Dissertation > Excellent graduate degree dissertation topics show

Cobalt crusts nonlinear ultrasound identification technology

Author: XieJiDong
Tutor: XiaYiMin
School: Central South University
Course: Mechanical and Electronic Engineering
Keywords: echo recognition of cobalt crusts feature extraction nonlinear classification multi-features fusion
CLC: TN912.34
Type: Master's thesis
Year: 2011
Downloads: 20
Quote: 0
Read: Download Dissertation

Abstract


Along with the rise of ocean strategy, the world’s major developed countries gradually focused their attention on marine mineral resources. Among them, the cobalt crusts in deep sea as a kind of marine strategic resource which is of the value of commercial exploitation has been drawn focuses on by all the coutries. Currently, the world’s major developed countries have begun the research on exploration and mining of cobalt crusts. To speed up our country’s pace on the exploration and mining of cobalt crusts, somen relative technical research on the underwater recongnitino on cobalt crusts is tried to do in this paper under the support from the National Natural Science Foundation project "Study on Extraction Mechanism of Collecting Cobalt Crusts Robot and Optimal Design of Extractive Institutions".Firstly, the two representative sediment echo’s feature extraction methods in wavelet domain:envelop feature of echoed tail wave and modulus maxima feature of echo are applied to echo feature extraction of cobalt crusts. By comparative experiment, it is indicated that because of the influence of surface topography, the distribution of these two feature samples in wavelet domain degrade and the effects of linear dimensionality reduction and linear classification results are poor by constrast with the sediments of smooth surface. To improve the linear classification results, two nonlinear methods based on kernel space: KFDA (Kernel Fisher Discriminant Analysis) nonlinear dimensionality reduction method and LSSVM (Least Square Support Vector Machine) nonlinear classification method are introduced on the application of the recognition of cobalt crusts. The experimental results show that using KFDA method nonlinear discriminant features can be extracted effectively and the samples’distribution in the reduced dimensional feature space can be improved, application LSSVM seafloor classification method can improve the results, using LSSVM method the seafloor classification method results can be improved. In order to further improve the classification results, a kind of nonlinear kernel features fusion method named as KECCA(Kernel ECCA) is proposed based on CCA(Canonical Correlation Analysis) and ECCA(Enhanced CCA) linear features fusion methods and a kind of nonlinear features fusion and recognition model "KECCA+PLS" is proposed in this paper. Experimental results demonstrate that the classification results can be further improved by using the proposed model. Finally, a comprehensive experiment is done on 19 kinds of major matter in deposits of cobalt crusts using "KECCA+PLS" model. In our experiment, the average correct recognition rate of cobalt crusts is 90.2%, the average error recognition of cobalt crusts is only 3.1%, using "KECCA+PLS" model obtain a good recognition effect.The research of this dissertation provides not only the theoretical base on the recognition of cobalt crusts, but also the good theoretical reference on the recognition of other marine mineral resources and provides the effective technical support to China’s deep-sea mining.

Related Dissertations

  1. Research on Automatic Detection Algorithm for Substructure Distress of Highway Pavement Based on SVM,U418.6
  2. ISAR Imaging Simulation of Space Targets and Target Recognition Based on ISAR Images,TN957.52
  3. Research on Feature Extraction and Classification of Pulse Waveform for Cholecystitis and Nephrotic Syndrome Diagnosis,TP391.41
  4. Application of Q-Learning in the Content-Based Image Retrieval Technology,TP391.41
  5. Research on Transductive Support Vector Machine and Its Application in Image Retrieval,TP391.41
  6. Research on Feature Extraction and Classification of Tongue Shape and Tooth-Marked Tongue in TCM Tongue Diagnosis,TP391.41
  7. Research on Visual Measurement for Spacecraft Rendezvous and Approach,TP391.41
  8. Research on the Image Real-Time Acquisition, Storage and Image Processing System,TP391.41
  9. Feature Extraction, Selection and Combination in Lipreading,TP391.41
  10. Multi-currency Notes Technology Research and Implementation,TP391.41
  11. The Research on Paper Currency Classification Method Based on Harr-Like Feature and Minimal Ball Including Samples,TP391.41
  12. Pavement Distress Recognition Based on Image,TP391.41
  13. Research on Visual Detection and Tracking of Mobile Robots,TP242.62
  14. Research on Fusion Algorithm of Hyper Spectral and High Spatial Resolution Remote Sensing Image,TP751
  15. An Approach for Identifying a Plant Resistance Gene Based on the Random Forest,Q943
  16. Tobacco Diseases Auto-Recognition Research Based on Image Processing Technology,S435.72
  17. Research on Nondestructive Detection Technology for External Qualities of Papayas Based-on Vision,S667.9
  18. Research on Identification System of Cashmere and Wool Fiber,TS101.921
  19. Research for Infrared Image Target Identification and Tracking Technology,TP391.41
  20. The Compression and Fusion Technique Research of Underwater Target Feature,TN911.7
  21. Research of Diagnosing Cucumber Diseases Based on Hyperspectral Imaging,S436.421

CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing > Speech Recognition and equipment
© 2012 www.DissertationTopic.Net  Mobile