Exogenous Forces in the Development of Our Banking System

A new method to optimize with orthonormal constraints is described, where a particular composition of plane (Givens) rotations is used to parameterize decision variables in terms of angles. It is showed that this parameterization is complete and that any orthonormal k-by-nmatrix can be derived to a set of no more than kn-k(k+1) angles. The technique is applied to the emph {feature extraction problem} where a linear subspace is optimized with respect to non-linear objective functions. The Optimal Discriminative Projection (ODP) algorithm is described. ODP is a data compression or feature extraction algorithm that combines powerful model optimization with regularization to avoid over training. The ODP is used primarily for classification problems

A Novel Feature Extraction Algorithm for Asymmetric Classification

A linear feature extraction technique for asymmetric distributions is introduced, the asymmetric class projection (ACP). By emph {asymmetric classification} is understood discrimination among distributions with different covariance matrices. Two distributions with unequal covariance matrices do not in general have a symmetry plane, a fact that makes the analysis more difficult compared to the symmetric case. The ACP is similar to linear discriminant analysis (LDA) in the respect that both aim at extracting discriminating features (linear combinations or projections) from many variables. However, the drawback of the well known LDA is the assumption of symmetric classes with separated centroids. The ACP, incontrast, works on (two) possibly concentric distributions with unequal covariance matrices. The ACP is tested on data from anarray of semiconductor gas sensors with the purpose of distinguish bad grain from good

A Linear Programming Approach to the Design of Thermostat Controllers of Interconnected Thermal Systems

In this note we investigate how to tune the thermostat hysteresis for a system of interconnected thermal processes. Using linear programming techniques and worst-case analysis we compute switch levels for the controller to make the system stay close to desired temperature levels. Both the case with and without amplitude bounded disturbances are treated. The same technique can also be applied to a system of interconnected tanks despite the fact that such a system is nonlinear. 1 Problem Description Many industrial processes are controlled using relays that turns heaters or pumps on and off when certain levels are reached. Usually the switch levels of the relays are tuned by hand to obtain required safety margins on for example temperatur or liquid levels. In some cases the relay controlled process interact with other processes which then are indirectly controlled by the relay. This makes it hard to predict how the setting of switch levels for the relay controlled process effects the pr..

A Novel Feature Extraction Algorithm for Asymmetric Classification II

A linear feature extraction technique for asymmetric distributions is introduced, the asymmetric class projection (ACP). By asymmetric classification is understood discrimination among distributions with different covariance matrices. Two distributions with unequal covariance matrices do not in general have a symmetry plane, a fact that makes the analysis more difficult compared to the symmetric case. The ACP is similar to linear discriminant analysis (LDA) in the respect that both aim at extracting discriminating features (linear combinations or projections) from many variables. However, the drawback of the well known LDA is the assumption of symmetric classes with separated centroids. The ACP, incontrast, works on (two) possibly concentric distributions with unequal covariance matrices. The ACP is tested on data from anarray of semiconductor gas sensors with the purpose of distinguish bad grain from good

A Linear Programming Approach to the Design of Thermostat Controllers of Interconnected Thermal Systems

We investigate how to tune the thermostat hysteresis for a system of interconnected thermal processes. Using linear programming techniques and the worst-case analysis we compute switch levels for the controller to make the system stay close to the desired temperature levels. Both the cases with and without amplitude bounded disturbances are treated. The same technique can also be applied to a system of interconnected tanks despite the fact that such a system is nonlinear

Efficient parameterization for the dimensional reduction problem

A new method to optimize with orthonormal constraints is described, where a particular composition of plane (Givens) rotations is used to parameterize decision variables in terms of angles. It is showed that this parameterization is complete and that any orthonormal k-by-nmatrix can be derived to a set of no more than kn-k(k+1) angles. The technique is applied to the emph {feature extraction problem} where a linear subspace is optimized with respect to non-linear objective functions. The Optimal Discriminative Projection (ODP) algorithm is described. ODP is a data compression or feature extraction algorithm that combines powerful model optimization with regularization to avoid over training. The ODP is used primarily for classification problems

A Novel Feature Extraction Algorithm for Asymmetric Classification II

A linear feature extraction technique for asymmetric distributions is introduced, the asymmetric class projection (ACP). By asymmetric classification is understood discrimination among distributions with different covariance matrices. Two distributions with unequal covariance matrices do not in general have a symmetry plane, a fact that makes the analysis more difficult compared to the symmetric case. The ACP is similar to linear discriminant analysis (LDA) in the respect that both aim at extracting discriminating features (linear combinations or projections) from many variables. However, the drawback of the well known LDA is the assumption of symmetric classes with separated centroids. The ACP, incontrast, works on (two) possibly concentric distributions with unequal covariance matrices. The ACP is tested on data from anarray of semiconductor gas sensors with the purpose of distinguish bad grain from good