) forecasts detailed weather for each farm based on the meteorological information provided by the Korea Meteorological Administration, and estimates the growth of crops and predicts a meteorological hazard that can occur during the growing period by using the estimated detailed meteorological information. For verification of early warning system, automated weather observation network was constructed in the study area. Moreover, a real-time web display system was built to deliver near real-time weather data collected from the observation network. The meteorological observation system collected diverse meteorological variables including temperature, humidity, solar radiation, rainfall, soil moisture, sunshine duration, wind velocity, and wind direction. These elements were collected every minute and transmitted to the server every ten minutes. The data display system is composed of three phases: the first phase builds a database of meteorological data collected from the meteorological observation system every minute; the second phase statistically analyzes the collected meteorological data at ten-minutes, one-hour, or one-day time step; and the third phase displays the collected and analyzed meteorological data on the web. The meteorological data collected in the database can be inquired through the webpage for all data points or one data point in the unit of one minute, ten minutes, one hour, or one day. Moreover, the data can be downloaded in CSV format.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.83-88
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• 2004

The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.187-188
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• 2006

In CDMA based mobile communication network, signaling messages are routed through Signaling Transfer Point(STP) which is responsible for MTP layer 3 switching. As WCDMA based UMTS network is considered as next generation technology allowing global roaming services, all nodes in networks have their own identity number called Global Title(GT). Therefore, it is essential to introduce Signaling Gateway(SG) responsible for SCCP layer switching to solve the problem each node has all GT tables including even all overseas nodes. In this paper, we propose the signaling network architecture in UMTS networks using SG and we show that we can reduce CAPEX and OPEX in each node. To show the validity of the proposed method some simulations are given in which the results can be expected by intuitive observation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.192-192
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• 2000

In this paper, a platoon merging is considered as a remote-controlled system with the state represented by a stochastic process. In this system, it becomes to encounter situations where a single decision maker controls a large number of subsystems, and observation and control signals are sent over a communication channel with finite capacity and significant transmission delays. Unlike classical estimation problem in which the observation is a continuous process corrupted by additive noise, there is a constraint that the observation must be coded and transmitted over a digital communication channel with finite capaci쇼. A recursive coder-estimator sequence is a state estimation scheme based on observations transmitted with finite communication capacity constraint. Using the coder-estimator sequence, the remote control station designs a feedback controller. In this paper, we introduce a stochastic model for the lead vehicle in a platoon of vehicles considering the angle between a road surface and a horizontal plane as a stochastic process. The simulation results show that the inter-vehicle distance and the deviation from the desired inter-vehicle distance are well regulated.

• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.435-442
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• 2017

The runway visual range affected by fog and so on is one of the important indicators to determine whether aircraft can take off and land at the airport or not. In the case of airports where transportation airplanes are operated, major weather forecasts including the runway visual range for local area have been released and provided to aviation workers for recognizing that. This paper proposes a runway visual range estimation model with a deep neural network applied recently to various fields such as image processing, speech recognition, natural language processing, etc. It is developed and implemented for estimating a runway visual range of local airport with a deep neural network. It utilizes the past actual weather observation data of the applied airfield for constituting the learning of the neural network. It can show comparatively the accurate estimation result when it compares the results with the existing observation data. The proposed model can be used to generate weather information on the airfield for which no other forecasting function is available.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.306-311
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• 2005

To analyze the consumer's purchase pattern, we must consider a factor which is a cultural, social, individual, psychological and so on. If we consider the internal state by the consumer's purchase, Both the consumer's purchase action and the purchase factor can be predicted, so the corporation can use effectively in suitable goods development in a consumer's preference. These factors need a technology that treat uncertain information, because it is difficult to analyze by directly information processing. Therefore, bayesian network manages elements those the observation of inner state such as consumer's purchase is difficult. In addition, it is interpretable about data that the observation is impossible. In this paper, we examine the seller's know-how and the way of consumer's purchase to analyze consumer's purchase action pattern through goods purchase. Also, we compose the bayesian network based on the examined data, and propose the method that predicts purchase patterns. Finally, we remove the data including unnecessary attribute using the bayesian network, and analyze the consumer's Purchase pattern using Kohonen's SOM method.