• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.75.1-75.1
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• 2019

The universe has been ionized in the post-reionization by several photon contributors. The dominant source to produce the hydrogen ionizing photons is not revealed so far. Faint quasars have been expected to generate UV photon budgets required to maintain ionization state of universe. Observational limits, however, hinder to discover them despite their higher number density than bright one. Consequently, the influence of faint quasars on post-reionization are not considered sufficiently. Therefore, a survey to find faint quasars at z ~ 5 is crucial to determine the main ionizing source in the post-reionization era. Deep images from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) allow us to search for quasar swith low luminosities in the ELAIS-N1 field. J band information are obtained by the Infrared Medium-deep Survey (IMS) and the UKIRT Infrared Deep Sky Survey (UKIDSS) - Deep ExtragalacticSurvey (DXS). Faint quasar candidates were selected from several multi-band color cut criteria based on simulated quasars on color-color diagram. To choose the reliable candidates with possible Lyman break, we have performed medium-bands observations. Whether a candidate is a quasar or a dwarf star contamination was decided by results from chi-square minimization of quasar/dwarf model fitting. Spectroscopic follow-up observations confirm three quasars at z ~ 5. 100% spectral confirmation success rate implies that the medium-band observations effectively select faint quasars with strong Lyman alpha emission.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.1-7
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• 2022

In this paper, a study was conducted to predict natural disasters in Afghanistan based on machine learning. Natural disasters need to be prepared not only in Korea but also in other vulnerable countries. Every year in Afghanistan, natural disasters(snow, earthquake, drought, flood) cause property and casualties. We decided to conduct research on this phenomenon because we thought that the damage would be small if we were to prepare for it. The Azure Machine Learning Studio used in the study has the advantage of being more visible and easier to use than other Machine Learning tools. Decision Forest is a model for classifying into decision tree types. Decision forest enables intuitive analysis as a model that is easy to analyze results and presents key variables and separation criteria. Also, since it is a nonparametric model, it is free to assume (normality, independence, equal dispersion) required by the statistical model. Finally, linear/non-linear relationships can be searched considering interactions between variables. Therefore, the study used decision forest. The study found that overall accuracy was 89 percent and average accuracy was 97 percent. Although the results of the experiment showed a little high accuracy, items with low natural disaster frequency were less accurate due to lack of learning. By learning and complementing more data, overall accuracy can be improved, and damage can be reduced by predicting natural disasters.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3164-3182
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• 2023

The CRUD on the fuel cladding under the pressurized water reactor (PWR) operating condition causes several issues. The CRUD can act as thermal resistance and increases the local cladding temperature which accelerate the corrosion process. The hideout of boron inside the CRUD results in axial offset anomaly and reduces the plant's shutdown margin. Recently, there are efforts to revise the acceptance criteria of emergency core cooling systems (ECCS), and additionally require the modeling of the thermal resistance effect of the CRUD during the performance analysis. There is an urgent need for the evaluation of the effect of the CRUD deposition on the cladding heat transfer under PWR operating conditions, but the experimental database is very limited. The experimental facility called DISNY was designed and constructed to analyze the CRUD-related multi-physical phenomena, and the performance analysis of the constructed DISNY facility was conducted. The thermal-hydraulic and water chemistry conditions to simulate the CRUD growth under PWR operating conditions were established. The design characteristics and feasibility of the DISNY facility were validated by the MARS-KS code analysis and separate performance tests. In the current study, detailed design features, design validation results, and future utilization plans of the proposed DISNY facility are presented.

• Tribology and Lubricants
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• v.39 no.2
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• pp.61-71
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• 2023

This study proposes a procedure for designing a labyrinth seal that meets both leakage flow rate and rotordynamic performance criteria (effective damping, amplification factor, separation margin, logarithmic decrement, and vibration amplitude). The seal is modeled using a one control volume (1CV) bulk flow approach to predict the leakage flow rate and rotordynamic coefficients. The rotating shaft is modeled with the finite element (FE) method and is assumed to be supported by two linearized bearings. Geometry, material and operating conditions of the rotating shaft, and the supporting characteristics of the bearings were fixed. A single labyrinth seal is placed at the center of the rotor, and the linearized dynamic coefficients predicted by the seal numerical model are inserted as linear springs and dampers at the seal position. Seal designs that satisfy both leakage and rotordynamic performance are searched by modifying five seal design parameters using the multi-grid method. The five design parameters include pre-swirl ratio, number of teeth, tooth pitch, tooth height and tooth tip width. In total, 12500 seal models are examined and the optimal seal design is selected. Finally, normalization was performed to select the optimal labyrinth seal designs that satisfy the system performance requirements.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.163-171
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• 2023

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed subpools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the wellknown privacy criteria: Entropy, Ubiquity, and Performance.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.1
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• pp.137-151
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• 2023

Stationary 3D whole-body scanners generally require 5 to 20 seconds of scanning time and cannot effectively detect armpit and crotch areas. Therefore, this study aimed to analyze the accuracy of a photogrammetric technique using a multi-camera system. First, dimensional accuracy was analyzed using a mannequin scan, comparing the differences between the scan-derived measurements and the direct measurements, with an allowable tolerance of ISO 20685-1:2018. Only 2 of 59 measurement items (ankle height and upper arm circumference, specifically) exceeded the ISO 20685-1:2018 criteria. When compared with the results of the eight stationary whole-body scanners assessed by the literature, the photogrammetric technique was found to have the advantage of scanning the top of the head, armpit, and crotch areas clearly. Second, this study found the photogrammetric technique is suitable for obtaining the body scans because it can minimize the perform scanning, resulting in a reduction of measurement errors due to breathing and uncontrolled movements. The error rate of the photogrammetry method was much lower than that of stationary 3D whole-body scanners.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.52-62
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• 2023

Various decontamination technologies have been developed for removing contaminated areas in industries. Although it is important to consider parameters such as safety, cost, and time when selecting the decontamination technology, till date their comparative study is missing. Furthermore, different decontamination technologies influence the decontamination effects in different ways. Therefore, this study compares different decontamination techniques for the steam generator using a multicriteria decision-making method. A steam generator is a large device comprising both low- and very low-level waste (LLW, VLLW) and reflects the difference in weights of the standards according to the classification of the waste. For LLW and VLLW decontaminations, chemical oxidizing reduction decontamination (CORD) and decontamination grit blasting were used as the preferred techniques, respectively, considering the purpose of decontamination differs based on the initial state of waste. An expert survey revealed that safety in LLW and waste minimization in VLLW exhibited high preference. This evaluation method can be applied not only to the comparison between each process, but also to the creation of process scenarios. Therefore, determining the decontamination approach using logical decision-making methods may improve the safety and economic feasibility of each step in the decommissioning process and ensure a public acceptance.

• Smart Structures and Systems
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• v.31 no.6
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• pp.545-559
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• 2023

Nanoparticle strips (NPS) are widely used as external reinforcers for two-way reinforced concrete slabs. However, the Structural Health Monitoring (SHM) of these slabs is a very important issue and was evaluated in this study. This study has been done analytically and numerically to optimize the placement of sensors. The properties of slabs and carbon nanotubes as composite sheets were considered isotopic and orthotropic, respectively. The nonlinear Finite Element Method (FEM) approach and suitable optimal placement of sensor approach were developed as a new MATLAB toolbox called DECOMAC by the authors of this paper. The Suitable multi-objective function was considered in optimized processes based on distributed ECOMAC method. Some common concrete slabs in construction with different aspect ratios were considered as case studies. The dimension and distance of nano strips in retrofitting process were selected according to building codes. The results of Optimal Sensor Placement (OSP) by DECOMAC algorithm on un-retrofitted and retrofitted slabs were compared. The statistical analysis according to the Mann-Whitney criteria shows that there is a significant difference between them (mean P-value = 0.61).

• Geomechanics and Engineering
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• v.34 no.5
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• pp.507-527
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• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.