• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.75-87
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• 2021

The purpose of this study is to derive the characteristics of interaction design for each stage of content composition so that it can be referred to in the planning and production of virtual reality safety education contents. It was confirmed that each of the following interaction design features was found in the three configuration steps: acquisition of situation response procedure knowledge, accident situation experiential learning, and content confirmation and evaluation. First, it was revealed that the quality of experience was controlled by increasing the fidelity of behaviors and reducing general and repetitive behaviors in order to emphasize the educational content-related experiences in the learner experience stage. Second, in order for learners to easily recognize main interaction objects in order to acquire information on safe behavior procedures in unfamiliar environments, use of spatial UI or signifiers using arrows or symbols, posts that specifically instruct actions, and multisensory signals Therefore, it was found to be important to emphasize essential actions in a way that lowers the degree of freedom of user experience, and the proportion of non-realistic interactions for cognitive interactions was found to increase. Lastly, in the confirmation and evaluation stage of the experience, it is important to use the meta UI to alleviate negative experiences such as physical damage after experiencing a safety accident situation,

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.146-153
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• 2022

Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.

• Journal of Dental Anesthesia and Pain Medicine
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• v.23 no.2
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• pp.101-110
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• 2023

Background: Dentists make various efforts to reduce patients' anxiety and fear associated with dental treatment. Dental sedation is an advanced method that dentists can perform to reduce patients' anxiety and fear and provide effective dental treatment. However, dental sedation is different from general dental treatment and requires separate learning, and if done incorrectly, can lead to serious complications. Therefore, sedation is performed by a limited number of dentists who have received specific training. This study aimed to investigate the proportion of dentists who practice sedation and the main sedatives they use in the context of the Republic of Korea. Methods: We used the customized health information data provided by the Korean National Health Insurance. We investigated the number of dental hospitals or clinics that claimed insurance for eight main sedatives commonly used in dental sedation from January, 2007 to September, 2019 at the Health Insurance Review and Assessment Service. We also identified the changes in the number of dental medical institutions by region and year and analyzed the number and proportion of dental medical institutions prescribing each sedative. Results: In 2007, 302 dental hospitals prescribed sedatives, and the number increased to 613 in 2019. In 2007, approximately 2.18% of the total 13,796 dental institutions prescribed sedatives, increasing to 3.31% in 2019. In 2007, 168 institutions (55.6%) prescribed N₂O alone, and in 2019, 510 institutions (83.1%) made claims for it. In 2007, 76 (25.1%) hospitals made claims for chloral hydrate, but the number gradually decreased, with only 29 hospitals (4.7%) prescribing it in 2019. Hospitals that prescribed a combination of N₂O, chloral hydrate, and hydroxyzine increased from 27 (8.9%) in 2007 to 51 (9%) in 2017 but decreased to 38 (6.1%) in 2019. The use of a combination of N₂O and midazolam increased from 20 hospitals (6.6%) in 2007 to 51 hospitals (8.3%) in 2019. Conclusion: While there is a critical limitation to the investigation of dental hospitals performing sedation using insurance claims data, namely exclusion of dental clinics providing non-insured treatments, we found that in 2019, approximately 3.31% of the dental clinics were practicing sedation and that N₂O was the most commonly prescribed sedative.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.1065-1079
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• 2012

This research is done with the objectives to examine and to identify how the main factors of airline services, which become fundamentals for airline customers to select their favorite one, affect their customer satisfaction and their customer preference, with a focus on adjustable influence of converting cost. The survey has been conducted with the population of the airline customers who use international airlines through Incheon International Airport and processed with the objectives to verify the influence of the main factors of airline services to customer satisfaction and customer preference and also to collect the verified data for adjustable influence of converting cost between customer satisfaction and customer preference, and its results are as follows: Firstly, airline services have significant impact on their customer satisfaction. Secondly, they also have significant impact on their customer preference, Thirdly, the converting cost between both of customer satisfaction and customer preference to airline services doesn't involve the adjustable function in consecutive cost nor in learning cost but it does involve the one in sunk cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.81-98
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• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

• Journal of The Korean Association of Information Education
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• v.1 no.1
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• pp.11-27
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• 1997

The purpose of this paper is to examine some effects of four menu types: icon menu, icontext menu, pulldown menu, and bar selection menu. Each have been mainly used with hypermedia instruction, learners' metacognition on their errors, menu search time, and scholastic achievements. A further purpose of this paper is suggest some strategies for enabling each learner to select menus more efficiently in order to enhance the learner's own learning effectiveness in hypermedia instruction. There were 100 college students selected as subjects for This study. They had never had experience in Hanoi hypermedia instruction. They were required to use four menu types at random in this experiement. The results of this study are as follows. The menu types had a significant effect on the low metacognition regulation group. Icontext menu types, especially, had a significant effect on achievements. In constrast to this, the menu types did not have any significant effect on the high metacognition regulation group.

• Annals of Clinical Neurophysiology
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• v.1 no.1
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• pp.1-9
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• 1999

Since Hans Berger reported the first paper on the human electroencephalogram in 1920s, huge technological advance have made it possible to use a number of electrophysiological approaches to neurological diagnosis in clinical neurology. In majority of the neurology training hospitals they have facilities of electroencephalography(EEG), electromyography(EMG), evoked potentials(EP), polysomnography(PSG), electronystagmography(ENG) and, transcranial doppler(TCD) ete. Clinicials and electrophysiologists should understand the technologic characteristics and general applications of each electrophysiological studies to get useful informations with using them in clinics. It is generally agreed that items of these tests are selected under the clinical examination, the tests are performed by the experts, and the test results are interpretated under the clinical background. Otherwise these tests are sometimes useless and lead clinicians to misunderstand the lesion site, the nature of disease, or the disease course. In this sense the clinical utility of neurophysiological tests could be summerized in the followings. First, the abnormal functioning of the nervous system and its environments can be demonstrated when the history and neurological examinations are equivocal. Second, the presence of clinically unsuspected malfunction in the nervous system can be revealed by those tests. Finally the objective changes can be monitored over time in the patient's status. Also intraoperative monitoring technique becomes one of the important procedures when the major operations in the posterior fossa or in the spinal cord are performed. In 1996, the Korean Society for Clinical Neurophysiology(KSCN) was founded with the hope that it will provide the members with the comfortable place for discussing their clinical and academic experience, exchanging new informations, and learning new techniques of the neurophysiological tests. The KSCN could collaborate with the International Federation of Clinical Neurophysiology(IFCN) to improve the level of the clinical neurophysiologic field in Korea as will as in Asian region.1 In this paper the clinical neurophysiological tests which are commonly used in clinical neurology and which will be delt with and educated by the KSCN in the future will be discussed briefly in order of EEG, EMG, EP, PSG, TCD, ENG, and Intraoperative monitoring.

• Journal of Society of Preventive Korean Medicine
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• v.18 no.2
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• pp.81-88
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• 2014

Objective : This study is a survey research that investigates the kinds of medicinal herbs actually used in Yaksun(medicianal food) restaurants, the frequency and the way of using herbs in Yaksun. Through this study, we assumed that it will be used basis data on further Korean Yaksun research. Method : We conducted survey targeting for 26 Yaksun restaurants and Temple food restaurants serving Yaksun cuisine(medicinal food) menu from July 2012 to January 2013. The questionnaire was composed of several parts including the kinds of medicinal herbs that was used in Yaksun, medicinal food types that use a lot of medicinal herbs, medicinal herbs criteria used in the selection of medicinal food, and education experience learning Yaksun cuisine. Results : Only 11 restaurants answered the questionnaire among the targeting restaurants of survey objects. The number of Herbs was investigated in each restaurant was maximum 65 kinds and minimum 7 kinds (average 32 kinds). All restaurants used Angelicae Gigantis Radix in their restaurant. And Nelumbinis Semen, Zingiberis Rhizoma, Glycyrrhizae Radix, Acanthopanacis Cortex, and Gardeniae Fructus are well used medicinal herbs in Yaksun. Types of medicinal food using a lot of herbs were rices porridges rice cakes, both vegetables salads and stews soups. Almost chefs or restaurant's representatives learned cooking medicinal food at temples, food research centers, university attached institutions, and cooking schools. Conclusion : Medicinal herbs used in Yaksun restaurants are familiar with Korean and easily available. These herbs has better efficacy, taste, scent, color in comparison of the others. For the development of Korean Yaksun, further research of divers parts in Yaksun materials should be conducted.