• Journal of Conservation Science
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• v.37 no.4
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• pp.391-401
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• 2021

Outdoor sculptures of modern art works are being damaged and deteriorated as they are exposed to the outdoor environment due to the nature of exhibition in the outdoor environment, but secure of basic data through the measures for conservation and advanced researches still remain in the early stage. The surface of "Conversion" which is exhibited in the Busan Cheonmasan Sculpture Park has been exfoliated and deteriorated due to outdoor exhibition for a long time, so systematic conservation and management of works are considered necessary. Prior to the conservation and management, this study conducted observation of cross section, analysis of inorganic components, FT-IR, Raman and Py-GC/Mass analysis to examine the nature and type of paints used for the work through a scientific analysis. As a result of analysis, paints used for the "Conversion" include paint mixed with silvery aluminium powder and white pigment, reddish paint mixed with toluidine red, bluish paint that mixed prussian blue and titanium white and mixture of phthalocyanine blue and titanium white. The result is expected to be used as basic data for selecting materials necessary for conservative treatment of and establishing a plan for conservative treatment of the "Conversion".

• Journal of the Korean Wood Science and Technology
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• v.47 no.4
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• pp.486-497
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• 2019

The non-isothermal and isothermal pyrolysis properties of H lignin and P lignin extracted from different biorefinery processes (such as supercritical water hydrolysis and fast pyrolysis) were studied using thermogravimetry analysis (TGA) and pyrolyzer-gas chromatography/mass spectrometry (Py-GC/MS). The lignins were characterized by ultimate/proximate analysis, FT-IR and GPC. Based on the thermogravimetry (TG) and derivative thermogravimetry (DTG) curves, the thermal decomposition stages were obtained and the pyrolysis products were analyzed at each thermal decomposition stage of non-isothermal pyrolysis. The isothermal pyrolysis of lignins was also carried out at 400, 500, and $600^{\circ}C$ to investigate the pyrolysis product distribution at each temperature. In non-isothermal pyrolysis, P lignin recovered from a fast pyrolysis process started to decompose and produced pyrolysis products at a lower temperature than H lignin recovered from a supercritical water hydrolysis process. In isothermal pyrolysis, guaiacyl and syringyl type were the major pyrolysis products at every temperature, while the amounts of p-hydroxyphenyl type and aromatic hydrocarbons increased with the pyrolysis temperature.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.34.3-34.3
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• 2019

We report our discovery of observational evidence for the coherence between galaxy rotation and the average motion of neighbors. Using the Calar Alto Legacy Integral Field Area (CALIFA) survey data analyzed with the Python CALIFA STARLIGHT Synthesis Organizer (PyCASSO) platform, and the NASA-Sloan Atlas (NSA) catalog, we estimate the angular momentum vectors of 445 CALIFA galaxies and build composite maps of their neighbor galaxies on the parameter space of velocity versus distance. The composite radial profiles of the luminosity-weighted mean velocity of neighbors show striking evidence for dynamical coherence between the rotational direction of the CALIFA galaxies and the average moving direction of their neighbor galaxies. The signal of such dynamical coherence is significant for the neighbors within 800 kpc distance from the CALIFA galaxies with a confidence level of $3.5{\sigma}$, when the angular momentum is measured at the outskirt ($Re<R{\leq}2Re$) of each CALIFA galaxy. We also find that faint or kinematically misaligned galaxies show stronger coherence with neighbor motions than bright or kinematically well-aligned galaxies do. Our results show that the rotation of a galaxy, particularly at its outskirt, may be significantly influenced by recent interactions with its neighbors.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.187-195
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• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.157-160
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• 2022

본 논문에서는 다중 인물 포함 단일 영상으로부터 파라미터 기반 3차원 휴먼 모델 생성 기법 중 최근 발표된 SOTA 기법 4가지에 대해 대표적인 데이터 셋들에 대해 사전 학습 모델을 사용한 복원 성능 비교 실험을 수행하였다. 실험결과, CLIFF 기법과 PyMAF-x 기법이 PARE 기법이나 ROMP 기법에 비해 우수한 결과를 보였다.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.161-164
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• 2022

Homomorphic Encryption (HE) schemes have been recently growing as a reliable solution to preserve users' information owe to maintaining and operating the user data in the encrypted state. In addition to that, several Neural Networks models merged with HE schemes have been developed as a prospective tool for privacy-preserving machine learning. Those mentioned works demonstrated that it is possible to match the accuracy of non-encrypted models but there is always a trade-off in the computation time. In this work, we evaluate the implementation of CKKS HE scheme operations over the layers of a LeNet5 convolutional inference model, however, owing to the limitations of the evaluation environment, the scope of this work is not to develop a complete LeNet5 encrypted model. The evaluation was performed using the MNIST dataset with Microsoft SEAL (MSEAL) open-source homomorphic encryption library ported version on Python (PyFhel). The behavior of the encrypted model, the limitations faced and a small description of related and future work is also provided.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.

• Journal of Information Science Theory and Practice
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• v.11 no.4
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• pp.82-91
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• 2023

Receiving 100 citations from indexed journals is not a common occurrence, although it does happen very rarely, and it is even more uncommon for a non-indexed article. Given this, the purpose of this present study is to perform a bibliometric analysis of 100 cited Scopus articles to a non-Scopus indexed article, using a case study of the article titled "Authorship trends and collaboration pattern in the marine sciences literature: A scientometric study" which was published by Elango and Rajendran in the International Journal of Information Dissemination and Technology in 2012. On October 15, 2022, the Scopus database was searched with the article title in the references field, and the resulting bibliographic data was exported as a comma-separated values file. The tools utilized for this analysis were ScientoPy, the Bibliometrix R Package, and VOSviewer. Based on the findings, most of the cited articles were published within the last three years, and international researchers have given more recognition compared to Indian researchers. The most popular topics were found to be bibliometrics, bibliometric analysis, and bibliometrix. The fact that all 100 cited articles were published across various subject disciplines used by Scopus to categorise sources demonstrates how well-established the citing article is within the scientific community.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.1
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• pp.14-17
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• 2024

Objectives: This is a case of air exposure assessment conducted after researchers complained of headaches and odor due to residual substances from fire extinguishing agents spread throughout the laboratory due to a malfunction of the fire extinguishing facility. Methods: A component analysis was conducted on the residual substances of a fire extinguishing agent spread in a laboratory using Py-GC-MS (pyrolysis gas chromatography mass spectrometry) at the research institute's own central equipment research center. As a result of the component analysis, several types of substances were detected. Among these, five types of substances subject to work environment measurement in the aromatic hydrocarbon series, which can affect headaches and odor, were selected as substances subject to exposure assessment in the air, and the measurement and analysis methods of the target substances were conducted in accordance with the KOSHA Guide for each substance. Conclusions: The measurement results showed that all 5 types of substances were not detected at locations A, B, and C. This is believed to be the result of the residual substances in the fire extinguishing agent being measured when approximately two months had elapsed after being exposed to the test bench, and the substances already exposed had volatilized and disappeared. In this survey, it is believed that the measurement process is more important than the measurement results.