• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.1025-1028
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• 2008

Using of maintenance system for gate bridge algorism, We made out algorism and engine for prediction of life cycle by neutralization, freezing-thawing and damage from sea wind. To objective of this system, user can use easily with maintenance system for gate bridge. Also, to improve of maintenance efficiency, web-program made out by superannuated evaluation and analysis of field exposure data. To develope web-program, we framing structure design of database, which is adapted to method of maintenance, repair, and reinforcing

• Journal of KIBIM
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• v.5 no.4
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• pp.53-62
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• 2015

Korea has been at the forefront of green growth initiatives. In 2008, the government declared the new vision toward 'low-carbon society and green growth'. The government subsidies and Feed-in Tariff (FIT) increased domestic usage of solar power by supplying photovoltaic housing and photovoltaic generation systems. Since 2000, solar power industry has been the world's fastest growing source with the annual growth rate of 52.5%. Especially, BIPV(Building Integrated Photovoltaic) systems are capturing a growing portion of the renewable energy market due to several reasons. BIPV consists of photovoltaic cells and modules integrated into the building envelope such as a roof or facades. By avoiding the cost of conventional materials, the incremental cost of photovoltaics is reduced and its life-cycle cost is improved. When it comes to atypical building, numerous problems occur because PV modules are flat, stationary, and have its orientation determined by building surface. However, previous studies mainly focused on improving installations of solar PV technologies on ground and rooftop photovoltaic array and developing prediction model to estimate the amount of produced electricity. Consequently, this paper discusses the problem during a planning and design stage of BIPV systems and suggests the method to select optimal design of the systems by applying the national strategy and economic policies. Furthermore, the paper aims to develop BIM tool based on the engineering knowledge from experts in order for non-specialists to design photovoltaic generation systems easily.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.35-43
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• 2019

The safety of industrial valves, which are the core parts of plant facilities, are managed by manpower and there are difficulties because of side area for inspection and limited accessibility due to the nature of facilities. The industrial valves used in plant facilities cause problems such as interrupted production; a loss of life due to leak or explosion of poisonous material and flammable gases, and difficulty in locating accident positions in the event of leakage or failure. Therefore, safety management and control systems based on IoT technology are needed. This study is about the development of risk factor prediction technique among the safety management of industrial valves through IoT- based wireless communication and the development of actuator control system. We have developed IoT-based industrial valve safety management techniques to prevent accidents caused by main risk factors by conducting an analysis of the structural characteristics of valves and an analysis of the causes of main risk factors through review of failure data and literature and an analysis of accident scenarios.

• Journal of Microbiology and Biotechnology
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• v.30 no.9
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• pp.1282-1289
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• 2020

Previously, we performed an in silico analysis of the Periplaneta americana transcriptome. Antimicrobial peptide candidates were selected using an in silico antimicrobial peptide prediction method. It was found that periplanetasin-5 had antimicrobial activity against yeast and gram-positive and gram-negative bacteria. In the present study, we demonstrated the anti-inflammatory activities of periplanetasin-5 in mouse macrophage Raw264.7 cells. No cytotoxicity was observed at 60 ㎍/ml periplanetasin-5, and treatment decreased nitric oxide production in Raw264.7 cells exposed to lipopolysaccharide (LPS). In addition, quantitative RT-PCR and enzyme-linked immunosorbent assay revealed that periplanetasin-5 reduced cytokine (tumor necrosis factor-α, interleukin-6) expression levels in the Raw264.7 cells. Periplanetasin-5 controlled inflammation by inhibiting phosphorylation of MAPKs, an inflammatory signaling element, and reducing the degradation of IκB. Through LAL assay, LPS toxicity was found to decrease in a periplanetasin-5 dose-dependent manner. Collectively, these data showed that periplanetasin-5 had anti-inflammatory activities, exemplified in LPS-exposed Raw264.7 cells. Thus, we have provided a potentially useful antibacterial peptide candidate with anti-inflammatory activities.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.2
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• pp.56-67
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• 2006

The reliability and failure mode effect analysis are effective means to achieve efficient and cost-reduction design for satellite development. The failure rate of COTS (Commercial-Off-The-Shelf) parts required for reliability analysis is not usually provided from the manufacturer. Space environment factors based on empirical data obtained from MIL-HDBK-217F can be applicable to the reliability calculation. As a radiation environment factor, the occurrence rate of SEL (Single Event Latch-up) is additionally incorporated for the failure rate prediction. In this paper, the statistical reliability analysis method for low-cost small satellite using COTS parts is suggested. This statistical reliability analysis was applied to HAUSAT-2 small satellite whose electronic boxes are consisted of many COTS parts to calculate the system reliability at the end of design mission life.

• Journal of Microbiology and Biotechnology
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• v.31 no.10
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• pp.1343-1349
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• 2021

Cockroaches live in places where various pathogens exist and thus are more likely to use antimicrobial compounds to defend against pathogen intrusions. We previously performed an in silico analysis of the Periplaneta americana transcriptome and detected periplanetasin-5 using an in silico antimicrobial peptide prediction method. In this study, we investigated whether periplanetasin-5 has anticancer activity against the human leukemia cell line K562. Cell growth and survival of K562 cells treated with periplanetasin-5 were decreased in a dose-dependent manner. By using flow cytometric analysis, acridine orange/ethidium bromide (AO/EB) staining and DNA fragmentation, we found that periplanetasin-5 induced apoptotic and necrotic cell death in leukemia cells. In addition, these events were associated with increased levels of the pro-apoptotic proteins Fas and cytochrome c and reduced levels of the anti-apoptotic protein Bcl-2. Periplanetasin-5 induces the cleavage of pro-caspase-9, pro-caspase-8, pro-caspase-3, and poly (ADP-ribose) polymerase (PARP). The above data suggest that periplanetasin-5 induces apoptosis via both the intrinsic and extrinsic pathways. Moreover, caspase-related apoptosis was further confirmed by using the caspase inhibitor carbobenzoxy-valyl-alanyl-aspartyl-[O-methyl]-fluoromethylketone (Z-VAD-FMK), which reversed the periplanetasin-5-induced reduction in cell viability. In conclusion, periplanetasin-5 caused apoptosis in leukemia cells, suggesting its potential utility as an anticancer therapeutic agent.

• The Journal of Information Systems
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• v.30 no.2
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.423-432
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• 2022

This paper addresses the safety concern that the SOH of batteries in electric vehicles decreases sharply when drivers change or their driving patterns change. Such a change can overload the battery, reduce the battery life, and induce safety issues. This paper aims to present the SOH as the changes on a dashboard of an electric vehicle in real-time in response to user pattern changes. As part of the training process I used battery data among the datasets provided by NASA, and built models incorporating linear regression and ARIMA, and predicted new battery data that contained user changes based on previously trained models. Therefore, as a result of the prediction, the linear regression is better at predicting some changes in SOH based on the user's pattern change if we have more battery datasets with a wide range of independent values. The ARIMA model can be used if we only have battery datasets with SOH data.

• Journal of Korean Neurosurgical Society
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• v.66 no.4
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• pp.382-392
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• 2023

Objective : The use of indocyanine green videoangiography (ICG-VA) to assess blood flow in the brain during cerebrovascular surgery has been increasing. Clinical studies on ICG-VA have predominantly focused on qualitative analysis. However, quantitative analysis numerical modelling for time profiling enables a more accurate evaluation of blood flow kinetics. In this study, we established a multiple exponential modified Gaussian (multi-EMG) model for quantitative ICG-VA to understand accurately the status of cerebral hemodynamics. Methods : We obtained clinical data of cerebral blood flow acquired the quantitative analysis ICG-VA during cerebrovascular surgery. Varied asymmetric peak functions were compared to find the most matching function form with clinical data by using a nonlinear regression algorithm. To verify the result of the nonlinear regression, the mode function was applied to various types of data. Results : The proposed multi-EMG model is well fitted to the clinical data. Because the primary parameters-growth and decay rates, and peak center and heights-of the model are characteristics of model function, they provide accurate reference values for assessing cerebral hemodynamics in various conditions. In addition, the primary parameters can be estimated on the curves with partially missed data. The accuracy of the model estimation was verified by a repeated curve fitting method using manipulation of missing data. Conclusion : The multi-EMG model can possibly serve as a universal model for cerebral hemodynamics in a comparison with other asymmetric peak functions. According to the results, the model can be helpful for clinical research assessment of cerebrovascular hemodynamics in a clinical setting.

• Computers and Concrete
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• v.32 no.2
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.