• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.553-568
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• 2022

The Household Income and Expenditure Survey is a representative survey of Statistics Korea, which aims to measure and analyze national income and consumption levels and their changes by understanding the current state of household balances. Recently, the disconnection problem in these time series caused by the large-scale reorganization of the survey methods in 2017 and 2019 has become an issue. In this study, we model the characteristics of the time series in the Household Income and Expenditure Survey up to 2016, and use the modeling to compute forecasts for linking the expenditures in 2017 and 2018. In order to evenly reflect the characteristics across all expenditure item series and to reduce the impact of a specific forecast model, we synthesize a total of 8 models such as regression models, time series models, and machine learning techniques. In particular, the noteworthy aspect of this study is that it improves the forecast by using the optimal combination technique that can exactly reflect the hierarchical structure of the Household Income and Expenditure Survey without loss of information as in the top-down or bottom-up methods. As a result of applying the proposed method to forecast expenditure series from 2017 to 2019, it contributed to the recovery of time series linkage and improved the forecast. In addition, it was confirmed that the hierarchical time series forecasts by the optimal combination method make linkage results closer to the actual survey series.

• Korean Journal of Community Nutrition
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• v.27 no.2
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• pp.81-93
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• 2022

Objectives: This study was conducted to investigate the experience and perception among adult women regarding weight control and the consumption of weight-control foods or health functional foods based on their body mass index (BMI). Methods: The subjects were 634 adult women from the Chungcheong province, Korea, and data were collected through a self-administered questionnaire from July 2021 through September 2021. The subjects were divided into four groups based on their BMI status: underweight (< 18.5 kg/m², 7.6%), normal weight (18.5 ~ 22.9 kg/m², 53.3%), overweight (23 ~ 24.9 kg/m², 19.7%), and obese (≥ 25 kg/m², 19.4%). Results: Over the past 3 years, almost two-thirds (68.6%) of the adult women had tried weight control measures, despite the fact that a significant proportion of them were normal or underweight. More importantly, 57.6% of subjects reported the consumption of weight-control foods, with a lower proportion in the underweight (35.4%) group compared to the normal (56.2%), overweight (62.4%), and obese (65.0%) groups. The food items used for weight control were mostly salads, chicken breasts, low fat (soy) milk, slimming tea, protein shakes, low-calorie cereals, and energy/protein bars among others. In addition, one-third (31.1%) of the subjects reported the use of health functional foods containing ingredients for fat reduction. A significantly higher proportion of these was from the overweight (36.0%) and obese (38.2%) groups compared to the underweight (20.8%) and normal weight (28.1%) groups. Products containing Garcinia cambogia extract, green tea, or Cissus extract, were popular among users. Subjects who were obese had a poorer perception of their health and body. Most subjects felt the need for correct information regarding weight control, but this number was significantly more in the higher BMI groups. Conclusions: Our results suggest that the use of weight-control foods or health functional foods is popular among adult women, especially those who are obese. Thus, nutrition education courses covering facts about weight control and practice need to be developed and provided based on the BMI status of subjects.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.180-193
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• 2021

The complex resistivity method is an exploration technique that can obtain various characteristic information of underground media by measuring resistivity and phase in the frequency domain, and its utilization has recently increased. In this paper, a three-dimensional inversion algorithm for the CR data was developed to increase the utilization of this method. The Poisson equation, which can be applied when the electromagnetic coupling effect is ignored, was applied to the modeling, and the inversion algorithm was developed by modifying the existing algorithm by adopting comlex variables. In order to increase the stability of the inversion, a technique was introduced to automatically adjust the Lagrangian multiplier according to the ratio of the error vector and the model update vector. Furthermore, to compensate for the loss of data due to noisy phase data, a two-step inversion method that conducts inversion iterations using only resistivity data in the beginning and both of resistivity and phase data in the second half was developed. As a result of the experiment for the synthetic data, stable inversion results were obtained, and the validity to real data was also confirmed by applying the developed 3D inversion algorithm to the analysis of field data acquired near a hydrothermal mine.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.3
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• pp.439-448
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• 2022

Recently, domestic small and medium-sized subcontractors participating in the overseas construction market are suffering from the continuous loss and damage due to the insufficient recognition of the importance of risk Sub-Clauses among conditions of subcontracts. Therefore, the need to derive risk Sub-Clauses for conditions of the subcontract has been raised, but until now, previous studies have been conducted focusing on deriving risk Sub-Clauses for standard conditions of contract for construction between the Employer and the Contractor. In this study, 52 risk Sub-Clauses were derived on the basis of the influence size of the Sub-Clauses through the Delphi technique targeting 94 Sub-Clauses of conditions of standard subcontract for construction edition 2011, issued by the International Federation of Consulting Engineers (FIDIC) and In addition, 33 key risk Sub-Clauses were finally derived through the PI Risk Matrix by Probability and Impact. The results of this study provide will useful information on key risk Sub-Clauses that need to be reviewed in advance to minimize contractual risks at the stage of bidding and signing contracts for overseas subcontract construction projects.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.31-41
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• 2023

Directly converting the focal depth and image size of computer-generated-hologram (CGH), which is obtained by calculating the interference pattern of light from the 3D image, is known to be quite difficult because of the less similarity between the CGH and the original image. This paper proposes a method for separately converting the each of focal length of the given CGH, which is composed of multi-depth images. Firstly, the proposed technique converts the 3D image reproduced from the CGH into a Light-Field (LF) image composed of a set of 2D images observed from various angles, and the positions of the moving objects for each observed views are checked using an object detection algorithm YOLOv5 (You-Only-Look-Once-version-5). After that, by adjusting the positions of objects, the depth-transformed LF image and CGH are generated. Numerical simulations and experimental results show that the proposed technique can change the focal length within a range of about 3 cm without significant loss of the image quality when applied to the image which have original depth of 10 cm, with a spatial light modulator which has a pixel size of 3.6 ㎛ and a resolution of 3840⨯2160.

• Smart Media Journal
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• v.11 no.10
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• pp.46-53
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• 2022

Today, due to the 4th industrial revolution and extensive R&D funding, domestic companies have begun to possess world-class industrial technologies and have grown into important assets. The national government has designated it as a "national core technology" in order to protect companies' critical industrial technologies. Particularly, technology leaks in the shipbuilding, display, and semiconductor industries can result in a significant loss of competitiveness not only at the company level but also at the national level. Every year, there are more insider leaks, ransomware attacks, and attempts to steal industrial technology through industrial spy. The stolen industrial technology is then traded covertly on the dark web. In this paper, we propose a system for detecting industrial technology leaks in the dark web environment. The proposed model first builds a database through dark web crawling using information collected from the OSINT environment. Afterwards, keywords for industrial technology leakage are extracted using the KeyBERT model, and signs of industrial technology leakage in the dark web environment are proposed as quantitative figures. Finally, based on the identified industrial technology leakage sites in the dark web environment, the possibility of secondary leakage is detected through the PageRank algorithm. The proposed method accepted for the collection of 27,317 unique dark web domains and the extraction of 15,028 nuclear energy-related keywords from 100 nuclear power patents. 12 dark web sites identified as a result of detecting secondary leaks based on the highest nuclear leak dark web sites.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• Korean Journal of Veterinary Service
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• v.45 no.4
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• pp.325-330
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• 2022

Distichiasis is one of the diseases commonly encountered in companion animals, and these abnormal eyelashes cause corneal ulcers, continuous eye irritation, eye pain, glare, epiphora, foreign body sensation and can cause corneal opacity and vision loss in severe cases. In this study, an eyelash epilation needle for animals was developed and applied to a real case, and the results were observed. In a case of corneal ulcer caused by distichiasis of a 2-year-old Shih Tzu, a high-frequency surgical instrument for animals was converted into an electric epilation needle to attempt a procedure to destroy the eyelash hair follicles on the upper eyelid. A epilation needle was developed to have a diameter of 0.1 mm and a length of 4 mm at the end of the handle of DOCTANZ 400, an electrosurgical instrument for animals only. In the procedure, 2~3 mm of an epilation needle was inserted into the hair follicle, and 1 watt of electric power was applied to the hair follicle for about 5 sec. to carry out electrolysis until white bubbles were generated around the meibomian glands thereby destroying the hair follicle. As a result, no eyelashes grew any longer in the treated area indicating that the treatment was successful. It is hoped that the method developed in this study will be applied so that it will be widely used as a treatment method for distichiasis in companion animals that can be frequently seen hereafter.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.