レーザー誘導破壊分光法を用いた水中で計測した熱水海底岩石の分類のための機械学習アルゴリズムの検討と統計解析

The aim is to study the use of machine learning algorithms for the classification hydrothermal seafloor rocks measured underwater using Laser-Induced Breakdown Spectroscopy. The rocks were classified concerning their labels assigned to each rock, and geological groups formed ternary diagram with the relative ratio of Cu-Pb-Zn. In this research the target rocks are obtained from deep-ocean in Okinawa Japan. These were hydrothermal deposit sea-floor rocks. Further, these rocks were brought into the laboratory and broken into pieces and made the pellets. The experimental setup which resembles the ocean, then to test using ChemiCam device which is a LIBS device which is specifically designed for the chemical elemental analysis in the deep ocean is used to fire the laser beams on rocks. The proposed methods for classification of rocks with respect to their labels and for geological group are evaluated using with and without linear detrend along with the principal component analysis (PCA) as a pre-processing step which significantly reduces the dimensionality of the data, with classification algorithms such as the support vector machine (SVM), k-nearest neighbor search (KNN) and artificial neural network (ANN) methods. The performance of the classification algorithms depends on the size of the dataset, to prove this the dataset has been divided into two sets of 100 laser shots of each rock and 300 laser shots of each rock. Additionally, removing the noise from the spectra such as linear trend using linear detrend operation from the data enhances the performance of the classification in terms of sensitivity. The best classification performance concerning the rock label concerning sensitivity is obtained using an SVM linear kernel algorithm with 95%. The best classification performance concerning the geological group is obtained using the SVM method with 98% accuracy. The one-sided Wilcoxon signed rank test is applied to the classification results in the rock label and group cases, and the results indicate that the SVM algorithm has statistical significance over the other algorithms while classifying the rock labels and rock group.九州工業大学博士学位論文 学位記番号：生工博甲第345号 学位授与年月日：平成31年3月25日1 Introduction|2 Machine Learning Models for Classification|3 Result and Discussion|4 Conclusion and Future Scope九州工業大学平成30年

Classification of metals by means of Laser-induced Breakdown Spectroscopy and chemometric methods

Táto diplomová práca sa zaoberá klasifikáciou kovov pomocou spektroskopie laserom indukovanej plazmy (LIBS) a chemometrických metód. Práca poskytuje prehľad o štúdiách na danú tému. Sú vybrané tri široko používané chemometrické klasifikačné metódy: "Soft Independent Modeling of Class Analogy" (SIMCA), "Partial Least Squares Discriminant Analysis" (PLS-DA) a variácia umelých neurónových sietí (ANN), "Feedforward Multilayer Perceptron". Rôzne prístupy k prieskumovej analýze su tiež preskúmané. Metódy sú stručne opísané. Následne sú klasifikátory experimentálne porovnané.This thesis deals with the classification of metals by means of laser-induced breakdown spectroscopy (LIBS) and chemometric methods. The work gives a review of the studies reported on the subject. Three widely used chemometric classification methods are selected: Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA) and a variation of Artificial Neural Networks (ANN), the Feedforward Multilayer Perceptron. Several approaches to exploratory data analysis are also considered. The methods are described, briefly stating their working principle. Subsequently, the performance of the classifiers is experimentally assessed, using several figures of merit.

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities

Interpreting Machine Learning Models and Application of Homotopy Methods

Neural networks have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications. We show that a trained neural network can be interpreted using flip points. A flip point is any point that lies on the boundary between two output classes: e.g. for a neural network with a binary yes/no output, a flip point is any input that generates equal scores for ``yes" and ``no". The flip point closest to a given input is of particular importance, and this point is the solution to a well-posed optimization problem. We show that computing closest flip points allows us, for example, to systematically investigate the decision boundaries of trained networks, to interpret and audit them with respect to individual inputs and entire datasets, and to find vulnerability against adversarial attacks. We demonstrate that flip points can help identify mistakes made by a model, improve its accuracy, and reveal the most influential features for classifications. We also show that some common assumptions about the decision boundaries of neural networks can be unreliable. Additionally, we present methods for designing the structure of feed-forward networks using matrix conditioning. At the end, we investigate an unsupervised learning method, the Gaussian graphical model, and provide mathematical tools for interpretation