• Journal of the Korean earth science society
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• v.30 no.5
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• pp.550-564
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• 2009

When magnetotelluric (MT) data are obtained in the vicinity of the coast, the surrounding seas make it difficult to interpret subsurface structure, especially the deep part of the subsurface. We introduce an iterative method to correct the sea effect, based on the previous topographic correction method that removes the distortion due to topographic changes in seafloor MT data. The method first corrects the sea effect in observed MT impedance, and then inverts corrected response in a model space without the sea. Due to mutual coupling between the sea and the subsurface structure, the correction and inversion steps are iterated until the changes in each result become negligible. The method is tested for 1- and 2-D structures using synthetic MT data produced by 3-D forward modeling including surrounding seas. In all cases, the method closely recovers the true structure assumed to generate synthetic responses after a few iterations.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.291-291
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• 2002

DGPS(Differential Global Positioning System) and RTK(RealTime Kinematic) is in one of today's most widely used surveying techniques. But It's use is restricted by the distance between reference station and rover station and it is difficult to process data in realtime by it's own orgnizational limitation in precise measurement of positioning. To meet these new demands, In This paper, new DGPS and RTK correction data services through Internet and PSTN(Public Switched Telephony Network) have been proposed. For this purpose, we implemented performance a DGPS and RTK error correction data transmission system for long-distance using the internet and PSTN network which allows a mobile user to increase the distance at which the rover receiver is located from the reference in realtime. and we analyzed and compared DGPS and RTK performance by experiments through the Internet and PSTN network with the distance and the time.

• Journal of IKEEE
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• v.24 no.1
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• pp.33-39
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• 2020

In this paper, we propose the design technique of the vehicle's load weight measuring system using tire pressure, which is one of the physical elements of tires. The proposed technique consists of four processes: noise correction by load and vibration, gas flow correction, data mixer and weight conversion. Noise correction by load and vibration eliminates noise that increases the tire's internal pressure due to external shocks and vibrations produced by the vehicle while it is in motion. In the gas flow correction process, the noise of the internal pressure of the tire is increased due to the temperature rise of the ground with respect to the data obtained through the noise correction process due to the load and vibration. In the data mixer process, the load and pressure on the tolerances the empty, median and the full load are classified according to the change in pressure of the tire that is delivered perpendicular to the tire in the event of cargo. In the weight conversion process, weight is expressed by weight through weight conversion algorithms using noise correction results by load and vibration and gas flow correction. The weight conversion algorithm calculates the weight conversion factor, which is the slope of the linear function with respect to the load and pressure change, and converts the weight. In order to evaluate the accuracy of the loading weight measurement system of the vehicle using the tire pneumatic system technique proposed in this paper, we propose the design technique of the vehicle's load weight measuring system using tire pressure, which is one of the physical elements of tires.. Noise correction results by load and vibration and gas flow data correction results showed reliable results. In addition, repeated weight precision test showed better weight accuracy than the standard value of 90% of domestic companies.

• Journal of the Korea Institute of Building Construction
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• v.8 no.2
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• pp.107-112
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• 2008

According to Korean Architectural Standard Specification (KASS) , at the design stage of the specified concrete strength, strength correction with each temperature level should be considered to secure required strength at 28 days even in low temperature condition, In this paper, the period for the strength correction at the stage of mixture design of the concrete using ordinary Portland cement(OPC) specified in KASS was determined with each region of south Korea based on the meteorological data of KMA(Korea meteorological administration) by applying KASS-5 regulation. In case of 28 days of strength control age, the period for strength correction with 6MPa was calculated to $50{\sim}60$ days and, with 3 MPa. to around 80 days. The period for the strength correction was shown to be decreased with the rise of altitude. The period to consider the delay of the strength development due to low temperature including the period of cold weather concrete was nearly 7 months around 1 year. References for determining the strength correction factors with each region of south Korea was provided in this paper. Further investigation of strength correction of the concrete containing blended cement is to studied.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1299-1312
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• 2019

As land remote sensing applications are expanding to the extraction of quantitative information, the importance of atmospheric correction is increasing. Considering the difficulty of atmospheric correction for land images, it should be applied when it is necessary. The quantitative information extraction and time-series analysis on biophysical variables in land surfaces are two major applications that need atmospheric correction. Atmospheric aerosol content and column water vapor, which are very dynamic in spatial and temporal domain, are the most influential elements and obstacles in retrieving accurate surface reflectance. It is difficult to obtain aerosol and water vapor data that have suitable spatio-temporal scale for high- and medium-resolution multispectral imagery. Selection of atmospheric correction method should be based on the availability of appropriate aerosol and water vapor data. Most atmospheric correction of land imagery assumes the Lambertian surface, which is not the case for most natural surfaces. Further BRDF correction should be considered to remove or reduce the anisotropic effects caused by different sun and viewing angles. The atmospheric correction methods of optical imagery over land will be enhanced to meet the need of quantitative remote sensing. Further, imaging sensor system may include pertinent spectral bands that can help to extract atmospheric data simultaneously.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Journal of Astronomy and Space Sciences
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• v.29 no.2
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• pp.97-101
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• 2012

Monitoring sunspots consistently is the most basic step required to study various aspects of solar activity. To achieve this goal, the observers must regularly calculate their own correction factor $k$ and keep it stable. Relatively recently, two observing teams in South Korea have presented interesting papers which claim that revisions that take the yearly-basis $k$ into account lead to a better agreement with the international relative sunspot number $R_i$, and that yearly $k$ apparently varies with the solar cycle. In this paper, using artificial data sets we have modeled the sunspot numbers as a superposition of random noise and a slowly varying background function, and attempted to investigate whether the variation in the correction factor is coupled with the solar cycle. Regardless of the statistical distributions of the random noise, we have found the correction factor increases as sunspot numbers increase, as claimed in the reports mentioned above. The degree of dependence of correction factor $k$ on the sunspot number is subject to the signal-to-noise ratio. Therefore, we conclude that apparent dependence of the value of the correction factor $k$ on the phase of the solar cycle is not due to a physical property, but a statistical property of the data.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.366-377
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• 2005

We find a module may that takes optimal correction rate of Korean spelling corrector. If there are a lot of module numbers of spelling corrector, it is difficult to calculate optimal correction rate of spelling corrector because permutation of N-modules is N!. This Korean spelling corrector consists of 19 modules. It is impossible to arrange 19 modules actually and the correction rate is various according to input data. We found the range of correction rate using parallel processing between modules and the optimal correction rate using sequential processing of modules. Input data that are used in an experiment is 753,191 eojeol's sets that happen in newspaper publishing company during several years. About this error set, theoretical maximum correction rate of spelling corrector is $97.28\%$ (732,764/753,191). But we got the optimal correction rate $96.62\%$ (727,750/733,191). This optimal correction rate is almost near to $99.31\%$ (727,750/732,764) of the maximum correction rate.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.209-214
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• 2019

Precise point positioning (PPP) requires precise orbit and clock products. International GNSS service (IGS) real-time service (RTS) data can be used in real-time for PPP, but it may not be possible to receive these corrections for a short time due to internet or hardware failure. In addition, the time required for IGS to combine RTS data from each analysis center results in a delay of about 30 seconds for the RTS data. Short-term orbit prediction can be possible because it includes the rate of correction, but the clock correction only provides bias. Thus, a short-term prediction model is needed to preidict RTS clock corrections. In this paper, we used a long short-term memory (LSTM) network to predict RTS clock correction for three minutes. The prediction accuracy of the LSTM was compared with that of the polynomial model. After applying the predicted clock corrections to the broadcast ephemeris, we performed PPP and analyzed the positioning accuracy. The LSTM network predicted the clock correction within 2 cm error, and the PPP accuracy is almost the same as received RTS data.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.280-286
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• 2022

In order to efficiently collect data, it is essential to locate the facilities and analyze the movement data. The current technology for location collection can collect data using a GPS sensor, but GPS has a strong straightness and low diffraction and reflectance, making it difficult for indoor positioning. In the case of indoor positioning, the location is determined by using wireless network technologies such as Wifi, but there is a problem with low accuracy as the error range reaches 20 to 30 m. In this paper, using BLE 4.2 built in Raspberry Pi, we implement Bluetooth Smart Ready. In detail, a beacon was produced for Advertise, and an experiment was conducted to support the serial port for data transmission/reception. In addition, advertise mode and connection mode were implemented at the same time, and a 3-count gradual algorithm and a quadrangular positioning algorithm were implemented for Bluetooth RSSI error correction. As a result of the experiment, the average error was improved compared to the first correction, and the error rate was also improved compared to before the correction, confirming that the error rate for position measurement was significantly improved.