• Journal of the korean society of oceanography
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• v.31 no.2
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• pp.64-74
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• 1996

Numerical experiments of OAT (Ocean Acoustic Tomography) are carried out in the East Sea of Korea where the canonical ocean has been perturbed by a mesoscale warm eddy and a thermal front. In order to estimate the horizontal and vertical structure of water temperature of the perturbed ocean, the experimental area is divided into 16 cells with 8 pairs of sources and receivers for a horizontal slice and the water column is divided into 8 layers for a vertical slice. The inversely estimated temperature field by using SVD (Singular Value Decomposition) method reveals the eddy and frontal structure clearly. The rms errors of the two horizontal slices are less than $0.4^{\circ}C$ and $1.7^{\circ}C$ at 400 m and 200 m depths, respectively, while the error in the vertical slice is less than $1.0^{\circ}C.$ For better estimation of temperature by OAT method, particularly for the East Sea, a range-dependent ray model should be used to solve the forward problem. At the same time, improvement in computing the refracted ray path between vertical layers is required to obtain more accurate travel time information. The results of the present experiment give rise to a possibility of application of OAT in remote sensing of the ocean thermal structure.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.153-164
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• 2008

According to the economic growth, many highways are constructed for increasing need of better life style. Especially roads and roadside facilities are used for accident prevention and offering mobility for drivers. For these purpose, roads and roadside facilities should be well maintained and managed. Now, many roads and roadside facilities are constructed in many areas. Because of traditional surveying method requires much time and surveying efforts, we designed and developed mobile mapping system for highway maintenance and management purpose using multi sensors. We tested our mobile mapping system and data management process. Using developed database, road managers can easily check the information of facility conditions, positions, and attributes. We are expecting low cost and efficient road maintenance process by using our system.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1531-1544
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• 2018

We detected the coseismic ionospheric disturbance generated by the earthquakes of magnitude 5.0 and greater in Korean Peninsula. We considered the seismic events such as Gyeongju earthquake in September 2016 with magnitude 5.8, the Pohang earthquake in November 2017 with magnitude 5.4, and the underground nuclear explosion from North Korea in September 2017 with magnitude 5.7. Although all GPS stations were not detected, the ionospheric disturbance induced by these earthquakes occurred approximately 10-30 minutes and 40-60 minutes after the events. We inferred that the time difference within each variation is due to the different focal depth and the geometry of epicenter, satellite, and GPS station. In the case of the Gyeongju earthquake, the earthquake had relatively deeper depth than the other earthquakes. However, the seismic magnitude was bigger and it occurred at nighttime when the ionospheric activity was stable. So we could observe such anomalous variations. It is considered that the ionospheric disturbance caused by the difference in velocity of the upward propagating waves generated by earthquake appears more than once. Our results indicate that the detection of ionospheric disturbances varies depending on the geometry of the GPS station, satellite, and epicenter or the detection method and that the apparent growth of amplitude in the time series varies depending on the focal depth or the site-satellite-epicenter geometry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.329-335
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• 2018

Satellite images, which are widely used in various applications, are very useful for monitoring the surface of the earth. Since satellite data is obtained from a remote sensor, it contains a lot of noise and errors depending on observation weather conditions during data acquisition and sensor malfunction status. Since the accuracy of the data affects the accuracy and reliability of the data analysis results, noise removal and data restoration for high quality data is important. In this study, we propose a reconstruction system that models the time dependent dynamic characteristics of satellite data using a multi-period harmonic model and performs adaptive data restoration considering the spatial correlation of data. The proposed method is a real-time restoration method and thus can be employed as a preprocessing algorithm for real-time reconstruction of satellite data. The proposed method was evaluated with both simulated data and MODIS NDVI data for six years from 2011 to 2016. Experimental results show that the proposed method has the potentiality for reconstructing high quality satellite data.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.220-223
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• 2004

Automatic image registration is an essential element of remote sensing because remote sensing system generates enormous amount of data, which are multiple observations of the same features at different times and by different sensor. The general process of automatic image registration includes three steps: 1) The extraction of features to be used in the matching process, 2) the feature matching strategy and accurate matching process, 3) the resampling of the data based on the correspondence computed from matched feature. For step 2) and 3), we have developed an algorithms for automated registration of satellite images with RANSAC(Random Sample Consensus) in success. However, for step 1), There still remains human operation to generate GCP Chips, which is time consuming, laborious and expensive process. The main idea of this research is that we are able to automatically generate GCP chips with comer detection algorithms without GPS survey and human interventions if we have systematic corrected satellite image within adaptable positional accuracy. In this research, we use SUSAN(Smallest Univalue Segment Assimilating Nucleus) algorithm in order to detect the comer. SUSAN algorithm is known as the best robust algorithms for comer detection in the field of compute vision. However, there are so many comers in high-resolution images so that we need to reduce the comer points from SUSAN algorithms to overcome redundancy. In experiment, we automatically generate GCP chips from IKONOS images with geo level using SUSAN algorithms. Then we extract reference coordinate from IKONOS images and DEM data and filter the comer points using texture analysis. At last, we apply automatically collected GCP chips by proposed method and the GCP by operator to in-house automatic precision correction algorithms. The compared result will be presented to show the GCP quality.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1763-1770
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• 2023

The availability of high-resolution satellite image time series data has led to an increase in change detection research. Various methods are being studied, such as satellite image pixel and object-level change detection algorithms, as well as algorithms that apply deep learning technology. In this paper, we propose a QGIS plugin-based system to enhance the utilization of these useful results and present an actual implementation case. The proposed system is a system for intensive change detection and monitoring of areas of interest, and we propose a convenient system expansion method for algorithms to be developed in the future. Furthermore, it is expected to contribute to the construction of satellite image utilization systems by presenting the basic structure of commercialization of change detection research.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.813-818
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• 2020

Recently, with the rapid increase in technology and users of smart devices, the smart watch market has grown, and its utility and usability are continuously expanding. The strengths of smartwatches are wearable portability, application immediacy, data diversity and real-time capability. Despite these strengths, smartwatches have limitations such as battery limitations, display and user interface size limitations, and memory limitations. In addition, there is a need to supplement developers and standard devices, operating system standard models, and killer application modules. In particular, monitoring and application of user's biometric information is becoming a major service for smart watches. The biometric information of such a smart watch generates a large amount of data in real time. In order to advance the biometric information service, stable peer-to-peer transmission of sensing data to a remote smartphone or local server storage must be performed. We propose a synchronization method to ensure wireless remote peer-to-peer transmission stability in a smart watch system. We design a wireless peer-to-peer transmission process based on this synchronization method, analyze asynchronous transmission process and proposed synchronous transmission process, and propose a transmission efficiency method according to an increase in transmission amount.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.5
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• pp.9-20
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• 2005

In this paper, we propose ubiTrack, a system which tracks users' location in indoor environments by employing infrared-based proximity method. Most of recently developed systems have focussed on performance and accuracy. For this reason, they adopted the idea of centralized management, which gathers all information in a main system to monitor users' location. However, these systems raise privacy concerns in ubiquitous computing environments where tons of sensors are seamlessly embedded into environments. In addition, centralized systems also need high computational power to support multiple users. The proposed ubiTrack is designed as a passive mobile architecture to relax privacy problems. Moreover, ubiTrack utilizes appropriate area as a unit to efficiently track users. To achieve this, ubiTrack overlaps each sensing area by utilizing the TDM (Time-Division Multiplexing) method. Additionally, ubiTrack exploits various filtering methods at each receiver and utilization module. The filtering methods minimize unexpected noise effect caused by external shock or intensity weakness of ID signal at the boundary of sensing area. ubiTrack can be applied not only to location-based applications but also to context-aware applications because of its associated module. This module is a part of middleware to support communication between heterogeneous applications or sensors in ubiquitous computing environments.

• Journal of Sensor Science and Technology
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• v.23 no.2
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• pp.99-104
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• 2014

Sensor arrays based on chemical sensors produce multidimensional patterns of data that may be used discriminate between different chemicals. For the human observer, visualization of multidimensional data is difficult, since the eye and brain process visual information in two or three dimensions. To devise a simple means of data inspection from the response of sensor arrays, PCA (Principal Component Analysis) or Sammon's nonlinear mapping technique can be applied. The PCA, which is a well-known statistical method and widely used in data analysis, has disadvantages including data distortion and the axes for plotting the dimensionally reduced data have no physical meaning in terms of how different one cluster is from another. In this paper, we have investigated two techniques and proposed a combination technique of PCA and nonlinear Sammom mapping for visualization of multidimensional patterns to two dimensions using data sets from odor sensing system. We conclude the combination technique has shown more advantages comparing with the PCA and Sammon nonlinear technique individually.