• Proceedings of the KSR Conference
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• 2007.11a
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• pp.734-743
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• 2007

The KTX station is one of special generators that produce a lot of trips caused by special land use such as university, airport, and super shopping mall. Special generators need special attention in developing travel demand models since the standard trip generation and distribution model in the conventional four-step approach do not provide reliable estimates of their travel patterns. New modeling approach, activity-based model, considering travel behavior of person, seem to be more appropriate for those special generators. Thus, this study introduces a spatial-temporal activity-based approach and how activity-based approach can be applied to estimation of travel demand at KTX stations.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.153-174
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• 2008

There have been two streams of the studies on development structure : conceptual model approach and statistical analysis approach. But In these days, the latter has been becoming the main approach owing to the development of multivariate statistical methods and statistical packages. In this study, we examine methodologies and results of the leading researches related to development structure based on statistical analysis and propose the future research directions. This analysis would be expected to contribute toward the construction of long-range development policies on each country.

• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1018-1030
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• 2013

The corpus callosum is the largest connective structure in the brain, and its shape and size are correlated to sex, age, brain growth and degeneration, handedness, musical ability, and neurological diseases. Manually segmenting the corpus callosum from brain magnetic resonance (MR) image is time consuming, error prone, and operator dependent. In this paper, two semi-automatic segmentation methods are present: the active contour model-based approach and the active shape model-based approach. We tested these methods on an MR image of the human brain and found that the active contour approach had better segmentation accuracy but was slower than the active shape approach.

• 한국연소학회:학술대회논문집
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• 2006.04a
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• pp.89-95
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• 2006

This study has numerically modelled the combustion processes of the turbulent swirling premixed lifted flames in the low-swirl burner (LSB). In these turbulent swirling premixed flames, the four tangentially-injected air jets induce the turbulent swirling flow which plays the crucial role to stabilize the turbulent lifted flame. In the present approach, the turbulence-chemistry interaction is represented by the level-set based flamelet model. Two-dimensional and three-dimensional computations are made for the various swirl numbers and nozzle length. In terms of the centerline velocity profiles and flame liftoff heights, numerical results are compared with experimental data The three-dimensional approach yields the much better conformity with agreements with measurements without any analytic assumptions on the inlet swirl profiles, compared to the two-dimensional approach. Numerical clearly results indicate that the present level-set based flamelet approach has realistically simulated the structure and stabilization mechanism of the turbulent swirling stoichiometric and lean-premixed lifted flames in the low-swirl burner.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.103-106
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• 2005

In this paper, we propose a language modeling approach to improve the performance of a large vocabulary continuous speech recognition system. The proposed approach is based on the active learning framework that helps to select a text corpus from a plenty amount of text data required for language modeling. The perplexity is used as a measure for the corpus selection in the active learning. From the recognition experiments on the task of continuous Korean speech, the speech recognition system employing the language model by the proposed language modeling approach reduces the word error rate by about 6.6 % with less computational complexity than that using a language model constructed with randomly selected texts.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.86-91
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• 2021

Calculating the scrutable core damage frequency (CDF) of nuclear power plants is an important component of the seismic probabilistic safety assessment (SPSA). In this work, a simple approach is developed to calculate CDF from minimal cut sets (MCSs) with non-rare events. When conventional calculation methods based on rare event approximations are employed, the CDF of industry SPSA models is significantly overestimated by non-rare events in the MCSs. Recently, quantification algorithms using binary decision diagrams (BDDs) have been introduced to prevent CDF overestimation in the SPSA. However, BDD structures are generated from a small part of whole MCSs due to limited computational memory, and they cannot be reviewed due to their complicated logic structure. This study suggests a simple approach for scrutinizing the CDF calculation based on whole MCSs in the SPSA system analysis model. The proposed approach compares the new results to outputs from existing algorithms, which helps in avoiding CDF overestimation.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.775-786
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• 2002

In this paper, we conducted an empirical study to investigate the design and weighting effects on descriptive and analytic statistics. The design and weighting effects were calculated for estimates produced from the 1998 small firm survey data. We considered the design and weighting effects on coefficients estimates of regression model using the design-based approach and the GEE approach.

• International Journal of Reliability and Applications
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• v.12 no.1
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• pp.41-48
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• 2011

This paper considers the maintenance model suggested by Jung and Park (2010) to adopt the Bayesian approach and obtain an optimal replacement policy following the expiration of NFRRW. As the criteria to determine the optimal maintenance period, we use the expected cost during the life cycle of the system. When the failure times are assumed to follow a Weibull distribution with unknown parameters, we propose an optimal maintenance policy based on the Bayesian approach. Also, we describe the revision of uncertainty about parameters in the light of data observed. Some numerical examples are presented for illustrative purpose.