• Journal of the Korean Magnetics Society
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• v.5 no.6
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• pp.937-946
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• 1995

In this research, the processing control of NiCuZn Ferrite has been developed. The relationship between initial permeability and bulk density of NiCuZn Ferrite with processing factors was studied. NiCuZn Ferrite, which calcinated at $700^{\circ}C$ for 3 hours, was ball milled for about 60 hours to make a size of $0.5\mu\textrm{m}$ followed by granulation using spray dryer. The physical properties and the magnetic properties of NiCuZn Ferrite were investigated with the processing factor, such as (i) granule size and forming pressure, and (ii) sintering temperature. The green density of NiCuZn Ferrite was largely depended on the forming pressure rather than its granule size. The green density of NiCuZn Ferrite was increased from $2.484\;g/cm^{3}$ to $3.002\;g/cm^{3}$ with increase in forming pressure. The bulk density of NiCuZn Ferrite was increased from $3.470\;g/cm^{3}$ to $4.754\;g/cm^{3}$ linearly with increase in sintering temperature. The relationship between initial permeability and bulk density of NiCuZn Ferrite was expressed with the empirical equation,$\mu_{i}=a+b_{\rho}+c_{\rho}^2$ at forming pressure and sintering temperature.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.11-17
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• 2020

In this paper, we propose a method to build a knowledge base based on natural language processing for intelligent battlefield awareness service. The current command and control system manages and utilizes the collected battlefield information and tactical data at a basic level such as registration, storage, and sharing, and information fusion and situation analysis by an analyst is performed. This is an analyst's temporal constraints and cognitive limitations, and generally only one interpretation is drawn, and biased thinking can be reflected. Therefore, it is essential to aware the battlefield situation of the command and control system and to establish the intellignet decision support system. To do this, it is necessary to build a knowledge base specialized in the command and control system and develop intelligent battlefield awareness services based on it. In this paper, among the entity names suggested in the exobrain corpus, which is the private data, the top 250 types of meaningful names were applied and the weapon system entity type was additionally identified to properly represent battlefield information. Based on this, we proposed a way to build a battlefield-aware knowledge base through mention extraction, cross-reference resolution, and relationship extraction.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.29-35
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• 2020

In this paper, we propose a new structured entity recognition DeNERT model. Recently, the field of natural language processing has been actively researched using pre-trained language representation models with a large amount of corpus. In particular, the named entity recognition, which is one of the fields of natural language processing, uses a supervised learning method, which requires a large amount of training dataset and computation. Reinforcement learning is a method that learns through trial and error experience without initial data and is closer to the process of human learning than other machine learning methodologies and is not much applied to the field of natural language processing yet. It is often used in simulation environments such as Atari games and AlphaGo. BERT is a general-purpose language model developed by Google that is pre-trained on large corpus and computational quantities. Recently, it is a language model that shows high performance in the field of natural language processing research and shows high accuracy in many downstream tasks of natural language processing. In this paper, we propose a new named entity recognition DeNERT model using two deep learning models, DQN and BERT. The proposed model is trained by creating a learning environment of reinforcement learning model based on language expression which is the advantage of the general language model. The DeNERT model trained in this way is a faster inference time and higher performance model with a small amount of training dataset. Also, we validate the performance of our model's named entity recognition performance through experiments.

• Polymer(Korea)
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• v.38 no.1
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• pp.24-30
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• 2014

Retinal pigment epithelium (RPE) plays an important role in maintaining the visual function and the degeneration of the RPE causes several retinal degeneration disease. In order to fabricate the suitable carrier for RPE transplantation, the hybrid poly(lactide-co-glycolide) (PLGA) film with hesperidin was prepared. Hesperidin has an anti-inflammatory and antioxidant characteristics. ARPE-19 was seeded on hesperidin/PLGA film and then, cell proliferation was determined by the MTT assay, and cell adhesion and cell morphology were confirmed by SEM. Also, RT-PCR was performed to confirm the expression of the specific genes, and AEC immunohistochemical staining was performed to determine the expression of RPE65. As a result, we confirmed that attachment, proliferation and phenotype maintenance of RPE cells were more excellent on hesperidin/PLGA film than PLGA film, thereby we were able to confirm the potential applications of hesperidin/PLGA film as tissue engineering carrier for regeneration of retina.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.823-827
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• 2010

This study was conducted to assess the propriety of models for soil water characteristics estimation in anthropogenic soil through the measurement of soil water content and soil water matric potential. Soil profile was characterized with four different soil layers. Soil texture was loamy sand for the first soil layer (from soil surface to 30 cm soil depth), sand for the second (30~70 cm soil depth) and the third soil layers (70~120 cm soil depth), and sandy loam for the fourth soil layer (120 cm < soil depth). Soil water retention curve (SWRC), the relation between soil water content and soil water matric potential, took a similar trend between different layers except the layer of below 120 cm soil depth. The estimation of SWRC and air entry value was better in van Genuchten model by analytical method than in Brooks-Corey model with power function. Therefore, it could be concluded that van Genuchten model is more desirable than Brook-Corey model for estimating soil water characteristics of anthropogenic soil accumulated with saprolite.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.1
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• pp.43-53
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• 2016

Objectives : Somatic symptoms after the exposure of psychological trauma frequently developed. However, the somatic symptoms are not covered under the diagnostic criteria of posttraumatic stress disorder(PTSD) in detail, although they are often associated with social and occupational functioning and patient-doctor relationships. The aim of this article is to highlight the potential mechanisms, the common manifestations, and the treatment of the somatic symptoms. Methods : This article studied the somatic symptoms searched using academic search engines like PubMed, Scopus, Google Scholar, KoreaMed and KISS from the earliest available date of indexing to March 31, 2016. Results : The mechanism of somatic symptoms after the exposure was described as psychological and physiological aspects. Psychological mechanism consisted of psychodynamic theory, cognitive behavioral theory, and others. Physiological mechanism involved changes in neuroendocrine and immune system, autonomic nervous system and central nervous system. Somatization associated with psychological trauma manifested various health conditions on head and neck, chest, abdominal, musculoskeletal, and dermatological and immune system. Few studies described the standardization of treatment for the somatic symptoms. Conclusions : Clinicians and disaster behavioral health providers should think of the accompanying somatic symptoms during intervention of psychological trauma and PTSD. Further studies are needed on the somatic symptoms seen in psychological trauma and PTSD.

• Journal of the Korean Society for Computer Game
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• v.31 no.4
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• pp.101-110
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• 2018

This thesis analyzed the correlation between the psychological characteristics of adolescents and the use of games in the first year students of the national high school in 2016. The game is one of the representative entertainment industries in Korea and is an industry that has a great influence on becoming a powerhouse as cultural contents. However, games and immersion are expressed as a negative vocabulary of game addiction and are referred to as social problems and youth problems, and they give negative perception to the content of games. All users have the right to enjoy the content, but only negative words are used in poisonous games and they are packed as if they are social problems. In the present study, many emotional problems that were derived from previous studies were not related to game use in general adolescents. Through the questions related to 'depression', students who are not motivated or energy. The results of this study are as follows. First, the positive effects of game and youth emotional problems were derived through the conclusion that students who are active and emotionally bright are more likely to use game contents. This study was conducted to help change the social perception of the game positively by breaking the social fixed point of view that the emotionally troubled adolescents engage with the game and cause social problems.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.465-481
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• 2022

Rock grouting is important to improve the waterproof efficiency and mechanical strength of rock medium with joint for utilizing the underground rock space such as tunnel. The grouting materials typically has been used the cement materials, which represent Bingham fluid model. This model can express the relationship of viscosity and yield strength. In addition, it is dependent with elapsed time. The grouting injection performance can be deteriorated with an increase of viscosity and yield strength in the grouting process if the time dependence is ignored. Therefore, in this study, the characteristics of viscosity and yield strength were investigated according to water-cement ratio and time dependence in the laboratory test. Numerical simulation was carried out to investigate the grouting performance according to the time dependence of characteristics in terms of the viscosity model. Given the results, the grouting injected distance and cumulative grout volume were significantly decreased when the time dependence of grouting material was considered. This study, considering the characteristics according to the time dependence of viscosity and yield strength, will be meaningful to the design of grouting injection in field applications.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.846-855
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• 2021

Eco-friendly and renewable energy sources are actively being researched in recent times, and of shore wind power generation requires advanced design technologies in terms of increasing the capacities of wind turbines and enlarging wind turbine installation vessels (WTIVs). The WTIV ensures that the hull is situated at a height that is not affected by waves. The most important part of the WTIV is the leg structure, which must respond dynamically according to the wave, current, and wind loads. In particular, the wave load is composed of irregular waves, and it is important to know the exact dynamic response. The dynamic response analysis uses a single degree of freedom (SDOF) method, which is a simplified approach, but it is limited owing to the consideration of random waves. Therefore, in industrial practice, the time-domain analysis of random waves is based on the multi degree of freedom (MDOF) method. Although the MDOF method provides high-precision results, its data convergence is sensitive and difficult to apply owing to design complexity. Therefore, a dynamic amplification factor (DAF) estimation formula is developed in this study to express the dynamic response characteristics of random waves through time-domain analysis based on different variables. It is confirmed that the calculation time can be shortened and accuracy enhanced compared to existing MDOF methods. The developed formula will be used in the initial design of WTIVs and similar structures.