• Korean Journal of Remote Sensing
• /
• v.40 no.2
• /
• pp.179-190
• /
• 2024

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.

• Tuberculosis and Respiratory Diseases
• /
• v.62 no.4
• /
• pp.299-307
• /
• 2007

Background: High mobility group box 1 (HMGB1) is a novel, late mediator of inflammation. This study compared the pro-inflammatory effects of LPS and HMGB1. The transcriptional factors that play an important role in mediating the HMGB1-induced stimulation of IL-8 were also evaluated. Methods: RAW264.7 cells were stimulated with either LPS (100 ng/ml) or HMGB1 (500 ng/ml). The $TNF-{\alpha}$, MIP-2 and $IL-1{\beta}$ levels in the supernatant were evaluated by ELISA at 0, 2, 4, 8, 12 and 24h after stimulation. An acute lung injury was induced by an injection of LPS (5 mg/kg) or HMGB1 (2.5 mg/kg) into the peritoneum of the Balb/c mice. The lung cytokines and MPO activity were measured at 4h (for LPS) or 24h (for HMGB1) after the injection. The transcriptional factor binding sites for NF-IL6, $NF-{\kappa}B$ and AP-1 in the IL-8 promoter region were artificially mutated. Each mutant was ligated with pIL-6luc and transfected into the RAW264.7 cells. One hour after stimulation with HMGB1 (500 ng/ml), the cell lysate was analyzed for the luciferase activity. Results: The expression of MIP-2, which peaked at 8h with LPS stimulation, increased sequentially until 24h after HMGB1 stimulation. An intraperitoneal injection of HMGB1, which induced a minimal increased in $IL-1{\beta}$ expression, provoked the accumulation of neutrophils the lung. A mutation of AP-1 as well as $NF-{\kappa}B$ in the IL-8 promoter region resulted in a lower luciferase activity after HMGB1 stimulation. Conclusion: The proinflammatory effects of HMGB1, particularly on IL-8, are mediated by both $NF-{\kappa}B$ and AP-1.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_1
• /
• pp.653-666
• /
• 2020

Understanding the vegetation structure is important to manage forest resources for sustainable forest development. With the recent development of technology, it is possible to apply new technologies such as drones and deep learning to forests and use it to estimate the vegetation structure. In this study, the vegetation structure of Gongju, Samchuk, and Seoguipo area was identified by fusion of drone-optical images and LiDAR data using Artificial Neural Networks(ANNs) with the accuracy of 92.62% (Kappa value: 0.59), 91.57% (Kappa value: 0.53), and 86.00% (Kappa value: 0.63), respectively. The vegetation structure analysis technology using deep learning is expected to increase the performance of the model as the amount of information in the optical and LiDAR increases. In the future, if the model is developed with a high-complexity that can reflect various characteristics of vegetation and sufficient sampling, it would be a material that can be used as a reference data to Korea's policies and regulations by constructing a country-level vegetation structure map.

• Development and Reproduction
• /
• v.18 no.4
• /
• pp.197-212
• /
• 2014

Osteosarcoma (OS) is one of the most common malignant primary bone tumors and NF-${\kappa}B$ appears to play a causative role, but the mechanisms are poorly understood. OS is one of the pleomorphic, highly metastasized and invasive neoplasm which is capable to generate osteoid, osteoclast and osteoblast matrix. Its high incidence has been reported in adolescent and children. Cell signal cascade is the pivotal functional mechanism acquired during the differentiation, proliferation, growth and survival of the cells in neoplasm including OS. The major limitation to the success of chemotherapy in OS is the development of multidrug resistance (MDR). Answers to all such queries might come from the knock-in experiments in which the combined approach of miRNAs with NF-${\kappa}B$ pathway is put into use. Abnormal miRNAs can modulate several epigenetical switching as a hallmark of number of diseases via different cell signaling. Studies on miRNAs have opened up the new avenues for both the diagnosis and treatment of cancers including OS. Collectively, through the present study an attempt has been made to establish a new systematic approach for the investigation of microRNAs, bio-physiological factors and their target pairs with NF-${\kappa}B$ to ameliorate oncogenesis with the "bridge between miRNAs and NF-${\kappa}B$". The application of NF-${\kappa}B$ inhibitors in combination with miRNAs is expected to result in a more efficient killing of the cancer stem cells and a slower or less likely recurrence of cancer.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.6
• /
• pp.671-679
• /
• 2020

In order to increase the food self-sufficiency rate, monitoring and analysis of crop conditions in the cultivated area is important, and the existing measurement methods in which agricultural personnel perform measurement and sampling analysis in the field are time-consuming and labor-intensive for this reason inefficient. In order to overcome this limitation, it is necessary to develop an efficient method for monitoring crop information in a small area where many exist. In this study, RGB images acquired from unmanned aerial vehicles and vegetation index calculated using RGB image were applied as deep learning input data to classify complex upland crops in small farmland. As a result of each input data classification, the classification using RGB images showed an overall accuracy of 80.23% and a Kappa coefficient of 0.65, In the case of using the RGB image and vegetation index, the additional data of 3 vegetation indices (ExG, ExR, VDVI) were total accuracy 89.51%, Kappa coefficient was 0.80, and 6 vegetation indices (ExG, ExR, VDVI, RGRI, NRGDI, ExGR) showed 90.35% and Kappa coefficient of 0.82. As a result, the accuracy of the data to which the vegetation index was added was relatively high compared to the method using only RGB images, and the data to which the vegetation index was added showed a significant improvement in accuracy in classifying complex crops.

• Korean Journal of Remote Sensing
• /
• v.36 no.6_2
• /
• pp.1579-1590
• /
• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• Journal of Preventive Medicine and Public Health
• /
• v.37 no.2
• /
• pp.127-132
• /
• 2004

Objectives : Cotinine, the major metabolite of nicotine, is a useful marker of exposure to tobacco smoke and self-reporting of smoking status is thought not to be reliable. This study aimed to evaluate the agreement between the smoking self-report among adolescents and the urinary cotinine test. Methods : The study subjects were 1226 middle and high school students in Hanam city, who were selected by stratified random sampling. The self-report about smoking behavior was compared with urine cotinine value measured with PBM $AccuSign^{\circledR}fi$ Nicotine(Princeton BioMeditech Corporation, USA). The percentage agreement, kappa and 95% confidence interval(CI) were calculated. Results : The overall percentage agreement was 88.6%, and those for boys, girls, middle school, general school and vocational school students were 87.3%, 90.1%, 93.7%, 85,5%, 90.7%, and 78.4%, respectively. The overall kappa index was 0.46(95% CI=0.39-0.54)for overall, .and those for boys, girls, middle school, general school and vocational school students were 0.56(95% CI=0.48-0.65), 0.20(95% CI=0.07-0.32), 0.21(95% CI=0.09-0.34), 0.55(95% CI=0.47-0.64), 0.42(95% CI=0.33-0.52), and 0.48(95% CI=0.36-0.60), respectively. Conclusion : The percentage agreement was relatively high but the kappa values very low for girls, and middle school students. Though the prevalence bias can be influenced by these results, the self-report was not a sufficient tool for the evaluation of adolescents' smoking status, especially in girls or middle school students.

• The Journal of Korean Medicine
• /
• v.40 no.3
• /
• pp.35-58
• /
• 2019

Objectives: The aim of this study was to investigate the anti-inflammatory effects of Curcuma longa rhizoma extract in an experimental rat model of osteoarthritis. Methods: Osteoarthritis was induced in rats by injecting monosodium iodoacetate (MIA) into the knee joint cavity of rats. The rats were divided into 5 groups (Normal, Control, positive comparison, low (CL) and high (CH) concentration groups). Rats in the low concentration (CL) group had MIA-induced osteoarthritis; they were treated with Curcuma longa rhizoma extract at a dose of 50mg/kg body weight. Rats in the high concentration (CH) group had MIA-induced osteoarthritis; they were treated with Curcuma longa rhizoma extract at a dose of 100mg/kg body weight. Hind paw weight distribution and ROS levels were measured. At the end of all treatments, changes in alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine levels were analyzed. In addition, inflammatory protein levels were evaluated by western blot analysis. Results: In this study, hind paw weight distribution significantly improved in the CL and CH groups, while. Reactive oxygen species (ROS) production significantly decreased in both. The levels of ALT, AST, BUN, and creatinine did not significantly change in either group. The production of nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4), $p47^{phox}$, and Ras-related C3 botulinum toxin substrate 1 (RAC1) decreased in both. Catalase, heme oxygenase-1 (HO-1) and superoxide dismutase (SOD) significantly increased in the CL and CH groups, respectively. Nuclear factor erythroid 2 (Nrf2) increased, but there were no significant differences between the experimental and control groups. Inflammatory cytokines, including nuclear factor-kappa Bp65 (NF-${\kappa}Bp65$), interleukin-1beta (IL-$1{\beta}$), and tumor necrosis factor-alpha (TNF-${\alpha}$), decreased significantly in both the CL and CH groups. Conclusions: Our results showed that Curcuma longa rhizoma extract has anti-inflammatory effects. Anti-inflammatory activity is regulated by the inhibition of inflammatory cytokines and mediators, such as NF-${\kappa}B$, therefore, it suppresses cartilage damage as well.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.10 no.1
• /
• pp.44-50
• /
• 2007

Purpose: The role of plain, abdominal radiography in childhood constipation has not been fully evaluated. The aim of this study was to determine the accuracy and reliability of scoring systems assessing a fecal load on plain, abdominal radiographs in children with functional constipation. Methods: Plain, abdominal radiographs from 38 constipated children and 39 control children were examined by four independent inspectors, pediatric residents. Four inspectors independently scored the radiographs according to three different scoring systems Barr, Blethyn, and Leech. No clinical information about the patients was available to the inspectors. Each abdominal radiograph was evaluated on two separate occasions, one week apart. Kappa coefficients were calculated as indicators of inter-and intra-inspector variability, coefficients <0.20, 0.21~0.40, 0.40~0.60, 0.61~0.80 and 0.81~1.00 were considered to indicate poor, fair, moderate, good, and very good agreement, respectively. Results: The Leech score showed the highest reproducibility: the inter-inspector agreement was uniformly very good on two separate occasions (${\kappa}$ values of 0.88, 0.91, 0.92, 0.86 in the first time and 0.81, 0.88, 0.89, 0.84 in the second time). Agreement using the Barr score was good (${\kappa}$ values of 0.66, 0.67, 0.69, 0.66 in the first time and 0.68, 0.65, 0.71, 0.68 in the second time). However, agreement for the Blethyn score was the lowest of the three scoring systems. The Leech scoring system had the highest sensitivity and specificity compared to the Barr scoring system for the diagnosis of functional constipation by plain, abdominal radiographs. Conclusion: The Leech score appeared to be a more accurate and reliable method because of its high sensitivity and specificity for evaluating the fecal load on plain, abdominal radiographs in children with functional constipation. Therefore, the Leech scoring system was found to be the most useful for assessment for the degree of constipation on plain, abdominal radiographs in children.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.682-685
• /
• 2004

This paper investigated a method for classifying emotional states by using pulse wave signal. It focused on finding effective features for emotional state classification. The emptional states considered here consisted of interest and neutral. Classification experiments utilized 65 and 60 samples of interest and neutral states respectively. We have investigated 19 features derived from pulse wave signals by using both time domain and frequency domain analysis methods with 2 classifiers of minimum distance (normalized Euclidean distanece) and ${\kappa}$-Nearest Neighbour. The Leave-one-out cross validation was used as an evaluation mehtod. Based on experimental results, the most efficient features were a combination of 4 features consisting of (i) the mean of the first differences of the smoothed pulse rate time series signal, (ii) the mean of absolute values of the second differences of thel normalized interbeat intervals, (iii) the root mean square successive difference, and (iv) the power in high frequency range in normalized unit, which provided 80.8% average accuracy with ${\kappa}$-Nearest Neighbour classifier.