• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.549-553
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• 2002

Although the magnetostrictive sensors have received much attention in recent years. the investigations on the selection of a desired mode have not been reported. The purpose of this investigation is to present a technique to select a desired mode in a solid ferromagnetic cylinder using a non-contact magnetostrictive sensor. To achieve this goal. we propose new bias magnet configurations to select longitudinal and flexural waves. A few experimental results confirm the validity of the present investigation.

• Journal of Korea Foundry Society
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• v.26 no.1
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• pp.27-33
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• 2006

Phase evolutions involving nucleation stages together with diffusional growth have been examined in order to provide a guideline for determining rate limiting stages during phase evolutions. In multiphase materials systems in coatings, composites or multilayered structures, diffusion treatments often result in the development of metastable/intermediate phases at the reaction interfaces. The development of metastable phases during solid state interdiffusion demonstrates that the nucleation reaction can be one controlling factor. Also, the concentration gradient and the relative magnitudes of the component diffusivities provide a basis for a phase selection and the application of a kinetic bias strategy in the phase selection. For multicomponent alloy systems, the identification of the operative diffusion pathway is central to control phase formation. Experimental access to the nucleation and growth stage is discussed in thin film multi layers and bulk samples.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.47-55
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• 2019

It is important to identify informative variables in high dimensional data analysis; however, it becomes a challenging task when covariates are contaminated by measurement error due to the bias induced by measurement error. In this article, we present a two-step approach for variable selection in the presence of measurement error. In the first step, we directly select important variables from the contaminated covariates as if there is no measurement error. We then apply, in the following step, orthogonal regression to obtain the unbiased estimates of regression coefficients identified in the previous step. In addition, we propose a modification of the two-step approach to further enhance the variable selection performance. Various simulation studies demonstrate the promising performance of the proposed method.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.11-22
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• 2023

In this paper, we propose an efficient satellite selection method for multi-constellation GNSS. The number of visible satellites has increased dramatically recently due to multi-constellation GNSS. By the increased availability, the overall GNSS performance can be improved. Whereas, due to the increase of the number of visible satellites, the computational burden in implementing advanced processing such as integer ambiguity resolution and fault detection can be increased considerably. As widely known, the optimal satellite selection method requires very large computational burden and its real-time implementation is practically impossible. To reduce computational burden, several sub-optimal but efficient satellite selection methods have been proposed recently. However, these methods are prone to the local optimum problem and do not fully utilize the information redundancy between different constellation systems. To solve this problem, the proposed method utilizes the inter-system biases and geometric assignments. As a result, the proposed method can be implemented in real-time, avoids the local optimum problem, and does not exclude any single-satellite constellation. The performance of the proposed method is compared with the optimal method and two popular sub-optimal methods by a simulation and an experiment.

• Survey Research
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• v.7 no.2
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• pp.53-69
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• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.

• Journal of Labour Economics
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• v.27 no.3
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• pp.1-23
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• 2004

To investigate the interdependence of the decisions on when to retire and how much consume before and after retirement, we compare the pre- (or post-) retirement consumption conditioned on the retirement decision with pre- (or post-) retirement consumption regardless of retirement decision by using the Korea Labor and Income Panel Study(KLIPS). We employ the two-stage switching regression for the econometric method to investigate the interdependence of two decisions of retirement and pre- or post retirement consumption. Then we test the existence of the interdependence in terms of the significance of the estimated selection biases which appear in the pre- (post-) retirement consumption equations for early and late retirees. In those equations, we also compare the income elasticity of the consumption of the early retirees with that of the late retirees. The empirical results show that there is negative selection bias in early retirees' consumption. These results imply that due to the early retirement decision early retirees would have consumed less than they actually have. The income elasticities of the consumption of the early retirees is smaller than that of the late retirees in pre- (or post-) retirement consumption equation. This result shows that relatively longer retirement period due 10 the early retirement affect the pre-retirement consumption. early retirees' marginal propensity to consume should be lower than that of the late retirees.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.273-284
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• 2022

The goal of this paper is to propose the use of machine learning platforms in education to train learners to recognize data biases. Learners can cultivate the ability to recognize when learners deal with AI data and systems when they want to prevent damage caused by data bias. Specifically, this paper presents a method of data bias education using MachineLearningforKids, focusing on the case of AI baseball referee. Learners take the steps of selecting a specific topic, reviewing prior research, inputting biased/unbiased data on a machine learning platform, composing test data, comparing the results of machine learning, and present implications. Learners can learn that AI data bias should be minimized and the impact of data collection and selection on society. This learning method has the significance of promoting the ease of problem-based self-directed learning, the possibility of combining with coding education, and the combination of humanities and social topics with artificial intelligence literacy.

• Journal of the Korean Society of Physical Medicine
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• v.18 no.4
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• pp.109-119
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• 2023

PURPOSE: The definition and scope of biofeedback are broad and lack a clear framework. Therefore, efforts are needed to clearly understand the exact range and definition of biofeedback based on the research and development conducted to date. Thus, the purpose of this study was to arrive at the definition and scope of biofeedback through a literature review and analysis of its application methods. METHODS: This study is a systematic literature review conducted to understand the various types and effects of biofeedback. International databases such as Google Scholar and PubMed were used. Domestic databases utilized for keyword searches included the Research Information Sharing Service (RISS) and the National Digital Science Library (NDSL). Quality assessment of the selected studies in the selection process was done using the Cochrane risk of bias, and the research was analyzed according to the population, intervention, control, and outcomes (PICO) format. RESULTS: Studies conducted between 2019 and 2021 were selected, with 4 papers falling under physiological classifications and 7 under biomechanical classifications. The quality assessment results showed that random sequence generation, allocation concealment, performance bias, and reporting bias were unclear. Detection bias was moderate, and attrition bias and other biases were low. Out of the 11 papers, 9 dealt with physical function outcomes, 5 with daily life activities, and 3 with mental functions. CONCLUSION: Physiological biofeedback tended to influence psychological factors more than physical functions, while biomechanical biofeedback tended to have a positive impact on physical functions.

• Korean Journal of Acupuncture
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• v.37 no.3
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• pp.131-144
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• 2020

Objectives : The aim of this study is to analyze the details of acupuncture treatment methods and the reporting quality of acupuncture on Carpal Tunnel Syndrome (CTS). Methods : Search was conducted in Pubmed, EMBASE, and Cochrane Library for acupuncture studies on CTS. The reporting quality of acupuncture treatment was assessed using the following guidelines: Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA) for analyzing the method of acupuncture treatment, Consolidated Standards of Reporting Trials (CONSORT) for analyzing study design and study process, and Risk of Bias (ROB) for analyzing bias. The number of reported items was calculated and evaluated as a proportion. The reported proportion of each study was classified into three grades: Grade A (% score ≥75), Grade B (50≤ % score <75), and Grade C (% score <50). Results : A total of 9 Randomized Controlled Trials (RCTs) were included in this study. All trials reported 12 items (66.67%) on average in STRICTA guidelines. Five studies were conducted with manual acupuncture and 3 studies were conducted with electroacupuncture. PC7 (Daereung) was most frequently used to treat CTS. In STRICTA guideline evaluation, 3 studies were classified as Grade A, 5 studies were classified as Grade B, and 1 study was classified as Grade C. In the CONSORT statement assessment, all trials reported an average of 20.56 items. Of the 9 RCTs, 6 studies were classified as Grade B and 3 studies were classified as Grade C. In ROB assessment, most studies showed a low (63.49%) or unclear (26.98%) risk of bias. The selective reporting bias and the incomplete outcome data bias were found to have the lowest risk of bias, and the allocation concealment of selection bias was found to have the most unclear risk of bias. Conclusions : Recent acupuncture studies on CTS showed moderate reporting quality. However, more detailed reports on acupuncture are still needed to establish more solid evidence of acupuncture treatment.

• Journal of Oriental Neuropsychiatry
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• v.28 no.3
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• pp.165-182
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• 2017

Objectives: The purpose of this systematic review was to understand clinical usefulness of Emotional Freedom Techniques (EFT) on students' mental health. Methods: Ten databases were included to extract clinical studies on effects of EFT intervention with students. Characteristics of selected studies were described, and biases were assessed with Risk of Bias (RoB) or Risk of Bias Assessment for Non-Randomized Studies (RoBANS). Results: A total of 14 clinical trials were extracted for analysis. There were 8 randomized-controlled trials (RCTs), 2 non-randomized-controlled trials (nRCTs), and 4 before-after studies. EFT have significant clinical usefulness in public speaking anxiety, test anxiety, stress, depression, learning related emotions, adolescent anxiety, and eating issues. The risk of selection bias in most studies was high or uncertain. Conclusions: EFT is an effective clinical technique for managing students' mental health issues. However, the included studies have been conducted with relatively poor quality and small sample size. Clinical trials with high quality study design and well-designed EFT education programs are needed to generalize clinical usefulness.