• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.2
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• pp.390-407
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• 2021

The study aims to assess the current status of sustainable clothing habits from the perspective of consumers. Awareness and management behavior regarding microplastics from fashion products and usage of fleece and faux fur were investigated. A random online survey involving 413 women was conducted to figure out their perceptions on microplastics that are shedded from fashion products such as fleece and faux fur. The results indicate that 73.6% were not aware of the fact that microplastic is released during the washing process of fleece and faux fur. Furthermore, only 26.6% of the respondents who were aware of microplastics from clothing washing were making efforts to reduce its emission. The respondents considered product sustainability more in the selection stage than in the management stage (p<.001). The results revealed that, although the respondents were highly aware of the risk of environmental pollution that microplastics pose, they were neither fully cognizant of the fact that microplastics may come from fashion products, nor did they make efforts to reduce its emissions. Compared with respondents in their 20's, respondents in the age of 30-40 years seemed more aware of microplastics from fashion products and exerted more effort to reduce its emission.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.29-43
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• 2019

In recent years, as the network traffic in the WSN(Wireless Sensor Network) has been increased by the growing number of IoT wireless devices, SDWSN(Software-Defined Wireless Sensor Network) and its security that aims a secure SDN(Software-Defined Networking) for efficiently managing network resources in WSN have received much attention. In this paper, we study on how to efficiently and securely design a PUF(Physical Unclonable Function)-assisted group key distribution scheme for the SDWSN environment. Recently, Huang et al. have designed a group key distribution scheme using the strengths of SDN and the physical security features of PUF. However, we observe that Huang et al.'s scheme has weak points that it does not only lack of authentication for the auxiliary controller but also it maintains the redundant synchronization information. In this paper, we securely design an authentication process of the auxiliary controller and improve the vulnerabilities of Huang et al.'s scheme by adding counter strings and random information but deleting the redundant synchronization information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.97-103
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• 2019

Electronic contract systems are widely used to integrate and manage the contract management process based on the development of ICT technology. Recently, improvement methods using block chain technology are being studied. However, contract management systems have processing performance, security vulnerabilities, data entry, and service accessibility issues. In this paper, we propose a block - chain based contract management platform with block chain, smart contract, and Rest API. The suggested platform includes the RPBFT algorithm which solves the processing performance and security vulnerability of the existing consensus authentication algorithm, and the algorithm to prevent data entry and enhance transparency of participants. The block-chain-based contract management platform proposed in this paper provides a use environment with improved processing performance, security, reliability, and transparency, and can be used through API without burdening construction. Therefore, The effect can be expected.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.875-880
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• 2019

A RC4 stream cipher is widely used for security applications such as IEEE 802.11 WEP, IEEE 802.11i TKIP and so on, because it can be simply implemented to dedicated circuits and achieve a high-speed encryption. RC4 is also used for systems with limited resources like IoT, but there are performance limitations. RC4 consists of two stages, KSA and PRGA. KSA performs initialization and randomization of S-box and K-box and PRGA produces cipher texts using the randomized S-box. In this paper, we initialize the S-box and K-box in the randomization of the KSA stage to reduce the initialization delay. In the randomization, we use clusters to process swap operation between elements of S-box in parallel and can generate two cipher texts per clock. The proposed RC4 cipher hardware can initialize S-box and K-box without any delay and achieves about 2 times to 6 times improvement in KSA randomization and key stream generation.

• Tribology and Lubricants
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• v.35 no.1
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• pp.16-23
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• 2019

In this study, we aim to analyze the effects of both contact layer properties and surface roughness on contact resistance. The contact has a great influence on performance in terms of electrical conduction and heat transfer. The two biggest factors determining contact resistance are the presence of surface roughness and the surface layer. For this reason we calculated the contact resistance by considering both factors simultaneously. The model of this study to calculate contact resistance is as follows. First, the three representative surface parameters for the GW model are obtained by Nayak's random process. Then, the apparent contact area, real contact area, and contact number of asperities are calculated using the GW model with the surface parameters. The contact resistance of a single surface layer is calculated using Mikic's constriction equation. The total contact resistance is approximated by the parallel connection between the same asperity contact resistances. The results of this study are as follows. The appropriate thickness with reduction effect for contact resistance is determined according to the difference in conductivity between the base layer and surface layer. It was confirmed that the standard deviation of surface roughness has the greatest influence on surface roughness parameters. The results of this study will be useful for selecting the surface material and surface roughness when the design considering the contact resistance is needed.

• Coupled systems mechanics
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• v.8 no.2
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• pp.169-184
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• 2019

This research work presents a study that aims to assess the dynamic structural behaviour and also investigate the human comfort levels of a reinforced concrete building, when subjected to nondeterministic wind dynamic loadings, considering the effect of masonry infills on the global stiffness of the structural model. In general, the masonry fills most of the empty areas within the structural frames of the buildings. Although these masonry infills present structural stiffness, the common practice of engineers is to adopt them as static loads, disregarding the effect of the masonry infills on the global stiffness of the structural system. This way, in this study a numerical model based on sixteen-storey reinforced concrete building with 48 m high and dimensions of $14.20m{\times}15m$ was analysed. This way, static, modal and dynamic analyses were carried out in order to simulate the structural model based on two different strategies: no masonry infills and masonry infills simulated by shell finite elements. In this investigation, the wind action is considered as a nondeterministic process with unstable properties and also random characteristics. The fluctuating parcel of the wind is decomposed into a finite number of harmonic functions proportional to the structure resonant frequency with phase angles randomly determined. The nondeterministic dynamic analysis clearly demonstrates the relevance of a more realistic numerical modelling of the masonry infills, due to the modifications on the global structural stiffness of the building. The maximum displacements and peak accelerations values were reduced when the effect of the masonry infills (structural stiffness) were considered in the dynamic analysis. Finally, it can be concluded that the human comfort evaluation of the sixteen-storey reinforced concrete building can be altered in a favourable way to design.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.533-540
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• 2021

To evaluate the performance of a system, one-dimensional 3-neighborhood cellular automata(CA) based pseudo-random generators are widely used in many fields. Although two-dimensional CA and one-dimensional 5-neighborhood CA have been applied for more effective key sequence generation, designing symmetric one-dimensional 5-neighborhood CA corresponding to a given primitive polynomial is a very challenging problem. To solve this problem, studies on one-dimensional 5-neighborhood CA synthesis, such as synthesis method using recurrence relation of characteristic polynomials and synthesis method using Krylov matrix, were conducted. However, there was still a problem with solving nonlinear equations. To solve this problem, a symmetric one-dimensional 5-neighborhood CA synthesis method using a transition matrix of 90/150 CA and a block matrix has recently been proposed. In this paper, we detail the theoretical process of the proposed algorithm and use it to obtain symmetric one-dimensional 5-neighborhood CA corresponding to high-order primitive polynomials.