• 재활복지
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• v.20 no.3
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• pp.83-104
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• 2016

The purpose of this study was to identify improvement in locomotor skills by physical activity programs. Method of this study indicates that the current literature (2004-2015) were reviewed and the data from 24 studies with 518 disabled children were analyzed by using CMA3 (Comprehensive Meta-Analysis ver.3) program. Analyzing the data of the primary studies included gender, age, type of disabilities, duration of the physical activity program intervention(weeks, session per week, minutes per session), run, gallop, hop, leap, horizontal jump, and slide. For sensitivity analysis, publication bias and outlier were reviewed. Results of analysis indicates that the overall effect size of improvement in locomotor skills by physical activity programs was 1.143. There were large effect size in categorical analyses. Autistic spectrum among type of disabilities was 1.697 and run among 6 of locomotor skills was 1.019. 8~10 aged was 0.920 and the intervention of 100~120minutes(1.261)per session, 3sessions(1.078) per week, 16~20(1.587)weeks was found to be more larger than the others. In conclusion, improvement in locomotor skills by program participation showed that treated group was 37% more effective than control group.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.135-153
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• 2018

E-Card Service for Child Nutrition Program is one of the main public policy services nowadays. In case of inconvenience during the use of the e-cards, it is recommended to cooperate with related organizations in order to promptly handle and provide guidance, and thoroughly manage child feeding service such as hygiene, nutrition and kindness etc. To do so, it is very important to provide food service that meets local actual conditions and children's needs in a cost effective manner for the underage who are worried about the poorly-fed by understanding the pattern of child feeding e-card service. Hence. this paper aims to investigate how child feeding e-card service efficiently provides meals according to the local situation and children's needs through big data analysis and to propose a method of identifying welfare conditions according to the purpose of service with actual application examples. The results suggest that, first of all, this study is able to judge appropriateness of public institution's policy in a timely and repetitive manner through non-standard data analysis such as Naver News and transaction data. Secondly, this paper proposes a multi-layered analysis framework, which performs online open data analysis to detect policy issues, visualizes retrieval and preprocessing of real data, and performs abnormal pattern recognition. These will be worthy of reference to other similar projects.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.183-192
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• 2012

This study analyzes the travel behavior for short-term demand forecasting model of KTX. This research suggests the following. First, the outlier criteria is considered to appropriate twice the standard deviation of the traffic. Second, the result of a homogeneity test using ANOVA analysis has been divided into weekdays(Mon Thu and weekends(Fri Sun). Third, a cluster analysis for O/D pairs using trip frequency, traffic averages and th distance between stations was performed.

• Journal of Ocean Engineering and Technology
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• v.20 no.3 s.70
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• pp.54-60
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• 2006

For optimal design of a deep-sea ocean mining collector system, based on self-propelled mining vehicle, it is imperative to develop and validate the dynamic model of a tracked vehicle traveling on soft deep seabed. The purpose of this paper is to evaluate the fidelity of the dynamic simulation model by means of response surface methodology. Various statistical techniques related to response surface methodology, such as outlier analysis, detection of interaction effect, analysis of variance, inference of the significance of design variables, and global sensitivity analysis, are examined. To obtain a plausible response surface model, maximum entropy sampling is adopted. From statistical analysis and prediction for dynamic responses of the tracked vehicle, conclusions will be drawn about the accuracy of the dynamic model and the performance of the response surface model.

• Restorative Dentistry and Endodontics
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• v.46 no.3
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• pp.37.1-37.11
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• 2021

Objectives: This systematic review evaluated the efficacy of the supplementary use of the XP-endo Finisher on bacteria content reduction in the root canal system. Materials and Methods: In-vitro studies evaluating the use of the XP-endo Finisher on bacteria content were searched in four databases in July 2020. Two authors independently screened the studies for eligibility. Data were extracted, and risk of bias was assessed. Data were meta-analyzed by using random-effects model to compare the effect of the supplementary use (experimental) or not (control) of the XP-endo Finisher on bacteria counting reduction, and results from different endodontic protocols were combined. Four studies met the inclusion criteria while 1 study was excluded from the meta-analysis due to its high risk of bias and outlier data. The 3 studies that made it to the meta-analysis had an unclear risk of bias for at least one criterion. Results: No heterogeneity was observed among the results of the studies included in the meta-analysis. The study excluded from the meta-analysis assessing the bacteria counting deep in the dentin demonstrated further bacteria reduction upon the use of the XP-endo Finisher. Conclusions: This systematic review found no evidence supporting the supplementary use of the XP-endo Finisher on further bacteria counting the reduction in the root canal.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.55-65
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• 2011

In this paper, a novel method of marker detection robust against marker occlusion in augmented reality is proposed. the proposed method consists of four steps. In the first step, in order to effectively detect an occluded marker, we first utilize the Affine-SIFT (ASIFT, Affine-Scale Invariant Features Transform) for detecting matching points between an enrolled marker and an input images with an occluded marker. In the second step, we apply the Principal Component Analysis (PCA) for eliminating outlier of the matching points in the enrolled marker. And then matching points are projected to the first and second axis for longest value and the shortest value of an ellipse are determined by average distance between the projected points and a center of the points. In the third step, Convex-hull vertices including matching points are considered as polygon vertices for estimating a geometric affine transformation. In the final step, by estimating the geometric affine transformation of the points, a marker robust against a marker occlusion is detected. Experimental results have shown that the proposed method effectively detects occlude markers.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.519-527
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• 2003

Speech is much influenced by the existence of outliers which are introduced by such an unexpected happenings as additive background noise, change of speaker's utterance pattern and voice detection errors. These kinds of outliers may result in severe degradation of speaker recognition performance. In this paper, we proposed the GMM based on robust principal component analysis (RPCA-GMM) using M-estimation to solve the problems of both ouliers and high dimensionality of training feature vectors in speaker identification. Firstly, a new feature vector with reduced dimension is obtained by robust PCA obtained from M-estimation. The robust PCA transforms the original dimensional feature vector onto the reduced dimensional linear subspace that is spanned by the leading eigenvectors of the covariance matrix of feature vector. Secondly, the GMM with diagonal covariance matrix is obtained from these transformed feature vectors. We peformed speaker identification experiments to show the effectiveness of the proposed method. We compared the proposed method (RPCA-GMM) with transformed feature vectors to the PCA and the conventional GMM with diagonal matrix. Whenever the portion of outliers increases by every 2%, the proposed method maintains almost same speaker identification rate with 0.03% of little degradation, while the conventional GMM and the PCA shows much degradation of that by 0.65% and 0.55%, respectively This means that our method is more robust to the existence of outlier.

• Smart Structures and Systems
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• v.21 no.5
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.6
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• pp.394-399
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• 2005

Near Infrared Spectroscopy (NIRS) has been used as a tool for the rapid, accurate and nondestructive assay of the fresh rice leaf in nitrogen content. NIRS used in this study was visible and near infrared spectroscopy type instrument, Foss model 6500. To obtain a useful calibration equation, standard regression between the data was analyzed by chemical analysis and by NIRS method. Accuracy of calibration equation for nitrogen content on fresh leaf of rice were 0.879, 0.858 and 0.819, respectively. Accuracy of calibration equation after outlier treatment increased as 0.017, 0.02 and 0.061 improved each with 0.896, 0.878 and 0.880, respectively. Calibration equation combined using merge function after accuracy of calibration equation more increased by 0.911. Difference analysis value between calibration equation and lab value by kjeldahl showed $0.001\%$. With this as same result is the possibility of closing the deterioration of the sample in order to omit a construction and pulverization process it is judged with the fact that the nitrogen content measurement of the fresh rice leaf which the possibility of reducing an hour and an expense is by a near infrared spectroscopy technique will be possible.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.539-556
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• 2019

This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, "no matter what regression lines" and "no matter what the number of change points".