• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.13-20
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• 2022

It's proposed and analyzed ML(Machine Learning) models to predict vehicle FC(Fuel Consumption) in real-time. The test driving was done for a car to measure vehicle speed, acceleration, road gradient and FC for training dataset. The various ML models were trained with feature data of speed, acceleration and road-gradient for target FC. There are two kind of ML models and one is regression type of linear regression and k-nearest neighbors regression and the other is classification type of k-nearest neighbors classifier, logistic regression, decision tree, random forest and gradient boosting in the study. The prediction accuracy is low in range of 0.5 ~ 0.6 for real-time FC and the classification type is more accurate than the regression ones. The prediction error for total FC has very low value of about 0.2 ~ 2.0% and regression models are more accurate than classification ones. It's for the coefficient of determination (R²) of accuracy score distributing predicted values along mean of targets as the coefficient decreases. Therefore regression models are good for total FC and classification ones are proper for real-time FC prediction.

• Archives of Plastic Surgery
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• v.49 no.4
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• pp.488-493
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• 2022

Background Currently, the BREAST-Q can effectively measure patient's satisfaction on the quality of life from the patient's perspective in relation to different type of breast reconstruction. However, evaluation of patient satisfaction and cosmetic outcomes in breast reconstruction may have potential to led bias. Methods To maximize the benefits of using BREAST-Q to evaluate clinical outcome, we performed comparative study focused on the correlation between postoperative BREAST-Q and cosmetic outcomes assessed by medical professionals. For the current analysis, we used three postoperative BREAST-Q scales (satisfaction with breast, psychosocial well-being, and sexual well-being). The Ten-Point Scale by Visser et al was applied to provide reproducible grading of the postoperative cosmetic outcomes of the breast. The system includes six subscales that measured overall aesthetic outcome, volume, shape, symmetry, scarring, and nipple-areolar complex. The photographic assessments were made by five medical professionals who were shown photographs on a computer screen in a random order. Obtained data were stored in Excel and evaluated by Spearman's correlations using SPSS Statistics. Results We enrolled 92 women in this study, 10 did not respond to all scales of postoperative BREAST-Q, the remaining 82 women had undergone breast reconstruction. The correlation between BREAST-Q score and aesthetic score measured by Ten-Point Scale for the three BREAST-Q scales all show positive values in Spearman's correlation coefficient. Conclusion A significant correlation without any bias observed was found between the patient's satisfaction measured by BREAST-Q after breast reconstruction and the medical expert's aesthetic evaluation.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.327-341
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• 2022

Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• The Journal of Korean Physical Therapy
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• v.35 no.1
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• pp.13-18
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• 2023

Purpose: The purpose of this study was to investigate the effects of posterior oblique sling activation on the muscle activities of the gluteus maximus (GM), multifidus (MF), and biceps femoris (BF) during three different prone hip extension exercises in healthy male individuals. Methods: Twenty healthy subjects participated in this study. An electromyography device was used to measure the muscle activities of the GM, MF, and BF. Each subject was asked to perform three different prone hip extensions as follows: [1) Prone hip extension with knee flexion + hip abduction 30°; PHE1, 2) Prone hip extension with knee flexion + hip abduction 30° and shoulder abduction 125°; PHE2, 3) Prone hip extension with knee flexion + hip abduction 30° and shoulder abduction 125° with 1kg loading; PHE3, in random order. A one-way repeated measures analysis of the variance and a Bonferroni post hoc test were used to analyze the results. The statistical significance was set at α=0.01. Results: The muscle activity of the GM was significantly different between the three positions (P_adj<0.01). The muscle activity of the GM was significantly greater during PHE3 compared with PHE1 and PHE2 (P_adj<0.01). The BF muscle activity was significantly lower during PHE3 compared with PHE1 and PHE2 (P_adj< 0.01). There was no significant difference in the muscle activity of the MF (P_adj<0.01). The ratio of the muscle activity (ratio=GM/BF) during PHE3 was significantly greater compared to PHE1 and PHE2 (P_adj< 0.01). Conclusion: The GM activity and GM/BF ratio during the PHE3 exercise were significantly greater compared to that during PHE1 and PHE2. Therefore, the PHE3 exercise could be recommended as a selectively effective GM activation exercise while decreasing the muscle activity of the BF.

• Structural Engineering and Mechanics
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• v.81 no.2
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• pp.219-230
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• 2022

Due to the large number of railway bridges along China's high-speed railway (HSR) lines, which cover a wide area with many lines crossing the seismic zone, the possibility of a HSR train running over a bridge when an earthquake occurs is relatively high. Since the safety performance of the train will be threatened, it is necessary to study the safety of trains running over HSR bridges during earthquakes. However, ground motion (GM) is highly random and selecting the appropriate ground-motion intensity measures (IMs) for train running safety analysis is not trivial. To deal this problem, a model of a coupled train-bridge system under seismic excitation was established and 104 GM samples were selected to evaluate the correlation between 16 different IMs and train running safety over HSR bridges during earthquakes. The results show that spectral velocity (SvT₁) and displacement (SdT₁) at the fundamental period of the structure have good correlation with train running safety for medium-and long-period HSR bridges, and velocity spectrum intensity (VSI) and Housner intensity (HI) have good correlation for a wide range of structural periods. Overall, VSI and HI are the optimal IMs for safety analysis of trains running over HSR bridges during earthquakes. Finally, based on VSI and HI, the IM thresholds of an HSR bridge at different speed were analyzed.

• Asia Marketing Journal
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• v.15 no.1
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• pp.155-168
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• 2013

This study describes consumers' movie choices in conjunction with other group members and attempts to reassess the effect of the online word of mouth (WOM) source in a joint decision context. The tendency of many people to go to movies in groups has been mentioned in previous literature but there is no modeling research that studies movie choice from the group decision perspective. We found that ignoring the group movie-going perspective can result in a misunderstanding, especially underestimation of genre preference and the impact of the WOM variables. Most of the studies to measure online WOM effects were done at the aggregate level, and the role of online WOM variables(volume vs valence) is mixed in the literature. We postulate that group-level analysis might offer insight to resolve these mixed understanding of WOM effects in the literature. We implemented the study via a random effect model with group-level heterogeneity. Romance, drama, and action were selected as genre variables; valence and volume were selected as online WOM variables. A choice-based conjoint survey was used for data collection and the models was estimated via Bayesian MCMC method. The empirical results show that (i) both genre and online WOM are important variables when consumers choose movies, especially as group, and (ii) the WOM valence effect are amplified more than the volume effect does as individuals are engaged in group decision. This research contributes to the literature in several ways. First, we investigate movie choice from a group movie-going perspective that is more realistic and consistent with the market behavior. Secondly, the study sheds new light on the WOM effect. At group-level, both valence and volume significantly affect movie choices, which adds to the understanding of the role of online WOM in consumers' movie choice.

• The korean journal of orthodontics
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• v.53 no.6
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• pp.374-392
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• 2023

Objective: Assess and evaluate the different indicators of oral health-related quality of life (OHRQoL) among patients treated with clear aligners (CAs) versus those treated with conventional fixed orthodontics (FAs). Methods: An electronic search was performed on the database is Web of Science, Scopus, and Embase databases. Randomized and non-randomized control trials, cross-sectional, prospective cohort and retrospective trials were included. Quality was assessed with risk of bias tool and risk of bias in non-randomised studies. Meta-analyses were performed with random effects models, estimating the standardized and non-standardized mean differences, odds ratio and risk ratio as the measure of effect. The effect on time was determined using a meta-regression model. Results: Thirty one articles were included in the qualitative synthesis and 17 in the meta-analysis. CAs had a significantly lower negative impact on QoL, with an "important" effect size, while the influence of time was not significant. Periodontal indicators plaque index (PI), gingival index (GI), probing depth (PD), and bleeding on probing show significantly better values in patients treated with CAs, with moderate to large effect sizes. PI and GI have a significant tendency to improve over time. In microbiological indicators, CAs present a lower biofilm mass without differences in the percentage of patients with high counts of Streptococcus mutans and Lactobacilli bacteria. The risk of white spot lesion onset is ten times lower in carriers of CAs. Conclusions: Patients wearing CAs show better periodontal indicators, less risk of white spot development, less biofilm mass and a better QoL than patients with FAs.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.663-675
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• 2023

Purpose: Skewness is an indicator used to measure the asymmetry of data distribution. In the past, product quality was judged only by mean and variance, but in modern management and manufacturing environments, various factors and volatility must be considered. Therefore, skewness helps accurately understand the shape of data distribution and identify outliers or problems, and skewness can be utilized from this new perspective. Therefore, we would like to propose a statistical quality control method using skewness. Methods: In order to generate data with the same mean and variance but different skewness, data was generated using normal distribution and gamma distribution. Using Minitab 18, we created 20 sets of 1,000 random data of normal distribution and gamma distribution. Using this data, it was proven that the process state can be sensitively identified by using skewness. Results: As a result of the analysis of this study, if the skewness is within ± 0.2, there is no difference in judgment from management based on the probability of errors that can be made in the management state as discussed in quality control. However, if the skewness exceeds ±0.2, the control chart considering only the standard deviation determines that it is in control, but it can be seen that the data is out of control. Conclusion: By using skewness in process management, the ability to evaluate data quality is improved and the ability to detect abnormal signals is excellent. By using this, process improvement and process non-sub-stitutability issues can be quickly identified and improved.

• Journal of Korean Academy of Nursing
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• v.12 no.2
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• pp.1-26
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• 1982

The main purpose of this study was to test the effect of the structured information on the sleep amount of the patients undergoing open heart surgery. This study has specifically addressed to the Following two basic research questions: (1) Would the structed in formation influence in the reduction of sleep disturbance related to anxiety and Physical stress before and after the operation? and (2) that would be the effects of the structured information on the level of preoperative state anxiety, the hormonal change, and the degree of behavioral change in the patients undergoing an open heart surgery? A Quasi-experimental research was designed to answer these questions with one experimental group and one control group. Subjects in both groups were matched as closely as possible to avoid the effect of the differences inherent to the group characteristics, Baseline data were also. collected on both groups for 7 days prior to the experiment and found that subjects in both groups had comparable sleep patterns, trait anxiety, hormonal levels and behavioral level. A structured information as an experimental input was given to the subjects in the experimental group only. Data were collected and compared between the experimental group and the control group on the sleep amount of the consecutive pre and post operative days, on preoperative state anxiety level, and on hormonal and behavioral changes. To test the effectiveness of the structured information, two main hypotheses and three sub-hypotheses were formulated as follows; Main hypothesis 1: Experimental group which received structured information will have more sleep amount than control group without structured information in the night before the open heart surgery. Main hypothesis 2: Experimental group with structured information will have more sleep, amount than control group without structured information during the week following the open heart surgery Sub-hypothesis 1: Experimental group with structured information will be lower in the level of State anxiety than control group without structured information in the night before the open heart surgery. Sub-hypothesis 2 : Experimental group with structured information will have lower hormonal level than control group without stuctured information on the 5th day after the open heart surgery Sub-hypothesis 3: Experimental group with structured information will be lower in the behavioral change level than control group without structured information during the week after the open heart surgery. The research was conducted in a national university hospital in Seoul, Korea. The 53 Subjects who participated in the study were systematically divided into experimental group and control group which was decided by random sampling method. Among 53 subjects, 26 were placed in the experimental group and 27 in the control group. Instruments; (1) Structed information: Structured information as an independent variable was constructed by the researcher on the basis of Roy's adaptation model consisting of physiologic needs, self-concept, role function and interdependence needs as related to the sleep and of operational procedures. (2) Sleep amount measure: Sleep amount as main dependent variable was measured by trained nurses through observation on the basis of the established criteria, such as closed or open eyes, regular or irregular respiration, body movement, posture, responses to the light and question, facial expressions and self report after sleep. (3) State anxiety measure: State Anxiety as a sub-dependent variable was measured by Spi-elberger's STAI Anxiety scale, (4) Hormornal change measure: Hormone as a sub-dependent variable was measured by the cortisol level in plasma. (5) Behavior change measure: Behavior as a sub-dependent variable was measured by the Behavior and Mood Rating Scale by Wyatt. The data were collected over a period of four months, from June to October 1981, after the pretest period of two months. For the analysis of the data and test for the hypotheses, the t-test with mean differences and analysis of covariance was used. The result of the test for instruments show as follows: (1) STAI measurement for trait and state anxiety as analyzed by Cronbachs alpha coefficient analysis for item analysis and reliability showed the reliability level at r= .90 r= .91 respectively. (2) Behavior and Mood Rating Scale measurement was analyzed by means of Principal Component Analysis technique. Seven factors retained were anger, anxiety, hyperactivity, depression, bizarre behavior, suspicious behavior and emotional withdrawal. Cumulative percentage of each factor was 71.3%. The result of the test for hypotheses show as follows; (1) Main hypothesis, was not supported. The experimental group has 282 minutes of sleep as compared to the 255 minutes of sleep by the control group. Thus the sleep amount was higher in experimental group than in control group, however, the difference was not statistically significant at .05 level. (2) Main hypothesis 2 was not supported. The mean sleep amount of the experimental group and control group were 297 minutes and 278 minutes respectively Therefore, the experimental group had more sleep amount as compared to the control group, however, the difference was not statistically significant at .05 level. Thus, the main hypothesis 2 was not supported. (3) Sub-hypothesis 1 was not supported. The mean state anxiety of the experimental group and control group were 42.3, 43.9 in scores. Thus, the experimental group had slightly lower state anxiety level than control group, howe-ver, the difference was not statistically significant at .05 level. (4) Sub-hypothesis 2 was not supported. . The mean hormonal level of the experimental group and control group were 338 ㎍ and 440 ㎍ respectively. Thus, the experimental group showed decreased hormonal level than the control group, however, the difference was not statistically significant at .05 level. (5) Sub-hypothesis 3 was supported. The mean behavioral level of the experimental group and control group were 29.60 and 32.00 respectively in score. Thus, the experimental group showed lower behavioral change level than the control group. The difference was statistically significant at .05 level. In summary, the structured information did not influence the sleep amount, state anxiety or hormonal level of the subjects undergoing an open heart surgery at a statistically significant level, however, it showed a definite trends in their relationships, not least to mention its significant effect shown on behavioral change level. It can further be speculated that a great degree of individual differences in the variables such as sleep amount, state anxiety and fluctuation in hormonal level may partly be responsible for the statistical insensitivity to the experimentation.