• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.107-116
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• 2021

In recent years, the research on audience satisfaction after watching programs has been carried out in various fields. However, there is no precedent for the study of simply analyzing the influencing factors of audience satisfaction with the newly established CCTV-9 channel. For CCTV-9, how to explore the strategy of industrial development based on the needs of the audience in the era of big data is a very important part. This article exploratively focuses on the influencing factors related to CCTV-9 audience satisfaction. Using questionnaires, 101 samples of the satisfaction with the channel of men and women of different ages, education backgrounds, majors, and incomes were collected to test, and 9 hypotheses were tentatively proposed as relevant influencing factors of channel satisfaction. Through empirical analysis, this research searches for the determinants. The reliability and validity of the measurement were properly analyzed, and all hypotheses were statistically tested. The empirical results show that: subject matter, program format, program scheduling, program broadcast time, channel advertising, simulcast series of documentaries, diversified communication platforms, brand image packaging and audience satisfaction are significantly positively correlated.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.371-384
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• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.409-420
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• 2012

Since the Industrial Revolution, CO2 levels have been increasing with climate change. In this study, Analyze time-series changes in snow cover quantitatively and predict the vanishing point of snow cover statistically using remote sensing. The study area is Mt. Kilimanjaro, Tanzania. 23 image data of Landsat-5 TM and Landsat-7 ETM+, spanning the 27 years from June 1984 to July 2011, were acquired. For this study, first, atmospheric correction was performed on each image using the COST atmospheric correction model. Second, the snow cover area was extracted using the NDSI (Normalized Difference Snow Index) algorithm. Third, the minimum height of snow cover was determined using SRTM DEM. Finally, the vanishing point of snow cover was predicted using the trend line of a linear function. Analysis was divided using a total of 23 images and 17 images during the dry season. Results show that snow cover area decreased by approximately $6.47km^2$ from $9.01km^2$ to $2.54km^2$, equivalent to a 73% reduction. The minimum height of snow cover increased by approximately 290 m, from 4,603 m to 4,893 m. Using the trend line result shows that the snow cover area decreased by approximately $0.342km^2$ in the dry season and $0.421km^2$ overall each year. In contrast, the annual increase in the minimum height of snow cover was approximately 9.848 m in the dry season and 11.251 m overall. Based on this analysis of vanishing point, there will be no snow cover 2020 at 95% confidence interval. This study can be used to monitor global climate change by providing the change in snow cover area and reference data when studying this area or similar areas in future research.

• The Journal of Art Theory & Practice
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• no.6
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• pp.77-99
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• 2008

The purpose of this study is to show that the reason behind the ambivalent characteristic displayed in Niki de Saint Phalle's works is in her trauma and how such characteristic can be extracted from her works. During her creative years, Saint Phalle worked on various materials from different genres such as assemblages, shooting paintings, a series on Bride and Monster, 'Nana', 'Tarot Garden'and public sculptures. One commonality found among her various works is the ambivalent characteristic that contains contrasting elements simultaneously. Saint Phalle suffered a terrible psychological damage inflicted by her parents during her childhood. Specifically, she was sexually assaulted by her father and emotionally neglected by her mother, the trauma that affected her for the rest of her life. As a result, she came to develop extreme love- hate relationships with her parents and this became the main reason for the ambivalent characteristic displayed in her works. The love-hate relationship Saint Phalle developed can be identified through various researches done on the subject of the affect of sexual assault. It is common for incest victims to develop ambivalent feelings towards the perpetrator and Saint Phalle was no exception. Dissociation disorder and a snake well explain the trauma from her father. It is a generally accepted belief in the field of psychology that dissociation disorder commonly occurs to children who experience incest. And dissociation disorder is similar to the characteristic of ambivalence in the sense that a single entity contains more than two contrasting elements at the same time. In addition, the amputated doll objects used in her assemblages coincide with the expression of body detachment of people with dissociation disorder. These facts clearly indicate that the trauma from her father is showing through in her works. A snake is a subject matter that reflects the ambivalent tendency of Saint Phalle that resulted from her trauma. She remembers her father's rape as an image of a snake which is related to a phallic symbol in mythology or art reflecting her trauma. Moreover, she displays a similar pattern of ambivalent emotion like love and hate or fear towards a snake and her father. This is also confirmed by her portrayal of a snake as a monster or reversely as a creature with fundamental vitality in her works. The lack of affection from her mother can be explained by her mother's maternal deprivation. It appears that Saint Phalle's mother possessed all the causes for maternal deprivation such as maternal separation, personality disorder and inappropriate attitude towards child rearing. Especially, a study that shows mother's negative attitude towards child breeding tends to increase dissociation experience of children is another important evidence that supports Saint Phalle's dissociation tendency. These traces of Saint Phalle's trauma are clearly revealed in her assemblages and shooting paintings. The violent objects in her assemblages such as a hammer, razor, nail represent the rage and defensiveness towards her father. The objects such as fragments of broken plates of feminine patterns, pots and mirrors that her mother used symbolize the affection towards her mother. On the other hand, the destructed objects can be interpreted as her hate and resentment towards her mother. Shooting paintings contain her extreme fury and hate. Things such as acts of shooting and the image associated with blood after shooting are blunt expressions of her bursts of emotions. I have tried to define and classify the ambivalent characteristics shown in her assemblages and shooting paintings as hate, rage, violence, calm, love and pleasure according to the frame of Thanatos and Eros. Out of the six, hate, rage, violence and clam are associated with Thanatos while love and pleasure are associated with Eros and they correspondingly form an ambivalent structure. These ambivalent characteristics can be found in her assemblages and shooting paintings. The objects in her assemblages such as a razor, saw, hammer imply hate, rage, violence and the silence felt throughout her works represent calmness. And, as mentioned, the feminine objects can be seen as symbolizing love. In shooting paintings, hate, rage, violence can be found in the use of force and in the traces of watercolor after shooting, and a sense of pleasure in her feelings of catharsis after her shooting. Moreover, a shielded calmness can be found on the plywood all covered with plaster before the shooting. This study looked into the ambivalent characteristic of Saint Phalle's works by examining her trauma to find its correlation, and a meaning of this study can be found from the fact that it refocused the origin of Saint Phalle who is generally known as a feminist artist. Additionally, a meaning of the study can be found also from the fact that it examined the ambivalent characteristics of her works through a frame of Thanatos and Eros.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.363-375
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• 2024

Waterbody change detection using satellite images has recently been carried out in various regions in South Korea, utilizing multiple types of sensors. This study utilizes optical satellite images from Landsat and Sentinel-2 based on Google Earth Engine (GEE) to analyze long-term surface water area changes in four monitored small and medium-sized water supply dams and agricultural reservoirs in South Korea. The analysis covers 19 years for the water supply dams and 27 years for the agricultural reservoirs. By employing image analysis methods such as normalized difference water index, Canny Edge Detection, and Otsu'sthresholding for waterbody detection, the study reliably extracted water surface areas, allowing for clear annual changes in waterbodies to be observed. When comparing the time series data of surface water areas derived from satellite images to actual measured water levels, a high correlation coefficient above 0.8 was found for the water supply dams. However, the agricultural reservoirs showed a lower correlation, between 0.5 and 0.7, attributed to the characteristics of agricultural reservoir management and the inadequacy of comparative data rather than the satellite image analysis itself. The analysis also revealed several inconsistencies in the results for smaller reservoirs, indicating the need for further studies on these reservoirs. The changes in surface water area, calculated using GEE, provide valuable spatial information on waterbody changes across the entire watershed, which cannot be identified solely by measuring water levels. This highlights the usefulness of efficiently processing extensive long-term satellite imagery data. Based on these findings, it is expected that future research could apply this method to a larger number of dam reservoirs with varying sizes,shapes, and monitoring statuses, potentially yielding additional insights into different reservoir groups.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.74-83
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• 2005

The Riparian Buffer Zone has many potential values including the preservation of water quality as well as being ecologically friendly. This study aims to quantitatively analyze the landscape structure index of the Riparian Buffer Zone in the Kyoung-an stream and to produce base information necessary for proper management. The study used aerial images that were applied to geometric corrections for a time series from 1966 to 2000 for land data and also used FRAGSTATS, which is a type of ARCVIEW extension module, as an analysis tool. An analysis of land use change and the Landscape Index revealed that the area of farm land has decreased and that the area of residential property has increased. In addition, there was a slight change for land used for purposes other than farming or for residence. The results of analyzing the Landscape Structure Index, revealed that the NP has increased from 437 in 1966 to 695 in 2000. This data reveals that the change of land use is influenced by various artificial factors. The NPS, which represents the declining degree of patch, decreased from 9.441 to 5.934, revealing that the change of land use has been progressing considerably. In regard to forest areas, land use reduced somewhat but did not indicate a significant change. Therefore, an analysis of the total index reveals that the edge of patch has become more complicated and that the variation index of patch has increased significantly. However, this study reveals that barriers to block pollution have weakened as a result and that there is a need to concentrate on the implementation and the management of the Riparian Buffer Zone. Consequently, this study reveals that substantial research is necessary in order to carry out the proper management of the Riparian Buffer Zone, especially in light of the distribution type of each patch and the change in conditions regarding them.

• Progress in Medical Physics
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• v.26 no.4
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• pp.286-293
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• 2015

The purpose of this study was to evaluate the set up accuracy using stereotactic body frame and frameless immobilizer for lung stereotactic body radiation therapy (SBRT). For total 40 lung cancer patients treated by SBRT, 20 patients using stereotactic body frame and other 20 patients using frameless immobilizer were separately enrolled in each group. The setup errors of each group depending on the immobilization methods were compared and analyzed. All patients received the dose of 48~60 Gy for 4 or 5 fractions. Before each treatment, a patient was first localized to the treatment isocenter using room lasers, and further aligned with a series of image guidance procedures; orthogonal kV radiographs, cone-beam CT, orthogonal fluoroscopy. The couch shifts during these procedures were recorded and analyzed for systematic and random errors of each group. Student t-test was performed to evaluate significant difference depending on the immobilization methods. The setup reproducibility was further analyzed using F-test with the random errors excluding the systematic setup errors. In addition, the ITV-PTV margin for each group was calculated. The setup errors for SBF were $0.05{\pm}0.25cm$ in vertical direction, $0.20{\pm}0.38cm$ in longitudinal direction, and $0.02{\pm}0.30cm$ in lateral direction, respectively. However the setup errors for frameless immobilizer showed a significant increase of $-0.24{\pm}0.25cm$ in vertical direction while similar results of $0.06{\pm}0.34cm$, $-0.02{\pm}0.25cm$ in longitudinal and lateral directions. ITV-PTV margins for SBF were 0.67 cm (vertical), 0.99 cm (longitudinal), and 0.83 cm (lateral), respectively. On the other hand, ITV-PTV margins for Frameless immobilizer were 0.75 cm (vertical), 0.96 cm (longitudinal), and 0.72 cm (lateral), indicating less than 1 mm difference for all directions. In conclusion, stereotactic body frame improves reproducibility of patient setup, resulted in 0.1~0.2 cm in both vertical and longitudinal directions. However the improvements are not substantial in clinic considering the effort and time consumption required for SBF setup.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.