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- 저자 : Dongmei Pei (검색결과 3 건)
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Pei Dongmei,Meng Fanjun,Wang HaiLong 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.8
In order to reduce the data storage and improve data compression ratio of the stiffness matrix of 3D finite element, after analyzed the relationship between nonzero submatrix and generalized adjacent nodes of the stiffness matrix, this paper proposes an improved stiffness matrix compression algorithm, which combined negative sign compressed sparse line and a rider to store binary classification method. Then the improved algorithm is applied to the storage of the stiffness matrix of 3D-FEM. Through experimental simulation, the results show that this method saves a lot of storage space to ensure the validity of data for finite element analysis.
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Research Progress of Visual Inspection of Tray Seedling and the System of Automatic Transplanting
Dongmei Pei,Fanjun Meng,HaiLong Wang 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.7
As a kind of modern technology of growing seedling, Tray seedling is efficient with little plant diseases and insect pests, and save labors, which make it develop rapidly in domestic. When growing seedlings, there are not only holes that are not germinant or missed, but also inferior seedlings, which lead to leakage of planting and empty of planting during follow-up mechanized transplanting. In order to utilize each area of holes and seedbeds, holes without seedlings need to be transplanted filling the gaps with seedlings. The paper introduces research of visual inspection of tray seedling and the system of automatic transplanting domestic and overseas, explores the research progress of automatic transplanting system from four aspects, which are technic of visual inspection, end effector, control system and route optimization. The paper proposes existing problems and development of domestic visual inspection and automatic transplanting system, which offers references for follow-up study and marketing application.
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Dongmei Pei,Shuang Wang,Chenhui Bao 연세대학교의과대학 2024 Yonsei medical journal Vol.65 No.4
Purpose: The purpose of this study was to use data mining methods to establish a simple and reliable predictive model based on the risk factors related to gallbladder stones (GS) to assist in their diagnosis and reduce medical costs. Materials and Methods: This was a retrospective cross-sectional study. A total of 4215 participants underwent annual health ex aminations between January 2019 and December 2019 at the Physical Examination Center of Shengjing Hospital Affiliated to Chi na Medical University. After rigorous data screening, the records of 2105 medical examiners were included for the construction of J48, multilayer perceptron (MLP), Bayes Net, and Naïve Bayes algorithms. A ten-fold cross-validation method was used to verify the recognition model and determine the best classification algorithm for GS. Results: The performance of these models was evaluated using metrics of accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve. Comparison of the F-measure for each algorithm revealed that the F-measure values for MLP and J48 (0.867 and 0.858, respectively) were not statistically significantly different (p>0.05), although they were significantly higher than the F-measure values for Bayes Net and Naïve Bayes (0.824 and 0.831, respectively; p<0.05). Conclusion: The results of this study showed that MLP and J48 algorithms are effective at screening individuals for the risk of GS. The key attributes of data mining can further promote the prevention of GS through targeted community intervention, improve the outcome of GS, and reduce the burden on the medical system.
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