• Journal of Periodontal and Implant Science
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• v.54 no.1
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• pp.3-12
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• 2024

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• Journal of Sport and Applied Science
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• v.8 no.3
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• pp.1-6
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• 2024

Purpose: This study aims to verify the effectiveness of acute anodal transcranial direct current stimulation (A-tDCS) using Halo Sport headset device on golf performance in professional golfers. Research design, data, and methodology: Eight professional golfers who voluntary participated in high-level golf tournaments were recruited in this study. They attended one single-session intervention which was stimulated by Halo Sport headset device (n=8). The A-tDCS halo sport session lasted for 20 minutes and stimulated on the motor control area of the cortex (M1). Four golf swing performance tasks (driver, iron, 100-yard shot, 50-yard shot) were performed before and after halo intervention. Key indicators of golf swing performance (Club Speed, Face Angle, Ball Speed, Smash Factor, Spin Rate, Side, Carry, Total) were collected by Trackman launch monitor. Results: In Halo session, there were no found statistical significance in driver, iron and approach (100, 50 yards) after intervention (P<0.05). Even some of variables (face angle, smash factor, carry and total distance) in driver, distance accuracy of 100 yards and direction accuracy of 50 yards were slightly improved tendency, but it was not significant (P<0.05). Meanwhile, there was any enhance all of variables in iron. Conclusions: As a result, the current study concludes that the acute A-tDCS halo sport intervention has no effect on the positive golf performance improvement for professional players. Further implications were discussed.

• The Korean Journal of Emergency Medical Services
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• v.11 no.2
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• pp.51-66
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• 2007

Purpose: With a view to providing basic data to develop cardiopulmonary resuscitation education suitable for elementary students, the cardiopulmonary resuscitation education was conducted to grasp students' knowledge, skills accuracy and the attitude change before and after the education. Methods: Convenience sampling was made on fourth and fifth graders(total-35 students) of S elementary school located in K city, Chungcheongnam-do, and this was a pre-experiment research designed before and after choosing a single group. In terms of methods, specifically we, researchers ; 1) Handed out questionnaires to students directly to make them fill in firsthand and collected the questionnaires. 2) Utilized PPT materials based on 2005 AHA guideline and DVD materials of AHA, to give students theoretical education of cardiopulmonary resuscitation. We used Anne/SkillReporter$^{(R)}$ torso produced by Leardal Inc, and Little Anne to conduct practical education individually. 3) Asked students to give Anne/SkillReporter$^{(R)}$ torso cardiopulmonary resuscitation five times with the ratio of 30 : 2, and then one of researchers filled in the evaluation sheet individually. 4) Evaluated the accuracy of students' ability to perform the resuscitation based on the record of Anne/SkillReporter$^{(R)}$ integrated printer(which was the objective tool to grasp students' skills accuracy). 5) Gave out questionnaires to make students fill them in and then collected them. after completing the practical evaluation. Results: 1) In case of the attitude about cardiopulmonary resuscitation, Students' confidency rose from 19.28%(before the education) to 93.57(after the education)- which is a positive change. 2) As the result of the education, some elementary students scored 11 points (full score-16 points), up from 5 points before the education, in terms of the knowledge about cardiopulmonary resuscitation. The average point also reached 13.14 points(after the education), jump from 8.37(before the education), which was the rise of 29.8%. 3) When it comes to the practical performance, the skills accuracy was 80.93% on average, and the calculation method was as follows: total items were 16, and each item was marked form 0 to 2 points, meaning the full score was 32 points. The minimum score was 19 points and the maximum was 32($M{\pm}SD=25.90{\pm}2.88$), which was calculated based on percentage. 4) Regarding skills accuracy, respiration accuracy(%)($M{\pm}SD=30.20{\pm}27.16$) was higher than pressure accuracy(%) ($M{\pm}SD=15.34{\pm}25.27$). Conclusion: The result showed that students' attitude on cardiopulmonary resuscitation changed positively. and meaningful difference(p = .00) existed in the change of students' knowledge. In terms of skills accuracy. chest compression and airway control showed high accuracy, but the result of Anne/SkillReporter$^{(R)}$ performance showed that the accuracy of chest compression was lower than that of mouth-to-mouth resuscitation.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4E
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• pp.144-151
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• 2005

We evaluate the performance of emotion recognition via speech signals when a plain speaker talks to an entertainment robot. For each frame of a speech utterance, we extract the frame-based features: pitch, energy, formant, band energies, mel frequency cepstral coefficients (MFCCs), and velocity/acceleration of pitch and MFCCs. For discriminative classifiers, a fixed-length utterance-based feature vector is computed from the statistics of the frame-based features. Using a speaker-independent database, we evaluate the performance of two promising classifiers: support vector machine (SVM) and hidden Markov model (HMM). For angry/bored/happy/neutral/sad emotion classification, the SVM and HMM classifiers yield $42.3\%\;and\;40.8\%$ accuracy, respectively. We show that the accuracy is significant compared to the performance by foreign human listeners.

• Journal of the Ergonomics Society of Korea
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• v.24 no.2
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• pp.45-49
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• 2005

The FSA(Force Sensitive Application) system measures hand force by using force resistance sensors. Compared to conventional hand force measurement systems such as Lafayette hand dynamometer and Jamar hydraulic hand dynamometer, the FSA system can be applied to analyze use of hand forces while the hand is manipulating objects for a task, However, the measurement performance of the FSA system has not been objectively evaluated. The present study tested the FSA system in terms of stability, repeatability, accuracy, and linearity. It is shown that the FSA system has good stability (CV$\leq$0.02) and linearity($R^2$=0.82), but has low repeatability(CV=$0.11{\sim}0.19$) and accuracy(22% of underevaluation on average). This performance result indicates that measurements from the FSA system should be used for relative comparison rather than for absolute comparison.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.1
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• pp.39-44
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• 2010

A method is presented of dynamic determination of mode transition probability for IMM in order to improve the accuracy performance of maneuvering target tracking for air traffic control surveillance processing system under multiple radar environment. It is shown that dynamic determination of mode transition probability based on the time intervals between the data input from multiple radars gives the optimized performance in terms of position estimation accuracy.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.212-216
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• 2003

The performance of active power filter depends on the inverter characteristic, the control method, and the accuracy of reference signal generator. The accuracy of reference generator is the most critical item to determine the performance of active power filter. This paper introduces a novel reference signal generator composed of improved adaptive predictive filter. The performance of proposed reference signal generator was verified by means of simulation with MATLAB. The simulation result confirm that the proposed reference signal generator can be utilized for the active power filter.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.575-578
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• 2004

The performance of an active power filter(APF) depends on the inverter characteristic, the control method, and the accuracy of reference signal generator. The accuracy of reference generator is the most critical item to determine the performance of active power filter. This paper introduces a novel reference signal generator composed of improved adaptive predictive filter. The performance of proposed reference signal generator was first verified through a simulation with MATLAB. Furthermore, the application of feasibility was evaluated through experimenting with a single-phase APF prototype based on the proposed reference generator, which was implemented using the TMS320C31 floating-point signal processor. Both simulations and experimental results confirm that our reference signal generator can be used successfully in practical active power filters.