• Journal of Life Science
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• v.33 no.8
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• pp.612-622
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• 2023

T-cell deficiency may occur in various clinical conditions including congenital defects, cell/organ transplantation, HIV infection and aging. In this regard, the development of artificial thymus has recently been attracting much attention. To achieve this aim, the development of techniques for 3D culture of thymic stromal cells is necessary because thymocytes grown only in a 3D thymic microenvironment can be differentiated fully to become mature, immunocompetent T cells; the same cannot be achieved for thymocytes grown in 2D. This study aimed to develop a nanotechnology-based 3D culture technique using polymeric scaffolds for thymic epithelial cells (TECs), the main component of thymic stromal cells. Scanning electron microscopic observation revealed that the pores of both PCL and PCL/PLGA scaffolds were filled with TECs. Interestingly, TECs grown in 3D on polydopamine-coated scaffolds exhibited enhanced cell attachment and proliferation compared to those grown on non-coated scaffolds. In addition, the gene expression of thymopoietic factors was upregulated in TECs cultured in 3D on polydopamine-coated scaffolds compared to those cultured in 2D. Taken together, the results of the present study demonstrate an efficient 3D culture model for TECs using polymeric scaffolds and provide new insights into a novel platform technology that can be applied to develop functional, biocompatible scaffolds for the 3D culture of thymocytes. This will eventually shed light on techniques for the in vitro development of T cells as well as the synthesis of artificial thymus.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.555-566
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• 2023

In this study, a composite phase change material (CPCM) produced using the SOL-GEL technique was developed as a thermal energy storage medium for low-temperature applications. Tetradecane and activated carbon (AC) were used as the core and supporting materials, respectively. The tetradecane phase change material (PCM) was impregnated into the porous structure of AC using the vacuum impregnation method, and a thin layer of silica gel was coated on the prepared composite using the SOL-GEL process, where tetraethyl orthosilicate (TEOS) was used as the silica source. The thermal performance of the CPCM was analysed using differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). DSC results showed that the pure tetradecane PCM had melting and freezing temperatures of 6.4℃ and 1.3℃ and corresponding enthalpies 226 J/g and 223.8 J/g, respectively. The CPCM exhibited enthalpy of 32.98 J/g and 27.7 J/g during the melting and freezing processes at 7.1℃ and 2.4℃, respectively. TGA test results revealed that the AC is thermally stable up to 500℃, which is much higher than the decomposition temperature of the pure tetradecane, which is around 120℃. Moreover, in the case of AC-PCM and CPCM thermal degradation started at 80℃ and 100℃, respectively. The chemical stability of the CPCM was studied using Fourier-transform infrared (FT-IR) spectroscopy, and the results confirmed that the developed composite is chemically stable. Finally, the surface morphology of the AC and CPCM was analysed using scanning electron microscopy (SEM), which confirmed the presence of a thin layer of silica gel on the AC surface after the SOL-GEL process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.377-380
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• 2022

An application called an app can be downloaded and used on mobile devices. Among them, Android-based apps have the disadvantage of being implemented on an open source basis and can be exploited by anyone, but unlike iOS, which discloses only a small part of the source code, Android is implemented as an open source, so it can analyze the code. However, since anyone can participate in changing the source code of open source-based Android apps, the number of malicious codes increases and types are bound to vary. Malicious codes that increase exponentially in a short period of time are difficult for humans to detect one by one, so it is efficient to use a technique to detect malicious codes using AI. Most of the existing malicious app detection methods are to extract Features and detect malicious apps. Therefore, three ways to select the optimal feature to be used for learning after feature extraction are proposed. Finally, in the step of modeling with optimal features, ensemble techniques are used in addition to a single model. Ensemble techniques have already shown results beyond the performance of a single model, as has been shown in several studies. Therefore, this paper presents a plan to select the optimal feature and implement a learning model for Android app-based malicious code detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.41-43
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• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.1-11
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• 2023

Smart factory environments and digital twin environments are established, and today's factories accumulate vast amounts of production data and are managed in real time as visualized results suitable for user convenience. Production simulation techniques are in the spotlight as a way to prevent delays in delivery and predict factory volatility in situations where production schedule planning becomes difficult due to the diversification of production products. With the development of the digital twin environment, new packages are developed and functions of existing packages are updated, making it difficult for users to make decisions on which packages to use to develop simulations. Therefore, in this study, the concept of Discrete Event Simulation (DES) performed based on discrete events is defined, and the characteristics of various simulation packages were compared and analyzed. To this end, studies that solved real problems using discrete event simulation software for 10 years were analyzed, and three types of software used by the majority were identified. In addition, each package was classified by simulation technique, type of industry, subject of simulation, country of use, etc., and analysis results on the characteristics and usage of DES software were provided. The results of this study provide a basis for selection to companies and users who have difficulty in selecting discrete event simulation package in the future, and it is judged that they will be used as basic data.

• Nuclear Engineering and Technology
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• v.26 no.3
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• pp.401-410
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• 1994

Validation study of two radionuclide migration models for single fracture developed in geologic medium the hydrodynamic dispersion diffusion model(HDDM) and the channeling dispersion diffusion model(CDDM), was studied by migration experiment of tracers through an artificial granite fracture on the labolatory scale. The tracers used were Uranine and Sodium lignosulfonate know as nonsorbing material. The flow rate ranged 0.4 to 1.5 cc/min. Related parameters for the models were estimated by optimization technique. Theoretical breakthrough curves with experimental data were compared. In the experiment, it was deduced that the surface sorption for both tracers did not play an important role while the diffusion of Uranine into the rock matrix turned out to be an important mass transfer mechanism. The parameter characterizing the rock matrix diffusion of each model agreed well The simulated result showed that the amount of flow rate could not tell the CDDM from the HDDM quantitatively. On the other hand, the variation of fracture length gave influence on the two models in a different degree. The dispersivity of breakthrough curve of the CDDM was more amplified than that of the CDDM when the fracture length was increased. A good agreement between the models and experimental data gave a confirmation that both models were very useful in predicting the migration system through a single fracture.

• Journal of the Korean Geotechnical Society
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• v.22 no.9
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• pp.45-59
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• 2006

The successful performance of any numerical geotechnical simulation depends on the accuracy and efficiency of the numerical implementation of constitutive model used to simulate the stress-strain (constitutive) response of the soil. The corner stone of the numerical implementation of constitutive models is the numerical integration of the incremental form of soil-plasticity constitutive equations over a discrete sequence of time steps. In this paper a well known two-surface soil plasticity model is implemented using a generalized implicit return mapping algorithm to arbitrary convex yield surfaces referred to as the Closest-Point-Projection method (CPPM). The two-surface model describes the nonlinear behavior of coarse-grained materials by incorporating a bounding surface concept together with isotropic and kinematic hardening as well as fabric formulation to account for the effect of fabric formation on the unloading response. In the course of investigating the performance of the CPPM integration method, it is proven that the algorithm is an accurate, robust, and efficient integration technique useful in finite element contexts. It is also shown that the algorithm produces a consistent tangent operator $\frac{d\sigma}{d\varepsilon}$ during the iterative process with quadratic convergence rate of the global iteration process.

• Journal of the Korean Geotechnical Society
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• v.25 no.7
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• pp.35-46
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• 2009

Pressure grouting is a common technique in geotechnical engineering to increase the stiffness and strength of the ground mass and to fill boreholes or void space in a tunnel lining and so on. Recently, the pressure grouting has been applied to a soil-nailing system which is widely used to improve slope stability. The soil-nailing design has been empirically performed in most geotechnical applications because the interaction between pressurized grouting paste and the adjacent ground mass is complicated and difficult to analyze. The purpose of this study is to analyze the increase of pullout resistance induced by pressurized grouting with the aid of performing laboratory model tests and field tests. In this paper, two main causes of pullout resistance increases induced by pressurized grouting were verified: the increase of mean normal stress and the increase of coefficient of pullout friction. From laboratory tests, it was found that dilatancy angle could be estimated by modified cavity expansion theory using the measured wall displacements. The radial displacement increases with dilatancy angle decrease and the dilatancy angle increases with injection pressure increase. The measured pullout resistance obtained from field tests is in good agreement with the estimated one from the modified cavity expansion theory.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.337-352
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• 2023

In general, Rainfall-Runoff model parameter set is optimized using the entire data to calculate unique parameter set. However, Korea has a large precipitation deviation according to the season, and it is expected to even worsen due to climate change. Therefore, the need for hydrological data considering seasonal characteristics. In this study, we conducted regional sensitivity analysis(RSA) using the conceptual Rainfall-Runoff model, GR4J aimed at the Soyanggang dam basin, and clustered combining the RSA results with hydrometeorological data using Self-Organizing map(SOM). In order to consider the climate characteristics in parameter estimation, the data was divided based on clustering, and a calibration approach of the Rainfall-Runoff model was developed by comparing the objective functions of the Global Optimization method. The performance of calibration was evaluated by statistical techniques. As a result, it was confirmed that the model performance during the Cold period(November~April) with a relatively low flow rate was improved. This is expected to improve the performance and predictability of the hydrological model for areas that have a large precipitation deviation such as Monsoon climate.