• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.611-620
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• 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 the Korea Society of Computer and Information
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• v.26 no.12
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• pp.159-168
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• 2021

Building Information Modeling (BIM) data is a digitized construction design by worldwide construction design stands rules. Some research are being conducted to utilize blockchain for safe sharing and trade of BIM data, but there is no way to store BIM data directly in the blockchain due to the size of BIM data and technical limitation of the blockchain. In this paper, we propose a method of storing BIM data by combining a distributed file system and a blockchain. We propose two network overlays for storing BIM data, and we also propose generating the Level of Detail (LOD)-based merkle tree for efficient verification of BIM data. In addition, this paper proposes a system design for distributed storage of BIM data by using blockchain besu client and IPFS client. Our system design has a result that the processing speed stably increased despite the increase in data size.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.166-173
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• 2008

Recently, problem of earth environment being attended with international issue, people are concerned about the environmentally-friendly and renewable biomass energy. Especially, the forest biomass is more important because Korea have to control carbon footprint for Kyoto Protocol and Convention on Climate Change. In case of Korea, forest area covers the land about 2/3 of all country. It is needed that more economical and efficient method to estimate the biomass by remote sensing data which include wide coverage and is progressed by one-step. In this study, we estimate forest biomass with LiDAR data and aerial photograph. Three biomass equation is used and estimate mean biomass of single tree and entire biomass in plots. The results are compared with field data. $R^2$ of the mean biomass of single tree is greater than 0.8 and that of entire biomass in plots is greater than 0.65. In conclusion, the method using remote sensing data is verified more economical and efficient than previous field data method.

• Journal of the Society of Naval Architects of Korea
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• v.39 no.3
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• pp.75-80
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• 2002

In these days of severe struggle for existence, the world has changed a great deal to global and digital oriented period. The enterprises try to introduce new management and production system to adapt such a change. But, if the only new technologies are applied to an enterprise without definite analysis about manufacturing, failure fellows as a logical consequence. Hence, enterprise must analyze manufacturing system definitely and needs new methodologies to mitigate risk. This study suggests that the new approach, which is systems approach for process improvement, is organized to systems analysis, systems diagnosis, and systems verification. Systems analysis analyzes manufacturing systems with object-oriented methodology-UML(Unified Modeling language) from a point of product, process, and resource view. Systems diagnosis identifies the constraints to optimize the system through scientific management or TOC(Theory of constraints). Systems verification shows the solution with virtual manufacturing technique applied to the core problem which emerged from systems diagnosis. This research shows the artifacts to improve the productivity with the above methodology applied to forming shop. UML provides the definite tool for analysis and re-usability to adapt itself to environment easily. The logical tree of TOC represents logical tool to optimize the forming shop. Discrete event simulator-QUEST suggests the tool for making a decision to verify the optimized forming shop.

• Ecology and Resilient Infrastructure
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• v.9 no.2
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• pp.83-92
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• 2022

The 2020 summer extreme flood severely disturbed the riparian ecosystem of the Seomjin River. Some trees were killed by the flood impact, whereas others have recovered through epicormic regeneration after the disturbance. At the same time, several tree individuals newly germinated. This research aimed to explain the recovery of the riparian ecosystem by spatial proximity between each tree individual of different characteristics, such as "dead", "recovered", and "newly germinated". A spatial point pattern analysis based on K and g-functions revealed that the newly germinated trees and the existing trees were distributed in the spatially clumping patterns. However, further detailed analysis revealed that the new trees were statistically less attracted to the recovered trees than the dead trees, implying competitive interactions hidden in the facilitative interactions. Habitat amelioration by the existing trees positively affected the growth of the new trees, while "living" existing trees were competing with the new trees for resources. This research is expected to provide new knowledge in this era of rapid climate change, which likely induces stronger and more frequent natural disturbance than before. Environmental factors have been widely used for ecosystem modeling, but species interactions, represented by the relative spatial distribution of plant individuals, are also valuable factors explaining ecosystem dynamics.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.509-520
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• 2015

College Scholastic Ability Test(CSAT) is a primary test to evaluate the study achievement of high-school students and used by most universities for admission decision in South Korea. Because its level of difficulty is a significant issue to both students and universities, the government makes a huge effort to have a consistent difficulty level every year. However, the actual levels of difficulty have significantly fluctuated, which causes many problems with university admission. In this paper, we build two types of data-driven prediction models to predict correct answer rate and to identify significant factors for CSAT English test through accumulated test data of CSAT, unlike traditional methods depending on experts' judgments. Initially, we derive candidate question-specific factors that can influence the correct answer rate, such as the position, EBS-relation, readability, from the annual CSAT practices and CSAT for 10 years. In addition, we drive context-specific factors by employing topic modeling which identify the underlying topics over the text. Then, the correct answer rate is predicted by multiple linear regression and level of difficulty is predicted by classification tree. The experimental results show that 90% of accuracy can be achieved by the level of difficulty (difficult/easy) classification model, whereas the error rate for correct answer rate is below 16%. Points and problem category are found to be critical to predict the correct answer rate. In addition, the correct answer rate is also influenced by some of the topics discovered by topic modeling. Based on our study, it will be possible to predict the range of expected correct answer rate for both question-level and entire test-level, which will help CSAT examiners to control the level of difficulties.

• The Journal of Society for e-Business Studies
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• v.13 no.3
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• pp.51-66
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• 2008

The Business Process Management System(BPMS) has received more attentions as companies increasingly realize the importance of business processes. However, traditional BPMS has focused mainly on correct modeling and exact automation of process flow, and paid little attention to the achievement of final goals of improving process efficiency and innovating processes. BPMS usually generates enormous amounts of log data during and after execution of processes, where numerous meaningful rules and patterns are hidden. In the present study we employ the data mining technique to find out useful knowledge from the complicated process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and the framework.

• Computers and Concrete
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• v.6 no.3
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• pp.203-223
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• 2009

Regression analysis (RA) can establish an explicit formula to predict the strength of High-Performance Concrete (HPC); however, the accuracy of the formula is poor. Back-Propagation Networks (BPNs) can establish a highly accurate model to predict the strength of HPC, but cannot generate an explicit formula. Genetic Operation Trees (GOTs) can establish an explicit formula to predict the strength of HPC that achieves a level of accuracy in between the two aforementioned approaches. Although GOT can produce an explicit formula but the formula is often too complicated so that unable to explain the substantial meaning of the formula. This study developed a Backward Pruning Technique (BPT) to simplify the complexity of GOT formula by replacing each variable of the tip node of operation tree with the median of the variable in the training dataset belonging to the node, and then pruning the node with the most accurate test dataset. Such pruning reduces formula complexity while maintaining the accuracy. 404 experimental datasets were used to compare accuracy and complexity of three model building techniques, RA, BPN and GOT. Results show that the pruned GOT can generate simple and accurate formula for predicting the strength of HPC.

• Computers and Concrete
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• v.19 no.6
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• pp.651-658
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• 2017

High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.04a
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• pp.172-175
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• 2005

The numerical simulation is performed for the acoustic emission and the wave propagation due to fiber breakage in single fiber composite plates by the finite element transient analysis. The acoustic emission and the following wave motions from a fiber breakage under a static loading is simulated to investigate the applicability of the explicit finite element method and the equivalent volume force model as a simulation tool of wave propagation and a modeling technique of an acoustic emission. For such a simple case of the damage event under static loading, various parameters affecting the wave motion are investigated for reliable simulations of the impact damage event. The high velocity and the small wave length of the acoustic emission require a refined analysis with dense distribution of the finite element and a small time step. In order to fulfill the requirement for capturing the exact wave propagation and to cover the 3-D simulation, we utilize the parallel FE transient analysis code and the parallel computing technology.