CHIPS IMAGE PROCESSING TO PREDICT PULP BRIGHTNESS USING FUZZY LOGIC TECHNIQUES
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1 PAPTAC Annual Meeting 2005 CHIPS IMAGE PROCESSING TO PREDICT PULP BRIGHTNESS USING FUZZY LOGIC TECHNIQUES Sofiane Achiche 1 Luc Baron 1 Marek Balazinski 1 Mokhtar Benaoudia 2 1 École Polytechnique de Montréal Montréal (QC) Canada 2 Centre de Recherche Industrielle du Québec Sainte-Foy (QC) Canada
2 Outline Introduction Fuzzy Decision Support System (FuzzyFlou) Automatic Generation of Fuzzy Knowledge Bases Chip Image Processing Results Conclusion 2/23
3 Outline Introduction Fuzzy Decision Support System (FuzzyFlou) Automatic Generation of Fuzzy Knowledge Bases Chip Image Processing Results Conclusion 3/23
4 Introduction TMP process is influenced by a large number of variables The relationship between the wood chips characteristics and pulp quality is vague A lot of information can be drawn from image analysis Genetically generated FKBs are used to model the above-mentioned relationship 4/23
5 Outline Introduction Fuzzy Decision Support System (FuzzyFlou) Automatic Generation of Fuzzy Knowledge Bases Clustering Results Conclusion 5/23
6 Fuzzy Decision Support System Rule-based approach to decision making done using fuzzy logic techniques, based on the compositional rule of inference Used to handle imprecise knowledge Knowledge can be collected by a human expert or generated through an automatic learning process 6/23
7 Fuzzy Decision Support System FDSS software screenshot 7/23
8 Outline Introduction Fuzzy Decision Support System (FuzzyFlou) Automatic Generation of Fuzzy Knowledge Bases Clustering Validation Results Conclusion 8/23
9 Automatic Generation of Fuzzy Knowledge Bases Coding The genotype of an FKB : coding of its parameters into chromosomes The genotype contains : Input/Output Premises Fuzzy Rules Evolution is obtained by the reproduction of the best individuals Reproduction performed by crossover of genotypes of the parents to obtain offspring 9/23
10 Genetic Algorithm Automatic learning process Final Solution 10/23
11 Automatic Generation of Fuzzy Knowledge Bases Natural Selection Natural Selection based on the performance criterion : Approximation error Δ rms = = i 1 Fitness value φ rms = N L Δ L ( RBCGA rms 100 output N data output ) 2 11/23
12 Automatic Generation of Fuzzy Knowledge Bases Animation 12/23
13 Outline Introduction Fuzzy Decision Support System (FuzzyFlou) Automatic Generation of Fuzzy Knowledge Bases Chip Image Processing Results Conclusion 13/23
14 Chips Image Processing The chip management system (CMS ) An Innovative device that allows online measurements of several chips characteristics The main sensor of the CMS is an RGB camera and frame grabber The used parameters are the Hue (H), Saturation (S) and Luminance (L) Coordinates. 14/23
15 Chips Image Processing H, S and L color coordinates 15/23
16 Outline Introduction Fuzzy Decision Support System (FuzzyFlou) Automatic Generation of Fuzzy Knowledge Bases Clustering Results Conclusion 16/23
17 Results Input/output variables The input variables are: H S L Bleaching concentration (hydrogen peroxide) The output variable is: ISO brightness of the pulp 17/23
18 Results Predicted FKB Using 90% of the experimental data 18/23
19 Results Predicted FKB Δ rms = 2.10% for this FKB for the learning file (φ rms =94.30%) Δ rms = 2.21% for the testing file 19/23
20 Results ISO Brightness : No Bleaching Similar variations 20/23
21 Results ISO Brightness : 5% peroxide 21/23
22 Outline Introduction Fuzzy Decision Support System (FuzzyFlou) Automatic Generation of Fuzzy Knowledge Bases Clustering Validation Results Conclusion 22/23
23 Conclusion The RBCGA produced satisfactory FKBs using exclusively image processing information along with the % of peroxide concentration. The FKB adds to the general knowledge about the relationship between wood chips and pulp quality The FKB allows optimizing the bleaching consumption The FKB presented a very stable performances The RBCGA reached a very good balance between the accuracy of the FKB and its simplicity 23/23
24 Questions? 24/23
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