• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.1
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• pp.29-41
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• 2023

In landscape studies, the concept of naturalness was vast in its categories from physical space to cognitive systems, making it difficult to define terms at once. Therefore, this study summarized the concept and evaluation attributes of 'naturalness' used in the literature through systematic review (SR), and identified the scope of individual attributes that constitute the meaning of naturalness. In addition, the individual attributes classified in previous studies were identified as the meaning chain, one of the cognitive linguistic research methods, and applied to papers targeting naturalness among domestic landscape studies to organize a polysemous meaning system. Meaning chain is a suitable method for grasping words whose meaning expands in a chain due to family resemblance around prototypical meaning, and the dimension is classified according to the classification of naturalness evaluation items and a multi-semantic chain system of naturalness concepts discussed in domestic academia. The results of the study are as follows. First, the attributes of naturalness extracted through foreign landscape literature were classified into four areas: nature perceived as wilderness, nature as non-artificiality, nature as visual landscape, and nature as experience, and 13 detailed attributes. Second, these detailed attributes are generally consistent with domestic landscape studies, but their specific cases were different, and a Korean context was presented in perception of time accumulation, also they suggested that there may be a mutual conflict between naturalness attributes.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.107-115
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• 2023

High turbidity in source water can have adverse effects on water treatment plant operations and aquatic ecosystems, necessitating turbidity management. Consequently, research aimed at predicting river turbidity continues. This study developed a multi-class classification model for prediction of turbidity using LightGBM (Light Gradient Boosting Machine), a representative ensemble machine learning algorithm. The model utilized data that was classified into four classes ranging from 1 to 4 based on turbidity, from low to high. The number of input data points used for analysis varied among classes, with 945, 763, 95, and 25 data points for classes 1 to 4, respectively. The developed model exhibited precisions of 0.85, 0.71, 0.26, and 0.30, as well as recalls of 0.82, 0.76, 0.19, and 0.60 for classes 1 to 4, respectively. The model tended to perform less effectively in the minority classes due to the limited data available for these classes. To address data imbalance, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm was applied, resulting in improved model performance. For classes 1 to 4, the Precision and Recall of the improved model were 0.88, 0.71, 0.26, 0.25 and 0.79, 0.76, 0.38, 0.60, respectively. This demonstrated that alleviating data imbalance led to a significant enhancement in Recall of the model. Furthermore, to analyze the impact of differences in input data composition addressing the input data imbalance, input data was constructed with various ratios for each class, and the model performances were compared. The results indicate that an appropriate composition ratio for model input data improves the performance of the machine learning model.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1679-1692
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• 2023

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• 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.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.393-407
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• 2012

This study focuses on the correction of topographic effects caused by a combination of solar elevation and azimuth, and topographic relief in single optical remote sensing imagery, and by a combination of changes in position of the sun and topographic relief in comparative analysis of multi-temporal imageries. For the Jeju Island, Republic of Korea, where Mt. Halla and various cinder cones are located, a Landsat 7 ETM+ imagery and ASTER GDEM data were used to normalize the topographic effects on the imagery, using two topographic normalization methods: cosine correction assuming a Lambertian condition and assuming a non-Lambertian c-correction, with kernel sizes of $3{\times}3$, $5{\times}5$, $7{\times}7$, and $9{\times}9$ pixels. The effects of each correction method and kernel size were then evaluated. The c-correction with a kernel size of $7{\times}7$ produced the best result in the case of a land area with various land-cover types. For a land-cover type of forest extracted from an unsupervised classification result using the ISODATA method, the c-correction with a kernel size of $9{\times}9$ produced the best result, and this topographic normalization for a single land cover type yielded better compensation for topographic effects than in the case of an area with various land-cover types. In applying the relative radiometric normalization to topographically normalized three multi-temporal imageries, more invariant spectral reflectance was obtained for infrared bands and the spectral reflectance patterns were preserved in visible bands, compared with un-normalized imageries. The results show that c-correction considering the remaining reflectance energy from adjacent topography or imperfect atmospheric correction yielded superior normalization results than cosine correction. The normalization results were also improved by increasing the kernel size to compensate for vertical and horizontal errors, and for displacement between satellite imagery and ASTER GDEM.

• The Journal of Engineering Geology
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• v.15 no.2 s.42
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• pp.213-227
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• 2005

This study aims to assess the problems with investigation method and to suggest the complementary solutions by comparing the predicted data from surface investigation with the outcome data from underground cavern. In the study area, one(NE-1) of 6 fracture zones predicted during the surface investigation was only confirmed in underground caverns. Therefore, it is necessary to improve the confidence level for prediction. In this study, the fracture classification criteria was quantitatively suggested on the basis of the BHTV images of NE-1 fracture zone. The major orientation of background fractures in rock mass was changed at the depth of the storage cavern, the length and intensity were decreased. These characteristics result in the deviation of predieted predicted fracture properties and generate the investigation bias depending on the bore hole directions and investigated scales. The evaluation of hydraulic connectivity in the surface investigation stage needs to be analyze by the groundwater pressures and hydrochemical properties from the monitoring bore hole(s) equipped with a double completion or multi-packer system during the test bore hole is pumping or injecting. The hydraulic conductivities in geometric mean measured in the underground caverns are 2-3 times lower than those from the surface and furthermore the horizontal hydraulic conductivity in geometric mean is six times lower than the vertical one. To improve confidence level of the hydraulic conductivity, the orientation of test hole should be considered during the analysis of the hydraulic conductivity and the methodology of hydro-testing and interpretation should be based on the characteristics of rock mass and investigation purposes.

• Childhood Kidney Diseases
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• v.3 no.1
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• pp.80-87
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• 1999

Purposes : Renal involvement is a potentially serious complication of systemic lupus erythematosus (SLE). There have been only few studies of lupus nephritis in pediatric age. In this study, the clinical manifestations, pathologic findings, response to treatment, and clinical course of lupus nephritis in children were analyzed. And the results will provide basic data for future nation-wide prospective multi-center study. Methods . The medical records of 46 children clinically and pathologically diagnosed to have lupus nephritis at Seoul National University Children's Hospital during 1986 to 1997 were analyzed retrospectively. Results : 1) The median age of diagnosis of lupus nephritis was 12.8 years ($2\;years\~\;15year$ 8months), and the sex ratio was 1:2.5. 2) FANA($85.7\%$), anti-ds-DNA antibody ($78.0\%$), and malar rash ($60.8\%$) were the most common findings among the classification criteria by ARA Decreased C3 was detected in $88.9\%$ of patients. 3) Hematuria ($87.0\%$) was the most common renal symptom, and WHO class IV lupus nephritis was identified in 41 cases by renal biopsy. 4) In most of patients, the disease activity was controlled relatively well with a single or combined therapy of prednisolone, azathioprine, or cyclophosphamide. The response revealed no difference according to the mode of treatment. 5) Infection, especially of Varicella-Zoster virus and candida, was the most common complication during the disease course. Conclusion : The renal involvement was noted in $87.0\%$ of childhood SLE, and $89.1\%$ of renal lesions was WHO class IV lupus nephritis known to associated with poor long-term prognosis. So, aggressive treatment using immunosuppressants in the early disease course may be helpful to increase long-term prognosis of lupus nephritis. A prospective multi-center study is necessary to analyze the therapeutic efficacy of various treatment modalities.

• The Korean Journal of Mycology
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• v.23 no.1 s.72
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• pp.92-104
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• 1995

Ten species of Cordyceps species were collected throughout Kangwon province including Chuncheon Dongsanmyun KNU forest experiment from June to September, 1993. Collected Cordyceps species were identified as Cordyceps militaris, C. roseostromata, C. kyushuensis, C. scarabaeicola, Phytocordyceps ninchukiospora, C. nutans, Paecilomyces tenuipes, C. sphecocephala, Hymenostilbe odonatae, Torrubiella sp.. C. militaris, type species of Cordyceps species, was mainly formed on pupae of Lepidoptera and found after the rainy season around July. Fruiting body of C. roseostromata was morphologically similar to those of C. militaris, but relatively small in size and they were also found on lawn or pupa of Lepidoptera. Fruiting body of C. scarabaeicola was found on adult Scarabaeidae specifically and collect fruiting bodies of C. kyushuensis were on larva of moth. C. nutans and C. sphecocephala had host specificity on Hemiptera and Hymenoptera, respectively. Each species formed elliptical fertile part attach to the slim and carneous stalk and they were collected the most in specimen number through whole season of the summer. Ascospore of Phytocordyceps ninchukiospora on seed was characterized by two viable, multiseptate, fusiform units linked end-to-end by a long, filiform connective. Paecilomyces tenuipes, imperfect stage of the genus Cordyceps is multi-infective fungi that attack all stages of all groups of insects. Hymenostilbe odonatae attacks only adult Odonata and Torrubiella sp. formed on spider was difficult to collect because it was found the back side of leaf. As results of cultural test PDA medium showed the best mycelial growth. In the experiment of effect of the acidity inside of the media, C. militaris was good on pH 5, C. nutans and Phytocordyceps ninchukiospora were good on pH 6 and Paecilomyces tenuipes was on pH 7 and C. scarabaeicola was on pH 9. All isolates tested showed the best mycelial growth at $20^{\circ}C$. Morphologically similar isolates were used to analyze protein banding pattern among and within species. As a result, C. militaris, C. roseostromata and C. kyushuensis were clustered into close species and C. scarabaeicola and Phytocordyceps ninchukiospora were relatively distant from those species.