請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42414
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 謝志豪(Chih-Hao Hsieh) | |
dc.contributor.author | Chun-Yi Chang | en |
dc.contributor.author | 張君屹 | zh_TW |
dc.date.accessioned | 2021-06-15T01:13:22Z | - |
dc.date.available | 2009-07-31 | |
dc.date.copyright | 2009-07-31 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-07-29 | |
dc.identifier.citation | Beaugrand, G., P. C. Reid, F. Ibanez, J. Alistair, and M. Edwards. 2002. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296: 1692-1694.
Bell, J. L., and R. R. Hopcroft. 2008. Assessment of ZooImage as a tool for the classification of zooplankton. Journal of Plankton Research 30: 1351-1367. Benfield, M. C. and others 2007. RAPID- Research on Automated Plankton Identification. Oceanography 20: 12-26. Culverhouse, P. F., R. Ellis, R. G. Simpson, R. Williams, R. W. Pierce, and J. T. Turner. 1994. Automatic categorization of 5 species of Cymatocylis (Protozoa, Tintinnida) by artificial neural network. Marine Ecology Progress Series 107: 273-280. Culverhouse, P. F. and others 1996. Automatic classification of field-collected dinoflagellates by artificial neural network. Marine Ecology Progress Series 139: 281-287. Faith, D. P., P. R. Minchin, and L. Belbin. 1987. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69: 57-68. Fernandes, J. A., X. Irigoien, G. Boyra, J. A. Lozano, and I. Inza. 2009. Optimizing the number of classes in automated zooplankton classification. Journal of Plankton Research 31: 19-29. Gong, G.-C. and others 2006. Reduction of primary production and changing of nutrient ratio in the East China Sea: Effect of the Three Gorges Dam? Geophysical Research Letters 33: L07610. Grosjean, P., M. Picheral, C. Warembourg, and G. Gorsky. 2004. Enumeration, measurement, and identification of net zooplankton samples using the ZOOSCAN digital imaging system. ICES Journal of Marine Science 61: 518-525. Herman, A. W. 1988. Simultaneous measurement of zooplankton and light attenuance with a new optical plankton counter. Continental Shelf Research 8: 205-221. Herman, A. W., B. Beanlands, and E. F. Phillips. 2004. The next generation of Optical Plankton Counter: the Laser-OPC. Journal of Plankton Research 26: 1135-1145. Ide, K., K. Takahashi, A. Kuwata, M. Nakamachi, and H. Saito. 2008. A rapid analysis of copepod feeding using FlowCAM. Journal of Plankton Research 30: 275-281. Lee, H.-J., and S.-Y. Chao. 2003. A climatological description of circulation in and around the East China Sea. Deep Sea Research Part II: Topical Studies in Oceanography 50: 1065-1084. Legendre, P., and L. Legendre. 1998. Numerical Ecology, 2nd English Edition. Elsevier, Amsterdam. Liaw, A., and M. Wiener. 2002. Classification and Regression by randomForest. R News 2: 18-22. Picheral, M. 2008. Zooprocess Manual version 5.08. www.zooscan.com. R Development Core Team. 2008. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org. Stephane, G. 2007. PLANKTON IDENTIFIER: a software for automatic recognition of planktonic organisms. http://www.obs-vlfr.fr/~gaspari/Plankton_Identifier/index.php Tang, X. and others 1998. Automatic Plankton Image Recognition. Artificial Intelligence Review 12: 177-199. Tungate, D., and E. Reynolds. 1980. The Lowestoft on-line particle counting system. MAFF Direct. Fisheries Research, Lowestoft. Fisheries Technical Report 58: 1-11. Wiebe, P. H., and M. C. Benfield. 2003. From the Hensen net toward four-dimensional biological oceanography. Progress In Oceanography 56: 7-136. Wohlers, J. and others 2009. Changes in biogenic carbon flow in response to sea surface warming. Proceedings of the National Academy of Sciences 106: 7067-7072. Zuo, T., R. Wang, Y. Q. Chen, S. W. Gao, and K. Wang. 2006. Autumn net copepod abundance and assemblages in relation to water masses on the continental shelf of the Yellow Sea and East China Sea. Journal of Marine Systems 59: 159-172. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42414 | - |
dc.description.abstract | 本研究目的在於發展一套浮游動物影像自動化辨認系統。以東海的浮游動物為材料,探討此系統在這個西太平洋陸棚海域的實用性。東海的水文環境狀況複雜,從沿岸地區受到長江淡水注入的影響,直至東海陸棚外受黑潮的影響都包括在其中。在這樣複雜的水文狀況下,我想要測試水體訓練樣本的準確率會高於全域訓練樣本準確率的假設(水體專一性的影響)。在記錄了兩兩測站之間的交叉辨認率之後,發現辨認率隨著兩兩測站之間環境因子異質性的遞增而遞減;例如,同屬近岸地區的測站交互辨認率會高於以近岸測站去辨認遠岸測站。另外,交叉辨認率的矩陣結構與環境異質性的矩陣結構有顯著地相關性。這些結果確認了水體專一性的影響,因此假設成立。不符合預期的是,全域訓練樣本的正確率(平均75%)皆不比最好的水體訓練樣本來得差,我們發現這是因為大部分觀察到的正確率皆受到優勢類群的影響,顯示出水體訓練樣本在當下的分類解析度之中效能有限。但重點是,由自動化辨認所得到的群聚結構仍然可以解釋大部分的環境變異,說明了這套系統實用的可能性。 | zh_TW |
dc.description.abstract | We developed an automatic classification system for mesozooplankton in the East China Sea, a region with complicated environmental variations ranging from coastal areas affected by river runoff to the shelf break influenced by the Kuroshio. Considering the large variation of water masses, we test the hypothesis that a water mass-specific training set (WTS) would perform better than a regional training set (RTS, which is randomly assembled from all stations). To test the water mass specificity, we evaluated pair-wise cross predictions for all stations. We found that cross-prediction accuracy decreased with an increase in environmental dissimilarity; for example, mutual predictions between coastal stations performed better than those using coastal stations to predict Kuroshio stations. Furthermore, the cross-prediction matrix is significantly correlated with the similarity matrix derived from environmental variables. These results suggest clear water mass specificity in training sets. However surprisingly, the prediction accuracy (with an average of 75%) of RTS performs equally well as the best WTS results for each station. This is mainly due to the contribution of dominant zooplankton categories to the overall accuracy rate. This suggests the benefit of WTS is limited for the coarse taxonomic resolution (order or higher) employed here. The machine-identified species composition (albeit containing ~38.5% error) still explained a significant amount of variance associated with the environmental gradient, demonstrating the potential capability of the automatic classification system. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T01:13:22Z (GMT). No. of bitstreams: 1 ntu-98-R96241211-1.pdf: 1236638 bytes, checksum: afdce876f04eb393e2444bcadb715b1c (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | Introduction 1
Materials and Methods 5 Sample preparation 5 Sample digitizing 6 Construction of training sets 7 Machine learning algorithm 9 Data analysis 10 Results 12 Discussion 15 References 54 Appendices 57 | |
dc.language.iso | en | |
dc.title | 發展浮游動物影像自動化辨認系統 – 以東海生態系為例 | zh_TW |
dc.title | Development and application of an automatic image classification system for the mesozooplankton in the East China Sea | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 丘臺生(Tai-Sheng Chiu),李明安(Ming-An Lee) | |
dc.subject.keyword | 水體訓練樣本,全域訓練樣本,影像分析,機器學習,水文環境,浮游動物群聚結構, | zh_TW |
dc.subject.keyword | water mass-specific training set,regional training set,image analysis,machine-learning,hydrography,zooplankton community, | en |
dc.relation.page | 60 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-07-29 | |
dc.contributor.author-college | 理學院 | zh_TW |
dc.contributor.author-dept | 海洋研究所 | zh_TW |
顯示於系所單位: | 海洋研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-98-1.pdf 目前未授權公開取用 | 1.21 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。