• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.5
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• pp.125-131
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• 2021

The scientific understanding of Korean medicine theory remains largely unknown, since there is a lack of proper methods to investigate its complex and unique characteristics. Here, we introduce a drug-centered approach, a novel method to investigate Korean medicine theory by analyzing the mechanisms of herbal medicines. This method can be effectively conducted by employing network pharmacology that can analyze the systems-level mechanisms of herbal medicines on a large scale. Firstly, we introduce the method of network pharmacology that are applied to analyze the mechanisms of herbal medicines in a step-by-step manner. Then, we show how the drug-centered approach employing network pharmacology can be applied to investigate Korean medicine theory by describing studies that identify the biological correlates of the cold-hot nature of herbs, spleen qi deficiency syndrome, or Sasang constitution. Finally, we discuss the limitations and future directions of the proposed approach in two aspects: The methods of network pharmacology for a drug-centered approach and the process of inferring Korean medicine theory through it. We believe that a drug-centered approach employing network pharmacology will provide an advanced scientific understanding of Korean medicine theory and contribute to its development by generating biologically plausible hypothesis.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.13-21
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• 2023

This paper proposes a method of automatically generating a model through a machine learning technique by setting a hypothetical model in the form of a gray box or black box with unknown parameters, when the big data of the actual system is given. We implements the proposed framework and conducts experiments to find an appropriate model among various hypothesis models and compares the cost and fitness of them. As a result we find that the proposed framework works well with continuous systems that could be modeled with ordinary differential equation. This technique is expected to be used well for the purpose of automatically updating the consistency of the digital twin model or predicting the output for new inputs using recently generated big data.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.1-15
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• 2021

Due to long-term social distancing due to the spread of COVID-19, business trends of restaurant companies are being implemented in accordance with the changed environment such as packaging and subscription. However, even in this environment, upscale restaurants are generating high profits by trying to differentiate themselves from existing restaurants by providing high-quality services with the best facilities. Therefore, this study describes how customers' experience of upscale restaurant service influences behavioral loyalty. That is, the purpose of this study is to determine the effect of service experience on cognitive satisfaction and emotional attachment, and to examine the causal role of cognitive satisfaction and emotional attachment by confirming the relationship between these constructs and relationship commitment and behavioral loyalty. To verify this, data were collected through face-to-face interviews with 300 consumers who recently used a upscale restaurant. Of the collected data, 275 copies(91.6%) were used for the final analysis, and inaccurate or erroneous data among 25 response sheets were excluded. In this study, the validity and reliability of the data were checked and the research hypothesis was verified by using SPSS 21.0 and AMOS 20 statistical package. Frequency analysis was performed to confirm the demographic characteristics of the respondent. Structural equation model analysis(SEM) was used to confirm the fit of the research model and to verify the research hypothesis. As a result of the research hypothesis analysis, it was found that service experience had a positive effect on cognitive satisfaction, and cognitive satisfaction had a positive effect on emotional attachment, relationship commitment, and behavioral loyalty. Also, it was found that emotional attachment had a positive effect on relationship commitment and behavioral loyalty, and relationship commitment had a positive effect on behavioral loyalty. However, service experience did not affect emotional attachment. With this study, marketers and managers of upscale restaurants such as hotel restaurants need to accurately select their target audience, understand their service needs, and then present the appropriate service to them. In addition, they should not only induce cognitive satisfaction by providing excellent service to their customers, but also identify moments of truth and present appropriate services so that satisfied customers can strengthen their emotional attachment. In addition, it is necessary to strengthen the relationship with their firms by forming friendly relationships with customers who have high emotional attachment, and also to induce relationship commitment so that such customers have a strong sense of belonging and attachment to their firms.

• Journal of Food Hygiene and Safety
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• v.21 no.3
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• pp.145-152
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• 2006

Efforts have been made to explore the possibility of using garlic as an antimicrobial therapeutic agent since garlic extract and its individual sulfur compounds show antimicrobial activities against all kinds of microorganisms including bacteria, molds, yeasts and protozoa. Staphylococcus aureus has been the most studied bacteria along with many other Gram positive and negative pathogenic bacteria, including species of the genera Clostridium, Mycobacterium, Escherichia, Klebsiella, Bacillus, Salmonella and Shigella. Candida albicans has been the most studied among the eukaryotic microorganisms. A pathogenic protozoa, Giardia intestinalis, was also tested. All the microorganisms tested was inhibited by garlic extract or its sulfur components. Garlic has been known to be growth inhibitory only when fresh garlic is crushed, since allicin-generating reaction is enzyme-catalyzed. Allicin is known to be growth inhibitory through a non-specific reaction with sulfhydryl groups of enzyme proteins that are crucial to the metabolism of microorganisms. Another plausible hypothesis is that allicin inhibits specific enzymes in certain biological processes, e.g. acetyl CoA synthetase in fatty acid synthesis in microorganisms. Allicin transforms into other compounds like ajoene and various sulfides which are also inhibitory to microorganisms, but not as potent as their mother compound. It is reported recently that garlic heated at cooking temperatures is growth inhibitory especially against yeasts, and that the growth inhibitory compound is allyl alcohol thermally generated from alliin in garlic.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.25-38
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• 2017

This study investigates non-native speakers(NNS) of English use of backchannels with beginner-intermediate learners' use of 'uh', 'um(uhm)', 'and' and 'hm' suggesting a view as a possible pedagogical implication. The initial aim of this study was to learn this phenomenon and observe their conversation patterns to compare with previous studies. Based on the previous findings, the analyzed data using conventional Conversation Analysis (CA) methods indicate the possible presence of L1 topic markers, '-un' and '-nun' in the form of L2 backchannel tokens when uttered by beginning and intermediate level speakers of English and the presences of L2 backchannel tokens appear only in front of noun phrases. Additionally, these same words with these tokens and when translated back to Korean also require topic markers of '-un' and '-nun.' Finally, This study discusses possible pedagogical implications with the initial analysis of backchannel tokens for Korean EFL learners. In addition, the ultimate goal of this study is to refine this analysis with follow up experiments to validate this investigation into a working hypothesis generating discussions of this backchannel phenomenon from being viewed as a hindrance to as an positive influence that needs to be understood.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• Journal of Service Research and Studies
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• v.9 no.3
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• pp.73-86
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• 2019

The authors of this study conducted research on universities located in China, and the primary purpose of the study was to test whether the quality factors of university education service have a positive (+) effect on student satisfaction. The secondary purpose of the study was to identify and analyze whether university instruction and curriculum variables had a positive regulatory effect on the relationship between quality factors and student satisfaction. First, Research Hypothesis 1, which suggested that university education service quality factors would have a positive effect on student satisfaction, was adopted. As the second analytical process of the study, controlled regression analysis was used to verify whether instruction and the curriculum had a regulatory effect on the relationship between the university education service quality factors and student satisfaction. When the two variables were analyzed as control variables, the results showed that curriculum had a significant positive regulatory effect, and instruction was shown to not be suitable for generating win-win cooperation or synergistic effects. The contributions of the theoretical perspective of this study were the analysis of the relationship between university education service quality factors in Chinese universities and student satisfaction, and systematically linking instructions and the curriculum and analyzing the impact on student satisfaction. The study implies that it would be more effective to analyze additional factors in the operation of universities through in-depth analysis on instruction from a practical standpoint.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3417-3426
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• 2012

This study aims to determine how ownership structure (share-holding ratio of insiders, foreigners) affects agent costs (the portion of asset efficiency or non-operating expenses) through empirical analysis. However, as existing studies on correlations between ownership structure and agent costs adopted Pooled OLS Model, this study focused on additionally formulating Fixed Effect Model and Random Effect Model aimed to reflect the time of data formation and corporate effects as study models based on verification results on the suitability of Pooled-OLS Model before comparative analysis for the purpose of improvement of credibility and statistical validity of the results of empirical analysis based on the premise that the Pooled OLS Model is not reliable enough to verify massive panel data. The data has been accumulated over 10 years from 1998 to 2007 after the IMF crisis hit the nation, from a subject 331 companies except for financial institutions. As a result of the empirical analysis, verification of the suitability of model has determined that the Random Effect Model is appropriate in terms of asset efficiency among agent costs items. On the other hand, the Fixed Effect Model is appropriate in terms of non-operating costs. As a result of the empirical analysis according to the appropriate model, no hypothesis adopted in the Pooled OLS Model has been accepted. This suggests that developing an appropriate model is more important than other factors for the purpose of generating statistically significant empirical results by showing that different empirical results are produced according to the type of empirical analysis.

• Journal of Science Education
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• v.42 no.3
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• pp.308-321
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• 2018

The purpose of the study is to develop a questionnaire that examines unskeptical attitudes in scientific inquiry context. The questionnaire items were developed through literature research, expert review, and statistical analyses for validity and the differences in scores were identified by gender and tracks. A total of 363 high school students participated in the study. To explore the validity evidence of items, the Rasch analysis and the reliability of internal consistency were performed, and the two-way ANOVA was performed to compare the scores of the unskeptical attitudes between gender and academic track. Self-reporting and Likert-scaling 23 items were developed to measure unskeptical attitudes in scientific inquiry context. The items were developed in the sub-domain of scientific inquiry: 'questioning and hypothesis generating,' 'experiment designing,' and 'explaining and interpreting.' Second, the validity and reliability of the unskeptical were identified in a rigorous method. The validity of items were identified by multi-dimensional partial score model analysis through the Rasch model, and all 23 items were found to be fit to model. Various reliability evidences were also found to be appropriate. It was found that there were no significant differences of unskeptical attitude score between the gender and academic track except one comparison. The developed questionnaire could be used to check an unskeptical attitude in the course of scientific inquiry and to compare the effects of scientific inquiry classes.