The purpose of this paper is to examine the multidisciplinary design optimization (MDO) of a reentry vehicle. In this paper, optimization of a RV based on, minimization of heat flux integral and minimization of axial force coefficient integral and maximization of static margin integral along reentry trajectory is carried out. The classic optimization methods are not applicable here due to the c...

This paper describes a novel automatic control toolbox, designed for non-experienced practitioners. Fractional order (FO) controllers are easily tuned with the main purpose of easy practical implementation. Experimental step data required FO controller tuning. An embedded system identification algorithm uses to obtain process model as second plus dead-time (SOPDT) system. Finally, is computed b...

Contrastive learning methods have been widely adopted in numerous unsupervised and self-supervised visual representation methods. Such algorithms aim to maximize the cosine similarity between two positive samples while minimizing that of negative samples. Recently, Grill et al. propose an algorithm, BYOL [1], utilize only samples, completely giving up on ones, by introducing a Siamese-like asym...

Linear classifiers separate the data with a hyperplane. In this paper we focus on the novel method of construction of multithreshold linear classifier, which separates the data with multiple parallel hyperplanes. Proposed model is based on the information theory concepts – namely Renyi’s quadratic entropy and Cauchy-Schwarz divergence. We begin with some general properties, including data scale...

In this paper the objective is to enhance the spectral efficiency using combinations of transmit antenna selection with Linear Dispersion Code Selection. Both bit error rate minimization and throughput maximization criteria are here examined. The performance of the proposed spatial link adaptation scheme is evaluated under low mobility environments concluding that in case of linear receivers th...

LARGE MARGIN TRAINING OF ACOUSTIC MODELS FOR SPEECH RECOGNITION Fei Sha Advisor: Prof. Lawrence K. Saul Automatic speech recognition (ASR) depends critically on building acoustic models for linguistic units. These acoustic models usually take the form of continuous-density hidden Markov models (CD-HMMs), whose parameters are obtained by maximum likelihood estimation. Recently, however, there ha...

Feature selection for classification working in high-dimensional feature spaces can improve generalization accuracy, reduce classifier complexity, and is also useful for identifying the important feature “markers”, e.g., biomarkers in a bioinformatics or biomedical context. For support vector machine (SVM) classification, a widely used feature selection technique is recursive feature eliminatio...

Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modelled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can ...

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer sett...

The design of reduced-order observer for Linear Parameter Varying (LPV) time-delay systems is addressed. Necessary conditions guaranteeing critical structural properties for the observation error dynamics are first provided through nonlinear algebraic matrix equalities. An explicit parameterization of the family of observers fulfilling these necessary conditions is then derived. Finally, an app...