Reviews and Comments on Paper 492
Paper information
| Paper #492: Pablo H. Ibargüengoytia and Alberto Reyes. Constructing Virtual Sensors using Probabilistic Reasoning |
| Abstract: Modern control systems and other monitoring systems require the acquisition of values of most of the parameters involved in the process. Examples of processes are industrial procedures or medical treatments or financial forecasts. However, sometimes some parameters are inaccessible through the use of traditional instrumentation. One example is the blades temperature in a gas turbine during operation. Other parameters require costly instrumentation difficult to install, operate and calibrate. For example, the contaminant emissions of power plant chimney. One solution of this problem is the use of analytical estimation of the parameter using complex differential equations. However, these models sometimes are very difficult to obtain and to maintain according the changes in the processes. Other solution is to borrow an instrument and measure a data set with the value of the difficult variable and its related variables at all the operation range. Then, use an automatic learning algorithm that allows inferring the difficult measure, given the related variables. This paper presents the use of Bayesian networks that represents the probabilistic relations of all the variables in a process, in the design of a virtual sensor. Experiments are presented with the temperature sensors of a gas turbine. (file) |
Missing reviews
DUMMY HasRvw.Summary of received reviews and comments
Reviews superseded by other reviews are shown in the grey color in the table.
| confidence | score | ||||
| Review 1 | 4 | 2 | |||
| Review 2 | 3 | 2 | |||
Reviews and Comments
Review 1
| PC member: | |
| Overall rating: | 2 (accept: I will argue for this paper) |
| Confidence: | 4 |
| Relevance: Is this paper relevant for this conference? | 3 (strong accept) |
| Soundness: Is this paper technically sound and complete? | 2 (accept (I will argue for this paper)) |
| Are the claims sufficiently supported by experimental/theoretical results? | 1 (weak accept (vote accept but don't mind rejecting)) |
| Significance: Are the results/ideas interesting for other AI researchers? | 2 (accept (I will argue for this paper)) |
| Originality: Are the results or ideas novel and previously unknown? | 1 (weak accept (vote accept but don't mind rejecting)) |
| Readability: Is the paper well-organized and easy to understand? | 2 (accept (I will argue for this paper)) |
| Language: Is the paper written in correct English and style? | 2 (accept (I will argue for this paper)) |
| Format: Is the paper correctly and consistently formatted? | 2 (accept (I will argue for this paper)) |
| Review: | CONTRIBUTION OF THE PAPER: POSITIVE ASPECTS: NEGATIVE ASPECTS: CHANGES TO IMPROVE THE PAPER: FURTHER COMMENTS: ITEMS BELOW ARE JUSTIFICATION OF THE SCORES IF NEGATIVE: (1) IS THIS PAPER RELEVANT FOR THIS CONFERENCE? (2) IS THIS PAPER TECHNICALLY SOUND AND COMPLETE? (3) ARE THE CLAIMS SUFFICIENTLY SUPPORTED BY EXPERIMENTAL OR THEORETICAL RESULTS? (4) ARE THE RESULTS/IDEAS INTERESTING FOR OTHER AI RESEARCHERS? (5) ARE THE RESULTS OR IDEAS NOVEL AND PREVIOUSLY UNKNOWN? (6) IS THE PAPER WELL-ORGANIZED AND EASY TO UNDERSTAND? (7) IS THE PAPER WRITTEN IN CORRECT ENGLISH AND STYLE? (8) IS THE PAPER CORRECTLY AND CONSISTENTLY FORMATTED? |
| PC only: | |
| Time: | Jun 28, 15:26 |
Review 2
| PC member: | |
| Reviewer: | |
| Overall rating: | 2 (accept: I will argue for this paper) |
| Confidence: | 3 |
| Relevance: Is this paper relevant for this conference? | 2 (accept (I will argue for this paper)) |
| Soundness: Is this paper technically sound and complete? | 2 (accept (I will argue for this paper)) |
| Are the claims sufficiently supported by experimental/theoretical results? | 2 (accept (I will argue for this paper)) |
| Significance: Are the results/ideas interesting for other AI researchers? | 2 (accept (I will argue for this paper)) |
| Originality: Are the results or ideas novel and previously unknown? | 2 (accept (I will argue for this paper)) |
| Readability: Is the paper well-organized and easy to understand? | 3 (strong accept) |
| Language: Is the paper written in correct English and style? | 2 (accept (I will argue for this paper)) |
| Format: Is the paper correctly and consistently formatted? | 3 (strong accept) |
| Review: | CONTRIBUTION OF THE PAPER: The paper describes a method for constructing, learning and using a Bayesian network model of a sensor system in an industrial setting. The idea is to use other sensors to estimate what the reading on an expensive (or broken) sensor would be if it was in place (or working). The paper reviews BNs and shows how they can be applied to this virtual sensor problem in a fairly general way, and then shows a particular BN for a gas turbine application. Results show the approach looks promising. POSITIVE ASPECTS: A good paper - the approach is well described (and seems very appropriate for the problem), the results look promising and the claims are not overstated. NEGATIVE ASPECTS: CHANGES TO IMPROVE THE PAPER: dp is missing from equation 1 would be good to try to show how the results change with different discretisations - how big an effect does this really have? FURTHER COMMENTS: ITEMS BELOW ARE JUSTIFICATION OF THE SCORES IF NEGATIVE: (1) IS THIS PAPER RELEVANT FOR THIS CONFERENCE? (2) IS THIS PAPER TECHNICALLY SOUND AND COMPLETE? (3) ARE THE CLAIMS SUFFICIENTLY SUPPORTED BY EXPERIMENTAL OR THEORETICAL RESULTS? (4) ARE THE RESULTS/IDEAS INTERESTING FOR OTHER AI RESEARCHERS? (5) ARE THE RESULTS OR IDEAS NOVEL AND PREVIOUSLY UNKNOWN? (6) IS THE PAPER WELL-ORGANIZED AND EASY TO UNDERSTAND? (7) IS THE PAPER WRITTEN IN CORRECT ENGLISH AND STYLE? (8) IS THE PAPER CORRECTLY AND CONSISTENTLY FORMATTED? |
| PC only: | |
| Time: | Jul 13, 15:00 |