• Proceedings of the National Institute of Ecology of the Republic of Korea
• /
• v.3 no.2
• /
• pp.67-72
• /
• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Journal of Environmental Health Sciences
• /
• v.46 no.1
• /
• pp.96-102
• /
• 2020

Objectives: This study identified factors impacting radiation knowledge among aircrew, who are affected by cosmic radiation exposure due to their occupational environment. Methods: In September 2019 we conducted an online survey of aircrew through a Google link. We evaluated the level of radiation knowledge using a ten-item (10 points) questionnaire. The following exploratory variables were evaluated in relationship with the level of radiation knowledge using univariable linear regression models: sex, age, duration of employment, position level, company, marriage, education level, personal/family history of disease, and the number of times acquiring information on radiation through various channels (internet searching, watching television, reading newspaper, conversation about radiation with aircrew/non-aircrew, in-house training). With a p of 0.2 in univariable models, we built a multivariable linear regression model using a stepwise selection method. Results: The average radiation knowledge score of the 356 respondents was 7.22. Univariable linear regression analysis showed that radiation knowledge of the aircrew was associated with their company, position level, age, and number of conversations with other aircrew members. Our multivariable model showed that the radiation knowledge level of aircrew decreased as they had more conversations about radiation with other aircrew members and as their age increased. Conclusions: Korean air crew showed a lower level of radiation knowledge as their age and the number of conversations with colleagues increased. The study suggests that more education is needed in order for aircrew to gain accurate radiation knowledge.

• Journal of the Korean earth science society
• /
• v.34 no.7
• /
• pp.662-668
• /
• 2013

We examine the effects of El Ni$\tilde{n}$o on tropospheric ozone through the simulation of a Climate-Chemistry model for a 40-year period (1971-2010). The Empirical Orthogonal Function (EOF) analysis reveals that the tropospheric ozone concentration in the central-eastern Pacific decreases when the El Ni$\tilde{n}$o occurs, which is consistent with the observation. However, the increase of ozone over Indian Ocean-Indonesia regions is weak in the simulation compared to the observations. We analyze details of the 2006 El Ni$\tilde{n}$o event to understand the mechanism that caused the change of ozone due to El Ni$\tilde{n}$o. It is found that enhanced convection as well as higher water vapor followed by shortened lifetime has led to lower the tropospheric ozone. Downward motion induced by the changes of atmospheric circulation due to sea surface temperature forcing, together with the decrease of water vapor, has brought ozone produced in the upper troposphere over the Indian Ocean.

• Fashion, Industry and Education
• /
• v.14 no.2
• /
• pp.35-46
• /
• 2016

The purpose of this study was to examine female consumers' attitudes and purchase intentions toward intimate apparel brands. To understand female consumers' shopping behaviors for intimate apparel products, this study examined interrelationships among brand familiarity, perceived risk, attitudes, and purchase intentions toward intimate apparel brands. A conceptual model was developed by adopting perceived risk theory (Cox, 1967) and theory of reasoned action (Ajzen & Fishbein, 1980). A pre-survey using a paper and pencil was conducted to identify the most familiar intimate apparel brand to young female consumers. The majority of pre-survey respondents (66 female college students) indicated Victoria's Secret as the most prominent intimate apparel brand. Therefore, Victoria's Secret was used to examine possible effects of brand familiarity on perceived risk and attitudinal and behavioral responses toward the brand. Using a web-based survey, 384 complete responses were collected from young female college students between the ages of 18-29 at a Mid-southern U.S. university. A structural equation modeling was employed to test the proposed research model and hypotheses. Results showed positive, statistically significant associations among the four variables (e.g., brand familiarity, perceived risk, attitudes, and purchase intentions). The findings suggested that young female consumers who are familiar with a particular intimate apparel brand are likely to perceive a low level of risk, leading to positive, strong attitudes with purchase intentions toward that particular intimate apparel brand. This suggests establishing brand familiarity through integrated marketing communication is crucial for risk reduction strategy in intimate apparel shopping.

• Carbon letters
• /
• v.18
• /
• pp.49-55
• /
• 2016

Refuse-derived fuel (RDF) produced using municipal solid waste was pyrolyzed to produce RDF char. For the first time, the RDF char was used to remove aqueous copper, a representative heavy metal water pollutant. Activation of the RDF char using steam and KOH treatments was performed to change the specific surface area, pore volume, and the metal cation quantity of the char. N₂ sorption, Inductively Coupled Plasma-Atomic Emission Spectrometer (ICP-AES), and Fourier transform infrared spectroscopy were used to characterize the char. The optimum pH for copper removal was shown to be 5.5, and the steam-treated char displayed the best copper removal capability. Ion exchange between copper ions and alkali/alkaline metal cations was the most important mechanism of copper removal by RDF char, followed by adsorption on functional groups existing on the char surface. The copper adsorption behavior was represented well by a pseudo-second-order kinetics model and the Langmuir isotherm. The maximum copper removal capacity was determined to be 38.17 mg/g, which is larger than those of other low-cost char adsorbents reported previously.

• Korean Journal of Construction Engineering and Management
• /
• v.21 no.2
• /
• pp.47-58
• /
• 2020

The ICEC (Index of Construction Engineer's Competency) quantifies the competence of construction engineers using such parameters as experience, education, and qualifications and assigns four technical grades (expert, advanced, intermediate, and beginners) to construction engineers according to their scores for the efficient management and loading of technical personnel. However, as of 2020, the seventh year since its implementation, ICEC has shown many problems in its application, unlike its intended purpose. So institutional supplementation is required to provide improvement measures that can cope with the changing labor market environment and complement the current ICEC. Therefore, this study examined the current status of the career management system after the introduction of the ICEC, suggested a career index proportional to the competence of construction engineers from the beginner to the expert level, and developed an effective capability evaluation model I²CEC. The improved model presented in this study provides a means to comprehensively judge the performance, experience, and the professional work abilities of construction management engineers. Furthermore, the results of this study are expected to contribute to the development of efficient manpower and career management systems for enhancing the competitiveness of the domestic construction industry.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.34 no.12
• /
• pp.1980-1991
• /
• 2010

This study proposes the O P E N Triad framework as a future set of tools and perspectives for individual members and institutes to further their professional and academic potential as well as prospect and vitalize the future of the Korean Clothing and Textiles discipline through a global perspective. The millennial generation desires On-demand, Personal, Engaging, and Networked (O P E N) experiences effecting cultural change for creative and influential interaction in transactions, communication, and education. O P E N Individuals offers a WebSphere model as a holistic learning system that has a synergizing value of education across academic courses, industries, and cultures. Through a digitalized and virtualized class, it complements relevant technologies already familiar to the student population. By employing environmental scanning approaches, the most influential and viable future global issues related to the clothing and textiles discipline are identified and dialogued within O P E N Institutes. For future clothing and textiles institutes, this scanning allows them to be open to new ideas, to focus on inter-engagements, to collaborate among individuals, to associate as a part of web of people, organizations, and ideas, to personalize an institutes curricula, and to dialogue generative knowledge. O P E N Industries reveals three dominant future issues that cross academia and industry, sustainability, supply chain management, and social networking. In-depth interviews with U.S. industry experts identified interdependent gaps in global consumer experience practices and suggested the following gaps as future research areas: a standardized business model to the entrepreneurial model, strategic management to a sustainable competitive advantage, standardized to differentiated products, services and operations, market segmentation to global consumer clusters, business-driven marketplaces to consumer-engaged marketspaces, and excellent services to optimal experience. This O P E N Triad framework empowers millennial students, universities, and industries to anticipate and prepare for a radically changing world.

• Geomechanics and Engineering
• /
• v.25 no.1
• /
• pp.1-16
• /
• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• The Journal of Korean Society for School & Community Health Education
• /
• v.8 no.2
• /
• pp.97-108
• /
• 2007

Background & Objectives: Health management is performed at enterprises under the Industrial Safety and Health Act. At small and mid sized enterprises, the reality is that health management is poorly performed due to the shortage of resources, professional knowledge, and administrative capabilities, as well as the lack of recognition by company presidents, and generation difference. Purpose: The purpose of this study is to Provide basic materials to complement the future health management model by researching the extent of performing health-related tasks at small and mid sized enterprises and analyzing the related factors. Methods: The survey subjects were 130 small and mid sized enterprises nationwide which had received health management support from the Korean government. The data were collected using a systematic questionnaire at the companies from September 2005 to November 2005. The respondents were the Personnel for healthcare tasks. Results: The results indicate that the extent of performing health management tasks at the companies was significantly different in the working environment and task management field in terms of industry types and in the fields of the establishment of an industrial health system as well as working environments and task management in terms of regions. Also, a multiple regression analysis was performed step-by-step in order to research the factors that affect the execution of health management tasks at small and mid sized enterprises.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12spc
• /
• pp.427-436
• /
• 2021

The article reveals and theoretically substantiates the trends of foreign language teachers' professional training in universities of Ukraine in terms of European integration, which are systematized in three areas: macro-level (system of education), meso-level (universities) and micro-level (subjects of educational process). The article aims to substantiate the trends of foreign language teacher training in the context of European integration and the main directions of creative use of constructive ideas of European experience in the innovative development of education. The article lights up the system for improving foreign language teacher training in universities, which is based on updated goals, content and approaches to the implementation of basic concepts, principles and features of teacher training in European experience, enable us to improve the quality of teacher training, its competitiveness in the European labor market. In the article developed the conceptual model of strategic development of the university in the conditions of European integration. It is emphasized that information technologies provide great opportunities for the development of professional skills and intellectual potential of future professionals. At present, the computerization of the educational process in higher education institutions is considered as one of the first and most promising areas for improving the quality of education. The article offered directions of internationalization of educational activity of university in the conditions of European integration. Diagnostic tools for the development of the university in terms of integration into the European educational space, individual rating and ranking of structural units of the university have been developed; main directions of activity of the laboratory of the skill of the teacher of higher school and methodical recommendations on the creation and the organization of work of scientific laboratories.