• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.04a
• /
• pp.346-349
• /
• 2011

In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infrared) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.

• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.1
• /
• pp.45.2-46
• /
• 2021

The Hayabusa 2 mission target asteroid (162173) Ryugu is a near-Earth, carbonaceous (C-type) asteroid. Before the arrival, this asteroid is expected to be covered with mm- to cm- sized grains through the thermal infrared observations [1]. These grains are widely understood to be formed by past impacts with other celestial bodies and fractures induced by thermal fatigue [2]. However, the close-up images by the MASCOT lander showed lumpy boulders but no abundant fine grains [3]. Morota et al. suggested that there would be submillimeter particles on the top of these boulders but not resolved by Hayabusa 2's onboard instruments [4]. Hence, we conducted polarimetry of Ryugu to investigate microscopic grain sizes on its surface. Polarimetry is a powerful tool to estimate physical properties such as albedo and grain size. Especially, it is known that the maximum polarization degree (Pmax) and the geometric albedo (pV) show an empirical relationship depending on surface grain sizes [5]. We observed Ryugu from UT 2020 November 30 to December 10 at large phase angles (ranging from 78.5 to 89.7 degrees) to derive Pmax. We modified TRIPOL (Triple Range Imager and POLarimeter, [6]) to attach to the 1.8-m telescope at the Bohyunsan Optical Astronomy Observatory (BOAO). With this instrument, we observed the asteroid and determined linear polarization degrees at the Rc-band filter. We obtained sufficient data sets from 7 nights at this observatory to determine the Pmax value, and collaborated with other observatories in Japan (i.e., Hokkaido University, Higashi-Hiroshima, and Nishi-Harima) to acquire linear polarization degrees of the asteroid from total 24 nights observations with large phase angle coverage (From 28 to 104 degrees). The observational results have been published in Kuroda et al. (2021) [7]. We thus found the dominance of submillimeter particles on the surface of Ryugu from the comparison with other meteorite samples from the campaign observation. In this presentation, we report our activity to modify the TRIPOL for the 1.8-m telescope and the polarimetric performance. We also examine the rotational variability of the polarization degree using the TRIPOL data.

• Journal of The Geomorphological Association of Korea
• /
• v.28 no.1
• /
• pp.83-99
• /
• 2021

Burn severity analysis using satellite imagery has high capabilities for research and management in inaccessible areas. We extracted the forest fire area of the DMZ (Demilitarized Zone) in the western Imjin Estuary which is restricted to access due to the confrontation between South and North Korea. Then we analyzed the forest fire severity and recoverability using atmospheric corrected Surface Reflectance Level-2 data collected from Landsat-8 OLI (Operational Land Imagery) / TIRS (Thermal Infrared Sensor). Normalized Burn Ratio (NBR), differenced NBR (dNBR), and Relative dNBR (RdNBR) were analyzed based on changes in the spectral pattern of satellite images to estimate burn severity area and intensity. Also, we evaluated the recoverability after a forest fire using a land cover map which is constructed from the NBR, dNBR, and RdNBR analyzed results. The results of dNBR and RdNBR analysis for the six years (during May 30, 2014 - May 30, 2020) showed that the intensity of monthly burn severity was affected by seasonal changes after the outbreak and the intensity of annual burn severity gradually decreased after the fire events. The regrowth of vegetation was detected in most of the affected areas for three years (until May 2020) after the forest fire reoccurred in May 2017. The monthly recoverability (from April 2014 to December 2015) of forests and grass fields was increased and decreased per month depending on the vegetation growth rate of each season. In the case of annual recoverability, the growth of forest and grass field was reset caused by the recurrence of a forest fire in 2017, then gradually recovered with grass fields from 2017 to 2020. We confirmed that remote sensing was effectively applied to research of the burn severity and recoverability in the DMZ. This study would also provide implications for the management and construction statistics database of the forest fire in the DMZ.

• Smart Structures and Systems
• /
• v.33 no.1
• /
• pp.55-64
• /
• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• Food Science of Animal Resources
• /
• v.44 no.1
• /
• pp.74-86
• /
• 2024

Eggs that have been hard-boiled are frequently used as ready-to-eat food. Refrigerated and frozen storage of hard-boiled eggs causes issues, such as customer rejection owing to textural changes. The objective of this research is to ascertain how storage temperature affects hard-boiled eggs' alteration in texture over time. Medium-sized brown shell eggs were acquired from a local market, boiled at 100℃ for 15 min, and then stored at room temperature (25℃), refrigeration (4℃), and freezing (-18℃) conditions for 0, 12, 24, and 48 h. Fourier transform infrared spectroscopy (FTIR), texture profile, visual observation using a gemological microscope, free amino acid content, and color were measured. Freezing had a substantial impact on the eggs' hardness, gumminess, chewiness, and cohesiveness (p<0.05). The FTIR spectrums confirmed the textural changes in bonds of amide A (3,271 cm^-1), amide I (1,626.2 cm^-1), amide II (1,539.0 cm^-1), C=O stretch of COO^- (1,397 cm^-1), asymmetric PO₂^- stretch (1,240 cm^-1). Microscopic images confirmed structural changes in eggs stored at -18℃. The free amino acid content was lower in fresh and frozen eggs than in the rest (p<0.05). However, there was no discernible variation in the egg white's color when eggs were kept at 4℃ (p>0.05). Salmonella spp. was found exclusively in eggs kept at room temperature. In conclusion, hard-boiled eggs did not exhibit structural or chemical changes when stored at 4℃ for up to 48 h compared to freezing and room temperature conditions.

• Journal of the Korean Wood Science and Technology
• /
• v.52 no.3
• /
• pp.205-220
• /
• 2024

Various polylactic acid (PLA) blends were reinforced with untreated or silane-treated micro-sized cellulose fiber (MCF), successfully prepared as 3D printing filaments and then printed using a fused filament fabrication (FFF) 3D printer. In this study, we focused on developing 3D-printed MCF/PLA composites through silane treatment of MCF and investigating the effect of silane treatment on the various properties of FFF 3D-printed composites. Fourier transform infrared spectra confirmed the increase in hydrophobic properties of silane-treated MCF by showing the new absorption peaks at 1,100 cm^-1, 1,030 cm^-1, and 815 cm^-1 representing C-NH₂, Si-O-Si, and Si-CH₂ bonds, respectively. In scanning electron microscope images of silane-treated MCF filled PLA composites, the improved interfacial adhesion between MCF and PLA matrix was observed. The mechanical properties of the 3D-printed MCF/PLA composites with silane-treated MCF were improved compared to those of the 3D-printed MCF/PLA composites with untreated MCF. In particular, the highest tensile and flexural modulus values were observed for S-MCF10 (5,784.77 MPa) and S-MCF5 (2,441.67 MPa), respectively. The thermal stability of silane-treated MCF was enhanced by delaying the initial thermal decomposition temperature compared to untreated MCF. The thermal decomposition temperature difference at T₉₅ was around 26℃. This study suggests that the effect of silane treatment on the 3D-printed MCF/PLA composites is effective and promising.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.13 no.3
• /
• pp.165-173
• /
• 2010

A cloud detection method is introduced to improve the reliability of NOAA/AVHRR Sea Surface Temperature (SST) data processed during the daytime and nighttime in the TeraScan System. In daytime, the channels 2 and 4 are used to detect a cloud using the three tests, which are spatial uniformity tests of brightness temperature (infrared channel 4) and channel 2 albedo, and reflectivity threshold test for visible channel 2. Meanwhile, the nighttime cloud detection tests are performed by using the channels 3 and 4, because the channel 2 data are not available in nighttime. This process include the dual channel brightness temperature difference (ch3 - ch4) and infrared channel brightness temperature threshold tests. For a comparison of daytime and nighttime SST images, two data used here are obtained at 0:28 (UTC) and 21:00 (UTC) on May 13, 2009. 6 parameters was tested to understand the factors that affect a cloud masking in and around Korean Peninsula. In daytime, the thresholds for ch2_max cover a range 3 through 8, and ch4_delta and ch2_delta are fixed on 5 and 2, respectively. In nighttime, the threshold range of ch3_minus_ch4 is from -1 to 0, and ch4_delta and min_ch4_temp have the fixed thresholds with 3.5 and 0, respectively. It is acceptable that the resulted images represent a reliability of SST according to the change of cloud masking area by each level. In the future, the accuracy of SST will be verified, and an assimilation method for SST data should be tested for a reliability improvement considering an atmospheric characteristic of research area around Korean Peninsula.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.4
• /
• pp.363-373
• /
• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• Economic and Environmental Geology
• /
• v.44 no.6
• /
• pp.463-474
• /
• 2011

The Haenam epithermal mineralized zone is located in the southwestern part of South Korea, and hosts low sulfidation epithermal Au-Ag deposit (Eunsan-Moisan) and clay quarries (Okmaesan, Seongsan, and Chunsan). Epithermal deposits and accompanying hydrothermal alteration related to Cretaceous volcanism caused large zoned assemblages of hydrothermal alteration minerals. Advanced argillic-altered rocks with mineral assemblages of alunite-quartz, alunite-dickite-quartz, and dickite-kaolinite-quartz exposed on the Okmaesan, Seongsan, and Chunsan area. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with three visible and near infrared bands, six shortwave infrared bands, and five thermal infrared bands, was used to identify advanced argillic-altered rocks within the Haenam epithermal mineralized zone. The distinct spectral features of hydrothermal minerals allow discrimination of advanced argillic-altered rocks from non-altered rocks within the study area. Because alunite, dickite, and kaolinite, consisting of advanced argillic-altered rocks within the study area are characterized by Al-O-H-bearing minerals, these acid hydrothermal minerals have a strong absorption feature at $2.20{\mu}m$. The band combination and band ratio transformation cause increasing differences of DN values between advanced argillic-altered rock and non-altered rock. The alunite and dickite-kaolinite of advanced argillic-altered rocks from the Okmaesan, Seongsan, and Chunsan have average DN values of 1.523 and 1.737, respectively. These values are much higher than those (1.211 and 1.308, respectively) of non-altered area. ASTER images can remotely provide the distribution of hydrothermal minerals on the surface. In this way good relation between ASTER spectra analysis and field data suggests that ASTER spectral analysis can be useful tool in the initial steps of mineral exploration.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1607-1622
• /
• 2022

The purpose of this study is to determine the actual performance of cool roof in preventing absorbed solar radiation. The spatial correlation between surface temperature and absorbed solar radiation is the method by which the performance of a cool roof can be understood and evaluated. The research area of this study is the vicinity of Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where an actual cool roof is applied. FLIR Vue Pro R thermal infrared sensor, Micasense Red-Edge multi-spectral sensor and DJI H20T visible spectral sensor was used for aerial photography, with attached to the drone DJI Matrice 300 RTK. To perform the spatial correlation analysis, thermal infrared orthomosaics, absorbed solar radiation distribution maps were constructed, and land cover features of roof were extracted based on the drone aerial photographs. The temporal scope of this research ranged over 9 points of time at intervals of about 1 hour and 30 minutes from 7:15 to 19:15 on July 27, 2021. The correlation coefficient values of 0.550 for the normal roof and 0.387 for the cool roof were obtained on a daily average basis. However, at 11:30 and 13:00, when the Solar altitude was high on the date of analysis, the difference in correlation coefficient values between the normal roof and the cool roof was 0.022, 0.024, showing similar correlations. In other time series, the values of the correlation coefficient of the normal roof are about 0.1 higher than that of the cool roof. This study assessed and evaluated the potential of an actual cool roof to prevent solar radiation heating a rooftop through correlation comparison with a normal roof, which serves as a control group, by using high-resolution drone images. The results of this research can be used as reference data when local governments or communities seek to adopt strategies to eliminate the phenomenon of urban heat islands.