• Korean Journal of Metals and Materials
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• v.48 no.6
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• pp.523-532
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• 2010

Co-application of organic coating and cathodic protection has not provided enough durability to low-alloyed steels inseawater ballast tank (SBT) environments. An attempt has made to study the effect of alloy elements (Al, Cr, Cu, Mo, Ni, Si, W) on general and localized corrosion resistance of steels as basic research to develop new low-allowed steels resistive to corrosion in SBT environments. For this study, we measured the corrosion rate by the weigh loss method after periodic immersion in synthetic seawater at $60^{\circ}C$, evaluated the localized corrosion resistance by an immersion test in concentrated chloride solution with the critical pH depending on the alloy element (Fe, Cr, Al, Ni), determined the permeability of chloride ion across the rust layer by measuring the membrane potential, and finally, we analyzed the rust layer by EPMA mapping and compared the result with the E-pH diagram calculated in the study. The immersion test of up to 55 days in the synthetic seawater showed that chromium, aluminium, and nickel are beneficial but the other elements are detrimental to corrosion resistance. Among the beneficial elements, chromium and aluminium effectively decreased the corrosion rate of the steels during the initial immersion, while nickel effectively decreased the corrosion rate in a longer than 30-day immersion. The low corrosion rate of Cr- or Al-alloyed steel in the initial period was due to the formation of $Cr_2FeO_4$ or $Al_2FeO_4$, respectively -the predicted oxide in the E-pH diagram- which is known as a more protective oxide than $Fe_3O_4$. The increased corrosion rate of Cr-alloyed steels with alonger than 30-day exposure was due to low localized corrosion resistance, which is explained bythe effect of the alloying element on a critical pH. In the meantime, the low corrosion rate of Ni-alloyed steel with a longer than 30-day exposure wasdue to an Ni enriched layer containing $Fe_2NiO_4$, the predicted oxide in the E-pH diagram. Finally, the measurement of the membrane potential depending on the alloying element showed that a lower permeability of chloride ion does not always result in higher corrosion resistance in seawater.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.4
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• pp.381-388
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• 2020

The quality control of pathological specimens is important for accurate molecular pathology testing. This study evaluated that specimen factors affecting the DNA quality during tissue processing and sample types for BRAF, EGFR, and KRAS mutations tests. One thousand seven hundred and seventy-two molecular pathology tests were investigated for the factors influencing the DNA quality, such as sample type, formalin fixation time, and reexamination status. Cytology samples stored in a saline solution had better DNA quality than commercial cytology preservation. Tissue samples fixed in formalin within 24 hours had better DNA quality than the samples fixed over 24 hours. Between the types of samples, fresh tissue samples and tissue samples with a high tumor cell density had relatively better DNA quality than the formalin-fixed paraffin-embedded (FFPE) tissues and cytology specimens. Of real-time PCR, the non-PNA Ct value increased proportionally with samples held for longer than 24 hours in formalin, and that the formalin-fixed time affects the sample DNA quality. In conclusion, the appropriate tumor cellularity and 10% neutral formalin fixation time are the most important factors for maintaining the DNA quality. These factors should be managed properly for an accurate pathological molecular test to ensure optimal DNA quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.23-38
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• 2021

In this paper, we analyzed the possibility of utilizing LX-PPS GNSS permanent stations whose antennas are installed on the building rooftop for the purpose of high-precision GNSS positioning services. We picked 15 pairs of adjacent GNSS permanent stations operated by LX-PPS and NGII, and then produced 3-year-long time series using the high-precision data processing software called GIPSY. Patterns and trends of position estimates were compared and analyzed. Horizontal and vertical deviations including the linear velocities coincide with the well-known crustal deformation rates of the Korean peninsula. We also observed almost the same annual or seasonal patterns from those nearby sites. After detrending the linear velocity, the amplitude and phase of annual signals almost perfectly match each other within the baseline length of 2 km. By subtracting seasonal signals, the RMS and standard deviations in LX-PPS PPGR with respect to NGII KANR are about 1, 2, and 5 mm in the north-south, east-west, and vertical directions, respectively. From this analysis it can be concluded that the rooftop-installed LX-PPS sites show similar level of stability and positioning performance comparable to those ground-mounted NGII stations.

• Journal of Environmental Science International
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• v.30 no.8
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• pp.605-612
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• 2021

Supply electrical conductivity (EC) concentration of the nutrition solution is an important factor in the absorption of nutrients by plants and the management of the root zone, as it can control the vegetative/reproductive growth of a plant. Paprika usually undergoes its reproductive and vegetative growth simultaneously. Therefore, ensuring proper growth of the plant leads to increased yield of paprika. In this study, growth characteristics of paprika were examined according to the EC concentration of a coir and a rockwool substrate. The supply EC was 1.0, 2.0, and 4.0 mS·cm^-1 applied at the initial stages of the growth using the rockwool (commonly used by paprika farmers) and the coir substrate with a chip and dust ratio of 50:50 and 70:30. For up to 16 weeks of paprika growth, EC concentrations of 1.0 and 2.0 mS·cm^-1 were found to have a greater effect on the growth than EC at 4.0 mS·cm^-1. The normality (marketable) rate of fruit, the soluble solid content, and paprika growth showed that the coir was generally better than the rockwool regardless of the supply EC concentration. The values of the yield per plant at an EC concentration of 4.0 mS·cm^-1 was mostly similar at 1.6 kg (coir 50:50), 1.5 kg (coir 70:30) and 1.5 kg (rockwool), but the yield of the rockwool was 88%, which was lower than 98% and 94% yield of the coir substrate. Therefore, this concludes that coir substrate is more effective than rockwool at improving paprika productivity. The results also suggest that the use of coir substrate for paprika has many benefits in terms of reducing production costs and preventing environmental destruction during post-processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.4
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• pp.57-64
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• 2022

Chloride, which is one of the main deterioration factors in reinforced concrete structures, can degrade the performance of the structure due to chloride-induced corrosion of steel. Chloride content at steel depth or the rate of chloride penetration is necessary to determine deterioration of reinforced concrete or to calculate initiation time of steel corrosion caused by chloride attack. Chlorides in concrete are generally identified with typical two methods including chloride profiling using potentiometric titration method and discoloration method using AgNO₃ solution. The former is advantageous to estimate chloride penetration rate (diffusion coefficient in general) with measured chloride contents directly, but it is laborious. In the case of latter, while the result is obtained easily with the range of discoloration, the error may occur depending on workmanship when the depth of chloride ingress is measured. This study shows that chloride penetrated depth is evaluated with the results obtained from discoloration method through image analysis, thereby the error is minimized by workmanship. In addition, the effect of micro-crack in concrete is studied on chloride penetration. In conclusion, the depth of chloride penetration was quantified with image analysis and as it was confirmed that chlorides can rapidly penetrate through micro-cracks, caution is especially required for cracks in concrete structure.

• Journal of Korean Society of Disaster and Security
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• v.15 no.3
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• pp.101-113
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• 2022

This study is planned with the aim of developing a systematic construction process based on the scientific and engineering theory of the water-sealing grouting construction applied to the tunnel excavation process during the construction of the downtown underground traffic network, so that the construction quality of the relatively backward domestic tunnel water-sealing grouting construction is improved and continuously maintained no matter who constructs it. The main contents of the improved tunnel water-sealing grouting can be largely examined in the classification of tunnel water-sealing grouting application and the definition of grouting materials, the correlation analysis of groundwater pressure conditions with groundwater inflow, the study of the characteristic factors of bedrock, and the element technologies and injection management techniques required for grouting construction. Looking at the trends in global research, research in the field of theoretical-based science and engineering grouting is actively progressing in Nordic countries (Sweden, Finland, Norway, etc.), Japan, Germany, and the United States. Therefore, in this study, the algorithm is established through theoretical analysis of the elements of tunnel water-sealing grouting construction techniques to provide an integrated solution including a construction process that can effectively construct tunnel water-sealing grouting construction.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.21-30
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• 2024

As the self-driving car market continues to grow, the need for charging infrastructure is growing. However, in the case of a wireless charging system, stability issues are being raised because it requires a large amount of power compared with conventional wired charging. SAE J2954 is a standard for building autonomous vehicle wireless charging infrastructure, and the standard defines a communication method between a vehicle and a power transmission system. SAE J2954 recommends using physical media such as Wi-Fi, Bluetooth, and UWB as a wireless charging communication method for autonomous vehicles to enable communication between the vehicle and the charging pad. In particular, UWB is a suitable solution for indoor and outdoor charging environments because it exhibits robust communication capabilities in indoor environments and is not sensitive to interference. In this standard, the process for building a wireless power transmission system is divided into several stages from the start to the completion of charging. In this study, UWB technology is used as a means of fine alignment, a process in the wireless power transmission system. To determine the applicability to an actual autonomous vehicle wireless power transmission system, experiments were conducted based on distance, and the distance information was collected from UWB. To improve the accuracy of the distance data obtained from UWB, we propose a Single Model and Multi Model that apply machine learning and deep learning techniques to the collected data through a three-step preprocessing process.

• Information Systems Review
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• v.21 no.2
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• pp.117-137
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• 2019

In the Big Data era, data science has become popular with the production of numerous data in various domains, and the power of data has become a competitive power. There is a growing interest in unstructured data, which accounts for more than 80% of the world's data. Along with the everyday use of social media, most of the unstructured data is in the form of text data and plays an important role in various areas such as marketing, finance, and distribution. However, text mining using social media is difficult to access and difficult to use compared to data mining using numerical data. Thus, this study aims to develop Korean Natural Language Application (KoALA) as an integrated application for easy and handy social media text mining without relying on programming language or high-level hardware or solution. KoALA is a specialized application for social media text mining. It is an integrated application that can analyze both Korean and English. KoALA handles the entire process from data collection to preprocessing, analysis and visualization. This paper describes the process of designing, implementing, and applying KoALA applications using the design science methodology. Lastly, we will discuss practical use of KoALA through a block-chain business case. Through this paper, we hope to popularize social media text mining and utilize it for practical and academic use in various domains.