| 1. |
- Artursson, Tom, et al.
(författare)
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Variable reduction on electronic tongue data
- 2002
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Ingår i: Analytica Chimica Acta. - 0003-2670 .- 1873-4324. ; 452:2, s. 255-264
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Tidskriftsartikel (refereegranskat)abstract
- Reduction of the number of variables in data from a so-called electronic tongue contributes to simpler model calculations and less storage requirements. In this study, we have developed a model for this purpose. This model describes the response from the electrodes in the electronic tongue with two exponential functions plus a constant term, i(t) = k + kf e-ta + kc e-tß, where t is the time. From the model, five parameters which describe the signal are extracted. These parameters can be used as inputs instead of the original signal to any multivariate algorithm. The results show that the variables obtained are at least as good as the original data to separate between different classes, even though the number of parameters has been reduced between 80 and 199 times. © 2002 Elsevier Science B.V. All rights reserved.
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| 2. |
- Holmin, Susanne, et al.
(författare)
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Compression of electronic tongue data based on voltammetry - A comparative study
- 2001
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Ingår i: Sensors and actuators. B, Chemical. - 0925-4005 .- 1873-3077. ; 76:1-3, s. 455-464
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, three data compression methods are investigated to determine their ability to reduce large data sets obtained by a voltammetric electronic tongue without loss of information, since compressed data sets will save data storage and computational time. The electronic tongue is based on a combination of non-specific sensors and pattern recognition tools, such as principal component analysis (PCA). A series of potential pulses of decreasing amplitude are applied to one working electrode at a time and resulting current transients are collected at each potential step. Voltammograms containing up to 8000 variables are subsequently obtained. The methods investigated are wavelet transformation (WT) and hierarchical principal component analysis (HPCA). Also, a new chemical/physical model based on voltammetric theory is developed in order to extract interesting features of the current transients, revealing different information about species in solutions. Two model experiments are performed, one containing solutions of different electroactive compounds and the other containing complex samples, such as juices from fruits and tomatoes. It is shown that WT and HPCA compress the data sets without loss of information, and the chemical/physical model improves the separations slightly. HPCA is able to compress the two data sets to the largest extent, from 8000 to 16 variables. When data sets are scaled to unit variance, the separation ability improves even further for HPCA and the chemical/physical model. © 2001 Elsevier Science B.V.
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| 3. |
- Lindgren, David, et al.
(författare)
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A Novel Feature Extraction Algorithm for Asymmetric Classification
- 2002
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 4. |
- Lindgren, David, et al.
(författare)
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A Novel Feature Extraction Algorithm for Asymmetric Classification
- 2004
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Ingår i: IEEE Sensors Journal. - : IEEE Sensors Council. - 1530-437X .- 1558-1748. ; 4, s. 643-650
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Lindgren, David, et al.
(författare)
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A Novel Feature Extraction Algorithm for Asymmetric Classification II
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Spångéus, Per, et al.
(författare)
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Efficient parameterization for the dimensional reduction problem
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 7. |
- Spångéus, Per
(författare)
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New Algorithms for General Sensors, or, How to Improve Electronic Noses
- 2001
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of three parts. The first part describes some new algorithms that we have invented for use in the field of sensor technology. Sensor technology is evolving rapidly and new sensors such as electronic noses and tongues have emerged on the market. Most of these sensors are non-specific and needs to be trained before real life usage. We have developed algorithms to ease the training of these sensors. The first algorithm is a superior algorithm (called ODP) for supervised feature extraction. This algorithm outperforms PCA and LDA. It consists of powerful pre-processing - to avoid statistical problems - as well as a method for minimizing the classification error in the variable reduced space. ODP is protected by a patent application. The second new algorithm (called GBP) is used for variable reduction when data consists of sensory panel judgments of samples as either "good" or "bad". GBP is protected by a patent application. The third algorithm part consists of some algorithms for measuring the information content in multi-cluster data thereby facilitating objective statements about the performance of sensors and algorithms.In the second part of the thesis we try to model the response of the electronic tongue through a first and a second order model. The second order model has five parameters and shows very good fit with experimental data. It may in the future help to compress tongue data as well as doing noise rejection.The third part consists of one old work that I did on hybrid control systems and is poorly related the rest of the work in this thesis.
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