• Journal of the Korean Regional Science Association
• /
• v.39 no.1
• /
• pp.21-36
• /
• 2023

Entering the era of low growth due to changes in social and economic conditions, most cities across the country are actively promoting urban regeneration. Although urban regeneration is a project with huge national finances, a clear evaluation system has not yet been established. In order to ensure the sustainability of urban regeneration, it is necessary to secure the validity of urban regeneration policies and establish a reflux system to supplement the policies. The purpose of this study is to derive the limitations of the existing comprehensive performance evaluation and to develop an improved urban regeneration policy comprehensive performance evaluation technique based on spatial big data. The urban regeneration comprehensive performance evaluation technique differentiated the areas affected by the urban regeneration project and the surrounding areas based on the type of urban regeneration project and the presence or absence of large cities and middle cities. The effects of urban regeneration were quantitatively verified through relative comparison between the areas affected by urban regeneration projects and the surrounding areas of population, society, economy, industry, physical and environmental evaluation indicators.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.9
• /
• pp.6411-6418
• /
• 2015

To respond the climate change and environmental pollution, the studies on renewable energy policies are increasing. The renewable energy is a new growth engine technology represented by the green industry and green technology. At present, the investments for the renewable energy supply and technology development projects of three main strategy sectors such as sunlight, wind power and hydrogen fuel cell are implemented in our country, while they are still in the early stage, accordingly reducing those uncertainty for the research direction and investment fields is the most urgent issue among others. Thus, this study applied text mining method and multinominal topic model among the big data analysis methods on our country's newspaper articles concerning the renewable energy over the last 10 years, and then analyzed the core issues and global research trend, forecasting the renewable energy fields with the growth potential. It is predicted that these results of the study based on information and communication technology will be actively applied on the renewable energy fields.

• Journal of Environmental Health Sciences
• /
• v.34 no.1
• /
• pp.20-26
• /
• 2008

The objective of the study which utilised population based data was to determine the respiratory condition of elementary school children in Gangneung. From October 9th to December 14th, 2006, Pulmonary Function Tests (PFT) including Forced Vital Capacity (FVC) and Forced Expiratoy Volume in I Second $(FEV_1)$ were conducted on the target group of children using a spirometer. The prevalence of asthmatic symptoms was 29.8% among boys and 39.6% among girls. By using logistic regression, we found that family history of allergic rhinitis (OR=3.90, CI=1.05-14.51), experience of allergic conjunctivitis (OR=4.67, CI=1.54-14.16) and atopic dermatitis (OR=2.86, CI=1.17-7.05) significantly increased the asthmatic symptoms. Also, a family history of asthma and food allergy were associated with asthmatic symptoms. In relation to housing and environmental risk factors, residences under the ground (OR=3.59, CI=1.35-9.51) and big-size dolls (OR=2.71, CI=0.86-8.53) significantly increased the prevalence of asthmatic symptoms. For PFT, above four families, exposure of passive smoking and pets significantly reduced FVC in both groups (p<0.05). In girls, a big-size doll was significantly associated with decreased lung function (FVC and $FEV_1$). In boys, using bed significantly reduced $FEV_1$. Also, the risk of asthmatic symptoms was found to increase when the house has been built for 5 years or more, the house is close to a road $({\leq}100m)$, a gas/Kerosene heater or carpet is utilized within the house. However, their differences were not significant. It is concluded that genetic factor such as a family history of respiratory disease, allergic symptoms and housing risk factor are related to asthmatic symptoms. These results were worth noting because the findings will help address risk factors related respiratory symptoms especially in relation to housing and environment.

• Journal of Environmental Science International
• /
• v.27 no.7
• /
• pp.509-518
• /
• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• Journal of Korean Society on Water Environment
• /
• v.36 no.6
• /
• pp.611-620
• /
• 2020

Although process-based models have been a preferred approach for modeling freshwater aquatic systems over extended time intervals, the increasing utility of data-driven models in a big data environment has made the data-driven models increasingly popular in recent decades. In this study, international peer-reviewed journals for the relevant fields were searched in the Web of Science Core Collection, and an extensive literature review, which included total 2,984 articles published during the last two decades (2000-2020), was performed. The review results indicated that the rate of increase in the number of published studies using data-driven models exceeded those using process-based models since 2010. The increase in the use of data-driven models was partly attributable to the increasing availability of data from new data sources, e.g., remotely sensed hyperspectral or multispectral data. Consistently throughout the past two decades, South Korea has been one of the top ten countries in which the greatest number of studies using the data-driven models were published. Among the major data-driven approaches, i.e., artificial neural network, decision tree, and Bayesian model, were illustrated with case studies. Based on the review, this study aimed to inform the current state of knowledge regarding the biogeochemical water quality and ecological models using data-driven approaches, and provide the remaining challenges and future prospects.

• Journal of Environmental Science International
• /
• v.13 no.10
• /
• pp.883-890
• /
• 2004

The purpose of this study is to survey and analyze the growth, management and surrounding environment of the big and old trees in Kyoungju-si or the cultural assets alive in our history, and thereby, provide for some data useful to their reasonable protection and use of their surrounding areas. As a result of surveying the growth conditions of the big and old trees, it was found that the height of new grass was 10.5cm on average, the activity scale of the wood was 7.2k$\Omega$, the soil hardness was $16.7kg/cm^2$, the soil acidity was pH 4.8, and the soil moisture was $13.3\%$. Such findings suggest that the soil has been acidified by people's frequent passages, but that the other growth conditions are more or less normal. Hence, it is desirable to secure a sufficient space around the trees or reduce people's stamping pressure with some mechanisms. On the other hand, the visible conditions of the trees were found more or less normal, but many trees remained cut or barked (with some cavities), requiring an optimal treatment or measure. Lastly, as the population has decreased in the suburban traditional villages, the surrounding environment seems to be less vulnerable to people's frequent visits. Nevertheless, in consideration of the fact that there are only a few public space for the villagers, it is deemed necessary to rearrange or maintain some parts of the surrounding environment as public space for villagers or hikers.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.1
• /
• pp.25-29
• /
• 2024

In this study, research was conducted to predict the probability of cervical cancer occurrence associated with the use of hormonal contraceptives. Cervical cancer is influenced by various environmental factors; however, the human papillomavirus (HPV) is detected in 99% of cases, making it the primary attributed cause. Additionally, although cervical cancer ranks 10th in overall female cancer incidence, it is nearly 100% preventable among known cancers. Early-stage cervical cancer typically presents no symptoms but can be detected early through regular screening. Therefore, routine tests, including cytology, should be conducted annually, as early detection significantly improves the chances of successful treatment. Thus, we employed artificial intelligence technology to forecast the likelihood of developing cervical cancer. We utilized the logistic regression algorithm, a predictive model, through Microsoft Azure. The classification model yielded an accuracy of 80.8%, a precision of 80.2%, a recall rate of 99.0%, and an F1 score of 88.6%. These results indicate that the use of hormonal contraceptives is associated with an increased risk of cervical cancer. Further development of the artificial intelligence program, as studied here, holds promise for reducing mortality rates attributable to cervical cancer.

• Transactions of the Korean hydrogen and new energy society
• /
• v.33 no.2
• /
• pp.121-129
• /
• 2022

With the advent of the post-2020 climate regime, the hydrogen industry is growing rapidly around the world. In order to build the hydrogen economy, it is important to identify social issues related to hydrogen and prepare countermeasures for them. Accordingly, this study conducted a semantic network analysis on hydrogen news from NAVER. As a result of the analysis, the number of hydrogen news in 2020 increased by 4.5 times compared to 2016, and as of 2018, the hydrogen issue has shifted from an environmental aspect to an economic aspect. In addition, although the initial government-led hydrogen industry is expanding to the mobility field such as privately-led fuel cell electric vehicles and hydrogen fuel, terms showing concerns about the safety such as explosions are constantly being exposed. Thus, it is necessary not only to expand the hydrogen ecosystem through the participation of private companies, but also to promote hydrogen safety.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.12
• /
• pp.2348-2354
• /
• 2016

In recent days, IoT (Internet of Things) technology has been widely used in the field of agriculture, which enables the collection of environmental data and biometric data into the database. The availability of big data on agriculture results in the increase of the machine learning based analysis. Through the analysis, it is possible to forecast agricultural production and the diseases of livestock, thus helping the efficient decision making in the management of smart farm. Herein, we use the environmental and biometric data of Smart Pig farm to derive the accurate relationship model between the environmental information and the daily weight increase of swine and verify the accuracy of the derived model. To this end, we applied the M5P tree algorithm of machine learning which reveals that the wind speed is the major factor which affects the daily weight increase of swine.

• Journal of Information Processing Systems
• /
• v.17 no.6
• /
• pp.1035-1043
• /
• 2021

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.