• Science of Emotion and Sensibility
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• v.25 no.4
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• pp.45-52
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• 2022

Recently, interest in and demand for sensors that recognize physical activity and their products are increasing. In particular, the development of wearable materials that are flexible, stretchable, and able to detect the user's biological signals is drawing attention. In this study, an experiment was conducted to improve the dip-coating efficiency of a single-walled carbon nanotube dispersion solution after fine holes were made in a hydrophobic material with a micro needle. In this study, dip-coating was performed with a material that was not penetrated, and comparative analysis was performed. The electrical conductivity of the sensor was measured when the sensor was stretched using a strain universal testing machine (Dacell Co. Ltd., Seoul, Korea) and a multimeter (Keysight Technologies, Santa Rosa, CA, USA) was used to measure resistance. It was found that the electrical conductivity of a sensor that was subjected to needling was at least 16 times better than that of a sensor that was not. In addition, the gauge factor was excellent, relative to the initial resistance of the sensor, so good performance as a sensor could be confirmed. Here, the dip-coating efficiency of hydrophobic materials, which have superior physical properties to hydrophilic materials but are not suitable due to their high surface tension, can be adopted to more effectively detect body movements and manufacture sensors with excellent durability and usability.

• Fire Science and Engineering
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• v.29 no.3
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• pp.48-52
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• 2015

In this study, we have evaluated whether the mixing ratio is proper by creating a mixing device for foam proportioner that mainly is employed in practice utilizing a metering venturi type. In case of the mixing ratio for 3%, water under pressure of 76 mm in diameter and the original liquid of a foam fire extinguishing agent of 31.75 mm in diameter have showed up the fluctuation rate just as much as 3.1~3.5% of the mixing ratio. Because water under pressure of 101.6 mm in diameter and the original liquid of a foam fire extinguishing agent of 38.1 mm in diameter have showed up 3.3~3.7% of the fluctuation rate, water under pressure of 101.6 mm in diameter and the original liquid of a foam fire extinguishing agent of 38.1 mm in diameter have satisfied 3.0~3.9% of performance criterion. And also, in case of the 6% of mixture rate, water under pressure of 76.2 mm in diameter and the original liquid of a foam fire extinguishing agent of 31.75 mm in diameter have showed up the fluctuation rate just as much as 6.4~6.8% of the mixing ratio. Because water under pressure of 101.6 mm in diameter and the original liquid of a foam fire extinguishing agent of 38.1 mm in diameter have showed up 6.0~6.8% of the fluctuation rate, water under pressure of 101.6 mm in diameter and the original liquid of a foam fire extinguishing agent of 38.1 mm in diameter have satisfied 6.0~7.0% of performance criterion.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.673-683
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• 2008

This study improved the existing method of using the longitudinal strain and concept of influence line to develop Bridge Weigh-in-Motion system without axle detector using the dynamic strain of the bridge girders and concrete slab. This paper first describes the considered algorithms of extracting passing vehicle information from the dynamic strain signal measured at the bridge slab, girders, and cross beams. Two different analysis methods of 1) influence line method, and 2) neural network method are considered, and parameter study of measurement locations is also performed. Then the procedures and the results of field tests are described. The field tests are performed to acquire training sets and test sets for neural networks, and also to verify and compare performances of the considered algorithms. Finally, comparison between the results of different algorithms and discussions are followed. For a PSC I-girder bridge, vehicle weight can be calculated within a reasonable error range using the dynamic strain gauge installed on the girders. The passing lane and passing speed of the vehicle can be accurately estimated using the strain signal from the concrete slab. The passing speed and peak duration were added to the input variables to reflect the influence of the dynamic interaction between the bridge and vehicles, and impact of the distance between axles, respectively; thus improving the accuracy of the weight calculation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.794-801
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• 2023

The high seas, covering the majority of the world's oceans, hold invaluable marine resources crucial for the growth of the marine bio-industry. The High seas bioresources program of South Korea is at the forefront of these global efforts. This study aims to gauge public awareness and quantify the project benefits, offering insights for future policy decisions. The results revealed that the estimated annual average willingness to pay (WTP) was 3,778.8 KRW, equating to approximately 81.54 billion KRW when extrapolated to the entire national population. The implications of the study are twofold: The project benefits, based on WTP, are substantial, amounting to approximately 81.5 billion KRW annually. This provides critical reference material for future policy formulation, given the considerable WTP in comparison to the current investment. Although interest in international sea marine biological resources is growing, public awareness remains relatively low. However, the project plays a crucial role in building essential databases for the marine bio-industry and securing international sea marine biological resources. Public interest and sustained support are pivotal, not only for this project but also for future policy implementation. Strategies to enhance public awareness are essential, and the study results offer valuable input for future policy decisions.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of the Korea Institute of Building Construction
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• v.23 no.5
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• pp.537-546
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• 2023

For the purpose of fire delineation within buildings, steel walls in Korea are mandated to undergo rigorous certification as fire-resistant entities, substantiated via a series of qualitative assessments. Predominantly, these evaluations comprise the fire resistance test paired with supplementary examinations; specifically for steel walls, these encompass the gas hazard and panel bending strength tests. Given the prevalence of semi-noncombustible core materials, gas hazard tests are largely rendered superfluous, pivoting the focus solely onto the panel bending strength test during the certification trajectory. This particular test is designed to gauge the flexural robustness of individual wall panels. An enhanced bending strength is postulated to fortify both the structural integrity and thermal insulation of the wall by mitigating potential deformations. In this scholarly exploration, an analytical deep dive was undertaken into extant, valid certification test datasets. The endeavor aimed to ascertain the depth of correlation between the designated fire resistance metric and the bending strength, the latter being the sole supplementary assessment for steel walls. In distilling the findings, it was discerned that temperature elevations beyond baseline values exhibited no statistically salient linkage with the panel's bending strength.

• Korean Journal of Materials Research
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• v.33 no.10
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• pp.406-413
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• 2023

This study used optical and scanning electron microscopy to analyze the surface oxidation phenomenon that accompanies a γ'-precipitate free zone in a directional solidified CM247LC high temperature creep specimen. Surface oxidation occurs on nickel-based superalloy gas turbine blades due to high temperature during use. Among the superalloy components, Al and Cr are greatly affected by diffusion and movement, and Al is a major component of the surface oxidation products. This out-diffusion of Al was accompanied by γ' (Ni₃Al) deficiency in the matrix, and formed a γ'-precipitate free zone at the boundary of the surface oxide layer. Among the components of CM247LC, Cr and Al related to surface oxidation consist of 8 % and 5.6 %, respectively. When Al, the main component of the γ' precipitation phase, diffused out to the surface, a high content of Cr was observed in these PFZs. This is because the PFZ is made of a high Cr γ phase. Surface oxidation of DS CM247LC was observed in high temperature creep specimens, and γ'-rafting occurred due to stress applied to the creep specimens. However, the stress states applied to the grip and gauge length of the creep specimen were different, and accordingly, different γ'-rafting patterns were observed. Such surface oxidation and PFZ and γ'-rafting are shown to affect CM247LC creep lifetime. Mapping the microstructure and composition of major components such as Al and Cr and their role in surface oxidation, revealed in this study, will be utilized in the development of alloys to improve creep life.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.237-250
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• 2022

On September 28, 2020, the Korea Fair Trade Commission introduced a proposed bill entitled the "Fair Online Platform Intermediary Transactions Act." We quantify the impact of this proposed act on Naver, Korea's major digital platform. Finding a proper control unit is not an easy task in social science studies. We overcome this caveat by constructing a synthetic version of Naver using Abadie & Gardeazabal's (2003) synthetic control method. It appears that the economic cost of the proposed act is not negligible at all. Naver's opportunity loss amounted to 16.18% of its market capitalization (approximately 8.5 trillion won in comparison with its pre-regulation market capitalization). Any regulation-based approaches to resolving digital platform issues have both promises and pitfalls. The results highlight that regulatory bodies should carefully gauge the impact of such regulations, as we have seen with Naver's case.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.