• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.160-165
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• 2019

The conduct of seafarer is major cause of marine accidents. This study models the behavior of the seafarer based on the Hidden Markov Model (HMM). Additionally, through the path analysis of the behavior estimated by the model, the kind of situations, procedures and errors that may have caused the marine accidents were interpreted. To successfully implement the model, the seafarer behaviors were observed by means of the summarized verdict reports issued by the Korean Maritime Safety Tribunal, and the observed results converted into behavior data suitable for HMM learning through the behavior classification framework based on the SRKBB (Skill-, Rule-, and Knowledge-Based Behavior). As a result of modeling the seafarer behaviors by the type of vessels, it was established that there was a difference between the models, and the possibility of identifying the preferred path of the seafarer behaviors. Through these results, it is expected that the model implementation technique proposed in this study can be applied to the prediction of the behavior of the seafarer as well as contribute to the prioritization of the behavior correction among seafarers, which is necessary for the prevention of marine accidents.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.583-592
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• 2018

Recently, concern has arisen regarding the lowering of groundwater levels in the hinterland caused by the development of high-capacity radial collector wells in riverbank filtration areas. In this study, groundwater levels are estimated using Modflow software in relation to the water volume pumped by the radial collector well in Anseongcheon Stream. Using the water volume data, an artificial neural network (ANN) model is developed to determine the amount of water that can be withdrawn while minimizing the reduction of groundwater level. We estimate that increasing the pumping rate of the horizontal well HW-6, which is drilled parallel to the stream direction, is necessary to minimize the reduction of groundwater levels in wells OW-7 and OB-11. We also note that the number of input data and the classification of training and test data affect the results of the ANN model. This type of approach, which supplements ANN modeling with observed data, should contribute to the future groundwater management of hinterland areas.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.1-15
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• 2020

In this paper, we propose a novel method for converting the existing 2D processes in the design and construction phase of civil engineering, to the future BIM-based processes. First, we compare and analyze the actual application processes of the outputs of the existing 2D method and the outputs of the 3D BIM method, for the whole process of BIM design of earthworks and road structures and integrated utilization of quantity, process(4D) and construction cost(5D), in view of life-cycle. The proposed method acquire the outputs of the design phase integrating IFC international common standard file information and CBS/OBS/WBS standard classification scheme information, and acquire the outputs of the construction stage by using an integrated utilization module for quantity, process(4D) and construction cost(5D). Ultimately, we intend to commercialize the step by step technologies for BIM design and construction in civil engineering by using the proposed method.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.287-294
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• 2021

Modern small farms are important link components in the structure of the world agro-industrial complex. It ensures the food and nutritional sustainability of the country exclusively at the local regional level. The purpose of the research is to examine the role of farming in ensuring nutritional security and food stability based on the analysis of the Food Sustainability Index (FSI). Research methods: modeling, abstraction, analogy, analysis, synthesis, formalization, logical abstraction, theoretical cognition, systematization and classification, abstract-logical, etc. Results. Having analyzed the Food Sustainability Index for 2018, it has been established that there is a lack of a clear relationship between the pace of economic development and the level of food and nutritional sustainability. In addition, this study has identified the countries with the largest number of small farms, as well as the number of farms within the region. The correlation between the size of the farm and the area of agricultural land that it cultivates has been determined. The problems faced by small farms in the process of their activity have been analyzed. The programs implemented in the field of agro-industrial complex development by international profile institutions have been systematized. Particularly, the regional structure of agricultural development programs under the guidance of IFAD is defined, as well as the areas to which they are directed. Specific measures taken by governments to stimulate the development of small farms have been outlined. Reasonable conclusions have been formed based on the study. The direction of future research is seen in the assessment of the export potential of small farms in terms of range, volume of export deliveries and geographical direction of movement of their products.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.70-75
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• 2022

Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.347-353
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• 2022

In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1167-1174
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• 2022

With the sustainable development and transformation of the city, public art as a business card of the famous city of culture has become a hot topic of research. The intervention of public art in public space not only brings users a sense of space experience, but also becomes a unique carrier of urban and rural image making. Although there is much research on the classification, aesthetics and function of public art, there is few quantitative research on user satisfaction. This paper takes the basic features of airport public art as a research object and the basic features of airport public art as the theoretical basis to study the impact of the basic characteristics of airport public art on user satisfaction. Research methods were based on questionnaire data of 247 people, in which models and hypotheses were tested using SPSS 21.0 software, based on the induction and extraction of nine influential factors in the basic characteristics of public art. The study found that public interpretation, media patterns, color perception, modeling form, place perception, city image and memory have significant positive effects on user satisfaction. The sharedness of public art, cognition and communication in public culture and spatial relations do not affect satisfaction. Conclusion, inspiration and prospect provide suggestions for designers and reference data and theoretical support for public art evaluation.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.1
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• pp.17-33
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• 2023

Disasters in coastal regions are a constant source of damage due to their uncertainty and complexity, leading to the proposal of green infrastructure as a nature-based solution that incorporates the concept of resilience to address the limitations of traditional grey infrastructure. This study analyzed trends in research related to coastal disasters and green infrastructure by conducting a co-occurrence keyword analysis of 2,183 articles collected from the Web of Science (WoS). The analysis resulted in the classification of the literature into four clusters. Cluster 1 is related to coastal disasters and tsunamis, as well as predictive simulation techniques, and includes keywords such as surge, wave, tide, and modeling. Cluster 2 focuses on the social system damage caused by coastal disasters and theoretical concepts, with keywords such as population, community, and green infrastructure elements like habitat, wetland, salt marsh, coral reef, and mangrove. Cluster 3 deals with coastal disaster-related sea level rise and international issues, and includes keywords such as sea level rise (or change), floodplain, and DEM. Finally, cluster 4 covers coastal erosion and vulnerability, and GIS, with the theme of 'coastal vulnerability and spatial technique'. Keywords related to green infrastructure in cluster 2 have been continuously appearing since 2016, but their focus has been on the function and effect of each element. Based on this analysis, implications for planning and management processes using green infrastructure in response to coastal disasters have been derived. This study can serve as a valuable resource for future research and policy in responding to and managing various disasters in coastal regions.

• The korean journal of orthodontics
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• v.53 no.3
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• pp.194-204
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• 2023

Objective: To investigate sex-specific correlations between the dimensions of permanent canines and the anterior Bolton ratio and to construct a statistical model capable of identifying the sex of an unknown subject. Methods: Odontometric data were collected from 121 plaster study models derived from Caucasian orthodontic patients aged 12-17 years at the pretreatment stage by measuring the dimensions of the permanent canines and Bolton's anterior ratio. Sixteen variables were collected for each subject: 12 dimensions of the permanent canines, sex, age, anterior Bolton ratio, and Angle's classification. Data were analyzed using inferential statistics, principal component analysis, and artificial neural network modeling. Results: Sex-specific differences were identified in all odontometric variables, and an artificial neural network model was prepared that used odontometric variables for predicting the sex of the participants with an accuracy of > 80%. This model can be applied for forensic purposes, and its accuracy can be further improved by adding data collected from new subjects or adding new variables for existing subjects. The improvement in the accuracy of the model was demonstrated by an increase in the percentage of accurate predictions from 72.0-78.1% to 77.8-85.7% after the anterior Bolton ratio and age were added. Conclusions: The described artificial neural network model combines forensic dentistry and orthodontics to improve subject recognition by expanding the initial space of odontometric variables and adding orthodontic parameters.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.