• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.293-302
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• 2019

This study systematically analyzed computer-based driving intervention programs for seniors, to provide the academic background for driving intervention for seniors. Articles published from January 2009 till December 2018 were researched and analyzed. 'PubMed, Google Scholar, and Science Direct' were used to search articles published overseas, and 'RISS, KERIS, and KISS' searched for articles published in Korea. Based on the inclusion and exclusion criteria, totally 359 papers were retrieved, and 10 articles were finally analyzed; 8 articles (80%) were evidence level I, and 2 articles (20%) were evidence level III. Amongst the computer-based interventions, driving simulators (70%) were the most common, followed by two video image training (20%) and one Nintendo Wii program (10%). In most studies, driving simulators trained the cognitive and visual abilities of seniors and enhanced their abilities to cope with risk situations under various simulated circumstances. Other interventions were also reported to have a positive effect. For evaluating elderly drivers, the driving performance evaluation using a driving simulator was the most common; in addition, evaluations of attention, space-time ability, cognitive function, risk perception, depression and anxiety were also commonly used. We believe that it is appropriate to employ computer-based driving intervention programs for seniors to train and evaluate various domains. We expect that these interventions can be used as an effective tool for safe driving.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.99-108
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• 2021

There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.376-386
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• 2023

Due to climate changes, landslide hazards in the Republic of Korea (hereafter South Korea) continuously increase. To establish the effective landslide mitigation strategies, such as erosion control works, landslide hazard estimation in the long-term perspective should be proceeded considering the influence of climate changes. In this study, we examined the change in landslide-damaged areas in South Korea responding to climate change scenarios using the multivariate regression method. Data on landslide-damaged areas and rainfall from 1981-2010 were used as a training dataset. Sev en indices were deriv ed from rainfall data as the model's input data, corresponding to rainfall indices provided from two SSP scenarios for South Korea: SSP1-2.6 and SSP5-8.5. Prior to the multivariate regression analysis, we conducted the VIF test and the dimension analysis of regression model using PCA. Based on the result of PCA, we developed a regression model for landslide damaged area estimation with two principal components, which cov ered about 93% of total v ariance. With climate change scenarios, we simulated landslide-damaged areas in 2030-2100 using the regression model. As a result, the landslide-damaged area will be enlarged more than the double of current annual mean landslide damaged area of 1981-2010; It infers that landslide mitigation strategies should be reinforced considering the future climate condition.

• Restorative Dentistry and Endodontics
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• v.30 no.1
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• pp.38-48
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• 2005

The purpose of this study was to compare and evaluate the shaping ability of the three different Ni-Ti file systems used by undergraduate students. Fifty undergraduate students prepared 150 simulated curved root canals in resin blocks with three Ni-Ti file systems - $ProFile^{(R)}$ (PF), Manual $ProTaper^{(R)}$ (MPT), Rotary $ProTaper^{(R)}$ (RPT). Every student prepared 3 simulated root canals with each system respectively. After root canal preparation, the Ni-Ti files were evaluated for distortion or breakage Assessments were made according to the presence of various types of canal aberrations. The pre- and post-instrumented canal images were attained and superimposed. The instrumented root canal width were measured and calculated for the net transportation (deviation) and the centering ratio. Under the condition of this study, both $ProTaper^{(R)}$ systems allowed significantly more removal of root canal wall than the $ProFile^{(R)}$ system. In the important other aspects such as the centering ratio, there was no significant differences between the systems. Novice dental students were able to prepare curved root canals with any kinds of Ni-Ti file systems with little aberration and great conservation of tooth structure. Students want to learn effective methods and at the same time simple rotary procedures. The rotary $ProTaper^{(R)}$ systems were one of the most compatible to these students from the point of view of cutting ability The $ProFile^{(R)}$ system was also compatible in safe and gentle shaping.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Journal of Dental Rehabilitation and Applied Science
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• v.28 no.4
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• pp.371-383
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• 2012

Shaping the root canal system to maintain original canal curvature is essential to clinical success in endodontic treatment. Opposed to most root canals that are curved, endodontic instruments are made from straight metal blanks. They have a tendency of straightening the root canal during preparation and frequently result in procedural errors. A new treatment method to maintain original canal curvature during shaping has been introduced for preventing procedural errors. The aim of this study was to compare the ability of instruments to maintain original canal curvature of continuous rotary system and single file system. Thirty ISO 15, 0.02 taper, Endo Training Blocks(Dentsplay Maillefer) were used. Specimens were assigned to 1 of 3 groups for shaping: specimens in group 1 were shaped with ProFile #20/.06 at the WL. Specimens in group 2 were shaped with Mtwo #35/.04 at the WL. Specimens in group 3 were shaped with WaveOne Primary reciprocating files at the WL after the glide path was achieved with PathFile. Pre- and postinstrumentation digital images were superimposed and processed with Matlab r2010b(The MathWorks Inc, Natick, MA) software to analyze the curvature-radius ratio(CRr), representing canal curvature modification. Data for comparison on the ability of instruments to maintain original canal curvature depending on each Ni-Ti file were analyzed with 1-way ANOVA(P<.05). Data for comparison on the ability of instruments to maintain original canal curvature depending on each Ni-Ti file system were analyzed with independent t-test(P<.05). A statistically significant difference(P<0.05) was noted on each Ni-Ti file. ProFile and WaveOne instrumentations maintained the original canal curvature significantly better(P<0.05) than Mtwo file. There were no significant difference(P>0.05) between continuous rotary system and single file system. Under the conditions of this study, ProFile and WaveOne instruments maintained the original curvature significantly better than Mtwo file and were less modification of the canal curvature compared. There was no significant difference between continuous rotary system and single file system in shaping of simulated canals. As clinical practitioners, it may be advantages to use hybrid approach when root canal shapes depending on the design and usage of Ni-Ti files.

• Restorative Dentistry and Endodontics
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• v.32 no.4
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• pp.385-396
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• 2007

The aims of this study were to compare the shaping effect and safety between single length technique recommended by manufacturer and crown-down technique using Mtwo rotary file and to present a modified method in use of Mtwo file. Sixty simulated root canal resin blocks were used. The canals were divided into three groups according to instrument and the manner of using methods. Each group had 20 specimens. Group MT was instrumented with single length technique of Mtwo, group MC was instrumented with crown-down technique of Mtwo and group PT was instrumented with crown-down technique of ProTaper. All of the rotary files used in this study were operated by an electric motor. The scanned canal images of before and after preparation were superimposed. These superimposed images were evaluated at apical 1 to 8 mm levels Angle changes were calculated. The preparation time, weight loss, instrument failure and binding, canal aberrations, and centering ratio were measured. Statistical analysis of the three experimental groups was performed with ANOVA and Duncan's multiple range tests for post-hoc comparison and Fisher's exact test was done for the frequency comparison. In total preparation time, group MT and group MC were less than group PT. In the aberrations, group MT had more elbows than those of group MC and group PT. The binding of group MC was least and group MT was less than group PT (P < 0.05). Under the condition of this study, crown-down technique using Mtwo rotary file is better and safer method than single length technique recommended by the manufacturer.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.