Pattern recognition for Space Applications Center director's discretionary fund

Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study

Extended Fuzzy Clustering Algorithms

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes

Fuzzy clustering with volume prototypes and adaptive cluster merging

Two extensions to the objective function-based fuzzy clustering are proposed. First, the (point) prototypes are extended to hypervolumes, whose size can be fixed or can be determined automatically from the data being clustered. It is shown that clustering with hypervolume prototypes can be formulated as the minimization of an objective function. Second, a heuristic cluster merging step is introduced where the similarity among the clusters is assessed during optimization. Starting with an overestimation of the number of clusters in the data, similar clusters are merged in order to obtain a suitable partitioning. An adaptive threshold for merging is proposed. The extensions proposed are applied to Gustafson–Kessel and fuzzy c-means algorithms, and the resulting extended algorithm is given. The properties of the new algorithm are illustrated by various examples

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Modeling of objects using planar facets in noisy range images

Products designed and manufactured before the advent of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) technology have not been documented electronically. To avoid the laborious procedure of redesigning the parts, a reverse engineering approach can be adopted. This approach involves, taking a picture of the object and constructing a solid model from the image data. Range image is a three dimensional image of an object or a scene. This image can be obtained from special cameras, called range image cameras, or can be constructed from the Coordinate Measuring Machine\u27s (CMM) output data. Adaptive Fuzzy c-Elliptotype (AFC) clustering algorithm is used to identify the planar facets in a range image. A modified version of AFC algorithm can handle noisy range images. Unknown number of planar facets can be identified using the Agglomerative clustering approach. The object is reconstructed using segmented image data. The equations of the edge are obtained from the plane intersections. An edge validity criterion is developed to validate the existence of an edge. Vertices are the two extreme points on the edge. A Boundary representation of the object is developed. The information about this object is then passed to a CAD software using Initial Graphics Exchange Specification (IGES)

Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequences for dataset shift of including different regressand stratification schemes in cross-validation with regression data. The results obtained show that these allow for creating more similar training and test sets, reducing the presence of dataset shift related to cross-validation. The bias and deviation of the performance estimation results obtained by regression algorithms are improved using the highest amounts of strata, as are the number of cross-validation repetitions necessary to obtain these better results.MCIU/AEI/ERDF, UE PGC2018098860-B-I00ERDF Operational Programme 2014-2020Economy and Knowledge Council of the Regional Government of Andalusia, Spain MCIN/AEI CEX2020-001105-M A-FQM-345-UGR1

Brain electrical activity discriminant analysis using Reproducing Kernel Hilbert spaces

A deep an adequate understanding of the human brain functions has been an objective for interdisciplinar teams of scientists. Different types of technological acquisition methodologies, allow to capture some particular data that is related with brain activity. Commonly, the more used strategies are related with the brain electrical activity, where reflected neuronal interactions are reflected in the scalp and obtained via electrode arrays as time series. The processing of this type of brain electrical activity (BEA) data, poses some challenges that should be addressed carefully due their intrinsic properties. BEA in known to have a nonstationaty behavior and a high degree of variability dependenig of the stimulus or responses that are being adressed..

Wavelet Transform Based Classification of Invasive Brain Computer Interface Data

The input signals of brain computer interfaces may be either electroencephalogram recorded from scalp or electrocorticogram recorded with subdural electrodes. It is very important that the classifiers have the ability for discriminating signals which are recorded in different sessions to make brain computer interfaces practical in use. This paper proposes a method for classifying motor imagery electrocorticogram signals recorded in different sessions. Extracted feature vectors based on wavelet transform were classified by using k-nearest neighbor, support vector machine and linear discriminant analysis algorithms. The proposed method was successfully applied to Data Set I of BCI competition 2005, and achieved a classification accuracy of 94 % on the test data. The performance of the proposed method was confirmed in terms of sensitivity, specificity and Kappa and compared with that of other studies used the same data set. This paper is an extended version of our work that won the Best Paper Award at the 33rd International Conference on Telecommunications and Signal Processing

Personalized Finance Advisory through Case-based Recommender Systems and Diversification Strategies

Recommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield

Extended Fuzzy Clustering Algorithms

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuzzy clustering algorithms. This technical report proposes two extensions to the objective function based fuzzy clustering for dealing with these issues. First, the (point) prototypes are extended to hypervolumes whose size is determined automatically from the data being clustered. These prototypes are shown to be less sensitive to a bias in the distribution of the data. Second, cluster merging by assessing the similarity among the clusters during optimization is introduced. Starting with an over-estimated number of clusters in the data, similar clusters are merged during clustering in order to obtain a suitable partitioning of the data. An adaptive threshold for merging is introduced. The proposed extensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting extended algorithms are given. The properties of the new algorithms are illustrated in various examples