• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.10
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• pp.1651-1658
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• 2001

It is necessary to evaluate fatigue strength and reliability of the connecting rod which is core part in automotive engine to assure the high level of durability of automobile. For this purpose, the loading conditions in automotive engine is obtained by the dynamic analysis. Based on these results, the critical section was identified by the finite element analysis. The fatigue strength under constant amplitude was evaluated and the mean of the fatigue limit at R = -2.27 derived from the staircase method was 311.2MPa. And the failure probability( F$\sub$p/ ) derived from the strength-stress interference model is 0.0003% at the 99.99% confidence level and the mean factor of safety was 4.2.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.1 no.2
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• pp.193-205
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• 1994

The Study was carried out for the purpose of investigating the degree of perception in nursing activities. The data of this study were collected by self-reported questionnaire composed of 5 point rating scale measure the ideal level and the performance level of nurses activity. For the analysis of the data, percentage, MANOVA and ANOVA were 231 nurses in 3 general hospitals in Taegu. Data was administrated from October 4 through 14, 1994. The results were as follow : 1. The average mean score for the ideal level was 4.19 with a maximum possible score 5points. The highest mean score was Infection Controll and the lowest mean score was nutrition The average mean score for the performance level was 3.75, the highest mean score was fluid and electrolyte, the lowest mean score was nutrition. In the desirable nursing pergormance, Education was found the highest response above charge nurse, Medication was found the highest response above General nurse, environment was found the highest response above aide. 2. In the analysis of the relationship between the ideal level and th performance level, significant defference was found in age, position, career, marital status, occupation satisfaction, Nursing unit, parent. 3. In the analysis of the relationship between the ideal level and the performance level and the general characteristics, significant difference was found in marital status in the ideal level of direct nursing care, significant difference was found in age, position, marital status, nursing unit in the ideal level of indirect nursing care, significant difference was found in age, position, career, marital status, occupation satisfaction, nursing unit in the performance level of direct nursing care. significant difference was found in age (25-29) and above 30 career(4-7 and 7), occupation satisfaction(good and moderate, good and poor) in scheffe test of the performance level of direct nursing care.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.5
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• pp.677-684
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• 2008

Accurate estimation of dry matter intake (DMI) is a prerequisite to meet animal performance targets without penalizing animal health and the environment. The objective of the current study was to evaluate some of the existing models in order to predict DMI when lactating dairy cows were offered a total mixed ration containing a high level of concentrates and locally produced agricultural by-products. Six popular models were chosen for DMI prediction (Brown et al., 1977; Rayburn and Fox, 1993; Agriculture Forestry and Fisheries Research Council Secretariat, 1999; National Research Council (NRC), 2001; Cornell Net Carbohydrate and Protein System (CNCPS), Fox et al., 2003; Fuentes-Pila et al., 2003). Databases for DMI comparison were constructed from two different sources: i) 12 commercial farm investigations and ii) a controlled dairy cow experiment. The model evaluation was performed using two different methods: i) linear regression analysis and ii) mean square error prediction analysis. In the commercial farm investigation, DMI predicted by Fuentes-Pila et al. (2003) was the most accurate when compared with the actual mean DMI, whilst the CNCPS prediction showed larger mean bias (difference between mean predicted and mean observed values). Similar results were observed in the controlled dairy cow experiment where the mean bias by Fuentes-Pila et al. (2003) was the smallest of all six chosen models. The more accurate prediction by Fuentes-Pila et al. (2003) could be attributed to the inclusion of dietary factors, particularly fiber as these factors were not considered in some models (i.e. NRC, 2001; CNCPS (Fox et al., 2003)). Linear regression analysis had little meaningful biological significance when evaluating models for prediction of DMI in this study. Further research is required to improve the accuracy of the models, and may recommend more mechanistic approaches to investigate feedstuffs (common to the Asian region), animal genotype, environmental conditions and their interaction, as the majority of the models employed are based on empirical approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3630-3656
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• 2018

Degree distribution can provide basic information for structural characteristics and internal relationship in social network. It is a critical procedure for social network topology analysis. In this paper, based on the mean-field theory, we study a special type of social network with exponential distribution of time intervals. First of all, in order to improve the accuracy of analysis, we propose a spreading coefficient algorithm based on intimate relationship, which determines the number of the joined members through the intimacy among members. Then, simulation show that the degree distribution of follows the power-law distribution and has small-world characteristics. Finally, we compare the performance of our algorithm with the existing algorithms, and find that our algorithm improves the accuracy of degree distribution as well as reducing the time complexity significantly, which can complete 29.04% higher precision and 40.94% lower implementation time.

• International Journal of Highway Engineering
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• v.17 no.4
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• pp.135-141
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• 2015

PURPOSES: This study has been conducted to determine a homogeneous segment and integration to improve the efficiency of short-term traffic count. We have also attempted to reduce the traffic monitoring budget. METHODS: Based on the statistical approach, a homogeneous segment in the same road section is determined. Statistical analysis using t-test, mean difference, and correlation coefficient are carried out for 10-year-long (2004-2013) short-term count traffic data and the MAPE of fresh data (2014) are evaluated. The correlation coefficient represents a trend in traffic count, while the mean difference and t-score represent an average traffic count. RESULTS : The statistical analysis suggests that the number of target segments varies with the criteria. The correlation coefficient of more than 30% of the adjacent segment is higher than 0.8. A mean difference of 36.2% and t-score of 19.5% for adjacent segments are below 20% and 2.8, respectively. According to the effectiveness analysis, the integration criteria of the mean difference have a higher effect as compared to the t-score criteria. Thus, the mean difference represents a traffic volume similarity. CONCLUSIONS : The integration of 47 road segments from 882 adjacent road segments indicate 8.87% of MAPE, which is within an acceptable range. It can reduce the traffic monitoring budget and increase the count to improve an accuracy of traffic volume estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2450-2469
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• 2018

In this paper, we develop a spatiotemporal model to analysis of cellular user in underlay D2D communication by using stochastic geometry and queuing theory. Firstly, by exploring stochastic geometry to model the user locations, we derive the probability that the SINR of cellular user in a predefined interval, which constrains the corresponding transmission rate of cellular user. Secondly, in contrast to the previous studies with full traffic models, we employ queueing theory to evaluate the performance parameters of dynamic traffic model and formulate the cellular user transmission mechanism as a M/G/1 queuing model. In the derivation, Embedded Markov chain is introduced to depict the stationary distribution of cellular user queue status. Thirdly, the expressions of performance metrics in terms of mean queue length, mean throughput, mean delay and mean dropping probability are obtained, respectively. Simulation results show the validity and rationality of the theoretical analysis under different channel conditions.

• Imaging Science in Dentistry
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• v.38 no.3
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• pp.147-151
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• 2008

Purpose : The purpose of this study was to investigate whether fractal analysis of periapical and panoramic radiographs was useful in predicting osteoporosis risk. Materials and Methods : 37 postmenoposal women between the age of 42 and 79 were classified as normal and osteoporosis group according to the bone mineral density of lumbar vertebrae and periapical and panoramic radio-graphs were taken. Fractal dimensions at periapical areas of mandibular first molars were calculated to differentiate the two groups. Results : The mean fractal dimensions of normal group on periapical and panoramic radiographs were $1.413{\pm}0.079$, $1.517{\pm}0.071$ each. The mean fractal dimensions of osteoporotic group on periapical and panoramic radiographs were $1.498{\pm}0.086$, $1.388{\pm}0.083$ each. The mean fractal dimension from peripaical radiographs of osteoporotic group was statistically significantly higher than that of normal group. The mean fractal dimension from panoramic radiographs of osteoporotic group was statistically significantly lower than that of normal group. Conclusion : Fractal analysis using periapical and panoramic radiographs was useful in predicting osteoporosis.

• Journal of Korean Society for Quality Management
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• v.33 no.3
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• pp.91-104
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• 2005

Tolerance analysis plays an important role in design and manufacturing stages for reducing manufacturing cost by improving producibility. In most production processes encountered in practice, a process mean may shift or drift in the long run although process is in control. This study discusses the feasibility of three most common inflation factors(Bender, Gilson and Six Sigma) as a correction to Root Sum of Squares(RSS) method to compensate heuristically for a shift of process mean and nonnormal component distributions from simulation experiments and proposes the guidelines for choosing the inflation factor.

• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.47-52
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• 2006

The purpose of this study was to predict successful defibrillation in ventricular fibrillation using parameters extracted by wave analysis method and neuro-fuzzy. Total 15 dogs were tested for predicting successful defibrillation. Feature parameters were extracted for return of spontaneous circulation (ROSC) and non-ROSC by wave analysis method, and these parameters are an irregularity factor, spectral moments, mean power of level-crossing spectrum, and mean of alpha-significant value. Additionally, two parameters by analyzing method of frequency were extracted into a mean of power spectrum and a mean frequency. Then extracted parameters were analyzed in which parameters result to have high performance of discriminating ROSC and non-ROSC by a statistical method of t-test. The average of sensitivity and specificity were 62.5% and 75.0%, respectively. The average of positive predictive factor and negative predictive factor were 61.2% and 75.8%, respectively.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.