• Anatomy and Cell Biology
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• v.56 no.4
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• pp.474-481
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• 2023

The dental characteristics created by acquired dental treatments can be used as age estimators. This pilot study aimed to analyze the correlation between the number of teeth observed for dental characteristics and chronological age and to develop new non-invasive age estimation models. Dental features on panoramic radiographs (420 radiographs of subjects aged 20-89 years) were classified and coded. The correlation between the number of teeth for each selected code (codes V, X, T, F, P, and L) and age was observed, and multiple regression was performed to analyze the relationship between them. Eleven regression models with various combinations of dental sextants were presented. The model with the data from both sides of the posterior teeth on both jaws showed the best performance (root mean square error of 14.78 years and an adjusted R² of 0.461). The model with all teeth was the second-best. Based on these results, we confirmed statistically significant correlations between certain dental features and chronological age. We also observed that some regression models performed sufficiently well to be used as adjunctive methods in forensic practice. These results provide valuable information for the design and performance of future full-scale studies.

• Physical Therapy Rehabilitation Science
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• v.13 no.2
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• pp.213-222
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• 2024

Objective: Stroke is a leading cause of disability worldwide, often leaving survivors with significant cognitive and motor impairments. Dual-task (DT), which involves performing cognitive and motor tasks simultaneously, can influence brain activation patterns and functional recovery in stroke patients. Design: A systematic review Methods: Following PRISMA guidelines, databases including MEDLINE, CINAHL, Embase, and Web of Science were searched for studies assessing cortical activation via functional near-infrared spectroscopy (fNIRS) during DT performance in stroke patients. Studies were selected based on predefined eligibility criteria, focusing on changes in hemodynamic responses and their correlation with task performance. Results: Eight studies met the inclusion criteria. Findings indicate that DT leads to increased activation in the prefrontal cortex (PFC), premotor cortex (PMC), and posterior parietal cortex (PPC), suggesting an integrated cortical response to managing concurrent cognitive and motor demands. However, increased activation did not consistently translate to improved functional outcomes, highlighting the complex relationship between brain activation and rehabilitation success. Conclusions: DT interventions may enhance cortical activation and neuroplasticity in post-stroke patients, but the relationship between increased brain activity and functional recovery remains complex and requires further investigation. Tailored DT programs that consider individual neurophysiological and functional capacities are recommended to optimize rehabilitation outcomes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.782-804
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• 2011

In video-based recognition systems, stationary cameras are used to monitor an area of interest. These systems focus on a segmentation of the foreground in the video stream and the recognition of the events occurring in that area. The usual approach to discriminating the foreground from the video sequence is background subtraction. This paper presents a novel background subtraction method based on a probabilistic approach. We represent the posterior probability of the foreground based on the current image and all past images and derive an updated method. Furthermore, we present an efficient fusion method for the color and edge information in order to overcome the difficulties of existing background subtraction methods that use only color information. The suggested method is applied to synthetic data and real video streams, and its robust performance is demonstrated through experimentation.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.381-393
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• 2002

K-modes algorithm and ROCK(RObust Clustering using linKs) algorithm we useful clustering methods for large categorical data. In the paper, we investigate these algorithms and propose improved algorithms of them to correct their weakness. A simulation study shows that the proposed algorithms could increase the performance of data clustering.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1765-1770
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• 2005

Recently, particle filters have attracted attentions for nonlinear state estimation. In this approaches, a posterior probability distribution of the state variable is evaluated based on observations in simulation using so-called importance sampling. We proposed a new filter, Evolution Strategies based particle (ESP) filter to circumvent degeneracy phenomena in the importance weights, which deteriorates the filter performance, and apply it to simultaneous state and parameter estimation of nonlinear state space models. Results of numerical simulation studies illustrate the applicability of this approach.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.117-122
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• 2008

This paper presents a multi-robot localization based on multidimensional scaling (MDS) in spite of the existence of incomplete and noisy data. While the traditional algorithms for MDS work on the full-rank distance matrix, there might be many missing data in the real world due to occlusions. Moreover, it has no considerations to dealing with the uncertainty due to noisy observations. We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr$\ddot{o}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2424-2441
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• 2012

In this paper, we propose a fake iris detection method that combines the optical and textural features of the human eye. To extract the optical features, we used dual Purkinje images that were generated on the anterior cornea and the posterior lens surfaces based on an analytic model of the human eye's optical structure. To extract the textural features, we measured the amount of change in a given iris pattern (based on wavelet decomposition) with regard to the direction of illumination. This method performs the following two procedures over previous researches. First, in order to obtain the optical and textural features simultaneously, we used five illuminators. Second, in order to improve fake iris detection performance, we used a SVM (Support Vector Machine) to combine the optical and textural features. Through combining the features, problems of single feature based previous works could be solved. Experimental results showed that the EER (Equal Error Rate) was 0.133%.

• Annals of Clinical Neurophysiology
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• v.20 no.1
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• pp.12-17
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• 2018

Visual evoked potentials (VEPs) are frequently used to assess the anterior and posterior visual pathways. In particular, the use of VEPs have been increasing in various fields such as evaluation of the optic nerves in patients with multiple sclerosis. The performance of VEP test can be affected by various factors such as stimulus type and subject condition, and its interpretation is also difficult. However, there have been no guidelines for performing and interpreting VEPs in Korea. Therefore, we aimed to provide comprehensive information regarding basic requirement and interpretation for VEPs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.