• Journal of Life Science
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• v.33 no.7
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• pp.593-604
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• 2023

The ocean accounts for over 70% of the Earth's surface and is a space of largely unexplored unknowns and opportunities. Korea is a peninsula surrounded by the sea on three sides, emphasizing the importance of marine research. The ocean has an extremely complex environment with immense biological diversity. In terms of microbiology, the marine environment has varying factors like extreme temperature, pressure, solar radiation, salt concentration, and pH, providing ecologically unique habitats. Due to this variety, marine organisms have very different phylogenetic classifications compared with terrestrial organisms. Although various microorganisms inhabit the ocean, studies on the diversity, isolation, and cultivation of marine microorganisms and the secondary metabolites they produce are still insufficient. Research on bioactive substances from marine microorganisms, which were rarely studied until the 1990s, has accelerated in terms of natural products from marine Actinomycetes since the 2000s. Since then, industries for bioplastic and biofuel production, carbon dioxide capture, probiotics, and pharmaceutical discovery and development of antibacterial, anticancer, antioxidant, and anti-inflammatory drugs using bacteria, archaea, and algae have significantly grown. In this review, we introduce current research findings and the latest trends in life science and biotechnology using marine microorganisms. Through this article, we hope to create consumer awareness of the importance of basic and applied research in various natural product-related discovery fields other than conventional pharmaceutical drug discovery. The article aims to suggest pathways that may boost research on the optimization and application of future marine-derived materials.

• Smart Media Journal
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• v.12 no.1
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• pp.77-91
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• 2023

Interaction methods and technologies for mutual exploration based on user behavior are evolving variously. Especially, in recent years, with the development of a wide range of sensors, they have developed from contact to non-contact methods. However, developers' senseless definitions of the interaction methods have made the exploration process quite complicated, which rather creates the hassle of users needing to learn the interaction guide defined by the developers before experiencing the exhibition contents. In this context, in order to make visitors smoothly communicate with exhibition contents, a preliminary study on easy interaction for users of various ages is needed, and in particular, research on improving the usability of user interaction is also essential when developing non-contact exhibition contents. So, in this study, a method to reduce the confusion between developers and users was sought by researching non-contact interaction that could be universally interacted with in the field of exhibition contents and proposing behavior designs. First, based on the narrative structure of cultural resources, existing studies were reviewed and the points of interactions as cultural contents were derived. Then the most efficient search process was selected among non-contact behaviors based on hand gestures that allow users to naturally guess and learn interaction methods. Furthermore, on the basis of the meaning of non-linear narrative-based interaction and the analysis results of spatial behavior elements, affordance behavior with high learning effect and efficiency was derived. Through this research process, an action that helps users to understand non-contact interaction naturally in the process of exploring exhibition-type cultural contents and to utilize non-contact interaction in the process of immersion in exhibition contents is proposed as a final model.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Journal of the Korea Society for Simulation
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• v.33 no.2
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• pp.1-11
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• 2024

A cyber-physical system is a technology that connects the physical systems of a manufacturing environment with a cyber space to enable simulation. One of the major challenges in this technology is the seamless communication between these two environments. In complex manufacturing processes, it is crucial to adapt to various protocols of manufacturing equipment and ensure the transmission and reception of a large volume of data without delays or errors. In this study, we propose a method for constructing agent models for real-time simulation-capable cyberphysical systems. To achieve this, we design data collection units as independent agent models and effectively integrate them with existing simulation tools to develop the overall system architecture. To validate the proposed structure and ensure reliability, we conducted empirical testing by integrating various equipment from a real-world smart microfactory system to assess the data collection capabilities. The experiments involved testing data delay and data gaps related to data collection cycles. As a result, the proposed approach demonstrates flexibility by enabling the application of various internal data collection methods and accommodating different data formats and communication protocols for various equipment with relatively low communication delays. Consequently, it is expected that this approach will promote innovation in the manufacturing industry, enhance production line efficiency, and contribute to cost savings in maintenance.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.28-53
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• 2024

In general, high-level radioactive waste (HLW) generated as a result of nuclear power generation should be disposed within the country. Determination of the disposal site and host rock for HLW deep geological repository is an important issue not only scientifically but also politically, economically, and socially. Considered host rock types worldwide for geological disposal include crystalline rocks, sedimentary rocks, volcanic rocks, and salt dome. However, South Korea consists of various rock types except salt dome. This paper not only analyzed the geological and rock mechanical characteristics on a nationwide scale with the preliminary results on various rock type studies for the disposal host rock, but also reviewed the characteristics and possibility of various rock types as a host rock through deep drilling surveys. Based on the nationwide screening for host rock types resulted from literature review, rock distributions, and detailed case studies, Jurassic granites and Cretaceous sedimentary rocks (Jinju and Jindong formations) were derived as a possible candidate host rock types for the geological disposal. However, since the analyzed data for candidate rock types from this study is not enough, it is suggested that the disposal rock type should be carefully determined from additional and detailed analysis on disposal depth, regional characteristics, multidisciplinary investigations, etc.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.236-256
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• 2011

Forest carbon stocks change due to land use change is an important data required by UNFCCC(United Nations framework convention on climate change). Spatially explicit estimation of forest carbon stocks based on IPCC GPG(intergovernmental panel on climate change good practice guidance) tier 3 gives high reliability. But a current estimation which was aggregated from NFI data doesn't have detail forest carbon stocks by polygon or cell. In order to improve an estimation remote sensing and GIS have been used especially in Europe and North America. We divided research trends in main countries into 4 categories such as remote sensing, GIS, geostatistics and environmental modeling considering spatial heterogeneity. The easiest way to apply is combination NFI data with forest type map based on GIS. Considering especially complicated forest structure of Korea, geostatistics is useful to estimate local variation of forest carbon. In addition, fine scale image is good for verification of forest carbon stocks and determination of CDM site. Related domestic researches are still on initial status and forest carbon stocks are mainly estimated using k-nearest neighbor(k-NN). In order to select suitable method for forest in Korea, an applicability of diverse spatial data and algorithm must be considered. Also the comparison between methods is required.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.749-758
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• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.

• Korean Journal of Environmental Biology
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• v.18 no.1
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• pp.77-93
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• 2000

Field survey on the spatio-temporal distribution of water quality and chlorophyll a concentration, and the environmental factors on the variation of phytoplankton biomass were carried out at the 23 stations for four seasons in the Shiahae, southwestern coast of Korean Peninsula from February to October in 1995. I made an analysis on biological factor as chlorophyll a concentration as well as environmental factors such as water temperature, salinity and nutrients; ammonia, nitrite, nitrate, dissolved inorganic nitrogen, phosphate, N/P ratio, silicate and Si/P ratio. The waters in the Shiahae were not stratified due to the tidal mixing and high velocity of tidal current. And the high productivity in photic layer were supported by high nutrients concentration from freshwater on lands and bottom waters The low depth of transparency in the Shiahae had a bad influence upon primary production and marine biology. In Shiahae had a sufficient nutrients for primary production during a year. Especially dissolved inorganic nitrogen and silicate were high, the other side, phosphate was low. The source of nutrients in summer and silicate supply depend on input of freshwater from lands, the other side, dissolved inorganic nitrogen and phosphate were depend on rather supplied from bottom layer by the mixing and input of seawater from outside than input of freshwater from lands. Phosphate seemed to become a limiting nutrient for the primary production at all area of Shiahae in winter and at the northern parts in other seasons. However, dissolved inorganic nitrogen seemed to do it at the southern parts in other seasons except winter. Silicate didn't become a limiting nutrient for diatoms in Shiahae. Phytoplankton biomass as measured by chlorophyll a concentration was very high all the year round, it was controlled by the combination of the several environmental factors, especially of nitrogen, phosphorus and the physical factors such as light intensity. [Spatio-temporal distribution, Seasonal fluctuation, Nnutrients, Chlorophyll a, Environmental factors, Nutrient source, Limiting Nutrient, Light, Shiahae] .