An investigation of K-means clustering to high and multi-dimensional biological data

PURPOSE – The K-means clustering algorithm has been intensely researched owing to its simplicity of implementation and usefulness in the clustering task. However, there have also been criticisms on its performance, in particular, for demanding the value of K before the actual clustering task. It is evident from previous researches that providing the number of clusters a priori does not in any way assist in the production of good quality clusters. The authors' investigations in this paper also confirm this finding. The purpose of this paper is to investigate further, the usefulness of the K-means clustering in the clustering of high and multi-dimensional data by applying it to biological sequence data. DESIGN/METHODOLOGY/APPROACH – The authors suggest a scheme which maps the high dimensional data into low dimensions, then show that the K-means algorithm with pre-processor produces good quality, compact and well-separated clusters of the biological data mapped in low dimensions. For the purpose of clustering, a character-to-numeric conversion was conducted to transform the nucleic/amino acids symbols to numeric values.1 pdf file.Barileé B. Baridam, M. M. Alihttp://www.emeraldsight.com/journalshtm?/issn=0368-492Xhb201

Using sequential deviation to dynamically determine the number of clusters found by a local network neighbourhood artificial immune system

Many of the existing network theory based artificial immune systems have been applied to data clustering. The formation of artificial lymphocyte (ALC) networks represents potential clusters in the data. Although these models do not require any user specified parameter of the number of required clusters to cluster the data, these models do have a drawback in the techniques used to determine the number of ALC networks. This paper discusses the drawbacks of these techniques and proposes two alternative techniqueswhich can be used with the local network neighbourhood artificial immune system. The end result is an enhanced model that can dynamically determine the number of clusters in a data set

Object Segmentation Using Active Contours: A Level Set Approach

Image segmentation is responsible for partitioning an image into sub-regions based on a preferred feature. Active contour models have widely been used for image segmentation. The use of level set theory has enriched the implementation of active contours with more flexibility and simplicity. The past models of active contours rely on a gradient based stopping function to stop the curve evolution. However, when using gradient information for noisy and textured images, the evolving curve may pass through, or stop far from the salient object boundaries. We propose using a polarity based stopping function. Comparing to the gradient information, the polarity information accurately distinguishes the boundaries or edges of the salient objects more precisely. With combining the polarity information with the active contour model, we obtain an efficient active contour model for salient object detection. Experiments are performed on several images to show the advantage of the polarity based active contour

Acoustic Emission Signal Classification for Gearbox Failure Detection

The purpose of this research is to develop a methodology and technique to determine the optimal number of clusters in acoustic emission (AE) data obtained from a ground test stand of a rotating H-60 helicopter tail gearbox by using mathematical algorithms and visual inspection. Signs of fatigue crack growth were observed from the AE signals acquired from the result of the optimal number of clusters in a data set. Previous researches have determined the number of clusters by visually inspecting the AE plots from number of iterations. This research is focused on finding the optimal number of clusters in the data set by using mathematical algorithms then using visual verification to confirm it. The AE data were acquired from the ground test stand that simulates the tail end of an H-60 Seahawk at Naval Air Station in Patuxant River, Maryland. The data acquired were filtered to eliminate durations that were greater than 100,000 ìs and 0 energy hit data to investigate the failure mechanisms occurring on the output bevel gear. From the filtered data, different AE signal parameters were chosen to perform iterations to see which clustering algorithms and number of outputs is the best. The clustering algorithms utilized are the Kohonen Self-organizing Map (SOM), k-mean and Gaussian Mixture Model (GMM). From the clustering iterations, the three cluster criterion algorithms were performed to observe the suggested optimal number of cluster by the criterions. The three criterion algorithms utilized are the Davies-Bouldin, Silhouette and Tou Criterions. After the criterions had suggested the optimal number of cluster for each data set, visual verification by observing the AE plots and statistical analysis of each cluster were performed. By observing the AE plots and the statistical analysis, the optimal number of cluster in the data set and effective clustering algorithms were determined. Along with the optimal number of clusters and effective clustering algorithm, the mechanisms of each cluster can be determined from the statistical analysis as well. From the results, the 5 cluster output using the Kohonen SOM clustering algorithm showed the distinct separation of clusters. Using the determined number of clusters and the effective clustering algorithms, the AE data sets were analyzed for the fatigue crack growth. Recorded data from the mid test and end test of the data acquisition period were utilized. After each set of clusters were associated with different mechanisms dependent on their AE characteristics. It was possible to detect the increase in the activities of the fatigue crack data points. This indicates that the fatigue crack is growing as the acquisition continued on the H-60 Seahawk ground test stand and that AE has a good potential for early crack detection in gearbox components

Neural Network Fatigue Life Prediction in Steel I-Beams Using Mathematically Modeled Acoustic Emission Data

The purpose of this research is to predict fatigue cracking in metal beams using mathematically modeled acoustic emission (AE) data. The AE data was collected from nine samples of steel I-beam that were subjected to three-point bending caused by cyclic loading. The data gathered during these tests were filtered in order to remove long duration hits, multiple hit data, and obvious outliers. Based on the duration, energy, amplitude, and average frequency of the AE hits, the filtered data were classified into the various failure mechanisms of metals using NeuralWorks® Professional II/Plus software based self-organizing map (SOM) neural network. The parameters from mathematically modeled AE failure mechanism data were used to predict plastic deformation data. Amplitude data from classified plastic deformation data is mathematically modeled herein using bounded Johnson distributions and Weibull distribution. A backpropagation neural network (BPNN) is generated using MATLAB®. This BPNN is able to predict the number of cycles that ultimately cause the steel I-beams to fail via five different models of plastic deformation data. These five models are data without any mathematical modeling and four which are mathematically modeled using three methods of bounded Johnson distribution (Slifker and Shapiro, Mage and Linearization) and Weibull distribution. Currently, the best method is the Linearization method that has prediction error not more than 17%. Multiple linear regression (MLR) analysis is also performed on the four sets of mathematically modeled plastic deformation data as named above using the bounded Johnson and Weibull shape parameters. The MLR gives the best prediction for the Linearized method which has a prediction error not more than 2%. The final conclusion made is that both BPNN and MLR are excellent tools for accurate fatigue life cycle prediction

Investigations on number selection for finite mixture models and clustering analysis.

by Yiu Ming Cheung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 92-99).Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.1.1 --- Bayesian YING-YANG Learning Theory and Number Selec- tion Criterion --- p.5Chapter 1.2 --- General Motivation --- p.6Chapter 1.3 --- Contributions of the Thesis --- p.6Chapter 1.4 --- Other Related Contributions --- p.7Chapter 1.4.1 --- A Fast Number Detection Approach --- p.7Chapter 1.4.2 --- Application of RPCL to Prediction Models for Time Series Forecasting --- p.7Chapter 1.4.3 --- Publications --- p.8Chapter 1.5 --- Outline of the Thesis --- p.8Chapter 2 --- Open Problem: How Many Clusters? --- p.11Chapter 3 --- Bayesian YING-YANG Learning Theory: Review and Experiments --- p.17Chapter 3.1 --- Briefly Review of Bayesian YING-YANG Learning Theory --- p.18Chapter 3.2 --- Number Selection Criterion --- p.20Chapter 3.3 --- Experiments --- p.23Chapter 3.3.1 --- Experimental Purposes and Data Sets --- p.23Chapter 3.3.2 --- Experimental Results --- p.23Chapter 4 --- Conditions of Number Selection Criterion --- p.39Chapter 4.1 --- Alternative Condition of Number Selection Criterion --- p.40Chapter 4.2 --- Conditions of Special Hard-cut Criterion --- p.45Chapter 4.2.1 --- Criterion Conditions in Two-Gaussian Case --- p.45Chapter 4.2.2 --- Criterion Conditions in k*-Gaussian Case --- p.59Chapter 4.3 --- Experimental Results --- p.60Chapter 4.3.1 --- Purpose and Data Sets --- p.60Chapter 4.3.2 --- Experimental Results --- p.63Chapter 4.4 --- Discussion --- p.63Chapter 5 --- Application of Number Selection Criterion to Data Classification --- p.80Chapter 5.1 --- Unsupervised Classification --- p.80Chapter 5.1.1 --- Experiments --- p.81Chapter 5.2 --- Supervised Classification --- p.82Chapter 5.2.1 --- RBF Network --- p.85Chapter 5.2.2 --- Experiments --- p.86Chapter 6 --- Conclusion and Future Work --- p.89Chapter 6.1 --- Conclusion --- p.89Chapter 6.2 --- Future Work --- p.90Bibliography --- p.92Chapter A --- A Number Detection Approach for Equal-and-Isotropic Variance Clusters --- p.100Chapter A.1 --- Number Detection Approach --- p.100Chapter A.2 --- Demonstration Experiments --- p.102Chapter A.3 --- Remarks --- p.105Chapter B --- RBF Network with RPCL Approach --- p.106Chapter B.l --- Introduction --- p.106Chapter B.2 --- Normalized RBF net and Extended Normalized RBF Net --- p.108Chapter B.3 --- Demonstration --- p.110Chapter B.4 --- Remarks --- p.113Chapter C --- Adaptive RPCL-CLP Model for Financial Forecasting --- p.114Chapter C.1 --- Introduction --- p.114Chapter C.2 --- Extraction of Input Patterns and Outputs --- p.115Chapter C.3 --- RPCL-CLP Model --- p.116Chapter C.3.1 --- RPCL-CLP Architecture --- p.116Chapter C.3.2 --- Training Stage of RPCL-CLP --- p.117Chapter C.3.3 --- Prediction Stage of RPCL-CLP --- p.122Chapter C.4 --- Adaptive RPCL-CLP Model --- p.122Chapter C.4.1 --- Data Pre-and-Post Processing --- p.122Chapter C.4.2 --- Architecture and Implementation --- p.122Chapter C.5 --- Computer Experiments --- p.125Chapter C.5.1 --- Data Sets and Experimental Purpose --- p.125Chapter C.5.2 --- Experimental Results --- p.126Chapter C.6 --- Conclusion --- p.134Chapter D --- Publication List --- p.135Chapter D.1 --- Publication List --- p.13

Gezielte Erzeugung mikroskopischer Schädigungsarten von Faserverbundwerkstoffen in Kombination mit Schallemissionsanalyse

Eine der größten Herausforderungen hinsichtlich der industriellen Anwendung von kohlenstofffaserverstärkten Kunststoffen (CFK) stellt das komplexe Versagensverhalten des Werkstoffs dar, da unter mechanischer Belastung eine Vielzahl von unterschiedlichen mikroskopischen Schadensmechanismen auftritt. Hierdurch werden oftmals erhebliche Materialpuffer verwendet, die dem enormen Leichtbaupotential entgegenwirken. Die Initialisierung einer jeden Schädigung ist mit der Entstehung und Abstrahlung einer elastischen Welle verbunden, die für jeden Schadensmechanismus charakteristisch ist und daher als Fingerprint der Schädigung betrachtet werden kann. Die sogenannte Schallemissionsanalyse stellt daher für die Untersuchung des Bruchverhaltens von CFK-Bauteilen eine geeignete Methode dar. Im Rahmen der vorliegenden Arbeit wird der Aufbau einer mikromechanischen Prüfvorrichtung vorgestellt, mit der verschiedene mikroskopische Schadensmechanismen von CFK-Materialien mit Hilfe von Modell-Verbunden gezielt einzeln erzeugt werden und mit Hilfe der Schallemissionsanalyse ausgewertet werden. Der Ansatz ermöglicht die Erzeugung und Analyse eines Faserbruchs, eines Faserauszugs (samt Faser-Matrix-Debonding) sowie eines Matrixrisses. Die aufgezeichneten Schallemissionssignale der unterschiedlichen Schadensmechanismen werden erstmals als Volumenwellen detektiert und deren reflexionsfreier Teil im Zeit- und Frequenzraum auf Gemeinsamkeiten und Unterschiede hin untersucht. Außerdem können die einzelnen Schädigungsarten optisch (Licht- und Rasterelektronenmikroskopie) und anhand ihrer Kraft-Weg-Signale charakterisiert werden. Im Rahmen dieser Arbeit wird eine Kohlenstofffaser/Epoxidharz-Kombination angewendet. Die mikromechanischen Eigenschaften der sich zwischen Faser und Matrix ausbildenden Interphase können durch die Variation der Oberflächenbehandlung der Kohlenstofffaser und des Aushärtezyklus des Epoxidharzes gezielt verändert und mit Hilfe von Einzelfaserauszugstests interpretiert werden. Ein weiterer wesentlicher Bestandteil der Arbeit ist die Entwicklung und Umsetzung des Einzelfaserfragmentierungstests, ebenfalls gestützt durch die Schallemissionsanalyse. Die verschiedenen Schadensmechanismen sollen anhand ihrer charakteristischen Gemeinsamkeiten lokalisiert und identifiziert werden. Dabei ist es möglich, den Einfluss der Aushärtetemperatur des Epoxidharzes auf die thermischen Spannungen der eingebetteten Kohlenstofffaser und die Folge für den Ablauf des Einzelfaserfragmentierungstests darzustellen.One of the main challenges concerning the industrial application of carbon fiber reinforced plastics (CFRP) is the complex failure behaviour of this class of materials, especially the occurence of several microscopic types of failure in the material under mechanical loading. As a consequence, there exists a large contradiction between a huge material buffer and the lightweight potential. Each failure mechanism is accompanied by a respective excitation of an elastic wave, so that the recorded acoustic emission signal is kind of a fingerprint of each failure type. The acoustic emission analysis is a powerfull tool to investigate the fracture behaviour of CFRP materials. In the following, the experimental setup of a micromechanical test stage is presented, which was constructed to allow the manufacturing of different microscopic failure types in CFRP materials by using single fiber compounds and a direct correlation to the excited acoustic emission signals. In the micromechanical test stage, a realization and analysis of fiber breakage, fiber pull-out (including fiber-matrix-debonding) and matrix cracking is possible. The acoustic emission signals are recorded as bulk waves, where a small observation window of the primary acoustic emission signal is free of reflections and can be investigated concerning similiarities and differences in the time and the frequency domain. The individual failure types are also analysed by optical means (light microscopy and scanning electron microscopy) and by the related force-displacement curves. In this work, the combination of a carbon fiber and an epoxy matrix is used. The mechanical properties of the fiber-matrix-interphase are altered by a variation of the surface treatment of the fiber and the curing cycle of the epoxy resin, which can be interpreted by single-fiber pull-out-tests. A further essential part of this work is the development and implementation of the single-fiber fragmentation test and a direct correlation to the respective acoustic emission signals. The certain failure mechanisms are localized and identified according to similar characteristics in acoustic emission signals. It is possible to show the influence of the curing temperature on the thermal stresses inside the embedded fiber and its consequences for the single-fiber fragmentation test