• Sim-seong Yeon-gu
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• v.29 no.2
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• pp.127-156
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• 2014

This thesis is an psychological study investigating the meanings of skin from the perspective of analytical psychology. Skin, as the outermost layer of our body, protects the body and carries out essential physiologic functions. It is an organ of the body and also psychological contents can be expressed on it in various forms. We can find sociocultural connotations of skin, some of which are demonstrated in our language. Skin may become a carrier of persona which defines a person's role in the society. And it can be a place where ego is expressed. Eros is the principle of relationship by Jung's definition and skin is the space where eros is realized intensely. Skin may carry meanings as a symbol of transformation. Skin disease can be interpreted as a message from Self in certain cases. The theme of casting off skin in myths and dreams can be an analogy of an individual's sacrifice for individuation, and putting on a skin may imply taking special properties in psychological level.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.11-20
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• 2024

In this paper, we propose the BAG (Bit-width Aware Generator) and the Intermediate Layer Knowledge Distillation using Channel-wise Attention to reduce the knowledge gap between a quantized network, a full-precision network, and a generator in GDFQ (Generative Data-Free Quantization). Since the generator in GDFQ is only trained by the feedback from the full-precision network, the gap resulting in decreased capability due to low bit-width of the quantized network has no effect on training the generator. To alleviate this problem, BAG is quantized with same bit-width of the quantized network, and it can generate synthetic images, which are effectively used for training the quantized network. Typically, the knowledge gap between the quantized network and the full-precision network is also important. To resolve this, we compute channel-wise attention of outputs of convolutional layers, and minimize the loss function as the distance of them. As the result, the quantized network can learn which channels to focus on more from mimicking the full-precision network. To prove the efficiency of proposed methods, we quantize the network trained on CIFAR-100 with 3 bit-width weights and activations, and train it and the generator with our method. As the result, we achieve 56.14% Top-1 Accuracy and increase 3.4% higher accuracy compared to our baseline AdaDFQ.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.149-158
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• 2024

Photo booths have traditionally provided a fun and easy way to capture and print photos to cherish memories. These booths allow individuals to capture their desired poses and props, sharing memories with friends and family. To enable diverse expressions, generative AI-powered photo booths have emerged. However, existing AI photo booths face challenges such as difficulty in taking group photos, inability to accurately reflect user's poses, and the challenge of applying different concepts to individual subjects. To tackle these issues, we present CINEMAPIC, a photo booth system that allows users to freely choose poses, positions, and concepts for their photos. The system workflow includes three main steps: pre-processing, generation, and post-processing to apply individualized concepts. To produce high-quality group photos, the system generates a transparent image for each character and enhances the backdrop-composited image through a small number of denoising steps. The workflow is accelerated by applying an optimized diffusion model and GPU parallelization. The system was implemented as a prototype, and its effectiveness was validated through a user study and a large-scale pilot operation involving approximately 400 users. The results showed a significant preference for the proposed system over existing methods, confirming its potential for real-world photo booth applications. The proposed CINEMAPIC photo booth is expected to lead the way in a more creative and differentiated market, with potential for widespread application in various fields.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.17-23
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• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Journal of KIISE:Information Networking
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• v.29 no.3
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• pp.286-298
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• 2002

The Remote Procedure Call(RPC) has been traditionally used for Inter Process Communication(IPC) among precesses in distributed computing environment. As distributed applications have been complicated more and more, the Mobile Agent paradigm for IPC is emerged. Because there are some paradigms for IPC, researches to evaluate and compare the performance of each paradigm are issued recently. But the performance models used in the previous research did not reflect real distributed computing environment correctly, because they did not consider the evacuation elements for providing security services. Since real distributed environment is open, it is very vulnerable to a variety of attacks. In order to execute applications securely in distributed computing environment, security services which protect applications and information against the attacks must be considered. In this paper, we evaluate and compare the performance of the Remote Procedure Call with that of the Mobile Agent in IPC paradigms. We examine security services to execute applications securely, and propose new performance models considering those services. We design performance models, which describe information retrieval system through N database services, using Petri Net. We compare the performance of two paradigms by assigning numerical values to parameters and measuring the execution time of two paradigms. In this paper, the comparison of two performance models with security services for secure communication shows the results that the execution time of the Remote Procedure Call performance model is sharply increased because of many communications with the high cryptography mechanism between hosts, and that the execution time of the Mobile Agent model is gradually increased because the Mobile Agent paradigm can reduce the quantity of the communications between hosts.

• Journal of Plant Biotechnology
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• v.35 no.3
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• pp.169-176
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• 2008

Salicylic acid(SA) is a phytohormone that is related to plant defense mechanism. The SA accumulation is triggered by abiotic and biotic stresses. SA acts as a signal molecular compound mediating systemic acquired resistance and hypersensitive response in plant. Although the role of SA has been studied extensively, an understanding of the SA regulatory mechanism is still lacking in plants. In order to comprehend SA regulatory mechanism, we have been transformed with a SID2 promoter:GUS::LUC fusion construct into siz1-2 mutant and wild plant(Col-0). SIZ1 encodes SUMO E3 ligase and negatively regulates SA accumulation in plants. SID2(SALICYLIC ACID INDUCTION DEFICIENT2) is a crucial enzyme of SA biosynthesis. The Arabidopsis SID2 gene encodes isochorismate synthase(ICS) that controls SA level by conversion of chorismate to isochorismate. We compared the regulation of SID2 in wild-type and siz1-2 transgenic plants that express SID2 promoter:GUS::LUC constructs respectively. The expressions of $\beta$-GLUCURONIDASE and LUCIFERASE were higher in siz 1-2 transgenic plant without any stress treatment. SID2 promoter:GUS::LUC/siz1-2 transgenic plant will be used as a starting material for isolation of siz1-2 suppressor mutants and genes involved in SA-mediated stress signaling pathway.

• Cartoon and Animation Studies
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• s.51
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• pp.293-319
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• 2018

Thanks to the advancement of the related technologies and equipment, today's video contents like movies, animations and soap operas are rapidly expanding their expressible cinematic imagination area. In order to fulfill the elevated visual expectations of audiences and realize exciting storytelling and fantastic world, the fusion of different techniques is actively used, and the reality for visual effects and image synthesis is increasing more and more. Accordingly, recent VFX-oriented movies using CG have a much more complicated production process than before. Therefore, the importance of Pre-visualization, aka Pre-vis is becoming bigger in the planning process for sophisticated design. Pre-vis means that the advance visualization for stories or directing ideas in the planning process before starting production of movies or animations. 3D animated Pre-visualization realizing directors' abstract and ambiguous ideas in 3 dimensional environment in advance is, as a powerful means for visual storytelling, briskly used focusing on the VFX film industry on which the present CG is broadly used, and the role of Pre-vis throughout productions has increased compared to the past. The studies, however, on the role and utility of Pre-vis are not enough. Therefore, this study was conducted on the role of Pre-vis used for present VFX movie productions using the examples of 3D animated Pre-visualization production in which the researcher of this study participated. In this study, the role of the Pre-vis that is subdivided presently, is divided into and 3D animatics and their each role is analyzed with the example images. Through this, the characteristics that Pre-vis should have are clarified and the concept of the advantages and utility led by the use of Pre-vis in productions is strengthened. The goal of this study is to induce active uses of Pre-vis throughout productions after forming consensus about the various roles of Pre-vis and their utility.

• Journal of Life Science
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• v.20 no.8
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• pp.1235-1240
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• 2010

Natural compounds have been studied to substitute synthetic antioxidants. In this study, the anti-oxidant activity of 70% acetone extracts from the Vaccinium oldhami fruit was investigated for utilization as ingredients for the cosmetic and bio-industries. Anti-oxidant activity was determined by determining total polyphenolic content, electron donating ability, nitric oxide (NO) radical scavenging activity, $ABTS{\cdot}^+$ cation radical scavenging activity and hydrogen peroxide scavenging activity. The polyphenolic content of 70% acetone extracts of the Vaccinium oldhami fruit was 55.972 mg TAE/g. In electron donating activity, 70% acetone extracts of the Vaccinium oldhami fruit showed an effect of 93.9%, which was similar to BHA effect at a concentration of 500 ${\mu}g/ml$. In the NO radical scavenging ability, 70% acetone extracts of the Vaccinium oldhami fruit showed 60% at 500 ${\mu}g/ml$. $ABTS{\cdot}^+$ cation radical scavenging activity of the Vaccinium oldhami fruit at a concentration of 1000 ${\mu}g/ml$ was 75.7%. Also, hydrogen peroxide scavenging activity of 70% acetone extracts showed 80.8% at 100 ${\mu}g/ml$, whichwas higher than BHA. In the natural compound market, the most important factors are the ability to obtain high effects of a material in low concentrations and a long-lasting supply. The Vaccinium oldhami fruit can be harvested every year - this fulfills one of the requirements. From these results, we can confirm that the Vaccinium oldhami fruit has anti-oxidant abilities and has potential as a natural anti-oxidant agent to be utilized in the cosmeceutical and bio-industries.