• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.29-50
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• 2020

Purpose: Targeting the members of manufacturing and Service industry Quality Control team this study is going is to carry out research about whether the outcome of OCB(Organizational Citizenship Behaviors) and organization can be brought about by organizational culture that is suitable for them. this study tries to identify the direct･indirect causal relationship between these variables and OCB by selecting organizational culture as a leading variable and job satisfaction as a parameter. Methods: SPSS 22.0 was used for data analysis and AMOS 18.0 statistical program for structural equation model analysis. For the descriptive statistics this study verified reliability analysis, feasibility analysis, structural equation model analysis, research hypothesis, and mediating effects. Results: As a result of path analysis estimating the regression coefficients for the linear structure analysis of the correlations between variables for the hypothesis verification, the rational culture among the organizational culture types of the manufacturing Quality Control team showed a positive (+) effect on the job satisfaction, and hierarchical culture has negative(-) effect on job satisfaction. Conclusion: This study suggested that the composition and friendly behavior of desirable organizational culture has a very close relationship in connection between job satisfaction and OCB by examining the causal relationship between OCB for improvement activities for organizational culture by establishing the organizational culture and job satisfaction of the manufacturing Quality Control team.

• Journal of the Earthquake Engineering Society of Korea
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• v.24 no.5
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• pp.219-232
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• 2020

The slip-weakening model developed by Ohnaka and Yamashita is extended over the breakdown zone by equating the scaling relationships for the breakdown zone and the whole rupture area. For the extension, the study uses the relationship between rupture velocity and radiation efficiency, which was derived in the theory of linear elastic fracture mechanics, and the definition of f_max given in the specific barrier model proposed by Papageorgiou and Aki. The results clearly show that the extended scaling relationship is governed by the ratio of rupture velocity to S wave velocity, and the velocity ratio can be determined by the ratio of characteristic frequencies of a Fourier amplitude spectrum, which are corner frequency, f_c, and source-controlled cut-off frequency, f_max, or vice versa. The derived relationship is tested by using the characteristic frequencies extracted from previous studies of more than 130 shallow crustal events (focal depth less than 25 km, M_W 3.0~7.5) that occurred in Japan. Under the assumption of a dynamic similarity, the rupture velocity estimated from f_max/f_c and the modified integral timescale give quite similar scale-dependence of the rupture area to that given by Kanamori and Anderson. Also, the results for large earthquakes show good agreement to the values from a kinematic inversion in previous studies. The test results also indicate the unavailability of the spectral self-similarity proposed by Aki because of the scale-dependent rupture velocity and the rupture velocity-dependent f_max/f_c; however, the results do support the local similarity asserted by Ohnaka. It is also remarkable that the relationship between the rupture velocity and f_max/f_c is quite similar to Kolmogorov's hypothesis on a similarity in the theory of isotropic turbulence.

• The Journal of Sustainable Design and Educational Environment Research
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• v.17 no.2
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• pp.1-10
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• 2018

Due to the sudden drop in the number of students currently, Korea is facing a turning point in the urgent need to revise the school acceptance plan policy. Especially, in the case of the decrease of the number of students, the reckless establishment of a school in a certain area has an adverse effect of further reducing the number of students in the nearby school. The purpose of this study is to provide basic data on the appropriate scale policy through the correlation between the opening school year and the number of enrolled students in 19 out of 1,179 schools. The results of the linear regression analysis are as follows : First, in 19 cities nationwide, 17 cities except Suwon city and Anyang city had a hypothesis that 'the number of new school students is more than the number of existing school students'. Second, not only the results of the analysis of 1,179 schools in Korea, but also the results of regional analysis showed a hypothesis. Third, there are many small schools and oversized schools in the national city. It is necessary to establish an appropriate school policy.

• Journal of Korean Elementary Science Education
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• v.42 no.1
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• pp.109-126
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• 2023

In this study, I took the evidence-explanation (E-E) continuum perspective to examine the epistemological implications of scientific reasoning cases designed by preservice elementary teachers during their simulation teaching. The participants were four preservice teachers who conducted simulation instruction on the seasons and high/low air pressure and wind. The selected discourse episodes, which included cases of inductive, deductive, or abductive reasoning, were analyzed for their epistemological implications-specifically, the role played by the reasoning cases in the E-E continuum. The two preservice teachers conducting seasons classes used hypothetical-deductive reasoning when they identified evidence by comparing student-group data and tested a hypothesis by comparing the evidence with the hypothetical statement. However, they did not adopt explicit reasoning for creating the hypothesis or constructing a model from the evidence. The two preservice teachers conducting air pressure and wind classes applied inductive reasoning to find evidence by summarizing the student-group data and adopted linear logic-structured deductive reasoning to construct the final explanation. In teaching similar topics, the preservice teachers showed similar epistemic processes in their scientific reasoning cases. However, the epistemological implications of the instruction were not similar in terms of the E-E continuum. In addition, except in one case, the teachers were neither good at abductive reasoning for creating a hypothesis or an explanatory model, nor good at using reasoning to construct a model from the evidence. The E-E continuum helps in examining the epistemological implications of scientific reasoning and can be an alternative way of transmitting scientific reasoning.

• Journal of Korea Technology Innovation Society
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• v.17 no.2
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• pp.380-420
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• 2014

We increasingly see the importance of employees acquiring enough expert capability or innovation capability to prepare for ever growing uncertainties in their operation domains. However, despite the above circumstances, there have not been an enough number of researches on how operational input components for employees' innovation outcome, innovation activities such as acquisition, exercise and promotion effort of employee's innovation capability, and their resulting innovation outcome interact with each other. This trend is believed to have been resulted because most of the current researches on innovation focus on the units of country, industry and corporate entity levels but not on an individual corporation's innovation input components, innovation outcome and innovation activities themselves. Therefore, this study intends to avoid the currently prevalent study frames and views on innovation and focus more on the strategic policies required for the enhancement of an organization's innovation capabilities by quantitatively analyzing employees' innovation outcomes and their most suggested relevant innovation activities. The research model that this study deploys offers both linear and structural model on the trio of learning, innovation capability and innovation outcome, and then suggests the 4 relevant hypotheses which are quantitatively tested and analyzed as follows: Hypothesis 1] The different levels of innovation capability produce different innovation outcomes (accepted, p-value = 0.000<0.05). Hypothesis 2] The different amounts of learning time produce different innovation capabilities (rejected, p-value = 0.199, 0.220>0.05). Hypothesis 3] The different amounts of learning time produce different innovation outcomes. (accepted, p-value = 0.000<0.05). Hypothesis 4] the innovation capability acts as a significant parameter in the relationship of the amount of learning time and innovation outcome (structural modeling test). This structural model after the t-tests on Hypotheses 1 through 4 proves that irregular on-the-job training and e-learning directly affects the learning time factor while job experience level, employment period and capability level measurement also directly impacts on the innovation capability factor. Also this hypothesis gets further supported by the fact that the patent time absolutely and directly affects the innovation capability factor rather than the learning time factor. Through the 4 hypotheses, this study proposes as measures to maximize an organization's innovation outcome. firstly, frequent irregular on-the-job training that is based on an e-learning system, secondly, efficient innovation management of employment period, job skill levels, etc through active sponsorship and energization community of practice (CoP) as a form of irregular learning, and thirdly a model of Yί=f(e, i, s, t, w)+${\varepsilon}$ as an innovation outcome function that is soundly based on a smart system of capability level measurement. The innovation outcome function is what this study considers the most appropriate and important reference model.

• Journal of KIISE
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• v.43 no.12
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• pp.1396-1403
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• 2016

In streaming data analysis where underlying data distribution may be changed or the concept of interest can drift with the progress of time, the ability to adapt to concept drift can be very powerful especially in the process of incremental learning. In this paper, we develop a general framework for an adaptive incremental classifier on data stream with concept drift. A distribution, representing the performance pattern of a classifier, is constructed by utilizing the distance between the confidence score of a classifier and a class indicator vector. A hypothesis test is then performed for concept drift detection. Based on the estimated p-value, the weight of outdated data is set automatically in updating the classifier. We apply our proposed method for two types of linear discriminant classifiers. The experimental results on streaming data with concept drift demonstrate that the proposed adaptive incremental learning method improves the prediction accuracy of an incremental classifier highly.

• Advances in environmental research
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• v.6 no.2
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• pp.83-94
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• 2017

Air quality planning and management requires accurate and consistent records of the air quality parameters. Limited number of monitoring stations and inconsistent measurements of the air quality parameters is a very serious problem in many parts of India. It becomes difficult for the authorities to plan proactive measures with such a limited data. Estimation models can be developed using soft computing techniques considering the physics behind pollution dispersion as they can work very well with limited data. They are more realistic and can present the complete picture about the air quality. In the present case study spatio-temporal models using Linear Genetic Programming (LGP) have been developed for estimation of air quality parameters. The air quality data from four monitoring stations of an Indian city has been used and LGP models have been developed to estimate pollutant concentration of the fifth station. Three types of models are developed. In the first type, models are developed considering only the pollutant concentrations at the neighboring stations without considering the effect of distance between the stations as well the significance of the prevailing wind direction. Second type of models are distance based models based on the hypothesis that there will be atmospheric interactions between the two stations under consideration and the effect increases with decrease in the distance between the two. In third type the effect of the prevailing wind direction is also considered in choosing the input stations in wind and distance based models. Models are evaluated using Band Error and it was observed that majority of the errors are in +/-1 band.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.43-59
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• 2007

The purpose of the study was to examine the effectiveness of Neuro Feedback training by observing the pre and post brainwave measurement results of about 50 (experimental group 25. comparative group 25) subjects who have shown psychological difficulties in studying. attention deficit, and personalities. The study took place at Neuro Feedback training Center B. in between the months of July 2006 and May 2007. The methodology involved in the study included the Coloring Analysis Program of the Brain Quotient Test. As the brain waves are adjusted by timeseries linear analysis. the brain function quotients can reflect the functional states of the brain. Through the test, three parameters relaxation, attention and concentration-were initially measured for one minute each and the lowest parameter out of the three was selected as the training mode or improvement target. The training took place two or three times a week. for about 40 to 60 minutes per session. Because the clients have come to the training center at different times. the researcher sampled the results of only those who had attended more than 30 training sessions. The tool used to measure the psychological reaction was POMS (Profile of Mood State). while the tool used to measure the emotional and attention-deficit characteristics was the Amen Clinic ADD Type questionnaire. Hypothesis testing included t-test. The result of the study showed the Theta: SMR ratio of (left)p = .013. (right) p = .019. The result also confirmed the differences of both ATQ(left) p = .011. (right)p = .030 and SQ(left) p = .017. (right) p = .022. The result confirmed of emotional p = .000. attention-deficit characteristics p = .000. The result of the study suggest Neuro Feedback technique's possibility in positively affecting the subjects' mental state and attention-deficit characteristics.

• Ocean and Polar Research
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• v.29 no.4
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• pp.379-389
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• 2007

This paper presents an attempt to determine natural background levels of heavy metals which could be used for assessing heavy metal contamination. For this study, a large archive dataset of heavy metal concentration (Cu, Cr, Ni, Pb, Zn) for more than 900 surface sediment samples from various Korean coastal environments was newly compiled. These data were normalized for aluminum (grain-size normalizer) concentration to isolate natural factors from anthropogenic ones. The normalization was based on the hypothesis that heavy metal concentrations vary consistently with the concentration of aluminum, unless these metals are of anthropogenic origin. So, the samples (outliers) suspected of receivingany anthropogenic input were removed from regression to ascertain the "background" relationship between the metals and aluminum. Identification of these outliers was tested using a model of predicted limits at 95%. The process of testing for normality (Kolmogorov-Smirnov Test) and selection of outliers was iterated until a normal distribution was achieved. On the basis of the linear regression analysis of the large archive (please check) dataset, background levels, which are applicable to heavy metal assessment of Korean coastal sediments, were successfully developed for Cu, Cr, Ni, Zn. As an example, we tested the applicability of this baseline level for metal pollution assessment of Masan Bay sediments.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.41-49
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• 2019

We are to build an unsupervised machine learning-based language model which can estimate the amount of information that are in need to process words consisting of subword-level morphemes and syllables. We are then to investigate whether the reading times of words reflecting their morphemic and syllabic structures are predicted by an information-theoretic measure such as surprisal. Specifically, the proposed Morfessor-based unsupervised machine learning model is first to be trained on the large dataset of sentences on Sejong Corpus and is then to be applied to estimate the information-theoretic measure on each word in the test data of Korean words. The reading times of the words in the test data are to be recruited from Korean Lexicon Project (KLP) Database. A comparison between the information-theoretic measures of the words in point and the corresponding reading times by using a linear mixed effect model reveals a reliable correlation between surprisal and reading time. We conclude that surprisal is positively related to the processing effort (i.e. reading time), confirming the surprisal hypothesis.