• Journal of Microbiology and Biotechnology
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• v.31 no.9
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• pp.1311-1322
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• 2021

Microbially induced calcium carbonate precipitation (MICP) has recently become an intelligent and environmentally friendly method for repairing cracks in concrete. To improve on this ability of microbial materials concrete repair, we applied random mutagenesis and optimization of mineralization conditions to improve the quantity and crystal form of microbially precipitated calcium carbonate. Sporosarcina pasteurii ATCC 11859 was used as the starting strain to obtain the mutant with high urease activity by atmospheric and room temperature plasma (ARTP) mutagenesis. Next, we investigated the optimal biomineralization conditions and precipitation crystal form using Plackett-Burman experimental design and response surface methodology (RSM). Biomineralization with 0.73 mol/l calcium chloride, 45 g/l urea, reaction temperature of 45℃, and reaction time of 22 h, significantly increased the amount of precipitated calcium carbonate, which was deposited in the form of calcite crystals. Finally, the repair of concrete using the optimized biomineralization process was evaluated. A comparison of water absorption and adhesion of concrete specimens before and after repairs showed that concrete cracks and surface defects could be efficiently repaired. This study provides a new method to engineer biocementing material for concrete repair.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.305-317
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• 2021

An AMD control system is usually built based on the original model of a target building. As a result, the fact leads a large calculation workload exists. Therefore, the orders of a structural model should be reduced appropriately. Among various model-reduction methods, a suitable reduced-order model is important to high-rise buildings. Meanwhile, a partial structural information is discarded directly in the model-reduction process, which leads to the accuracy reduction of its controller design. In this paper, an optimal technique is selected through comparing several common model-reduction methods. Then, considering the dynamic characteristics of a high-rise building, an improved balanced truncation (BT) method is proposed for establishing its reduced-order model. The abandoned structural information, including natural frequencies, damping ratios and modal information of the original model, is reconsidered. Based on the improved reduced-order model, a new reduced-order controller is designed by a regional pole-placement method. A high-rise building with an AMD system is regarded as an example, in which the energy distribution, the control effects and the control parameters are used as the indexes to analyze the performance of the improved reduced-order controller. To verify its effectiveness, the proposed methodology is also applied to a four-storey experimental frame. The results demonstrate that the new controller has a stable control performance and a relatively short calculation time, which provides good potential for structural vibration control of high-rise buildings.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.65-72
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• 2020

In this paper, we developed a web-based DApp system based on a private blockchain by applying machine learning techniques to automatically identify Android malicious apps that are continuously increasing rapidly. The optimal machine learning model that provides 96.2587% accuracy for Android malicious app identification was selected to the authorized experimental data, and automatic identification results for Android malicious apps were recorded/managed in the Hyperledger Fabric blockchain system. In addition, a web-based DApp system was developed so that users who have been granted the proper authority can use the blockchain system. Therefore, it is possible to further improve the security in the Android mobile app usage environment through the development of the machine learning-based Android malicious app identification block chain DApp system presented. In the future, it is expected to be able to develop enhanced security services that combine machine learning and blockchain for general-purpose data.

• Journal of Ginseng Research
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• v.46 no.2
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• pp.283-289
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• 2022

Background: Sarcopenia, defined as loss of muscle mass and strength with age, becomes a public health concern as the elderly population increases. This study aimed to determine whether the mixture of soluble whey protein hydrolysate (WPH) and Panax ginseng berry extract (GBE) has a synergetic effect on sarcopenia and, if so, to identify the relevant mechanisms and optimal mixing ratio. Methods: In the first experiment, C57BL/6 mice were hindlimb immobilized for one-week and then administered WPH 800 mg/kg, GBE 100 mg/kg, WPH 800 mg/kg+ GBE 100 mg/kg mixture, and Fructus Schisandrae extract (SFE) 200 mg/kg for two weeks. In the second experiment, experimental design was same, but mice were administered three different doses of WPH and GBE mixture (WPH 800 mg/kg+ GBE 100 mg/kg, WPH 800 mg/kg+ GBE 90 mg/kg, WPH 1000 mg/kg+ GBE 75 mg/kg). Results: In the first experiment, we confirmed the synergetic effect of WPH and GBE on muscle mass and identified that GBE was more effective on the protein synthesis side, and WPH tended to be slightly more effective for protein degradation. In the second experiment, among three different ratios, the WPH 800 mg/kg+ GBE 100 mg/kg was most effective for muscle mass and strength. The mixtures activated muscle protein synthesis via PI3K/Akt/mTORc1 pathway and inhibited muscle protein degradation via suppressing ubiquitin-proteasome system (UPS) and autophagy-lysosome system (ALS), and these effects were more GBE dose-dependent than WPH. Conclusion: The WPH and GBE mixture having a synergetic effect is a potential agent to prevent sarcopenia.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.143-150
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• 2021

In the recent power industry, a change is taking place from manual meter reading to remote meter reading using AMI(Advanced Metering Infrastructure). If such the power data generated from the AMI is recorded on the blockchain, integrity is guaranteed by preventing forgery and tampering. As data sharing becomes transparent, new business can be created. However, Ethereum blockchain is not suitable for processing large amounts of transactions due to the limitation of processing speed. As a solution to overcome such the limitation, various On/Off-Chain methods are being investigated. In this paper, we propose a interface server using data sharding as a solution for storing large amounts of power data in Etherium blockchain environments. Experimental results show that our power-data processing system with sharding method lessen the data omission rate to 0% that occurs when the transactions are transmitted to Ethereum and enhance the processing speed approximately 9 times.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.204-211
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• 2022

In this study, Hollow Concrete Filled FRP Tube Pile(HCFFT Pile) was proposed as a model to utilize the advantages of composite piles and solve the problem of corrosion, which is a disadvantage of CFT piles, and a numerical analysis model was developed to analyze their behavior. The strain compatibility method was applied considering the damage plastic behavior of concrete, the yield plastic behavior of steel, and the elastic behavior of FRP. The flexural strength calculation equation of HCFFT piles was proposed considering the change of the FRP tube section according to the distance from the neutral axis. The flexural strength calculation equation, numerical analysis results, and experimental results were compared and analyzed to verify their adequacy. The results of this study can be used as basic data for the optimal design of various HCFFT piles using FRP.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.134-138
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• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.395-401
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• 2023

Sound Event Detection (SED) aims to identify not only sound category but also time interval for target sounds in an audio waveform. It is a critical technique in field of acoustic surveillance system and monitoring system. Recently, various models have introduced through Detection and Classification of Acoustic Scenes and Events (DCASE) Task 4. This paper explored how to design optimal parameters of DenseNet based model, which has led to outstanding performance in other recognition system. In experiment, DenseRNN as an SED model consists of DensNet-BC and bi-directional Gated Recurrent Units (GRU). This model is trained with Mean teacher model. With an event-based f-score, evaluation is performed depending on parameters, related to model architecture as well as model training, under the assessment protocol of DCASE task4. Experimental result shows that the performance goes up and has been saturated to near the best. Also, DenseRNN would be trained more effectively without dropout technique.

• Advances in nano research
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• v.15 no.4
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• pp.355-366
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• 2023

Biological corrosion, a crucial aspect of metal degradation, has received limited attention despite its significance. It involves the deterioration of metals due to corrosion processes influenced by living organisms, including bacteria. Soil represents a substantial threat to pipeline corrosion as it contains chemical and microbial factors that cause severe damage to water, oil, and gas transmission projects. To combat fouling and corrosion, corrosion inhibitors are commonly used; however, their production often involves expensive and hazardous chemicals. Consequently, researchers are exploring natural and eco-friendly alternatives, specifically nano-sized products, as potent corrosion inhibitors. This study aims to environmentally synthesize silver nanoparticles using an extract from Lagoecia cuminoides L and evaluate their effectiveness in preventing biological corrosion of buried pipes in soil. The optimal experimental conditions were determined as follows: a volume of 4 ml for the extract, a volume of 4 ml for silver nitrate (AgNO3), pH 9, a duration of 60 minutes, and a temperature of 60 degrees Celsius. Analysis using transmission electron microscopy confirmed the formation of nanoparticles with an average size of approximately 28 nm, while X-ray diffraction patterns exhibited suitable peak intensities. By employing the Scherer equation, the average particle size was estimated to be around 30 nm. Furthermore, antibacterial studies revealed the potent antibacterial activity of the synthesized silver nanoparticles against both aerobic and anaerobic bacteria. This property effectively mitigates the biological corrosion caused by bacteria in steel pipes buried in soil.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.1-12
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• 2024

Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.