• Journal of Korea Entertainment Industry Association
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• 제14권8호
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• pp.175-187
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• 2020

The purpose of this study is to discuss the elements and meanings of the performative scenography which has been revealed through the new directorial interpretation and deconstruction process of the musical 『All Shook Up』. The performative scenography characteristics after postmodernism aim to create individual active perceptions and various social meanings through audience's voluntary and particular emergence. To this end, the theoretical foundation of scenography was examined by periods in advance. Based on this, I attempted to establish performative scenography for synthesized scenic and media design through the reconstruction process for the 『All Shook Up Travelers』. As a result, I set up visual narrative based world of 『All Shook Up Travelers』 which was produced by text-based intense images for a direct medium in order to expand actors' inner narrative and established unique performative scenography of its own: 1. enhancing the adapted one's narratives for the actors' and audience's co-existence and detachment, 2. delivering its own independent meanings which have double meanings, 3. encouraging audience's critical and active perception experiences through collage and montage of media.

• Journal of the Korean Society of Floral Art and Design
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• 제42호
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• pp.133-144
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• 2020

Anthropomorphism is one of the commonly used appeal methods in the field of communication design. Almost all visual images that humans encounter are exposed to this anthropomorphism, and we are consciously or unconsciously utilizing or being used. In addition, anthropomorphism can be found in almost all cultures and arts rather than in a specific field of study. Therefore, in this paper, the personification is redefined based on the relationship formation structure based on the anthropomorphic cases observed in culture and art and the results of previous studies. In addition, the personification form is defined as two kinds of personification and inverse personification according to the subject of relationship formation based on the personification type and gesture list derived from the previous study on the personification technique. Through the application cases of anthropomorphic techniques, which are appealed across the design domain, the effective anthropomorphic application system was defined. The definition of anthropomorphic relationship formation and anthropomorphic application system provided a framework for anthropomorphic techniques that could lead to effective audience satisfaction in various media. In addition, through the personification application system that synchronizes the characteristics of the conceptual traits of the medium with the gesture list and the personification type classification, it was confirmed that a device for communicating with the owner can be provided with a powerful and effective personification technique.

• Journal of the Korea Society of Computer and Information
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• 제27권9호
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• Journal of the Korea Society of Computer and Information
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• 제27권12호
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Geophysics and Geophysical Exploration
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• 제25권3호
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• Journal of Korean Society of Forest Science
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• 제112권3호
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Journal of IKEEE
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• 제27권3호
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• Journal of Korean Home Economics Education Association
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• 제35권2호
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• pp.133-144
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• 2023

The goal of this study was to improve environmental awareness by systematically developing a hands-on online lesson for adults on making drink bags by upcycling discarded umbrella cloth. The lesson was developed using an ADDIE model. During the analysis stage, the instructional design direction was established based on the findings of previous studies. In the design stage, the operation of practical classes in the online environment was specifically planned. The contents of education and the training time were also determined. The materials developed during the development stage included a kit and theoretical information containing images to raise awareness of environmental pollution and the significance of upcycling, as well as videos and photos. During the implementation stage, two sessions were held three months apart. A total of 36 adults participated, with 18 participants in each session. In the evaluation stage, the first session participants provided feedback on class satisfaction, which led to improvements. Positive feedbacks were received from the second session participants, who expressed satisfaction with the smooth communication and easy approaches to the learning materials. In both instances, the surveys on environmental consciousness and attitudes yielded an overall average score of 4.27, indicating a generally positive evaluation.

• Journal of the Korean Society of Marine Environment & Safety
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• 제29권5호
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• pp.456-461
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• 2023

The Korea Maritime Environment Corporation is conducting a comprehensive survey of the national marine ecosystem under the commission of the Ministry of Oceans and Fisheries (MOF) to ensure continuous use of the ocean, preserve and manage the marine ecosystem. The survey has set major peaks to investigate changes in the marine ecosystem around the Korean Peninsula. However as the peak has been set around the coast, it is necessary to expand the scope of investigation to encompass offshore areas. Meanwhile, the Aids to Navigation Division of the MOF supports a comprehensive national marine ecosystem survey providing photographs of fouling organisms during the Aids to Navigation lifting inspection, however, the photographs are provided only in consultation with the Korea Maritime Environment Corporation. Therefore, a study was conducted to generate information on fouling organisms using deep learning-based image processing algorithms by the lifting Aids to Navigation and dorsal buoys so that Aids to Navigation could be used as the major component of a comprehensive national marine ecosystem. If the Aids to Navigation are used as the peak of the survey, they could serve as fundamental data to enhance their own value as well as analyze abnormal marine conditions and ecosystem changes in Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제43권5호
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.