• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.811-825
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• 2023

In accordance with the government's stance of actively promoting intelligent administrative service policies through data utilization, in the disaster and safety management field, it also is proceeding with disaster and safety management policies utilizing data and constructing systems for responding efficiently to new and complex disasters and establishing scientific and systematic safety policies. However, it is difficult to quickly and accurately grasp the on-site situation in the event of a disaster, and there are still limitations in providing information necessary for situation judgment and response only by displaying vast data. This paper focuses on deriving specific needs to make disaster situation management work more intelligent and efficient by utilizing intelligent information technology. Through individual interviews with workers at the Central Disaster and Safety Status Control Center, we investigated the scope of disaster situation management work and the main functions and usability of the geographic information system (GIS)-based integrated situation management system by practitioners in this process. In addition, the data built in the system was reclassified according to purpose and characteristics to check the status of data in the GIS-based integrated situation management system. To derive needed to make disaster situation management more intelligent and efficient by utilizing intelligent information technology, 3 strategies were established to quickly and accurately identify on-site situations, make data-based situation judgments, and support efficient situation management tasks, and implementation tasks were defined and task priorities were determined based on the importance of implementation tasks through analytic hierarchy process (AHP) analysis. As a result, 24 implementation tasks were derived, and to make situation management efficient, it is analyzed that the use of intelligent information technology is necessary for collecting, analyzing, and managing video and sensor data and tasks that can take a lot of time of be prone to errors when performed by humans, that is, collecting situation-related data and reporting tasks. We have a conclusion that among situation management intelligence strategies, we can perform to develop technologies for strategies being high important score, that is, quickly and accurately identifying on-site situations and efficient situation management work support.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Journal of Life Science
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• v.33 no.12
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• pp.1052-1061
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• 2023

This study was carried out to evaluate the effect of fermentation by B. subtilis (BPLE), L. brevis (LPLE), S. cerevisiae (SPLE) and C. militaris (CPLE) on the antioxidant activity of Protaetia brevitarsis larvae fed with mushroom substrates (king oyster mushroom). The total polyphenol content of Protaetia brevitarsis larvae (PLE), BPLE, LPLE, SPLE and CPLE were 58.07±0.67, 83.33±0.98, 79.21±1.32, 61.02±0.87 and 57.90±1.02 mg GAEs/extract g, respectively. The flavonoid contents of the PLE, BPLE, LPLE, SPLE and CPLE were 17.35±1.57, 19.49±0.95, 16.90±1.57, 18.12±0.95 and 16.99±0.95 mg QEs/extract g, respectively. The DPPH radical scavenging activity showed no significant difference between the PLE, BPLE, LPLE, SPLE and CPLE at a concentration of 0.2 mg/ml. However, at a concentration of 0.4 mg/ml or more, the DPPH radical scavenging activity of the BPLE and LPLE was higher than that of the PLE. The reducing power of the BPLE and LPLE was also higher than that of the PLE, and more than twice as high at a concentration of 0.8 mg/ml or more. The ORAC value of the BPLE (79.77±0.82 uM TEs/extract g) was higher than that of the PLE (61.34±0.97 uM TEs/extract g). A WST-1 assay of the RAW 264.7 cells indicated that the PLE, BPLE, LPLE, SPLE and CPLE showed no cytotoxicity.

• Korean Journal of Poultry Science
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• v.50 no.4
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• pp.293-301
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• 2023

This study aimed to investigate the effects of seasonal differences and crating densities on the behavior, surface temperature, and respiration rate of broilers during transportation. A total of 600 broilers aged 35 days were divided into 10 treatment groups based on five crating densities (10.3, 11.5, 12.8, 14.1, 15.4 birds/m²) with two seasons (i.e. summer and winter) to give six replicates. Broilers were allocated to the crates (1.00 m × 0.78 m × 0.26 m) in a randomized design. The transportation distance was 20 km for 40 minutes (average 30-50 km/h) during the early morning. The results revealed that standing behavior of broilers during transportation was shown more frequently (P<0.05) in winter. Moreover, sitting behavior was significantly shown (P<0.05) more at densities of 14.1 birds/m² and 15.4 birds/m² compared to other treatments. Standing behavior was significantly shown more (P<0.05) in winter and lower crating density. Changes in broilers surface temperature after transportation were higher (P<0.05) in summer. However, surface temperature after transportation was not affected (P>0.05) by crating density, and interaction between seasonal differences and crating density. Respiration rate was higher (P<0.05) in summer. But there were no effects (P>0.05) on respiration rate based on crating density, and interaction between seasonal differences and crating density. Based on the results of the present study, broilers transportation is recommended in winter rather than summer to minimize the climatic stress, but further research is required to identify proper crating densities to improve the broilers welfare during transportation.

• Journal of Life Science
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• v.33 no.12
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• pp.995-1001
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• 2023

This study investigated the effects of three music genres (classical, new age, and rock) on the stress levels of six Jeju crossbred horses (Jeju horse × Thoroughbred) in a horse stable. The horses were exposed to the three genres for seven days, and their stress levels were measured by analyzing physiological markers, including neurotransmitter (cortisol, β-endorphin, dopamine, serotonin, and oxytocin) plasma levels and creatine phosphokinase (CPK) and aldolase serum levels. The neurotransmitter analysis showed significant differences in cortisol levels between classical and new age music exposure. Dopamine levels decreased significantly only with new age exposure. Although there were no significant differences in β-endorphin levels between the three genres, β-endorphin levels decreased with increasing classical and new age music playback times and increased with increasing rock music playback times. There were no significant differences in serotonin levels between the three genres. Oxytocin levels decreased significantly with exposure to classical and rock music. The CPK and aldolase analyses showed that CPK levels decreased significantly only with exposure to new age music and increased after playback ended, while aldolase levels decreased significantly with classical and new age music exposure and increased after playback ended. These findings suggest that classical music and new age are the optimal music genres for the psychological stability of Jeju crossbred horses. Playing back an appropriate music genre could be used to improve breeding and promote the welfare of Jeju crossbred horses.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• Korean Journal of Culture and Social Issue
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• v.12 no.5_spc
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• pp.169-203
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• 2006

The purpose of this study is to compare happiness between Korean elderly residing in South Korea and those residing in New York City area in United States (Korean American elderly), and to investigate important factors determining their happiness. We collected data from 1,988 elderly residing in Chuncheon and Seoul in South Korea and 173 elderly residing in New York City area. All samples were over 65 years old in 2005. In general, Korean American elderly seemed happier than Korean elderly. Our specific data analysis showed that Korean American elderly had higher scores in non-agitation, satisfaction for aging, and satisfaction for life than Korean elderly. And they showed secure attachment more and insecure attachment less than Korean elderly. Social support network, satisfaction in psychological need, health, daily routines, and social background predicted happiness significantly for both Korean American elderly and Korean elderly. For American Korean elderly, satisfaction in psychological need predicted satisfaction for aging, satisfaction for life, and secure attachment significantly when social background, health, and daily routine were controlled. For Korean elderly, satisfaction in psychological need predicted all sub-factors of happiness significantly, and social support network also predicted happiness significantly when social background, health, and daily routine were controlled. Additional data analysis compared social support network, satisfaction in psychological need, health, daily routine, and social background between Korean American elderly and Korean elderly. Korean American elderly seemed to have more solid social support network and to have higher satisfaction in psychological needs than Korean elderly. Korean American elderly perceived themselves as healthier, and their life style related health was much healthier than Korean elderly. In social background, Korean American elderly had higher education and economic level than Korean elderly. Roles of social support network and satisfaction in psychological need for happy aging were discussed in terms of previous theoretical explanation of successful aging.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.37-50
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• 2023

Venture capital invests the necessary capital and supports management and technology in promising small and medium-sized venture companies in the early stages of start-up with promising technology and excellent manpower. It plays a role as a key player in the venture ecosystem that realizes profits by collecting the investment through various means after growth. Venture capital's job is to recruit various investors(LPs) to invest in small and medium-sized venture companies with growth potential through the formation of venture investment funds, and to collect investment as companies grow, distribute and reinvest. The main tasks of venture capitalists, which play the most important role in venture investment, are finding promising companies, corporate analysis and evaluation, investment screening, follow-up management, and investment recovery. Venture capital's success indicators are fund formation and return on investment, and venture capitalists are rewarded with annual salary, performance-based incentive, and promotion with work performance such as investment, exit, and fund formation. Compared to the recent rapidly growing venture investment market, investment manpower is insufficient, and venture capital is making great efforts to foster manpower and establish infrastructure and systems for long-term service, but research has been conducted mainly from a quantitative perspective. Accordingly, this study aims to empirically analyzed the impact of investment experience, delegation of authority, job fit, and peer relationships on fund formation and return on investment according to the characteristics of the venture capital industry. The results of these empirical studies suggested that future venture capital needs a job environment and manpower operation strategy so that venture capitalists with high job fit and investment experience can work for a long time.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.65-76
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• 2023

In the field of technology entrepreneurship and startups, the development of Artificial Intelligence(AI) has emerged as a key topic for business model innovation. As a result, venture firms are making various efforts centered on AI to secure competitiveness(Kim & Geum, 2023). The purpose of this study is to analyze the relationship between the development of GenAI technology and the startup ecosystem by analyzing domestic news articles to identify trends in the technology startup field. Using BIG Kinds, this study examined the changes in GenAI-related news articles, major issues, and trends in Korean news articles from 1990 to August 10, 2023, focusing on the emergence of ChatGPT before and after, and visualized the relevance through network analysis and keyword visualization. The results of the study showed that the mention of GenAI gradually increased in the articles from 2017 to 2023. In particular, OpenAI's ChatGPT service based on GPT-3.5 was highlighted as a major issue, indicating the popularization of language model-based GenAI technologies such as OpenAI's DALL-E, Google's MusicLM, and VoyagerX's Vrew. This proves the usefulness of GenAI in various fields, and since the launch of ChatGPT, Korean companies have been actively developing Korean language models. Startups such as Ritten Technologies are also utilizing GenAI to expand their scope in the technology startup field. This study confirms the connection between GenAI technology and startup entrepreneurship activities, which suggests that it can support the construction of innovative business strategies, and is expected to continue to shape the development of GenAI technology and the growth of the startup ecosystem. Further research is needed to explore international trends, the utilization of various analysis methods, and the possibility of applying GenAI in the real world. These efforts are expected to contribute to the development of GenAI technology and the growth of the startup ecosystem.