• Journal of Environmental Health Sciences
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• v.49 no.6
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• pp.362-371
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• 2023

Background: PM_2.5 pollution has been a persistent problem in South Korea, with concentrations consistently exceeding World Health Organization (WHO) guidelines. The aging of the population in the country further exacerbates the health impacts of PM_2.5 since older adults are more susceptible to the adverse effects of air pollution. Objectives: This study aims to evaluate how the health impact (premature death) due to long-term exposure to PM_2.5 in South Korea could change in the future according to the trend of change in the country's population structure. Methods: The study employs a relative risk function, which accounts for age-specific relative risks, to assess the changes in premature deaths by age and region at the average annual PM_2.5 concentration for 2022 and at PM_2.5 concentration improvement levels. Premature deaths were estimated using the Global Exposure Mortality Model (GEMM). Results: The findings indicate that the increase in premature deaths resulting from the projected population structure changes up to 2050 would significantly outweigh the health benefits (reduction in premature deaths) compared to 2012. This is primarily attributed to the rising number of premature deaths among the elderly due to population aging. Furthermore, the study suggests that the effectiveness of the current domestic PM_2.5 standard would be halved by 2050 due to the increasing impact of population aging on PM_2.5-related mortality. Conclusions: The study highlights the importance of considering trends in population structure when evaluating the health benefits of air pollution reduction measures. By comparing and evaluating the health benefits in reflection of changes in population structure to the predicted PM_2.5 concentration improvements at the provincial level, a more comprehensive assessment of regional air quality management strategies can be achieved.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.554-560
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• 2023

To prevent illegal timber distribution, DNA markers have been used to identify the species and origin. However, extracting high-quality DNA from timber is difficult because of its physical and chemical properties. In this study, we investigated whether the age of timber tissue influences the yield of DNA extraction and the success rate of polymerase chain reaction (PCR) to understand the relationship between the establishment time of the wood annual ring and the extracted DNA concentration (ng/μl), purity (A260/A280), and PCR success rate (%) from pinewood, a major Korean domestic species. According to the results, it was observed that as the distance from the cambium increased, indicating that the tissue was older, the concentration and purity of the extracted DNA decreased significantly. For the trnM-trnV (285 bp) and rpoC1 (298 bp) regions, the PCR success rate was 100%. However, for the rbcL (1.3 kb) region, the PCR success rate was 66.67%. Moreover, PCR amplification of the rbcL region failed at all points older than 30 years. Thus, it is deduced that as time passes, along with the decay of timber cells, DNA is degraded, leading to a decrease in DNA concentration, purity, and PCR success rate. The results of this study are expected to be beneficial for future applications, such as the species identification of timber, providing valuable insights and potential utilization in this field.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.461-470
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• 2023

While speech animation generation employing deep learning has been actively researched for English, there has been no prior work for Korean. Given the fact, this paper for the very first time employs supervised deep learning to generate Korean speech animation. By doing so, we find out the significant effect of deep learning being able to make speech animation research come down to speech recognition research which is the predominating technique. Also, we study the way to make best use of the effect for Korean speech animation generation. The effect can contribute to efficiently and efficaciously revitalizing the recently inactive Korean speech animation research, by clarifying the top priority research target. This paper performs this process: (i) it chooses blendshape animation technique, (ii) implements the deep-learning model in the master-servant pipeline of the automatic speech recognition (ASR) module and the facial action coding (FAC) module, (iii) makes Korean speech facial motion capture dataset, (iv) prepares two comparison deep learning models (one model adopts the English ASR module, the other model adopts the Korean ASR module, however both models adopt the same basic structure for their FAC modules), and (v) train the FAC modules of both models dependently on their ASR modules. The user study demonstrates that the model which adopts the Korean ASR module and dependently trains its FAC module (getting 4.2/5.0 points) generates decisively much more natural Korean speech animations than the model which adopts the English ASR module and dependently trains its FAC module (getting 2.7/5.0 points). The result confirms the aforementioned effect showing that the quality of the Korean speech animation comes down to the accuracy of Korean ASR.

• Journal of Environmental Health Sciences
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• v.50 no.1
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• pp.16-24
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• 2024

Background: Indoor PM_2.5 concentrations in residential houses can be affected by various factors depending on the season. This is because not only do the climate characteristics depend on the season, but the activity patterns of occupants are also different. Objectives: The purpose of this study is to compare factors affecting indoor PM_2.5 concentrations in apartments and detached houses in Daegu according to seasonal changes. Methods: This study included 20 households in Daegu, South Korea. The study was conducted during the summer (from July 10 to August 10, 2023) and the autumn (from September 11 to October 9, 2023). A sensor-based instrument for PM_2.5 levels was installed in the living room of each residence, and measurements were taken continuously for 24 hours at intervals of one minute during the measurement period. Based on the air quality monitoring system data in Daegu, outdoor PM_2.5 concentrations were estimated using ordinary kriging (OK) in Python. In addition, the indoor activities of the occupants were investigated using a time-activity pattern diary. The affecting factors of indoor PM_2.5 concentration were analyzed using multiple regression analysis. Results: Indoor and outdoor PM_2.5 concentrations of the residences during summer were 15.27±11.09 ㎍/m³ and 11.52±7.56 ㎍/m³, respectively. Indoor and outdoor PM_2.5 concentrations during autumn were 13.82±9.61 ㎍/m³ and 9.57±5.50 ㎍/m³, respectively. The PM_2.5 concentrations were higher in summer compared to autumn both indoors and outdoors. The primary factor affecting indoor PM_2.5 concentration in summer was occupant activity. On the other hand, during the autumn season, the primary affecting factor was outdoor PM_2.5 concentration. Conclusions: Indoor PM_2.5 concentration in residential houses is affected by occupant activity such as the inflow of outdoor PM_2.5 concentration, cooking, and cleaning, as found in previous studies. However, it was revealed that there were differences depending on the season.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.83-90
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• 2024

This study investigates the 3D printing characteristics of strain hardening cement composites (SHCC) reinforced by PVA fibers. Three SHCC mixtures with diverse fiber volume fractions (1.0% for F1.0 mixture, 1.5% for F1.5 mixture, and 1.8% for F1.8 mixture) were designed. Except for the F1.0 mixture, all mixtures met the necessary conditions for multiple micro-cracking, with higher fiber volume fractions more readily satisfying these conditions. The flow values of three SHCC mixtures were within the 3D printable range of 120~160 mm, exhibiting decreased flow values with increasing the fiber volume fractions. Observation of the printed SHCC surfaces indicated that the F1.0 mixture had a Level-3 (good) rating, while F1.5 and F1.8 were rated as Level-2 (average). Higher fiber volume fractions resulted in poorer surface quality, thus, further research needs to be performed for modulating SHCC mixture suitable for 3D printing. The uniaxial tension behavior showed that the F1.0 mixture failed at lower strain, whereas F1.5 and F1.8 exhibited higher strain performance with multiple micro-cracks occurring.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.17-24
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• 2024

In this paper, we propose a highly secure technique to hide confidential data in image pixels using a quadruple encryption techniques. In the proposed technique, the boundary surface where the image outline exists and the flat surface with little change in pixel values are investigated. At the boundary of the image, in order to preserve the characteristics of the boundary, one bit of confidential data that has been multiply encrypted is spatially encrypted again in the LSB of the pixel located at the boundary to hide the confidential data. At the boundary of an image, in order to preserve the characteristics of the boundary, one bit of confidential data that is multiplely encrypted is hidden in the LSB of the pixel located at the boundary by spatially encrypting it. In pixels that are not on the border of the image but on a flat surface with little change in pixel value, 2-bit confidential data that is multiply encrypted is hidden in the lower 2 bits of the pixel using location-based encryption and spatial encryption techniques. When applying the proposed technique to hide confidential data, the image quality of the stego-image is up to 49.64dB, and the amount of confidential data hidden increases by up to 92.2% compared to the existing LSB method. Without an encryption key, the encrypted confidential data hidden in the stego-image cannot be extracted, and even if extracted, it cannot be decrypted, so the security of the confidential data hidden in the stego-image is maintained very strongly. The proposed technique can be effectively used to hide copyright information in general commercial images such as webtoons that do not require the use of reversible data hiding techniques.

• Journal of the Korean Geotechnical Society
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• v.40 no.1
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• pp.5-14
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• 2024

PHC piles demonstrate superior resistance to compression and bending moments, and their factory-based production enhances quality assurance and management processes. Despite these advantages that have resulted in widespread use in civil engineering and construction projects, the design process frequently relies on empirical formulas or N-values to estimate the soil-pile friction, which is crucial for bearing capacity, and this reliance underscores a significant lack of experimental validation. In addition, environmental factors, e.g., the pH levels in groundwater and the effects of seawater, are commonly not considered. Thus, this study investigates the influence of vibrating machine foundations on PHC pile models in consideration of the effects of varying pH conditions. Concrete model piles were subjected to a one-month conditioning period in different pH environments (acidic, neutral, and alkaline) and under the influence of seawater. Subsequent repeated direct shear tests were performed on the pile-soil interface, and the disturbed state concept was employed to derive parameters that effectively quantify the dynamic behavior of this interface. The results revealed a descending order of shear stress in neutral, acidic, and alkaline conditions, with the pH-influenced samples exhibiting a more pronounced reduction in shear stress than those affected by seawater.

• Journal of Environmental Health Sciences
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• v.50 no.2
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• pp.146-156
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• 2024

Background: Children who use playground facilities are exposed to potential risks due to the high concentration of heavy metals contained in the finishing materials of facilities in children's playgrounds. Objectives: The purpose of this study was to investigate the concentration of heavy metals in the finishing materials of outdoor children's playgrounds where harmful heavy metals exist in Gwangju and to conduct human risk assessment for children and adults by age to find the risks and limitations. Methods: The bottom and top layers of double-painted paint were peeled off and collected together from the finishing materials of children's play facilities such as slides, swings, and seesaws in 147 children's parks in Gwangju. Heavy metals were analyzed using ICP-OES, etc., and human risk assessment was performed using the concentrations of heavy metals. Results: Based on 1.0E-04, which requires legal regulation, CTE was found to pose a carcinogenic risk for preschool children and no carcinogenic risk for the rest of the age groups. However, RME showed that both men and women of all ages had a carcinogenic risk. For reference, when the carcinogenic risk was based on 1.0E-06, CTE was found to pose a carcinogenic risk from infants to elementary school students, and RME was found to have a carcinogenic risk in all age groups. It was judged that there is a non-carcinogenic risk if the non-carcinogenic risk exceeds 1 based on the hazard index (HI) 1. In CTE, there was no non-carcinogenic risk, and RME for preschooler males (1.49E+00) and females (1.56E+00) were found to have non-carcinogenic risk. Conclusions: This study was meaningful in that it examines the differences in the current management of heavy metals concentration standards and potential carcinogenic and non-carcinogenic risks to the human body and discusses the relationship between heavy metals and human health effects.

• Korean Journal of Radiology
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• v.25 no.7
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• pp.613-622
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• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.