ダム下流河道における河床の低撹乱化に着目した水域生態系評価に関する研究

名古屋大学Nagoya University博士(工学)名古屋大学博士学位論文 学位の種類:博士(工学) (課程) 学位授与年月日:平成16年3月25日doctoral thesi

エネルギに基づく新型磨耗ダンパの開発に関する基礎研究

For the past several years, seismic isolation systems have been focused in the seismic design of industrial facilities. This paper deals with the response reduction effect by friction. Friction is expected to decrease the relative displacement by dissipating the vibration energy. The relative displacement between structure and support becomes larger at long period in the case of linear system. Therefore, the response reduction effect by friction should be utilized positively in the seismic design. However, the response magnification of acceleration is used in the seismic design. The acceleration becomes larger by stick and slip motion. Therefore, the response reduction effect by friction is evaluated from the viewpoint of energy. The ratio between elastic energy and input energy is utilized. In this paper, “Response Reduction Map” based on energy for long period friction system is suggested. This map shows the range that energy response ratio is less than the ratio of the linear system. The response reduction map without complex non-linear time history calculation can easily obtain the proper parameters, such as natural period or friction force. In this study, the response reduction map by the artificial earthquake wave calculated from the design spectrum is shown. Next, the advanced friction damper is developed by using above energy based result. The friction part of the advanced friction damper is constructed by using MR fluid. The characteristics of the friction part are evaluated.textapplication/pdfdepartmental bulletin pape

Evidence for Muon Neutrino Oscillation in an Accelerator-Based Experiment

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Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data-0

And without correction is shown on the - and -axis, respectively. The diagonal line is inserted to guide the eye. There is no significant change in the distribution of score magnitudes.<p><b>Copyright information:</b></p><p>Taken from "Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data"</p><p>http://www.biomedcentral.com/1471-2164/9/25</p><p>BMC Genomics 2008;9():25-25.</p><p>Published online 19 Jan 2008</p><p>PMCID:PMC2267710.</p><p></p

Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data-1

Lated with and without correction, is shown on the - and -axis, respectively. The diagonal line is inserted to guide the eye. The corrected imputation method results in larger variations of expression.<p><b>Copyright information:</b></p><p>Taken from "Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data"</p><p>http://www.biomedcentral.com/1471-2164/9/25</p><p>BMC Genomics 2008;9():25-25.</p><p>Published online 19 Jan 2008</p><p>PMCID:PMC2267710.</p><p></p

Predicting the grouping for a patient.

<p>Each ANN is trained to identify either high risk or low risk individuals. When combined into an ensemble, majority voting determines if the patient is classified as high risk, low risk, or intermediate risk. <i>N</i>/2 is chosen to be odd, in order to avoid ties in each subgroup.</p

Predictions for the test set.

<p>Overall the results for the three models are quite similar. One notable exception is for <i>lung</i> where the low risk group predicted by Rpart actually has the worst survival.</p

Group sizes for the test set.

<p>Group sizes for the test set.</p

Median survival time in 10 × 3 cross-validation.

<p>This is only possible to compute if a group’s survival rate reaches 0.5, which no grouping in <i>nwtco</i> did. Groupings in <i>lung</i> had so poor survival that even the low-risk groups could be included.</p

Schematic of the training procedure.

<p>It is important to have similar group sizes so that the properties of the survival curves can be compared. To be able to include Rpart in the comparison, it is necessary to compensate for its inability to pre-determine suitable group sizes. The predicted group sizes on the training data (only sizes of groups, not the actual predictions) are set as parameters on Cox and ANN to generate similarly sized risk groups later on the test/validation data.</p