• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1331-1344
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• 2021

This study is to evaluate the seismic capacity of the fire-damaged cabinet facility in a nuclear power plant (NPP). A prototype of an electrical cabinet is modeled using OpenSees for the numerical simulation. To capture the nonlinear behavior of the cabinet, the constitutive law of the material model under the fire environment is considered. The experimental record from the impact hammer test is extracted trough the frequency-domain decomposition (FDD) method, which is used to verify the effectiveness of the numerical model through modal assurance criteria (MAC). Assuming different temperatures, the nonlinear time history analysis is conducted using a set of fifty earthquakes and the seismic outputs are investigated by the fragility analysis. To get a threshold of intensity measure, the Monte Carlo Simulation (MCS) is adopted for uncertainty reduction purposes. Finally, a capacity estimation model has been proposed through the investigation, which will be helpful for the engineer or NPP operator to evaluate the fire-damaged cabinet strength under seismic excitation. This capacity model is presented in terms of the High Confidence of Low Probability of Failure (HCLPF) point. The results are validated by the proper judgment and can be used to analyze the influences of fire on the electrical cabinet.

• Smart Media Journal
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• v.12 no.2
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• pp.83-90
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• 2023

The advent of quantum computers threatens the security of existing hash functions. In this paper, we confirmed the implementation results of quantum circuits for domestic/international hash functions, LSH, SHA2, SHA3 and SM3, and conducted a comparative analysis. To operate the existing hash function in a quantum computer, it must be implemented as a quantum circuit, and the quantum security strength can be confirmed by estimating the necessary quantum resources. We compared methods of quantum circuit implementation and results of quantum resource estimation in various aspects and discussed ways to meet quantum computer security in the future.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.883-889
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• 2009

The importance of cost estimate in early stage such has been increasing due to market change and severe competition in construction industry. Because the adjustable budget is only 20% after design stage, most of the crucial decisions to influence cost is made in the early stage. However, in the early stage, the project scope is not defined completely so that estimator has inaccurate information to make critical decision. Therefore, this research suggests the cost estimate method using case-based reasoning. Case-based reasoning is appropriate for the early cost estimating, as it has the strength of rapidity and convenience in cost estimation. This research analyzes 84 actual data of public apartment on the scale of 11~15 stories. In order to extract the most similar case, at the first step this research identifies influence factors and calculates attribute similarity. In case-based reasoning, the most challenging task is determining attribute weight. At the third step, this research calculates case similarity which is aggregated attribute similarity multipled by attribute weight. Finally, extracts the most similar case which has the highest score of case similarity.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.50-55
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• 2023

Due to the unavailability of global positioning system (GPS) indoors, various indoor pedestrian positioning methods have been designed to estimate the position of the user using received signal strength (RSS) measurements from radio beacons, such as wireless fidelity (WiFi) access points and Bluetooth low energy (BLE) beacons. In indoor environments, radio-frequency (RF) signals are unpredictable and change over space and time because of multipath associated with reflection and refraction, shadow fading caused by obstacles, and interference among different devices using the same frequencies. Therefore, the outliers in the positional information obtained from the indoor positioning method based on RSS measurements occur often. For this reason, the performance of the positioning method can be degraded by the characteristics of the RF signal. To resolve this issue, a map-matching (MM) algorithm based on maximum probability (MP) estimation is applied to the indoor positioning method in this study. The MM algorithm locates the aberrant position of the user estimated by the positioning method within the limits of the adjacent pedestrian passages. Empirical experiments show that the positioning method can achieve higher positioning accuracy by leveraging the MM algorithm.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.2
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• pp.121-129
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• 2022

Density and sound speed contrasts (g and h, respectively), and swimming angle were measured for sandfish (Arctoscopus japonicus) without swimbladder. The density contrast was measured by the volume displacement method while the sound speed contrast was measured by the acoustic measurements of travel time (time-of-flight method). The swimming angle was measured by dividing it into daytime, nighttime, daytime feeding and nighttime feeding. The g was 1.001 to 1.067 with an average (± standard deviation) of 1.032 (± 0.017), and the h was 1.007 to 1.022 with an average (± standard deviation) of 1.015 (± 0.003). The swimming angles (mean ± standard deviation) were 16.8 ± 10.3° during the daytime, 1.9 ± 12.3° during the nighttime, 30.2 ± 12.6° in the daytime feeding and 35.0 ± 13.2° in the nighttime feeding. These results will provide important parameters input to calculate theoretical scattering models for estimating the acoustic target strength of sandfish.

• Annual Conference of KIPS
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• 2008.11a
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• pp.1207-1210
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• 2008

무선 센서 네트워크는 고정 인프라 없이 센서 노드만으로 정보를 수집하는 네트워크로서 센서들의 위치정보 식별은 매우 중요하다. 센서 노드간 거리 측정은 신호의 도착시간차(Time of Arrival: ToA), 신호세기(Received Signal Strength: RSS), 신호각도(Angle of Arrival: AoA)에 기반을 둔 방법 등이 있다. 무선 센서 네트워크에 배치되어 있는 각 센서 노드간 정확한 거리 식별을 위해 기존의 거리 측정 방법을 보완하여 거리 오차를 줄이는 ToA기반의 RSS보정 방법을 제안한다. 구체적으로 초음파를 통한 거리측정 값에 맵(RF-MAP)을 통해 보정한 RSS값을 가중치로 보정하여 기존의 거리 측정 방법보다 측정오차를 줄였다. 실험을 통해 제안한 방법은 기존 ToA보다 실내(5m×7m)에서 평균 0.1cm, 실외(10m×10m) 평균 0.6cm 측정 오차를 줄일 수 있음을 확인 할 수 있었다.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3617-3627
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• 2023

This study presents a method to solidify spent ion-exchange resin (IER) in a metakaolin-based geopolymer and shows results of mechanical strength, immersion, leaching, irradiation, and thermal cycling tests for waste acceptance criteria (WAC) to repository. The geopolymer waste form with 20 wt% of simulated spent IER met the WAC in South Korea (ROK), and the leaching tests of various sized-waste forms up to 15.0 × 30.0 cm and waste loadings up to 20 wt% for 1-5 d and 1-90 d achieved a leachability index, L_i > 6. In a leaching test for 5 d, the cumulative fraction leached (CFL) for Cs, which leached the most, was linearly correlated with the square root of leaching time for all waste forms, and L_i increased as the size of the waste form increased. The CFL was also correlated with elapsed time in the 90 d leaching test. The correlations among CFL, time, and volume-to-surface area ratio of waste forms used to estimate the L_i of Cs of a 200-L sized geopolymer with 15 wt% IER showed the L_i values as 14.73 (5 d) and 17.71 (90 d), respectively, indicating that the large-sized geopolymer waste form met the WAC.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.

• Transactions of Materials Processing
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• v.33 no.4
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• pp.248-254
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• 2024

The triply periodic minimal surface (TPMS) structure is characterized by a high surface-to-volume (S/V) ratio and the separated internal structure for flow. Combining the different TPMS structures can provide unique flow and strength characteristics. This paper investigates the effects of dimension, density and arrangement of the unit cell of the TPMS on contact and flow areas of combined TPMS structures. Several representative TPMS structures, including primitive, gyroid and diamond structures, are adopted to design gradient and heterogeneous types TPMS structures. The estimation method of contact and flow areas using an image processing technique is proposed. Python software is used to predict contact and flow area. The influence of the combination method of TPMS on contact and flow areas in the contact surface of combined TPMS structures with different shapes is investigated. Based on the results of the investigation, an appropriate combination method of TPMS structures is discussed.