• Phonetics and Speech Sciences
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• v.16 no.2
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• pp.49-58
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• 2024

This study aimed to compare the acoustic measurements of speech samples recorded from individuals with normal voices using various devices: the Computerized Speech Lab (CSL), a unidirectional wired pin-microphone (WIRED) suitable for smartphones, the built-in omnidirectional microphone (SMART) of smartphones, and Bluetooth-connected wireless earphones, specifically the Galaxy Buds2 Pro (WIRELESS). This study included 40 normal adults (12 males and 28 females) who had not visited an otolaryngologist for respiratory diseases within the past three months. Participants performed sustained vowel /a/ phonation for four seconds and reading tasks with sentences ("Walk") and paragraphs ("Autumn") in a sound-treated booth. Recordings were simultaneously conducted using the four different devices and synchronized based on the CSL-recorded samples for analysis using the MDVP, ADSV, and VOXplot programs. Compared with CSL, the Cepstral Spectral Index of Dysphonia (CSID_V, CSID_S) and Acoustic Voice Quality Index (AVQI) values were lower in the WIRED and higher in the SMART. The opposite trend was observed for the L/H spectral ratios (SR_V and SR_S), and the WIRELESS demonstrated task-specific discrepancies. Furthermore, both the fundamental frequency (F0) and the cepstral peak prominence of the vowel samples (CPP_V) had intraclass correlation coefficient (ICC) values above 0.9, indicating high reliability. These variables, F0 and CPP_V were considered highly reliable for voice recordings across different microphone types. However, caution should be exercised when analyzing and interpreting variables such as the SR, CSID, and AVQI, which may be influenced by the type of microphone used.

• The Korean Journal of Coaching Psychology
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• v.8 no.1
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• pp.1-50
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• 2024

The purpose of this study is to identify career barrier factors experienced by career interruption women, develop a tool to measure career barrier, and verify their validity. To this end, preliminary questions were developed by reviewing literature, conducting one-on-one in-depth interviews with 10 women on career interruption, and conducting an open questionnaire with 100 women on career interruption. The subjects of the study were married women aged 20 to 54 who had past employment experience, wanted to be re-employed, and experienced retirement due to marriage, pregnancy, childbirth, childcare, and family care, and the period of career interruption was selected for more than one year. After that, 63 questions were selected for 7 factors. A preliminary survey was conducted on 300 women with career interruption in Korea, and as a result, 63 questions of 6 factors were derived through exploratory factor analysis. The main survey was conducted with 44 questions of 6 factors by partially modifying the questions reflecting the important concepts in each factor. In this survey of 600 people, in order to verify the validity of the constituent concept of this test, the entire sample was divided into two groups, and group 1 (G1, N=309) conducted exploratory factor analysis and group 2 (G2, N=291) conducted confirmatory factor analysis. As a result of exploratory factor analysis for Group 1, 34 questions of 6 factors were finally derived, and a confirmatory factor analysis of Group 2(G2) was conducted to confirm the model fit of the derived factors, and it was confirmed that the model fit criteria were met. In order to verify the convergence validity of the developed career barrier scale, a correlation analysis was conducted with the career barrier test for female college students, and as a result of the analysis, the career barrier scale for women with career interruption and the career barrier test for female college students showed statistically significant correlation. In order to verify the validity of the criterion, the results of a correlation analysis with variables of job preparation behavior, job stress, state anxiety, and life satisfaction were all found to be statistically significant. Finally, the academic, practical, and policy significance and limitations of this study and future research directions were presented.

• Korean Journal of Environment and Ecology
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• v.38 no.3
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• pp.293-309
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• 2024

This study was conducted to provide basic data on the structure of the Carpinus turczaninowii community and the characteristics of the habitat environment for ex situ conservation. To identify the current ecological environment, 27 plots (each measuring 100m²) were selected for analyzing the detailed structure of plant communities in Anmyondo Isl.(Jungjangri San 14-217), Hwanggumsan Mt., Gayasan Mt.(Wonhyobong) and Palbongsan Mt.. The research methodology employed in this study was qualitative analysis. The TWINSPAN classification yielded a total of seven distinct communities. Group I represents the C. turczaninowii - Quercus mongolica community, Group II represents the C. turczaninowii - Pinus densiflora community, Group III represents the C. turczaninowii - P. densiflora community, Group IV represents the C. turczaninowii - Q. mongolica community, Group V represents the C. turczaninowii - Q. variabilis community, Group VI represents the C. turczaninowii - Prunus serrulata Lindl. var. pubescens community, and Group VII represents the C. turczaninowii - Styrax japonicus community. The species diversity ranged from 0.8056 to 1.1568, the importance value ranged from 0.1214 to 0.3024, and the similarity index ranged from 9.37% to 36.36%. Based on the correlation analysis of six environmental factors for the seven communities using RDA ordination, the results indicate that on the first axis, Altitude, Crown density, Bare rock, and Slope exhibited a positive correlation. In the C. turczaninowii - P. densiflora community (Group III) and C. turczaninowii - Q. mongolica community (Group IV), altitude, bare rock, and slope were analyzed as factors influencing vegetation distribution. In the C. turczaninowii - Q. variabilis community (Group V), C. turczaninowii - P. serrulata Lindl. var. pubescens community (Group VI), and C. turczaninowii - S. japonicus community (Group VII), crown density was analyzed as a factor influencing vegetation distribution.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.400-412
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• 2023

In this study, the biogeochemistry management (BGC-MAN) model was applied to North and South Korea pine and oak forest stands to evaluate the Net Primary Productivity (NPP), an indicator of forest ecosystem productivity. For meteorological information, historical records and East Asian climate scenario data of Shared Socioeconomic Pathways (SSPs) were used. For vegetation information, pine (Pinus densiflora) and oak(Quercus spp.) forest stands were selected at the Gwangneung and Seolmacheon in South Korea and Sariwon, Sohung, Haeju, Jongju, and Wonsan, which are known to have tree nurseries in North Korea. Among the biophysical information, we used the elevation model for topographic data such as longitude, altitude, and slope direction, and the global soil database for soil data. For management factors, we considered the destruction of forests in North and South Korea due to the Korean War in 1950 and the subsequent reforestation process. The overall mean value of simulated NPP from 1991 to 2100 was 5.17 Mg C ha^-1, with a range of 3.30-8.19 Mg C ha^-1. In addition, increased variability in climate scenarios resulted in variations in forest productivity, with a notable decline in the growth of pine forests. The applicability of the BGC-MAN model to the Korean Peninsula was examined at a time when the ecosystem process-based models were becoming increasingly important due to climate change. In this study, the data on the effects of climate change disturbances on forest ecosystems that was analyzed was limited; therefore, future modeling methods should be improved to simulate more precise ecosystem changes across the Korean Peninsula through process-based models.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.183-228
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• 2024

This study aimed to develop a competency evaluation scale tailored to the specific needs of key talent in the ICT startup sector. Existing competency assessment tools are mostly designed for environments in large corporations or traditional small and medium-sized enterprises, failing to adequately reflect the dynamic requirements of rapidly evolving startups. For startups, where a small number of individuals directly impact company success, key talent is a critical asset. Accordingly, this study sought to create a scale that measures the competencies suited to the challenges and opportunities faced by startups, helping domestic startups establish more effective talent management strategies. The research initially selected 71 items through a literature review and in-depth interviews. Based on expert feedback that emphasized the need for more precise and clear descriptions, the item descriptions were revised, and a total of 65 items were developed through four rounds of content validation. Following preliminary and main surveys, a final set of 58 items was developed. The main survey conducted further factor analysis based on the three broad competency factors?knowledge, skills, and attitude?identified in the preliminary survey. As a result, 10 latent factors emerged: 6 items for task comprehension, 6 items for practical experience (tacit knowledge), 6 items for collaboration, 9 items for management and problem-solving, 9 items for practical skills, 4 items for self-direction, 5 items for goal orientation, 5 items for adaptability, 5 items for relationship orientation, and 3 items for organizational loyalty. The developed scale comprehensively covers the multifaceted nature of competencies, allowing for a thorough evaluation of essential skills such as technical ability, teamwork, innovation, and leadership, which are critical for startups. Therefore, the scale provides a tool that helps startup managers objectively and accurately assess candidates' competencies. It also supports the growth of employees within startups, maximizing the overall organizational performance. By utilizing this tool, startups can build a strong internal talent pool and continuously enhance employees' competencies, thereby strengthening organizational competitiveness. In conclusion, the competency evaluation scale developed in this study is a customized tool that aligns with the characteristics of startups and plays a crucial role in securing sustainable competitiveness in rapidly changing market environments. Additionally, it offers practical guidance to support the successful growth of domestic startups and help them maintain their competitive edge in the market, contributing to the development of the startup ecosystem and the growth of the national economy.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.6-22
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• 2024

This paper examines the characteristics of wooden wells from the Three Kingdoms period that were discovered in the Yeongsan River basin, in addition to their functions based on the distribution of the remains and excavated artifacts found near the wells. A total of 11 wooden wells have been found at six archeological sites along the middle and upper reaches of the Yeongsan River basin. These wooden wells were built in a wider variety of forms than wells made of other materials due to the ease of processing resulting from the physical properties of wood. However, due to the limited geological conditions in which these wells can be installed and their rapid decay, the discovery of such wells is rare. They tend to be located in the clay and mud layers of old river channels or near river channels where it was relatively easy to obtain water from the riverbed. The wooden wells are mostly square or rectangular in shape and were assembled transversely, and some include support beams in their construction. The backfill was reinforced with either stone, a mixture of stone and clay, or a mixture of clay and pottery shards. The material mainly used was pine wood boards, with wood from chestnut trees being used as a sub-material. Various artifacts, such as pottery and wooden containers, animal bones, and seeds, have been excavated in small quantities. The excavated pottery items consist mostly of flat cups with a cover, mounted cups, pottery stands, wide-mouthed jars with a hole, and round pottery. Based on the environment and remains of the sites, the wells are thought to have been used for domestic and production purposes. The assumed primary function was to obtain domestic water, as most of the wells were located within residential spaces where the area's inhabitants lived. The wells were also used to obtain water for agricultural purposes, as well as for productive purposes such as for operating kilns and smelters. Lastly, the wooden wells were also found to be strongly associated with rites, as evidenced by the artifacts found inside them.

• Journal of Digestive Cancer Research
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• v.8 no.2
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• pp.102-108
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• 2020

Background: This study was performed to evaluate the effect of gastric cancer screening through analysis of screening-related data. Methods: We investigated claims data of gastric cancer from 2009 to 2015. We evaluated whether the screening was performed to prior to registration as patients with gastric cancer. The effect of gastric cancer screening was also analyzed by gender. Results: We collected total 196,293 patients with gastric cancer. 74% of them had previous experience of gastric cancer screening. In patients with screening, early gastric cancer was 33.4% and advanced gastric cancer was 17.3%. 22,548 (15.5%) patients were diagnosed with gastric cancer within 2 years after screening. In the case of patients without screening, early gastric cancer was 15.1% and advanced gastric cancer was 25.3%. In case of men, 76% of them confirmed gastric cancer through screening, and 70.2% of women confirmed the gastric cancer. In both men and women, the rate of early gastric cancer was higher among those with screening than those without screening. Conclusion: In this study, we were able to indirectly confirm the stage shift of gastric cancer screening. However, within 2 years after screening, not a few patients with gastric cancer were diagnosed. Therefore, more studies are warranted to in the future.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Journal of Environmental Impact Assessment
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• v.33 no.4
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• pp.143-154
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• 2024

Heat waves caused by climate change are rapidly increasing health damage to vulnerable groups, and to prevent this, the national, regional, and local governments are establishing climate crisis adaptation policy. A representative climate crisis adaptation policy to reduce heat wave damage is to expand the number of cooling centers. Because it is highly effective in a short period of time, most metropolitan local governments, except Jeonbuk, include the project as an adaptation policy. However, the criteria for selecting a cooling centers are different depending on the budget and non-budget, so the utilization rate and effectiveness of the cooling centers are all different. Therefore, in this study, we developed logistic regression models that can predict and evaluate areas with a high probability of expanding cooling centers in order to implement adaptation policy in local governments. In Incheon Metropolitan City, which consists of various heat wave-vulnerable environments due to the coexistence of the old city and the new city, a logistic model was developed to predict areas where heat waves can be cooling centered by dividing it into Ganghwa·Ongjin-gun and other regions, taking into account socioeconomic and environmental differences. As a result of the study, the statistical model for the Ganghwa·Ogjin-gun region showed that the higher the ground surface temperature and the more and more the number of elderly people over 65 years old, the higher the possibility of location of cooling centers, and the prediction accuracy was about 80.93%. The developed logistic regression model can predict and evaluate areas with a high potential as cooling centers by considering regional environmental and social characteristics, and is expected to be used for priority selection and management when designating additional cooling centers in the future.