• The Journal of Korean Academy of Prosthodontics
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• v.54 no.3
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• pp.193-202
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• 2016

Purpose: This study was performed in order to assess the effect of the surface treatment methods and the use of bonding agent on the shear bond strength (SBS) between the aged CAD-CAM (computer aided design-computer aided manufacturing) hybrid materials and added composite resin. Materials and methods: LAVA Ultimate (LU) and VITA ENAMIC (VE) specimens were age treated by submerging in a $37^{\circ}C$ water bath filled with artificial saliva (Xerova solution) for 30 days. The surface was ground with #220 SiC paper then the specimens were divided into 9 groups according to the combination of the surface treatment (no treatment, grinding, air abrasion with aluminum oxide, HF acid) and bonding agents (no bonding, Adper Single Bond 2, Single Bond Universal). Each group had 10 specimens. Specimens were repaired (added) using composite resin (Filtek Z250), then all the specimens were stored for 7 days in room temperature distilled water. SBS was measured and the fractured surfaces were observed with a scanning electron microscope (SEM). One-way ANOVA and Scheffe test were used for statistical analysis (${\alpha}=.05$). Results: Mostly groups with bonding agent treatment showed higher SBS than groups without bonding agent. Among the groups without bonding agent the groups with aluminum oxide treatment showed higher SBS. However there was no significant difference between groups except two subgroups within LU group, which revealed a significant increase of SBS when Single Bond Universal was used on the ground LU specimen. Conclusion: The use of bonding agent when repairing an aged LAVA Ultimate restoration is recommended.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.301-311
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• 2013

This study aims to analyze characteristics, health behaviors and health management level related to private health examination recipients in one general hospital. To achieve this, we analyzed 150,501 cases of private health examination data for 11 years from 2001 to 2011 for 20,696 participants in 2011 in a Dae-Jeon general hospital health examination center. The cluster analysis for classify private health examination group is used z-score standardization of K-means clustering method. The logistic regression analysis, decision tree and neural network analysis are used to periodic/non-periodic private health examination classification model. 1,000 people were selected as a customer management business group that has high probability to be non-periodic private health examination patients in new private health examination. According to results of this study, private health examination group was categorized by new, periodic and non-periodic group. New participants in private health examination were more 30~39 years old person than other age groups and more patients suspected of having renal disease. Periodic participants in private health examination were more male participants and more patients suspected of having hyperlipidemia. Non-periodic participants in private health examination were more smoking and sitting person and more patients suspected of having anemia and diabetes mellitus. As a result of decision tree, variables related to non-periodic participants in private health examination were sex, age, residence, exercise, anemia, hyperlipidemia, diabetes mellitus, obesity and liver disease. In particular, 71.4% of non-periodic participants were female, non-anemic, non-exercise, and suspicious obesity person. To operation of customized customer management business for private health examination will contribute to efficiency in health examination center.

• Journal of Life Science
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• v.18 no.2
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• pp.206-212
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• 2008

The quorum sensing (QS) regulatory network has been the subject of extensive studies during recent years and has also attracted a lot of attention because it both positively and negatively regulates various putative virulence factors, although initially considered to be a specialized system of Vibrio fischeri and related species. In this study, to identify the novel materials which inhibit QS system of microorganisms, extracts of eighteen natural products were tested by bioassay using N-(3-oxohexanoyl)-$_L$-homoserine lactone and N-(3-oxooctanoyl)-$_L$-homoserine lactone synthesized in this experiment and an Agrobacterium tumefaciens NT1 biosensor strain containing a traI::lacZ fusion. The result indicated that the extracts of cabbage, leek, and onion exhibited the QS inhibition activity. Thus, materials contained in the extracts were isolated via recycling preparative HPLC and were purified via a JAIGEL-LS255 column. The common fraction corresponding to a peak of the 83 min point of them quenched the quorum sensing of A. tumefaciens NT1 biosensor strain in ABMM containing X-gal and was designated quorum sensing inhibitor-83 min (QSI-83). The QSI-83 exhibited the heat stability and did not inhibit the growth of A. tumefaciens NTl. Furthermore, thin layer chromatography (TLC) results suggested that these novel materials may be antagonists of N-acyl homoserine lactone or may inhibit the QS autoinducer synthesis by Pseudomonas syringae pv. tabaci.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.1
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• pp.110-122
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• 1993

When voice is transmitted through packet switching network, there needs a overload control, that is, a control for the congestion which lasts short periods and occurrs in local extents. In this thesis, we analyzed the performance of the statistical packet voice multiplexer using the overload control strategy by bit dropping. We assume that the voice is coded accordng to (4,2) embedded ADPCM and that the voice packet is generated and transmitted according to the procedures in the CCITT recomendation G. 764. For the performance analysis, we must model the superposed packet arrival process to the multiplexer as exactly as possible. It is well known that interarrival times of the packets are highly correlated and for this reason MMPP is more suited for the modelling in the viewpoint of accuracy. Hence the packet arrival process in modeled as MMPP and the matrix geometric method is used for the performance analysis. Performance analysis is similar to the MMPP IG II queueing system. But the overload control makes the service time distribution G dependent on system status or queue length in the multiplexer. Through the performance analysis we derived the probability generating function for the queue length and using this we derived the mean and standard deviation of the queue length and waiting time. The numerical results are verified through the simulation and the results show that the values embedded in the departure times and that in the arbitrary times are almost the same. Results also show bit dropping reduces the mean and the variation of the queue length and those of the waiting time.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of Korean Society of Forest Science
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• v.37 no.1
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• pp.1-16
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• 1978

The study describes on the identification and morphological characteristics of each species, ecological characteristics and propagation techniques, and developing utilization measures of the Parthenocissus plants for environment conservation and revegetation of the babe rock-slopes. The following species and varieties are disscussed in this study; Parthenocissus tricuspidata (S. et Z.) Planch. var. veitchii Rehd. var. lowii Rehd. var. pupurea Hort. Parthenocissus quiquefolia (L.) Planch. var. engelmanni Rehd. var. saint-pauli Rehd. var. hirsuta Planch. Parthenocissus henryana Diels et Gilg. Parthenocissus thomsoni Planch. Parthenocissus heptaphylla Small. Parthenocissus inserta (Kern.) K. Fritsch. Parthenocissus laetevirens Rehd. Parthenocissus himalayana Planch. These are, in general, all vigorous self-clinging climbers that will quickly cover a wall and bare rock surfaces with a dense network of branch growths and beautiful green leaves which change to shades of scarlet and crimson before they fall in Autumn. Parthenocissus tricuspidata out of 8 species in the genus Parthenocissus is the most useful plant for the environment conservation including the bare rock-slope revegetation and for the production of food and shelter for wildlifes. This native of Korea clings by means of small rootlike holdfasts (adhesive discs) and holds (tendrils) to stone work or any other solid support, tenaciously.