| 1. |
- Gillblad, Tomas, et al.
(author)
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Implementation Problems for Activated Sludge Controllers
- 1978
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Reports (other academic/artistic)abstract
- The paper describes some problems that appear in the implementation of a computer control system in a wastewater treatment plant. The problems are related to the control authority of the actuators, the influence of the process design on the controller design, or the disturbance pattern. Some experience from two full scale activated sludge plants in Sweden are discussed.
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| 2. |
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| 3. |
- Olsson, Tomas, et al.
(author)
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Case-Based Reasoning for Explaining Probabilistic Machine Learning
- 2014
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In: International Journal of Computer Science & Information Technology (IJCSIT). - : Academy and Industry Research Collaboration Center (AIRCC). - 0975-4660 .- 0975-3826. ; 6:2, s. 87-101
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Journal article (peer-reviewed)abstract
- This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.
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| 4. |
- Olsson, Tomas, et al.
(author)
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Case-Based Reasoning for Explaining Probabilistic Machine Learning
- 2014. - 7
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In: International Journal of Computer Science and Information Technology. - : Academy and Industry Research Collaboration Center (AIRCC). - 0975-4660 .- 0975-3826. ; 6, s. 87-101
-
Journal article (peer-reviewed)abstract
- This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.
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| 5. |
- Olsson, Tomas, et al.
(author)
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Explaining probabilistic fault diagnosis and classification using case-based reasoning
- 2014
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In: Case-Based Reasoning Research and Development. - Cham : Springer International Publishing. - 9783319112084 - 9783319112091 ; , s. 360-374
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Conference paper (peer-reviewed)abstract
- This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.
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| 6. |
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| 7. |
- Olsson, Tomas, et al.
(author)
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Fault Diagnosis of Heavy Duty Machines : Automatic Transmission Clutches
- 2014
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In: Proceedings of the ICCBR 2014 Workshops.
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Conference paper (peer-reviewed)abstract
- This paper presents a generic approach to fault diagnosis of heavy duty machines that combines signal processing, statistics, machine learning, and case-based reasoning for on-board and off-board analysis. The used methods complement each other in that the on-board methods are fast and light-weight, while case-based reasoning is used off-board for fault diagnosis and for retrieving cases as support in manual decision mak- ing. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely in- tegrated with the anomaly detection model. As example application, the proposed approach has been applied to diagnosing the root cause of clutch slippage in automatic transmissions.
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