• Journal of Preventive Medicine and Public Health
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• v.38 no.1
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• pp.16-24
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• 2005

Objectives : To help develop strategies to cope with the changes arising from the rapid aging process by predicting the determining factors of intention to actual use of the charged long-term care services for elderly as perceived by the middle aged who play the major role of supports. Methods : Subjects were the parents (men 177, women 507) in their 40s of the students selected from a university of Busan city. A questionnaire survey was conducted for 4 weeks in October 2003 about the knowledge for long-term care service, the intention of actual use, and the preferences about the type of service suppliers. Data analysis was performed with frequency, chi-square test, and t-test using SPSS program (ver 10.0K), along with data mining using decision tree of Enterprise Miner V8.2 by SAS. Results : About half of the subjects (53.7%) had the actual experiences of elderly supports. Intentions to use the charged services were relatively high in home visiting nursing care service (40.1%) and long-term care facilities service (40.4%), and were influenced by previous knowledge about the services. The intentions were stronger in women, those with higher education, and those with greater income levels. Actual elderly supports were mostly (80%) done by women, and the perceived burdens for the supports were bigger in women and those of lower socioeconomic level. Desired charges were about 10,000 won for the bath service, 20,000 won for the rests services per day, and about 500,000 won for the long-term care facilities service per month. From the result of decision tree analysis, the job professionalism was the most important determining factor of intention to actual use of the services with validation as $63{\sim}71%$. Health and welfare mixed type facilities were preferred, and the most important consideration was the level of professionalism. Conclusions : Intention to actual use of the charged services was largely determined by the aspects of time and cost. Polices to increase the number of service suppliers and to decrease the burdens perceived by actual supporters were strongly recommended.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.821-831
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• 2013

As a result of recent research, it is reported that the periodic replacements criterion of rails is able to extend as grinding rail surface and using the continuous welded rail (CWR). In this study, we carried out fatigue tests on existing laid rails. Based on the test results, an S-N curve expressing the remaining life of laid rails at a fracture probability of 50% was obtained using weighted probit analysis suitable for small-sample fatigue data sets. As rails used for testing had different histories in terms of accumulated tonnage, the test data were corrected to average out the accumulated tonnage. We estimated the remaining service lives for laid rails on the urban railway using equations developed in the past to estimate rail base bending stress and that surface irregularities into consideration. Therefore, estimating the remaining service life of laid rails showed that the rail replacement period could be extended over 200 MGT, although it is necessary to remove longitudinal rail surface irregularities at welds by grinding. Also, the fatigue test results under fatigue limit, Haibach's rule appling half slope of S-N curve under the fatigue limit was considered more reasonable than modified Miner's rule for estimating rail fatigue life.

• Parasites, Hosts and Diseases
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• v.47 no.3
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• pp.235-241
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• 2009

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.2
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• pp.201-208
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• 2009

Recently, the wind energy has been widely used as a renewable energy resource due to lack and environmental issues of the mostly used fossil fuel. This work is to develop a 500W class small wind turbine blade which will be applicable to relatively low speed region like Korea and for the domestic use. For this blade a high efficiency wind turbine blade was designed with the proposing aerodynamic design procedure, and a light and low cost composite structure blade was designed considering fatigue life. Structural analyses including load case study, stress, deformation, buckling and vibration analysis were performed using the Finite Element Method. The fatigue life was estimated using the load spectrum analysis and the Miner rule. In order to evaluate the designed blade, the structural and aerodynamic performance tests were carried out, and the test results were compared with the analysis results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1461-1468
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• 2015

Mostly mine support(Rock bolt and Support) which is currently general for reinforcements of a large scale tunnel is applied, Some executing a reinforcement to Poor ground by cast-in-place shotcrete. However On domestic mine conditions that couldn't having every time after mine tunnel excavation, it couldn't ensure the field batch plant which is a fixed in an issue of economies and site security, constructing it by supply gets from shotcrete materials combined in neighborhood ready mixed concrete manufacturing plants. carried shotcrete in ready mixed concrete manufacturing plants as migration length and time are falling off in quality. But, it is difficult for construction quality control By quality control arbitrariness absence at on-scene. In the present study, carry out Laboratory Test by kinds and percentages of accelerating agent for evalution of Mechanical Property and Durability of Shotcrete.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3571-3587
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• 2021

For accurately analysing and forecasting the social networks of China's political, economic and social power elites, it is necessary to develop a database that collates their information. The development of such a database involves three stages: data definition, data collection and data quality maintenance. The present study recommends distinctive solutions in overcoming the challenges that occur in existing comparable databases. We used organizational and event factors to identify the Chinese power elites to be included in the database, and used their memberships, social relations and interactions in combination with flows data collection methodologies to determine the associations between them. The system can be used to determine the optimal relationship path (i.e., the shortest path) to reach a target elite and to identify of the most important power elite in a social network (e.g., degree, closeness and eigenvector centrality) or a community (e.g., a clique or a cluster). We have used three social network analysis tools (i.e., R, UCINET and NetMiner) in order to find the important nodes in the network. We compared the results of centrality rankings of each tool. We found that all three tools are providing slightly different results of centrality. This is because different tools use different algorithms and even within the same tool there are various libraries which provide the same functionality (i.e., ggraph, igraph and sna in R that provide the different function to calculate centrality). As there are chances that the results may not be the same (i.e. centrality rankings indicating the most important nodes can be varied), we recommend a comparison test using different tools to get accurate results.

• The Journal of Korean Academic Society of Nursing Education
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• v.30 no.2
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• pp.182-191
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• 2024

Purpose: The purpose of this study was to explore the characteristics of social networks among registered nurses in acute nursing care units. Methods: This study used a survey design. Four nursing units from two acute hospitals were selected using a convenience method, and 83 nurses from those nursing units participated in the study in July 2022. The positive influences among nurses included friendship, collaboration, advice, and referent networks, and the negative influences included avoidance and bullying networks. Using the NetMiner program, the k-means clustering technique was applied to create groups of nodes with similar characteristics. The general characteristics of the participants were analyzed by mean, standard deviation, frequency, and ANOVA or chi-squared test. Results: As a result of dividing the 83 nurse participants into four clusters, positive influencers, silent peers, unwelcome peers, and active bullies were identified. Positive influence group nurses were frequently mentioned in the friendship, collaboration, advice, and referent networks. On the other hand, nurses in the unwelcome group and the active bullying group were frequently mentioned in the avoidance and bullying networks. Conclusion: Social networks that have a positive or negative impact on nursing performance are created through different relationships between nurses. Nurse managers can use the findings to create a more supportive and collaborative environment. Further research is needed to develop intervention programs to improve interactions and relationships between fellow nurses.

• The Korean Journal of Pesticide Science
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• v.19 no.3
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• pp.317-322
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• 2015

This study was conducted to obtain basic data for deriving the appropriate application amount of insecticide for effective pest control. We have investigated the correlation among the application amount of insecticide, the adhesion amount of active ingredient and the pest control effect. The linear standard curve of acetamiprid was $R^2=0.9994$, and the scope of the recovery factor was between 71% and 93% with less than 6% of the coefficient of variation. During the test conducted in 2015, the application amount was reduced to 302 L/10a which was 70 L less than the previous year and the spraying pressure was reduced as well. After analyzing the adhesion amount of active ingredient, it was found that a correlation coefficient of adhesion amount of the active ingredients became lower from 82% to 69%, that indicated insecticide liquid was evenly distributed compared to 2014. Also more than 95% of control effect was displayed, thereby indicating that there is a close relation with the correlation coefficient of the adhesion amount of active ingredient. It also presented that the adhesion amount of active ingredient on the ground was 2.2 times more than that on the leaves of apple trees.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.