• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Sijohaknonchong
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• v.23
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• pp.161-188
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• 2005

The purpose of this study was to delve into the morphological types of Sijo in an effort to determine the morphological structure of Sasul-sijo, and it's also attempted to present standard about how to discriminate Pyong-si, Eos-sijo and Sasul-sijo from one another from a morphological standpoint. It's suggested that Si with tee Jangs, six verses and 12 stanzas or more, with three Jangs, seven verses and 14 stanzas or more, and with three Jangs, eight verses and 16 stanzas or more should respectively be called Pyong-sijo, Eos-sijo and Sasul-sijo. After what Sijo was and what's not were discussed, how to distinguish Eos-sijo from Sasul-sijo was described, and finally, the structure of Sasul-sijo was presented. As for Sijo and non-Sijo, the types of works that consisted of tee Jangs, like Sijo, yet didn't suit its framework and Yuljo and were written in Chinese characters were regarded as non-Sijo. Concerning discrimination between Eos-si and Sasul-sijo, the type of Sijo that included one more or higher number of verse(s) and two more or higher number of stanzas in one of three Jangs was defined as Eos-sijo, and the type of Sijo that involved two more or higher number of verses and four more or higher number of stanzas in one of three Jangs was called Sasul-sijo. In other words, Eos-sijo contained one more verse in one of tee Jangs, and Sasul-sijo included one more Jang in one tee Jangs. The sort of Sijo that contained one more Jang in one of three Jangs could be viewed as Sasul-sijo. Regarding the structure of Sasul-si, there should be three Jangs, eight verses and 16 stanzas in one piece of Sasul-sijo. Any type of Sijo that contained two more or higher number of verses and four more or higher number of stanzas could be called Sasul-sijo. Such an addition of verse and stanza could done in various ways. The examples were (1) adding stanzas the first Jang, 2) adding stanzas to the second Jang, (3) adding stanzas to the final Jang, (4) adding stanzas to both the first and Second Jangs, (5) adding stanzas to th the second and final Jangs, and (6) adding stanzas to all the first, second and third Jangs at the same time. Besides, there was an extremely broad gap between the numbers of verse and stanza in Sasul-sijo, which ranged from a low of eight stanzas to a high of 87 ones in one of three Jangs.

• Tuberculosis and Respiratory Diseases
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• v.45 no.4
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• pp.736-745
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• 1998

Background: Respiratory muscle interaction is further profoundly affected by a number of pathologic conditions. Hyperinflation may be particularly severe in chronic obstructive pulmonary disease(COPD) patients, in whom the functional residual capacity(FRC) often exceeds predicted total lung capacity(TLC). Hyperinflation reduces the diaphragmatic effectiveness as a pressure generator and reduces diaphragmatic contribution to chest wall motion. Ultrasonography has recently been shown to be a sensitive and reproducible method of assessing diaphragmatic excursion. This study was performed to evaluate how differences of diaphragmatic excursion measured by ultrasonography associate with normal subjects and COPD patients. Methods: We measured diaphragmatic excursions with ultrasonography on 28 healthy subjects(l6 medical students, 12 age-matched control) and 17 COPD patients. Ultrasonographic measurements were performed during tidal breathing and maximal respiratory efforts approximating vital capacity breathing using Aloka KEC-620 with 3.5 MHz transducer. Measurements were taken in the supine posture. The ultrasonographic probe was positioned transversely in the midclavicular line below the right subcostal margin. After detecting the right hemidiaphragm in the B-mode the ultrasound beam was then positioned so that it was approximately parallel to the movement of middle or posterior third of right diaphragm. Recordings in the M-mode at this position were made throughout the test. Measurements of diaphragmatic excursion on M-mode tracing were calculated by the average gap in 3 times-respiration cycle. Pulmonary function test(SensorMedics 2800), maximal inspiratory(PImax) and expiratory mouth pressure(PEmax, Vitalopower KH-101, Chest) were measured in the seated posture. Results: During the tidal breathing, diaphragmatic excursions were recorded $1.5{\pm}0.5cm$, $1.7{\pm}0.5cm$ and $1.5{\pm}0.6cm$ in medical students, age-matched control group and COPD patients, respectively. Diaphragm excursions during maximal respiratory efforts were significantly decreased in COPD patients ($3.7{\pm}1.3cm$) when compared with medical students, age-matched control group($6.7{\pm}1.3cm$, $5.8{\pm}1.2cm$, p< 0.05}. During maximal respiratory efforts in control subjects, diaphragm excursions were correlated with $FEV_1$, FEVl/FVC, PEF, PIF, and height. In COPD patients, diaphragm excursions during maximal respiratory efforts were correlated with PEmax(maximal expiratory pressure), age, and %FVC. In multiple regression analysis, the combination of PEmax and age was an independent marker of diaphragm excursions during maximal respiratory efforts with COPD patients. Conclusion: COPD subjects had smaller diaphragmatic excursions during maximal respiratory efforts than control subjects. During maximal respiratory efforts in COPD patients, diaphragm excursions were well correlated with PEmax. These results suggest that diaphragm excursions during maximal respiratory efforts with COPD patients may be valuable at predicting the pulmonary function.