• 한국연소학회:학술대회논문집
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• 2004.06a
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• pp.102-111
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• 2004

Partial quenching structure of turbulent diffusion flames in a turbulent mixing layer is investigated by the method of flame hole dynamics to develope a prediction model for the turbulent lift off. The present study is specifically aimed to remedy the problem of the stiff transition of the conditioned partial burning probability across the crossover condition by adopting level-set method which describes propagating or retreating flame front with specified propagation speed. In light of the level-set simulations with two model problems for the propagation speed, the stabilizing conditions for a turbulent lifted flame are suggested. The flame hole dynamics combined with level-set method yields a temporally evolving turbulent extinction process and its partial quenching characteristics is compared with the results of the previous model employing the flame-hole random walk mapping. The probability to encounter reacting' state, conditioned with scalar dissipation rate, demonstrated that the conditional probability has a rather gradual transition across the crossover scalar dissipation rate in contrast to the stiff transition of resulted from the flame-hole random walk mapping and could be attributed to the finite response of the flame edge propagation.

• Journal of the Korean Society of Combustion
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• v.9 no.2
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• pp.18-29
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• 2004

Partial quenching structure of diffusion flames in a turbulent mixing layer has been investigated by the method of flame hole dynamics in oder to develope a prediction model for the phenomenon of turbulent flame lift off. The present study is specifically aimed to remedy the shortcoming of the stiff transition of the conditioned partial burning probability across the crossover condition by employing the level-set method which enables us to include the effect of finite flame edge propagation speed. In light of the level-set simulation results with two models for the edge propagation speed, the stabilizing conditions for turbulent lifted flame are suggested. The flame hole dynamics combined with the level-set method yields a temporally evolving turbulent extinction process and its partial quenching characteristics is compared with the results of the previous model employing the flame-hole random walk mapping based on three critical scalar dissipation rates. The probability to encounter reacting state, conditioned with scalar dissipation rate, demonstrated that the conditional probability has a rather gradual transition across the crossover scalar dissipation rate. Such a smooth transition is attributed to the finite response of the flame edge propagation.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.5
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• pp.195-200
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• 2014

Typical methods of active contour model for object contour extraction are snake and level. Snake is usually faster than level set, but has limitation to compute topology of objects. Level set on the other hand is slower but good at it. In this paper, a new object contour extraction algorithm to use advantage of each is proposed. The algorithm is composed of two main steps. In the first step, snake is used to extract the rough contour and then in the second step, level set is applied to extract the complex contour exactly. 5 binary images and 2 natural images with different contours are simulated by a proposed algorithm. It is shown that speed is reduced and contour is better extracted.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.795-797
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• 2014

The visibility polygon of a simple polygon P is the set of points which are visible from a visibility source in P such as a point or an edge. Since a visibility polygon is the set of points, the set operations such as intersection and union can be executed on them. The intersection(resp. union) of two visibility polygons is the set of points which are visible from both (resp. either) of the corresponding two visibility sources. As previous results, there exist O(n) time algorithms for the set operations of two visibility polygons with total n vertices. In this paper, we present $O(log^2n)$ time algorithms for solving the problems on a reconfigurable mesh with size $O(n^2)$.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.1
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• pp.89-99
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• 1993

A solid modeller was developed in this paper. Representation scheme of modelling is nonmanifold B-rep. While namy B-rep modellers use edges as basic element of modelling, this modeller is triangle-based. New modelling element, split triangle that is composed of 3 pure half-edges and has no information about the adjoining triangles, was defined to enlarge the range of representation. Corresponding algorithms for Boolean set operations were also developed.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.479-487
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• 2021

In this paper, we propose an Sound Event Detection (SED) model using self-training based on a noisy student model. The proposed SED model consists of two stages. In the first stage, a mean-teacher model based on an Residual Convolutional Recurrent Neural Network (RCRNN) is constructed to provide target labels regarding weakly labeled or unlabeled data. In the second stage, a self-training-based noisy student model is constructed by applying different noise types. That is, feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise are used here. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4. The experiments show that the single model and ensemble model of the proposed SED based on the noisy student model improve F1-score by 4.6 % and 3.4 % compared to the top-ranked model in DCASE 2020 challenge Task 4, respectively.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.35-40
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• 2017

Out-of-limit (OOL) alarm method that is threshold checking of telemetry value is widely used for the satellites fault diagnosis and health monitoring. However, it requires engineering knowledge and effort to define delicate threshold value and has limitations that anomalous behaviors within the defined limits can't be detected. In this paper, we propose a satellite anomalous behavior detection system through fuzzy model that is composed by important statistical feature selected by rough-set theory. Not pre-defined anomaly is detected because only normal state data is used for fuzzy model. Also, anomalous behavior within the threshold limit is detected by using statistic feature that can be collected without engineering knowledge. The proposed system successfully detected non-ordinary state for battery temperature telemetry.

• The Journal of Engineering Geology
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• v.13 no.2
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• pp.171-191
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• 2003

The objective of this present study is to understand long term(500 years) thermohydromechanical interaction behavior on joints adjacent to a repository cavern, when high level radioactive wastes are disposed of within discontinuous granitic rock masses, and then, to contribute this understanding to the development of a disposal concept. The model includes a saturated discontinuous granitic rock mass, PWR spent nuclear fuels in a disposal canister surrounded with compacted bentonite inside a deposition hole, and mixed bentonite backfilled in the rest of the space within a repository cavern. It is assumed that two joint sets exist within a model. Joint set 1 includes joints of $56^{\circ}$ dip angle, spaced 20m apart, and joint set 2 is in the perpendicular direction to joint set 1 and includes joints of $34^{\circ}$ dip angle, spaced 20m apart. The two dimensional distinct element code, UDEC is used for the analysis. To understand the joint behavior adjacent to the repository cavern, Barton-Bandis joint model is used. Effect of the decay heat from PWR spent fuels on the repository model has been analyzed, and a steady state flow algorithm is used for the hydraulic analysis.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.241-246
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• 2020

In this paper, we propose a neural network model to converge the marine electric propulsion system and deep learning algorithm to predict the DC/DC converter output current in the electric propulsion regeneration system and to predict the battery charge during regeneration. In order to experiment with the proposed neural network, the input voltage and current of the PCM were measured and the data set was secured on the prototype PCM board. In addition, in order to improve the learning results in the insufficient data set, the scale of the data set was increased through data fitting and its learning was executed further. After learning, the difference between the data prediction result of the neural network model and the actual measurement data was compared. The proposed neural network model effectively showed the prediction of battery charge according to changes in input voltage and current. In addition, by predicting the characteristic change of the analog circuit constituting the DC/DC converter through a neural network, it is determined that the characteristics of the analog circuit should be considered when designing the regeneration system.