• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.124-131
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• 2016

Baseline correction is very important due to influence on performance of spectral analysis in application of spectroscopy. Baseline is often estimated by parameter selection using visual inspection on analyte spectrum. It is a highly subjective procedure and can be tedious work especially with a large number of data. For these reasons, it is an objective and automatic procedure is necessary to select optimal parameter value for baseline correction. Asymmetrically reweighted penalized least squares (arPLS) based on penalized least squares was proposed for baseline correction in our previous study. The method uses a new weighting scheme based on the generalized logistic function. In this study, we present an automatic selection of optimal parameter for baseline correction using arPLS. The method computes fitness and smoothness values of fitted baseline within available range of parameters and then selects optimal parameter when the sum of normalized fitness and smoothness gets minimum. According to the experimental results using simulated data with varying baselines, sloping, curved and doubly curved baseline, and real Raman spectra, we confirmed that the proposed method can be effectively applied to optimal parameter selection for baseline correction using arPLS.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.6-15
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• 2012

Purpose : The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and $T_2{^*}$ quantification in the liver. Materials and Methods: In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of $T_2{^*}$and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a $B_0$ field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and $T_2{^*}$ from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field. Results: After correction, in the phantom experiments, the estimated $T_2{^*}$ and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 ${\mu}T/m$ with increased homogeneity in $T_2{^*}$ and fat fractions obtained. Conclusion: The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.

• Journal of Gifted/Talented Education
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• v.27 no.1
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• pp.17-35
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• 2017

The purpose of this study is to analyze the learning objectives characteristics of educational programs of centers for the university affiliated science-gifted education using semantic network analysis, we examined the applicability of semantic network analysis in analyzing learning objectives by comparing the results of analysis with Bloom's revised taxonomy. For this purpose, 702 learning objectives presented in 169 science subjects were selected as subjects to be analyzed. After classifying and coding the learning objectives according to Bloom's revised taxonomy, we conducted a semantic network analysis to investigate the relationship between learning objectives. The results of the analysis are as follows. First, we looked at the number of learning objectives used for each subject, and about 3 elementary school levels and about 6 middle school levels were used. Second, the knowledge dimension such as 'factual and conceptual knowledge' and cognitive process dimension such as 'remember', 'understand', and 'create' was high regardless of the research method and school level. Third, the results of analysis based on the weighting through the semantic network analysis method, the elementary school level emphasize activities th be applied to the actual experimental process through learning about scientific facts, while the middle school level emphasize the understanding of scientific facts and concepts themselves. As a result, it can be seen that the semantic network analysis can analyze characteristics of various learning objectives rather than the conventional simple statistical analysis.

• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.31-40
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• 2010

The purpose of this study is to formulate an evaluation model and to select evaluation factors for designing scenic roads and designating existing roads as scenic roads. The definition of a "scenic road" contains the purpose not just of travel or access but visiting itself. The value of a scenic road can be enhanced through the preservation of natural environment along the road or the installation of artificial facilities. In order to promote objective scenic road selection and evaluation, the authors used Analytic Hierarchy Process (AHP), which is a multi-criteria decision making method. The weighting value was 0.382 for the value of landscape along the road, 0.154 for the value of landscape in the road, 0.269 for the characteristics of the road, and 0.196 for the management of the road.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.11-16
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• 2022

As an interest in the development of artificial intelligence(AI) technology in the water supply sector increases, we have developed an AI algorithm that can predict improvement decision-making ratings through repetitive learning using the data of pipe condition evaluation results, and present the most reliable prediction model through a verification process. We have developed the algorithm that can predict pipe ratings by pre-processing 12 indirect evaluation items based on the 2020 Han River Basin's basic plan and applying the AI algorithm to update weighting factors through backpropagation. This method ensured that the concordance rate between the direct evaluation result value and the calculated result value through repetitive learning and verification was more than 90%. As a result of the algorithm accuracy verification process, it was confirmed that all water pipe type data were evenly distributed, and the more learning data, the higher prediction accuracy. If data from all across the country is collected, the reliability of the prediction technique for pipe ratings using AI algorithm will be improved, and therefore, it is expected that the AI algorithm will play a role in supporting decision-making in the objective evaluation of the condition of aging pipes.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.

• Journal of Korean Ophthalmic Optics Society
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• v.13 no.4
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• pp.25-30
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• 2008

Purpose: In this paper, we have analyzed the problems of the Oh's report which is used to the basic data for supply and demand of medical technicians and studied a proposal for improvement to control system and supply and demand of korean optometrists. Methods: We have analyzed errors of Oh's report including supply and demand for medical technicians and management policy, expecting number for future optician, inaccurate estimation by limited data (employment rate, retirement rate, mortality rate) and an incorrect method of measurement for future supply and demand. Results: Oh's report showed the 18% error for estimation of supply which exclude the irregular entrance students. The estimation of supply was calculated by graduation rate 62.6% (college and University of Technology are 78.9% and 85.98% respectively), employment rate 65.8% (the average employment between 2002 and 2007 is 73.96%) and retirement rate is 2.3% (the retirement of pharmacists is 1.3%) but it showed the significant differences to objective data. For estimate the suitable ratio of optometrists to the population, the ratio use of medical facilities by an age group was used, and suggested spectacle wearers 1,280 persons (populations 2,928 persons) per optometrist but the different from reference of Germany (4,706 persons), America (1,789 persons) and Korea (1,825 persons/an optometrist) are applied to estimation on supply. This report applied the low employment rate and argued that maintain the present situation, but claimed that utilize unemployment persons. The above result has induced double weighting effect on estimation of supply. Conclusions: To solve the related problems of supply and demand, we have to make a search for exact data and optimum application model, have to take an example of nation similar job category as Germany and the research result of the job satisfaction into consideration. After we get the integrated research result, we must carried out the policy with fairness and balance for the estimation of supply and demand. Therefore exact research is required prior to beginning policy establishment, government and related group have to make a clear long-term plan and permanent organization for medical technician to establish supply and demand of medical technician.