• Applied Microscopy
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• v.48 no.4
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• pp.102-109
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• 2018

Multiple synaptic boutons (MSBs) have been reported to be synapse with two or more postsynaptic terminals in one presynaptic terminal. These MSBs are known to be increased by various brain stimuli. In the motor cortex, increased number of MSB was observed in both acrobat training (AC) model and traumatic brain injury (TBI) model. Interestingly one is a physiological stimuli and the other is pathological insult. The purpose of this study is to compare the connectivity of MSBs between AC model and TBI model in the cerebral motor cortex, based on the hypothesis that the connectivity of MSBs might be different according to the models. The motor cortex was dissected from perfused brain of each experimental animal, the samples were prepared for routine transmission electron microscopy. The 60~70 serial sections were mounted on the one-hole grid and MSB was analyzed. The 3-dimensional analysis revealed that 94% of MSBs found in AC model synapse two postsynaptic spines from same dendrite. But, 28% MSBs from TBI models synapse two postsynaptic spines from different dendrite. This imply that the MSBs observed in motor cortex of AC model and TBI model might have different circuits for the processing the information.

• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.3
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• pp.227-236
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• 2021

Background: Emergencies in outpatient clinics are rare. However, potentially catastrophic events can be challenging to manage due to a variety of factors, including limited equipment and staff. The purpose of this quality improvement project was to improve the staff knowledge and familiarity with critical performance elements for emergencies encountered in the setting of a periodontics clinic. Methods: Emergency cognitive aids tailored to the clinic's resources were created for anaphylaxis, airway obstruction, and sublingual hemorrhage. The project pre-post-test repeated measures design evaluated the effectiveness of cognitive aids using a combination of hands-on simulation, written knowledge assessments, and self-efficacy surveys. Training sessions and simulations were provided to the clinic's existing care teams made up of a periodontist and two dental assistants with an anesthetist who was present for simulations involving sedation. Due to the small sample size (N = 14) and non-normal distribution, all metrics were evaluated using non-parametric statistics. Results: Significant improvements were found in knowledge assessment (-2.310, P = 0.021) and self-efficacy (-2.486, P = 0.013) scores when retention after a training session before and after the introduction of cognitive aid was compared. The mean simulation scores and times improved steadily or reached maximum scores during the project progression. Conclusion: Training sessions before and after cognitive aid introduction were effective in improving knowledge, self-efficacy, and simulation performance. Future projects should focus on validating the process for creating contextualized cognitive aids and evaluating the effectiveness of these cognitive aids in larger samples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• Progress in Superconductivity and Cryogenics
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• v.24 no.3
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• pp.24-29
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• 2022

One of the recent proposals with nuclear magnetic resonance (NMR) is a multi-bore NMR which consists of array of magnets which could present possibilities to quickly cope with pandemic virus by multiple inspection of virus samples. Low temperature superconductor (LTS) can be a candidate for mass production of the magnet due to its low price in fabrication as well as operation by applying the helium zero boil-off technology. However, training feature of LTS magnet still hinders the low cost operation due to multiple boil-offs during premature quenches. Thus in this paper, LTS magnet with low mechanical stress is designed targeting the "training-free" LTS magnet for mass production of magnet array for multi-bore NMR. A thorough process of an LTS magnet design is conducted, including the analyses as the followings: electromagnetics, mechanical stress, cryogenics, stability, and protection. The magnet specification was set to 4.7 T in a winding bore of 85 mm, corresponding to the MR frequency of 200 MHz. The stress level is tolerable with respect to the wire yield strength and epoxy crack where mechanical disturbance is less than the minimum quench energy.

• Korean Journal of Exercise Nutrition
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• v.25 no.4
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• pp.17-23
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• 2021

[Purpose] This study aimed to investigate the effect of education based on the health belief model on the physical activity of the staff of the University of Medical Sciences. [Methods] This semi-experimental study was conducted on 130 university staff aged 25-50 years from the Hamadan University of Medical Sciences. Inclusion criteria were having at least 1 year of work experience, lack of acute and chronic physical and mental illnesses, and not using drugs that affect physical activity. The samples were randomly divided into two groups. The experimental group received three training sessions based on the health belief model. Before and 2 months after training, the control and experimental groups were evaluated via the following questionnaires: (1) demographic information questionnaire, (2) Health Belief Model Questionnaire, and (3) International Physical Activity Questionnaire. Finally, data were analyzed statistically. [Results] The training process resulted in a significant increase in the mean scores of the health belief model constructs in the experimental group, but changes in the control group were not significant. Self-efficacy was the strongest predictor of physical activity. [Conclusion] The health belief model is a useful model for improving individuals' understanding of the benefits of physical activity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2095-2101
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• 2008

In this paper, we consider a performance improvement of neural network for predicting defect size of steam generator tube using early stopping. Usually, neural network is trained until MSE becomes less than a prescribed error goal. The smaller the error goal, the greater the prediction performance for the trained data. However, as the error goal is decreased, an over fitting is likely to start during supervised training of a neural network, which usually deteriorates the generalization performance. We propose that, for the prediction of an axisymmetric defect size, early stopping can be used to avoid the over-fitting. Through various experiments on the axisymmetric defect samples, we found that the difference bet ween the prediction error of neural network based on early stopping and that of ideal neural network is reasonably small. This indicates that the error goal used for neural network training for the prediction of defect size can be efficiently selected by early stopping.

• Computers and Concrete
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• v.15 no.5
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• pp.759-770
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• 2015

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.22-29
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• 2016

Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.