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선박조종시뮬레이션의 최저 시행에 관한 연구
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | PANBAOFENG | - |
| dc.date.accessioned | 2017-02-22T06:22:03Z | - |
| dc.date.available | 2017-02-22T06:22:03Z | - |
| dc.date.issued | 2015 | - |
| dc.date.submitted | 57071-01-11 | - |
| dc.identifier.uri | http://kmou.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002175235 | ko_KR |
| dc.identifier.uri | http://repository.kmou.ac.kr/handle/2014.oak/9411 | - |
| dc.description.abstract | In the year of 2010 the Korean government introduced an enforced maritime traffic safety assessment act for the purpose of enhancing traffic safety. The act was mandated by the law to design a new port or modify an exiting one. According to Korea Maritime Safety Act, the assessment of propriety of marine traffic system comprises safety of channel transit and berthing/unberthing maneuver, safety of mooring, and safety of marine traffic flow. The safety of channel transit and berthing/unberthing maneuver can be evaluated only by ship-handling simulation which is carried out by sea pilots working with the port concerned. The vessel's proximity measure is an important factor for evaluating traffic safety. The proximity measure is composed of vessel's closest distance to channel boundary and probability of grounding/collision. Therefore, the probability of grounding cannot be ignored. According to central limit theorem, a sample has a normal distribution on condition that its size is more than 30. However, more than 30 simulation runs lead to an increase in the assessment period which results in difficulty in employing sea pilots. Hence, this paper aims to analyze the minimum sample size for evaluating vessel's proximity. In this research, mean and standard deviation of ten cases are obtained from the latest maritime traffic safety assessment. The probability of grounding is within 10-4. Then each case generates twenty random sample sets, each set constitutes the sample 3, 4, 5, 6, 7, 9 and 11，at the same time it calculates the h value and confidence interval of each sample sets. Then the box-plots which consists of twenty sample boxes is drawn, and the mean line and confidence interval are also shown on the box-plots. In the box-plots, the X-axis refers to the sample set, while the Y-axis is CPA to channel boundary. Based on the size of confidence interval, the change of confidence interval span, the relative position of mean line and box figures, it can be indicated that the minimum number of simulation should be more than 5. After accumulating the mean and standard deviation curves of confidence interval, when the size of simulation runs is larger than 5, a much smaller mean and standard deviation of confidence interval than those in 3 can be obtained. In each case of this study, the 20 sample sets of data are obtained randomly by the parameter mean and standard deviation. If the actual data is used, it is difficult to ensure the 20 sample sets of actual data have the same mean and standard deviation. As such, it is meaningless to use the actual data to analyse. Thus this study uses the random data instead of actual data to analyse. In conclusion, this paper proposes a minimum sample size of 5, that is, the simulation should be carried out more than five times. It is recommended that actual data should be used to carry out analysis and other tests other than KS test should be applied to goodness of fit for the sample distribution. | - |
| dc.description.tableofcontents | Contents List of Tables ⅳ List of Figures ⅵ Abstract ⅷ 1. Introduction 1 1.1 Background and purpose of the study 1 1.2 Related researches of the study 2 1.3 Methodology of the study 2 2. Design of the study 4 2.1 Population and sample 4 2.2 Research process 4 2.3 Statistical method in the study 6 2.3.1 Introduction of box-plots 6 2.3.2 The use of box-plots and normal distribution function 7 3. Generation and analysis of random sample sets with less than collision probability of 10-5 10 3.1 Determination of parameters for generation of random sample set 10 3.2 Generation and analysis of random sample set from case 1 to case 5 11 3.2.1 Data of case 1 11 3.2.2 Box-figures and confidence interval of case 1 15 3.2.3 Data of case 2 16 3.2.4 Box-figures and confidence interval of case 2 20 3.2.5 Data of case 3 21 3.2.6 Box-figures and confidence interval of case 3 26 3.2.7 Data of case 4 26 3.2.8 Box-figures and confidence interval of case 4 32 3.2.9 Data of case 5 36 3.2.10 Box-figures and confidence interval of case 5 38 4. Generation and analysis of random sample sets with less than collision probability of 10-8 40 4.1 Determination of parameters for generation of random sample set 40 4.2 Generation and analysis of random sample set from case 6 to case 10 41 4.2.1 Data of case 6 41 4.2.2 Box-figures and confidence interval of case 6 42 4.2.3 Data of case 7 46 4.2.4 Box-figures and confidence interval of case 7 51 4.2.5 Data of case 8 52 4.2.6 Box-figures and confidence interval of case 8 57 4.2.7 Data of case 9 58 4.2.8 Box-figures and confidence interval of case 9 63 4.2.9 Data of case 10 64 4.2.10 Box-figures and confidence interval of case 10 69 5. The determination of the minimum simulation runs 71 5.1 Analysis of the compare result 71 5.2 Analysis of the stacking of mean and standard deviation of confidence interval span 72 6. Conclusion 75 References 78 Acknowledgement 80 List of Tables Table 3.1 The mean and standard deviation of each case 10 Table 3.2 Sample sets of three(3) runs 11 Table 3.3 Sample sets of four(4) runs 11 Table 3.4 Sample sets of five(5) runs 12 Table 3.5 Sample sets of six(6) runs 13 Table 3.6 Sample sets of three(3) runs 16 Table 3.7 Sample sets of four(4) runs 16 Table 3.8 Sample sets of five(5) runs 17 Table 3.9 Sample sets of six(6) runs 18 Table 3.10 Sample sets of three(3) runs 21 Table 3.11 Sample sets of four(4) runs 22 Table 3.12 Sample sets of five(5) runs 22 Table 3.13 Sample sets of six(6) runs 23 Table 3.14 Sample sets of three(3) runs 27 Table 3.15 Sample sets of four(4) runs 27 Table 3.16 Sample sets of five(5) runs 28 Table 3.17 Sample sets of six(6) runs 29 Table 3.18 Sample sets of three(3) runs 33 Table 3.19 Sample sets of four(4) runs 34 Table 3.20 Sample sets of five(5) runs 34 Table 3.21 Sample sets of six(6) runs 35 Table 4.1 The mean and standard deviation of each case 40 Table 4.2 Sample sets of three(3) runs 41 Table 4.3 Sample sets of four(4) runs 41 Table 4.4 Sample sets of five(5) runs 42 Table 4.5 Sample sets of six(6) runs 43 Table 4.6 Sample sets of three(3) runs 46 Table 4.7 Sample sets of four(4) runs 47 Table 4.8 Sample sets of five(5) runs 47 Table 4.9 Sample sets of six(6) runs 48 Table 4.10 Sample sets of three(3) runs 52 Table 4.11 Sample sets of four(4) runs 52 Table 4.12 Sample sets of five(5) runs 53 Table 4.13 Sample sets of six(6) runs 54 Table 4.14 Sample sets of three(3) runs 58 Table 4.15 Sample sets of four(4) runs 59 Table 4.16 Sample sets of five(5) runs 59 Table 4.17 Sample sets of six(6) runs 60 Table 4.18 Sample sets of three(3) runs 64 Table 4.19 Sample sets of four(4) runs 64 Table 4.20 Sample sets of five(5) runs 65 Table 4.21 Sample sets of six(6) runs 66 Table 5.1 The compare result of confidence interval span of case 1 to case 5 71 Table 5.2 The compare result of confidence interval span of case 6 to case 10 71 List of Figures Fig. 2.1 The Process of the study 5 Fig. 2.2 The meaning of box-plot 7 Fig. 2.3 The relationship between the box-plot and normal distribution 8 Fig. 2.4 The box-plots of 3 simulation runs(μ=79.35, σ=20.29) 8 Fig. 2.5 The box-plots of 10000 simulation runs(μ=79.35, σ=20.29) 9 Fig. 3.1 Mean of confidence interval span of case1 14 Fig. 3.2 Standard deviation of confidence interval span of case1 14 Fig. 3.3 Box-plots of case 1 15 Fig. 3.4 Mean of confidence interval span of case 2 19 Fig. 3.5 Standard deviation of confidence interval span of case 2 19 Fig. 3.6 Box-plots of case 2 20 Fig. 3.7 Mean of confidence interval span of case 3 24 Fig. 3.8 Standard deviation of confidence interval span of case 3 24 Fig. 3.9 Box-plots of case 3 26 Fig. 3.10 Mean of confidence interval span of case 4 30 Fig. 3.11 Standard deviation of confidence interval span of case 4 30 Fig. 3.12 Box-plots of case 4 32 Fig. 3.13 Mean of confidence interval span of case 5 36 Fig. 3.14 Standard deviation of confidence interval span of case 5 36 Fig. 3.15 Box-plots of case 5 38 Fig. 4.1 Mean of confidence interval span of case 6 44 Fig. 4.2 Standard deviation of confidence interval span of case 6 44 Fig. 4.3 Box-plots of case 6 45 Fig. 4.7 Mean of confidence interval span of case 7 49 Fig. 4.8 Standard deviation of confidence interval span of case 7 49 Fig. 4.9 Box-plots of case 7 51 Fig. 4.7 Mean of confidence interval span of case 8 54 Fig. 4.8 Standard deviation of confidence interval span of case 8 55 Fig. 4.9 Box-plots of case 8 57 Fig. 4.10 Mean of confidence interval span of case 9 61 Fig. 4.11 Standard deviation of confidence interval span of case 9 61 Fig. 4.12 Box-plots of case 9 63 Fig. 4.13 Mean of confidence interval span of case 10 67 Fig. 4.14 Standard deviation of confidence interval span of case 10 67 Fig. 4.15 Box-plots of case 10 69 Fig. 5.1 The superposed curves of mean and standard deviation of case 1 73 Fig. 5.2 The superposed curves of mean and standard deviation of case 10 73 | - |
| dc.language | eng | - |
| dc.publisher | 한국해양대학교 | - |
| dc.title | 선박조종시뮬레이션의 최저 시행에 관한 연구 | - |
| dc.type | Thesis | - |
| dc.date.awarded | 2015-08 | - |
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