• Cross-Cultural Studies
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• v.41
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• pp.275-298
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• 2015

This paper is to examine in detail the relevant aspects of "Nature" and "Elegant Beauty" appearring in Cho, Chi-Hoon's poems and poetics. It uses the same context to explain the reasons and grounds for the inevitable, corresponding closely to his poetry and poetics. It is the core of Cho Chi-Hoon's poetry to determine "natural" new artistic views and his vision of ultra-modernism. This is consistent with the precise feature of "Elegant Beauty" in the midst of aesthetic categories profoundly discusseing his poetics. He regards a "lyric" as a vision of ultra-modernism, to overcome divisions and conflicts of values, "Truth Good Beauty", which was caused by modern science. Furthermore, it includes many social issues in accordance with the differentiation and specialization of each area. It is inferred to have been attempted to produce specifically was found to shape new images of "Nature" in the dimension of his poems, "Elegant Beauty" is overwhelmed with the aesthetic excellence of the other categories in the dimension of his poetics in this context.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.458-465
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• 2019

Experimental animation is an independent art animation and a visual art that is integrated with the spirit of experimentation. Norman McLaren pursues original beauty in each piece through scientific exploration of visual expression technology and experiment that transcends common sense with great creativity and sensitivity, and the aesthetic value of animation is very high as the master of experimental animation that has brought innovation of real image media today. Therefore, it is meaningful to analyze the experimental expressions of Norman McLaren, who expanded the field of experimental animation by challenging various techniques and carried out a ceaseless search for motion creation between each frame, and to study the expressions of movement he focused on. The most significant feature of his movement expression was analyzed as repetition of motion and repetition of metamorphosis. I hope that this derivation will be understood as experimental animation of experimental method which requires more specific type of inquiry than simple question about experimental method. Also, I hope that it will be meaningful as a preliminary study to deeply explore the possibility and direction of animation creation in university education.

• The Research Journal of the Costume Culture
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• v.27 no.4
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• pp.384-397
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• 2019

The purpose of this study is to analyze the eco-friendly design characteristics of contemporary children's collections. Photos from FirstviewKorea were utilized for analysis; 29 brands were selected that included children's clothing collections featuring eco-friendly characteristics from 2007 to 2018. The results are as follows. First, naturalness was the most frequent characteristic of environmentally friendly children's collections. It was not conveyed in an eccentric way in any season, showed a relatively uniform distribution, and was seen in various ways, including printed on the fabric and expressed in $appliqu\acute{e}s$ and embroidery. Second, handcrafted features frequently changed according to seasonal trends. Various methods such as beading, embroidery, applique, sewing techniques, and handbags were used, which enhanced manual workability, discrimination from other designs. Third, traditionality is divided into the characteristics of ethnicity and revivalism. National traditions were expressed in the clothing and reflected the current generation while connecting to the past. Fourth, simplicity appeared in classic designs such as simple silhouettes, sparse decoration, natural colors, and comfortable dress length that is not tight on the body. Simplicity was not a frequent feature due to the characteristics of the children's clothing collections. Fifth, playfulness functioned to enhance the children's clothing's wear frequency. Although it was the least frequent of all the characteristics, it seemed to increase the design fun and the clothing's value by fusing with other characteristics such as handcraftedness and naturalness.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.361-366
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• 2018

For each character constituting learning data, different fonts are added in pixel unit to create MASK, and then pixel values belonging to the MASK are divided into three groups. The experimental data are modified into skeletal forms, and then regional boundary operation is used to create a boundary that distinguishes the background region adjacent to the skeleton of the character from the background of the modified experimental data. Discordance values between the modified experimental data and the MASKs are calculated, and then the MASK with the minimum value is found. This MASK is selected as a finally recognized result for the given experiment data. The recognition algorithm using skeleton of the character and the regional boundary operation can easily extend the learning data set by adding new fonts to the given learning data, and also it is simple to implement, and high character recognition rate can be obtained.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.657-665
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• 2021

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

• Korean Journal of Optics and Photonics
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• v.33 no.6
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• pp.310-316
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• 2022

In this paper, the difference in modulation transfer function (MTF) results according to the change in the angle of a slant target when measuring a presampled MTF was confirmed, and the difference was reduced by fitting the edge spread function graph obtained to reduce the error by the target's rotation. Due to the feature of the presampled MTF method, the spatial frequency changed due to the sensor's projected intensity being changed by the target's rotation, and it was confirmed that the difference in the MTF value occurred depending on the rotation angle of the target. In this paper, the MTF was calculated after fitting only one column of the acquired image. It was confirmed that the rotation error is smaller compared to the case of the presampled MTF method and this fitting method can be applied to a scene that contains various target angles, such as auto-focusing using the MTF.

• Journal of the Korea Computer Graphics Society
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• v.26 no.5
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• pp.15-24
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• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.70-75
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• 2022

Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1635-1641
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• 2021

Portable systems with built-in active noise control is required low power operation. Excessive anti noise search operation can lead to rapid battery consumption. A method that can adaptively cancel noise according to the operating conditions of the system is required and the methods of reducing power are becoming very important key feature in today's portable systems. In this paper, we propose the method of active noise control(ANC) using binary search algorithm in noisy systems. The implemented architecture detects a frequency component considered as noise from the input signal and by using the binary search algorithm, the system find out an appropriate amplitude value for anti-noise in a much faster time than the general linear search algorithm. Through the experimental results, it was confirmed that the proposed algorithm performs a successful functional operation.