| 1. |
- Byttner, Stefan, 1975-, et al.
(författare)
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Estimation of combustion variability using in-cylinder ionization measurements
- 2001
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the use of the ionization current to estimate the Coefficient of Variation for the Indicated Mean Effective Pressure, COV(IMEP), which is a common variable for combustion stability in a spark-ignited engine. Stable combustion in this definition implies that the variance of the produced work, measured over a number of consecutive combustion cycles, is small compared to the mean of the produced work. The COV(IMEP) is varied experimentally either by increasing EGR flow or by changing the air-fuel ratio, in both a laboratory setting (engine in dynamometer) and in an on-road setting. The experiments show a positive correlation between COV(Ion integral), the Coefficient of Variation for the integrated Ion Current, and COV(IMEP), when measured under low load on an engine in a dynamometer, but not under high load conditions. On-road experiments show a positive correlation, but only in the EGR and the lean burn case. An approach based on individual cycle classification for real-time estimation of combustion stability is discussed. © Copyright 2001 Society of Automotive Engineers, Inc.
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| 2. |
- Byttner, Stefan, 1975-, et al.
(författare)
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Strategies for handling the fuel additive problem in neural network based ion current interpretation
- 2001
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Ingår i: SAE Technical Paper Series. - Warrendale, PA : Society of Automotive Engineers. - 0148-7191.
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Konferensbidrag (refereegranskat)abstract
- With the introduction of unleaded gasoline, special fuel agents have appeared on the market for lubricating and cleaning the valve seats. These fuel agents often contain alkali metals that have a significant impact on the ion current signal, thus affecting strategies that use the ion current for engine control and diagnosis, e.g., for estimating the location of the pressure peak. This paper introduces a method for making neural network algorithms robust to expected disturbances in the input signal and demonstrates how well this method applies to the case of disturbances to the ion current signal due to fuel additives containing sodium. The performance of the neural estimators is compared to a Gaussian fit algorithm, which they outperform. It is also shown that using a fuel additive significantly improves the estimation of the location of the pressure peak. © 2001 Society of Automotive Engineers, Inc.
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| 3. |
- Erlandsson, Lena-Karin, et al.
(författare)
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Recognition of similarities (ROS): A methodological approach to analysing and characterising patterns of daily occupations
- 2004
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Ingår i: Journal of Occupational Science. - Melbourne : Taylor & Francis. - 1442-7591 .- 2158-1576. ; 11:1, s. 3-13
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Tidskriftsartikel (refereegranskat)abstract
- It has been proposed that patterns of daily occupations that promote health or cause illness should be possible to identify. By describing patterns as consisting of main, hidden, and unexpected occupations, this study aimed to develop and to evaluate a process for analysing and characterising subjectively perceived patterns of daily occupations. Yesterday diaries describing one day of 100 working married mothers were collected through interviews. The diaries were transformed into time-and-occupation graphs. An analysis based on visual interpretation of the patterns was performed. The graphs were grouped into the categories low, medium, or high complexity. In order to identify similarities the graphs were then compared both pair-wise and group-wise. Finally, the complexity and the similarities perspectives were integrated, identifying the most typical patterns of daily occupations representing low, medium, and high complexity. The visual differences in complexity were evident. However, in order to validate the ROS process developed, a measure expressing the probability of change was computed and was found to differ statistically significantly between the three groups, supporting the validity of the ROS process.
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| 4. |
- Rögnvaldsson, Thorsteinn, 1963-, et al.
(författare)
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Improved protein identification in mammalian cells using a new automatic peak detection algorithm
- 2002
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Ingår i: Proceedings - 50th ASMS Conference on Mass Spectrometry and Allied Topics. - Santa Fe : ASMS. ; , s. 785-786
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Konferensbidrag (refereegranskat)abstract
- A comparison of two automatic peak detection algorithms is presented. One algorithm comes with the Voyager 5 Data ExplorerTM program, the other is a new algorithm called Pepex® (short for PEptide Peak Extractor) from BioBridge Computing. The peak sets selected with both tools have been compared, against each other and against manual peak selections, on a large set of mass spectra obtained after tryptic in gel digestion of 2D-gel samples from human fetal fibroblasts. It is shown how much variation there is in peak sets, both when selected by human operators and when selected by automatic peak detection algorithms. This variation has been used as an advantage to gain significantly better protein identification results, using the Pepex tool, than what an experienced mass spectroscopist has achieved on the same data. The strongest improvement has been observed in weak spectra, where the signal peak intensities are low.
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| 5. |
- Rögnvaldsson, Thorsteinn, 1963-, et al.
(författare)
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Why neural networks should not be used for HIV-1 protease cleavage site prediction
- 2004
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Ingår i: Bioinformatics. - Oxford : Oxford University Press. - 1367-4803 .- 1367-4811. ; 20:11, s. 1702-1709
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Tidskriftsartikel (refereegranskat)abstract
- Several papers have been published where non-linear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease. Motivation: Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used. Results: We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers.
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| 6. |
- Samuelsson, Jim, et al.
(författare)
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PIUMS® - A new algorithm for protein identification using peptide fingerprints
- 2002
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Ingår i: Proceedings. - Santa Fe : ASMS. ; , s. 783-784
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Konferensbidrag (refereegranskat)abstract
- Piums®, a new protein identification tool for peptide fingerprints, is presented. Piums includes both a peak extraction tool (Pepex®) and a new protein scoring algorithm (Piped®). The basic ideas underlying the scoring algorithm are presented and it is demonstrated on some real sample spectra. It is shown, using simulated peak lists, how the scoring performance varies with contamination levels and protein sequence coverage, and that there is a boundary for when scoring is possible. Piums is fully scriptable and modularised. Each individual module can be used by itself, with input and output in XML format, to e.g. include it in an analysis chain. Piums is benchmarked against the Mascot software from Matrix Science LLC, The results indicate that Piums is more precise than the Mascot score, and about 5-10% more efficient than Mascot for real peak lists, for the same level of false positives.
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| 7. |
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