• Journal of the Korean Society of Safety
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• v.33 no.1
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• pp.109-117
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• 2018

This study examined the effect of Behavior Based Safety(BBS) program on safe behaviors of bus drivers and passengers. Four male bus drivers working at a H transportation company situated in Seoul participated in this study. BBS program consisted of education, prompts, and self-monitoring with goal setting. Dependent variables were the percentage of two safe driving behaviors of bus drivers (departure after stopping for 3 seconds, announcement for holding bus handles) and the percentage of one safe behavior of passengers getting on the busses those drivers drove (holding bus handles). A primary observer and two trained assistant observers measured two safe behaviors of the bus drivers with behavior checklists by riding on the busses and the passengers' safe behavior was observed by CCTV installed on each bus. An ABC multiple baseline design across participants was adopted. After baseline(A), education and prompts(B) and self-monitoring with goal setting(C) were introduced sequentially to each participant. The results showed that BBS program was effective to increase both bus drivers' and passengers' safe behaviors. Especially self-monitoring with goal setting was more effective in improving safe behaviors of bus drivers than education/prompts. These results suggest that education/prompts and self-monitoring with goal setting would be an alternative treatment technique to improve safety for lone workers such as bus drivers.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.549-559
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• 2015

In this paper, an efficient dynamic reactive motion planning method for an autonomous vehicle in a dynamic environment is proposed. The purpose of the proposed method is to improve the robustness of autonomous robot motion planning capabilities within dynamic, uncertain environments by integrating a virtual plane-based reactive motion planning technique with a sensor fusion-based obstacle detection approach. The dynamic reactive motion planning method assumes a local observer in the virtual plane, which allows the effective transformation of complex dynamic planning problems into simple stationary ones proving the speed and orientation information between the robot and obstacles. In addition, the sensor fusion-based obstacle detection technique allows the pose estimation of moving obstacles using a Kinect sensor and sonar sensors, thus improving the accuracy and robustness of the reactive motion planning approach. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles in hostile dynamic environments.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2017-2025
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• 2021

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33-0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.

• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.77-84
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• 2007

The intima media thickness(IMT) of the carotid artery from B mode ultrasound images has recently been proposed as the most useful index of individual atherosclerosis and can be used to predict major cardiovascular events. Ultrasonic measurements of the IMT are conventionally obtained by manually tracing interfaces between tissue layers. The drawbacks of this method are the inter and intra observer variability and its inefficiency. In this paper, we present a multiresolution snake method combined with the dynamic programming, which overcomes the various noises and sensitivity to initialization of conventional snake. First, an image pyramid is constructed using the Gaussian pyramid that maintains global edge information with smoothing in the images, and then the boundaries are automatically detected in the lowest resolution level by minimizing a cost function based on dynamic programming. The cost function includes cost terms which are representing image features and geometrical continuity of the vessel interfaces. Since the detected boundaries are selected as initial contour of the snake for the next level, this automated approach solves the problem of the initialization. Moreover, the proposed snake improves the problem of converging th the local minima by defining the external energy based on multiple image features. In this paper, our method has been validated by computing the correlation between manual and automatic measurements. This automated detection method has obtained more accurate and reproducible results than conventional edge detection by considering multiple image features.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.10-15
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• 2012

In a color image, people and especially facial patterns are important and interesting visual objects. Thus, effective skin color reproduction is essential, as skin color is a key memory color in color application systems. Previous studies suggested skin color reproduction by mapping only to the center value of preferred skin region. However, it is not suitable to determine one preference color because preference color from the observer's preference test is not dominant. In this paper, skin color reproduction using multiple preferred skin colors for each race is proposed. The proposed method first defines multiple preferred skin colors for each race according to their luminance level. After that, skin region is detected in an image. The race is then selected by calculating distance between average chromaticity of detected region and that of each racial skin from a database to assign preferred skin color for each race. Next, each corresponding preferred skin color is determined for each selected race. Finally, input skin color is proportionally mapped toward preferred skin color according to the difference between the input skin color and the preferred skin color for a smoothly reproduced skin color. In the experimental results, the proposed method gives better color correction on the objective and subjective evaluation than the previous methods.

• Journal of Veterinary Clinics
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• v.31 no.6
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• pp.495-501
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• 2014

The objective of this study was to evaluate the differences and reproducibility of Hounsfield unit (HU) value and volume measurements on different computed tomography (CT) scanner types and different collimations by using a gelatin phantom. The phantom consisting of five synthetic simulated calculus spanning diameters from 3.0 mm to 12.0 mm with 100 HU was scanned using a two-channel multi-detector row CT (MDCT) scanner, a four-channel MDCT scanner, and two 64-channel MDCT scanners. For all different scanner types, the thinnest possible collimation and the second thinnest collimation was used. The HU values and volumes of the synthetic simulated calculus were independently measured three times with minimum intervals of 2 weeks and by three experienced veterinary radiologists. ANOVA and Scheff$\acute{e}$ test for the multiple comparison were performed for statistical comparison of the HU values and volumes of the synthetic simulated calculus according to different CT scanner types and different collimations. The reproducibility of the HU value and volume measurements was determined by calculating Cohen's k. The reproducibility of HU value and volume measurements was very good. HU value varied between different CT scanner types, among different beam collimations. However, there was not statistically significant difference. The percent error (PE) decreased as the collimation thickness decreased, but the decrease was statistically insignificant. In addition, no statistically significant difference in the PEs of the different CT scanner types was found. It can be concluded that the CT scanner type insignificantly affects HU value and the volumetric measurement, but that a thinner collimation tends to be more useful for accurate volumetric measurement.

• Imaging Science in Dentistry
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• v.45 no.1
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• pp.15-22
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• 2015

Purpose: This study was performed to assess the reproducibility of identifying the sella turcica landmark in a three-dimensional (3D) model by using a new sella-specific landmark reference system. Materials and Methods: Thirty-two cone-beam computed tomographic scans (3D Accuitomo$^{(R)}$ 170, J. Morita, Kyoto, Japan) were retrospectively collected. The 3D data were exported into the Digital Imaging and Communications in Medicine standard and then imported into the Maxilim$^{(R)}$ software (Medicim NV, Sint-Niklaas, Belgium) to create 3D surface models. Five observers identified four osseous landmarks in order to create the reference frame and then identified two sella landmarks. The x, y, and z coordinates of each landmark were exported. The observations were repeated after four weeks. Statistical analysis was performed using the multiple paired t-test with Bonferroni correction (intraobserver precision: p<0.005, interobserver precision: p<0.0011). Results: The intraobserver mean precision of all landmarks was <1 mm. Significant differences were found when comparing the intraobserver precision of each observer (p<0.005). For the sella landmarks, the intraobserver mean precision ranged from $0.43{\pm}0.34mm$ to $0.51{\pm}0.46mm$. The intraobserver reproducibility was generally good. The overall interobserver mean precision was <1 mm. Significant differences between each pair of observers for all anatomical landmarks were found (p<0.0011). The interobserver reproducibility of sella landmarks was good, with >50% precision in locating the landmark within 1 mm. Conclusion: A newly developed reference system offers high precision and reproducibility for sella turcica identification in a 3D model without being based on two-dimensional images derived from 3D data.

• Investigative Magnetic Resonance Imaging
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• v.21 no.1
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• pp.9-19
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• 2017

Background: Normalized cerebral blood volume (nCBV) can be measured using manual or semiautomatic segmentation method. However, the difference in diagnostic performance on brain tumor differentiation between differently measured nCBV has not been evaluated. Purpose: To compare the diagnostic performance of manually obtained nCBV to that of semiautomatically obtained nCBV on glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) differentiation. Materials and Methods: Histopathologically confirmed forty GBM and eleven PCNSL patients underwent 3T MR imaging with dynamic susceptibility contrast-enhanced perfusion MR imaging before any treatment or biopsy. Based on the contrast-enhanced T1-weighted imaging, the mean nCBV (mCBV) was measured using the manual method (manual mCBV), random regions of interest (ROIs) placement by the observer, or the semiautomatic segmentation method (semiautomatic mCBV). The volume of enhancing portion of the tumor was also measured during semiautomatic segmentation process. T-test, ROC curve analysis, Fisher's exact test and multivariate regression analysis were performed to compare the value and evaluate the diagnostic performance of each parameter. Results: GBM showed a higher enhancing volume (P = 0.0307), a higher manual mCBV (P = 0.018) and a higher semiautomatic mCBV (P = 0.0111) than that of the PCNSL. Semiautomatic mCBV had the highest value (0.815) for the area under the curve (AUC), however, the AUCs of the three parameters were not significantly different from each other. The semiautomatic mCBV was the best independent predictor for the GBM and PCNSL differential diagnosis according to the stepwise multiple regression analysis. Conclusion: We found that the semiautomatic mCBV could be a better predictor than the manual mCBV for the GBM and PCNSL differentiation. We believe that the semiautomatic segmentation method can contribute to the advancement of perfusion based brain tumor evaluation.

• Annals of Clinical Neurophysiology
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• v.17 no.2
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• pp.61-67
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• 2015

Background: The aim of this study was to investigate the predictive value of snoring frequency in the diagnosis and severity of obstructive sleep apnea. Methods: Patients who underwent polysomnography with one or more of the following characteristics were included: 1) sleepiness, non-restorative sleep, fatigue, or insomnia symptoms; 2) arousal due to cessation of breathing or the occurrence of gasping or choking when waking up; and 3) habitual snoring, breathing interruptions, or both, noted by a bed partner or other observer. We analyzed the differences in clinical and polysomnographic variables between patients with and without obstructive sleep apnea and investigated the associations of those variables with obstructive sleep apnea severity. Results: One hundred ninety-three patients met the inclusion criteria, and 145 of the 193 patients were diagnosed with obstructive sleep apnea. Multiple logistic regression analysis showed that large neck circumference (p = 0.0054) and high snoring index (p = 0.0119) were independent predictors for obstructive sleep apnea. Moreover, between the obstructive sleep apnea severity groups, there was a strong tendency of difference in body mass index (p = 0.0441) and neck circumference (p = 0.0846). However, there was no significant difference in snoring frequency according to obstructive sleep apnea severity (p = 0.4914). Conclusions: We confirmed that snoring frequency is a predictor of obstructive sleep apnea. In addition, we showed for the first time that snoring frequency is not associated with obstructive sleep apnea severity, thus it is not a valuable marker for predicting obstructive sleep apnea severity.

• Therapeutic Science for Rehabilitation
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• v.10 no.4
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• pp.7-24
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• 2021

Objective : This study examined the characteristics of the literature involving a single-subject research design among positive behavior support intervention studies to improve problem behavior in children with autism spectrum disorders and assess the quality level. Methods : This is a literature study, and the targets of analysis were nine single-subject research papers published between 2011 and 2020. The subject papers were analyzed by dividing them into general characteristics and the qualitative levels according to the content of the research method. Results : Analysis of analyzing the contents of the study showed that the subjects were preschool and elementary school-age children at the same ratio. Furthermore, the study design involved mostly the middle and multiple baseline designs among the behaviors. All papers presented social validity, intervention fidelity, and observer reliability. Problem behaviors included self-injury and aggression behaviors, disturbing behaviors, and seat break-away behaviors, while the most dependent variables were measured through partial interval recording. As a result of confirming the intervention effect, the effect was confirmed in intervention, maintenance, and generalization. All analysis studies showed high-quality levels. Conclusion : This study confirmed the content and qualitative level of the thesis that applied the single-subject research design among positive behavior support intervention studies for problem behaviors of children with autism spectrum disorders. Positive behavior support intervention, an evidence-based intervention for children with autism spectrum disorders, was confirmed an effective intervention for autism spectrum disorders.