• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1329-1350
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• 2018

There have been many applications to observe Earth using synthetic aperture radar (SAR) since it could acquire Earth observation data without reference to weathers or local times. However researches about digital map generation using SAR have hardly been performed due to complex raw data processing. In this study, we suggested feasibility of producing digital map using SAR stereo images. We collected two sets, which include an ascending and a descending orbit acquisitions respectively, of KOMPSAT-5 stereo dataset. In order to suggest the feasibility of digital map generation from SAR stereo images, we performed 1) rational polynomial coefficient transformation from radar geometry, 2) digital resititution using KOMPSAT-5 stereo images, and 3) validation using digital-map-derived reference points and check points. As the results of two models, root mean squared errors of XY and Z direction were less than 1m for each model. We discussed that KOMPSAT-5 stereo image could generated 1:25,000 digital map which meets a standard of the digital map. The proposed results would contribute to generate and update digital maps for inaccessible areas and wherever weather conditions are unstable such as North Korea or Polar region.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.419-430
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• 2021

Remote sensing techniques using drone-based multispectral image were studied for fast and two-dimensional monitoring of algal blooms in the river. Drone is anticipated to be useful for algal bloom monitoring because of easy access to the field, high spatial resolution, and lowering atmospheric light scattering. In addition, application of multispectral sensors could make image processing and analysis procedures simple, fast, and standardized. Spectral indices derived from the active spectrum of photosynthetic pigments in terrestrial plants and phytoplankton were tested for estimating chlorophyll-a concentrations (Chl-a conc.) from drone-based multispectral image. Spectral indices containing the red-edge band showed high relationships with Chl-a conc. and especially, 3-band model (3BM) and normalized difference chlorophyll index (NDCI) were performed well (R²=0.86, RMSE=7.5). NDCI uses just two spectral bands, red and red-edge, and provides normalized values, so that data processing becomes simple and rapid. The 3BM which was tuned for accurate prediction of Chl-a conc. in productive water bodies adopts originally two spectral bands in the red-edge range, 720 and 760 nm, but here, the near-infrared band replaced the longer red-edge band because the multispectral sensor in this study had only one shorter red-edge band. This index is expected to predict more accurately Chl-a conc. using the sensor specialized with the red-edge range.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Journal of Oral Medicine and Pain
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• v.30 no.1
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• pp.69-77
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• 2005

The purposes of this study were to develop and introduce a novel intraoral appliance for bruxism composed of power switch and biofeedback device and further to examine inter- and intra-reliability of the appliance prior to clinical tests. The newly-developed appliance consisted of detection sensors, a central processing unit (CPU), a reactor and a storage unit and a displayer. Compact-sized, waterproof switches were selected as bruxism detection sensor and any sensor activation by clenching or grinding event was processed at the CPU and transmitted, by radio wave, to the reactor and storage unit and triggered auditory or vibratory signal, subsequently producing biofeedback to the patient with bruxism. The data on bruxing event in the storage unit can be displayed on the computer, making it possible analyzing frequency, duration and nature of bruxism. Cast models were obtained from ten volunteers with normal occlusion to evaluate reliability of the appliances. For inter-operator reliability on the intraoral appliances, each operator of the two fabricated the appliance for the same subject and compared the minimal contact forces provoking auditory biofeedback reaction in vertical, lateral and central directions. Intra-operator reliability was also investigated on the appliances made by a single operator at two separate times with an interval of two days. Conclusively, the newly-developed appliance is compact and safe to use in oral circumstance and easy to make. Furthermore, it had to be proven reliability excellent enough to apply in clinical settings. Thus, it is assumed that this appliance with the processor and the storage of data and auditory or vibratory biofeedback function is available and useful to analyze and control bruxism.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.3
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• pp.253-262
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• 1995

To develop a device for measuring fish freshness which could be move accurate and reliable than used freshness measuring systems. A new device based on digital circuit was designed using a microcomputer. The device was composed of a sensor part, 8096 microprocessor and a segment display. The effectiveness of device has been evaluated by the coefficient of correlation among the measured freshness stores such as electrical Q-value, K-value and amount of volatile basic nitrogen (VBN) of plaice, Paralichthys Olivaceus, during storage at $-3^{\circ}C,\;0^{\circ}C,\;5^{\circ}C,\;10^{\circ}C,\;and\;25^{\circ}C$. Q-values measured by a new device were more closely correlated with K-value (r=-0.978-\;-0.962,\;p<0.05) and VBN (r=-0.888-\;-0.988,\;p<0.05) in case of plaice meat. If more data would achieve using various fishes, this new designed device could be a valuable kit in fish market by its compact portability.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.449-461
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• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.437-444
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• 2017

Electric Pole is a supporting beam used for power transmission/distribution which is sensitive to external force change of environmental factors. Therefore, power facilities have many difficulties in terms of maintenance/conservation from external environmental changes and natural disasters that cause a great economic impact. The aerial wire cause elasticity due to the influence of temperature, or factors such as wind speed and wind direction, that weakens the electric pole. The situation may lead to many safety risk in day-to-day life. But, the safety assessment of the pole is carried out at the design stage, and aftermath is not considered. For the safety and maintenance purposes, it is very important to analyze the influence of weather factors on external forces periodically. In this paper, we analyze the acceleration data of the sensor nodes installed in electric pole for maintenance/safety purpose and use Kalman filter as noise compensation method. Fast Fourier Transform (FFT) is performed to analyze the influence of each meteorological factor, along with the meteorological factors on frequency components. The result of the analysis shows that the temperature, humidity, solar radiation, hour of daylight, air pressure, wind direction and wind speed were influential factors. In this paper, the influences of meteorological factors on frequency components are different, and it is thought that it can be an important factor in achieving the purpose of safety and maintenance.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.75-91
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• 2015

This study aims to analyse the U-City characteristics through the U-City historical mapping. The U-City characteristics were analysed by building the U-City historical map in terms of STIM model which consists of service, technology, infrastructure and management. The data for analysis is the National Informatization White Paper published by the NIA (National Information Society Agency) from 2002 to 2013. As a result, first, the U-City service were evolved from administration informatization, enterprise informatization, administration/living informatization and administration/space/private informatization through the intelligence facilities and space. Second, the U-City technology were changed through wire network, sensor/network, processing/super-highway network, convergence of network/security. Third, the U-City infrastructure have had evolutionary process such as wire computer network, wire/wireless network, intellectualization facility and intelligent facility space. Forth, the U-City management were carried out with making the unit network/infrastructure management, information connection/operating management and information integration/participation management. Therefore, the history of U-City has been making rapid development in government computerization, computer oriented society, information city and ubiquitous city.