• Journal of the Korea Computer Graphics Society
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• v.22 no.3
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• pp.21-29
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• 2016

Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.173-185
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• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• Journal of Distribution Science
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• v.14 no.11
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• pp.75-82
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• 2016

Purpose - In order for actively pursuing medium and long term policies of Gangwon region to be effectively and efficiently driven, efficacious and practical development strategies are needed. In terms of regional revitalization in most regions that are dependent on the primary industry like Gangwon-do Province, the maintaining of local community becomes difficult and there are limitations on the support from the central government and local governments. Therefore, local communities need to implement measures not only to be financially independent but also maintain and activate themselves. And community business can be adopted to be a proper strategy to cope with this change. This study drew importance of a community business model appropriate for Gangwon-do region to figure out success factors. Research design, data, and methodology - This study aimed to come up with importance of community business model for Gangwon-do region by using AHP Method. AHP Method, which was developed by Professor Saaty in 1970', is a methodology to simplify complex problems for a rational decision making. A survey targeting related public officials and expert group was carried out and a total of 30 questionnaires were collected for the analysis. Results - Analysis model used in this study was to prioritize community business models of Gangwon-do region. The second hierarchy was divided according to local restoration type, local resource utilization type, environment improvement type, and life support type. The third hierarchy consisted of 5 items such as network, the middle structure, program, government support, and human resources to measure each importance. As a result, in the second hierarchy, local resource utilization type had the highest importance. In the third hierarchy, the middle structure had the highest importance, followed by government support, program, network, and human resources. Collectively, the results suggested that important critical factors of community business model of Gangwon-do region was the importance of local resource utilization model and the middle structure. Conclusions - Not only should projects that are already operating in the region but next community business projects that are planning in the Gangwon-do region should be practically operated in view of the importance and the models derived from this study.

• Journal of Korean Society on Water Environment
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• v.25 no.6
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• pp.922-934
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• 2009

The transport of contaminants and spatial variation in a deep reservoir are certainly governed by the thermal structure of the reservoir. There has been continuous efforts to utilize three-dimensional (3D) hydrodynamic and water quality models for supporting reservoir management, but the efforts to validate the models performance using extensive field data were rare. The study was aimed to evaluate a 3D hydrodynamic model, ELCOM, in Daecheong Reservoir for simulating heat fluxes and stratification processes under hydrological years of 2001, 2006, 2008, and to assess the impact of internal wave on the reservoir mixing. The model showed satisfactory performance in simulating the water temperature profiles: the absolute mean errors at R3 (Hoenam) and R4 (Dam) sites were in the range of $1.38{\sim}1.682^{\circ}C$. The evaporative and sensible heat losses through the reservoir surface were maximum during August and January, respectively. The net heat flux ($H_n$) was positive from February to September, while the stratification formed from May and continued until September. Instant vertical mixing was observed in the reservoir during strong wind events at R4, and the model reasonably reproduced the mixing events. A digital low-pass filter and zero crossing method was used to evaluate the potential impact of wind-driven internal wave on the reservoir mixing. The results indicated that most of the wind events occurred in 2001, 2006, 2008 were not enough to develop persistent internal wave and effective mixing in the reservoir. ELCOM is a suitable 3D model for supporting water quality management of the deep and stratified reservoirs.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.121-129
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• 2009

Today, it is difficult to re-use and share spatial information, because of the explosive growth of heterogeneous information and specific characters of spatial information accumulated by diverse local agency. A spatial analysis of subsurface spatial informa-tion, one of the National Spatial Data Infrastructure, needs related spatial information such as, topographical map, geologic map, underground facility map, etc. However, current methods using standard format or spatial datawarehouse cannot consider a se-mantic hetergenity. In this paper, the layered ontology model which consists of generic concept, measuremnt scale, spatial model, and subsurface spatial information has developed. Also, the current ontology building method pertained to human experts is a expensive and time-consuming process. We have developed the MDA-based metamodel(UML Profile) of ontology that can be a easy under-standing and flexiblity of environment change. The semantic quality of devleoped ontology model has evaluated by reasoning engine, Pellet. We expect to improve a semantic sharing, and strengthen capacities for developing GIS experts system using knowledge representation ability of ontology.

• Journal of The Korean Astronomical Society
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• v.37 no.4
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• pp.151-157
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• 2004

It is investigated quantitative relations between the magnetic storm magnitude and the solar wind parameters such as the Interplanetary Magnetic Field (hereinafter, IMF) magnitude (B), the southward component of IMF (Bz), and the dynamic pressure during the main phase of the magnetic storm with focus on the role of the interplanetary shock (hereinafter, IPS) in order to build the space weather fore-casting model in the future capable to predict the occurrence of the magnetic storm and its magnitude quantitatively. Total 113 moderate and intense magnetic storms and 189 forward IPSs are selected for four years from 1998 to 2001. The results agree with the general consensus that solar wind parameter, especially, Bz component in the shocked gas region plays the most important role in generating storms (Tsurutani and Gonzales, 1997). However, we found that the correlations between the solar wind parameters and the magnetic storm magnitude are higher in case the storm happens after the IPS passing than in case the storm occurs without any IPS influence. The correlation coefficients of B and $BZ_(min)$ are specially over 0.8 while the magnetic storms are driven by IPSs. Even though recently a Dst prediction model based on the real time solar wind data (Temerin and Li, 2002) is made, our correlation test results would be supplementary in estimating the prediction error of such kind of model and in improving the model by using the different fitting parameters in cases associated with IPS or not associated with IPS rather than single fitting parameter in the current model.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.29-43
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• 2008

Although RDF ontologies might be expressed in XML tree model, existing methods for protection of XML documents are not suitable for securing RDF ontologies. The graph style and inference feature of RDF demands a new security model development. Driven by this goal, this paper proposes a new query-oriented model for the RDF ontology access control. The proposed model rewrites a user query using a three-layered ontology view. The proposal resolves the problem that the existing approaches should generate inference models depending on inference rules. Accessible ontology concepts and instances which a user can visit are defined as ontology views, and the inference view defined for controling an inference query enables a controlled inference capability for the user. This paper defines the three-layered view and describes algorithms for query rewriting according to the views. An implemented prototype with its system architecture is shown. Finally, the experiment and comparative evaluation result of the proposal and the previous approach is described.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1032-1038
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• 2021

Rate of penetration(ROP) is one of the important variables for maximizing the drilling performance. In order to maximize drilling efficiency, it is necessary to increase the drilling speed, and real-time ROP prediction is important so that the driller can identify problems during drilling. The ROP has a high correlation with the drillstring rotational speed, weight on bit, and flow rate. In this paper, the ROP was predicted using a data-driven supervised learning model trained from the drilling efficiency parameters. As a result of comparison through the performance evaluation metrics of the regression model, the root mean square error(RMSE) of the RF model was 4.20 and the mean absolute percentage error(MAPE) was 9.08%, confirming the best predictive performance. The proposed method can be used as a base model for ROP prediction when constructing a real-time drilling operation guide system.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2743-2759
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• 2020

A detailed computational fluid dynamics (CFD) simulation analysis model was developed using ANSYS CFX 16.1 and analyzed to simulate the basic design and internal flow characteristics of a 180 MW small modular reactor (SMR) with a natural circulation flow system. To analyze the natural circulation phenomena without a pump for the initial flow generation inside the reactor, the flow characteristics were evaluated for each output assuming various initial powers relative to the critical condition. The eddy phenomenon and the flow imbalance phenomenon at each output were confirmed, and a flow leveling structure under the core was proposed for an optimization of the internal natural circulation flow. In the steady-state analysis, the temperature distribution and heat transfer speed at each position considering an increase in the output power of the core were calculated, and the conceptual design of the SMR had a sufficient thermal margin (31.4 K). A transient model with the output ranging from 0% to 100% was analyzed, and the obtained values were close to the T_hot and T_cold temperature difference value estimated in the conceptual design of the SMR. The K-factor was calculated from the flow analysis data of the CFX model and applied to an analysis model in RELAP5/MOD3.3, the optimal analysis system code for nuclear power plants. The CFX analysis results and RELAP analysis results were evaluated in terms of the internal flow characteristics per core output. The two codes, which model the same nuclear power plant, have different flow analysis schemes but can be used complementarily. In particular, it will be useful to carry out detailed studies of the timing of the steam generator intervention when an SMR is activated. The thermal and hydraulic characteristics of the models that applied porous media to the core & steam generators and the models that embodied the entire detail shape were compared and analyzed. Although there were differences in the ability to analyze detailed flow characteristics at some low powers, it was confirmed that there was no significant difference in the thermal hydraulic characteristics' analysis of the SMR system's conceptual design.