Mathematical modelling in the analysis and design of hard and soft measurement systems

Khan, Sharief; Finkelstein, L.

doi:10.1016/j.measurement.2013.04.045

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Soft modelling is data modelling (drawing the right conclusions) with the use of specialist knowledge (expert knowledge) on the analysed process through appropriate measures on empirical data (mathematical soft modelling) [15]. Information on such a process may be residual or limited and models may be built on the basis of assumptions and dependencies obtained from the provided data analysis.…”

Section: State Of Artmentioning

confidence: 99%

Management and Production Engineering Review

Sika

Szajewski

Hajkowski

et al. 2019

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The paper presents an example of Instance-Based Learning using a supervised classification method of predicting selected ductile cast iron castings defects. The test used the algorithm of k-nearest neighbours, which was implemented in the authors' computer application. To ensure its proper work it is necessary to have historical data of casting parameter values registered during casting processes in a foundry (mould sand, pouring process, chemical composition) as well as the percentage share of defective castings (unrepairable casting defects). The result of an algorithm is a report with five most possible scenarios in terms of occurrence of a cast iron casting defects and their quantity and occurrence percentage in the casts series. During the algorithm testing, weights were adjusted for independent variables involved in the dependent variables learning process. The algorithms used to process numerous data sets should be characterized by high efficiency, which should be a priority when designing applications to be implemented in industry. As it turns out in the presented mathematical instance-based learning, the best quality of fit occurs for specific values of accepted weights (set #5) for number k = 5 nearest neighbours and taking into account the search criterion according to "product index".

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Section: State Of Artmentioning

confidence: 99%

Management and Production Engineering Review

Sika

Szajewski

Hajkowski

et al. 2019

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“…Due to the complex nature of the cement clinker production process, it is difficult to establish an accurate mechanistic model (Csernyei and Straatman, 2016; Mujumdar et al, 2007). In recent years, data-driven soft measurement-based modeling approaches have gained the attention of many experts and scholars (Guo and Liu, 2021; Khan and Finkelstein, 2013; Salami et al, 2021). Pani et al (2013) used raw material components as model input variables, and constructed three different neural network models to predict the content of f-CaO.…”

Section: Introductionmentioning

confidence: 99%

A multi-model fusion soft measurement method for cement clinker f-CaO content based on K-means ++ and EMD-MKRVM

Zhang

Wang

et al. 2022

Transactions of the Institute of Measurement and Control

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The content of free calcium oxide (f-CaO) in cement clinker is a key indicator for testing the quality of cement clinker. To address the problem that the content of f-CaO cannot be detected online, a multi-model fusion soft measurement method based on K-means++ clustering, empirical modal decomposition combined with multi-kernel relevance vector machines (EMD-MKRVM) is proposed to predict f-CaO content under different operating conditions. First, time-series analysis and matching of input variables with f-CaO content were performed, based on which a combination of empirical modal decomposition (EMD) and sample entropy (SE) denoising method was used to filter out high- frequency noise from the original data and extract effective signal information for reconstruction. Second, the K-means++ algorithm was used to cluster the processed training sample data, and multi-kernel relevance vector machine (MKRVM) sub-models were established by training the sample data of each sub-class and then the affiliation between the test samples and each sub-class was calculated as the weights of the sub-model output values, and the final model prediction output was obtained by multi-model fusion. Finally, the real data from cement plants were used for validation. The results show that compared with the single MKRVM model, multi–relevance vector machine (RVM) model, multi–support vector machine (SVM) model, and multi-MKRVM model using only EMD denoising method, the mean absolute error (MAE) of the multi-MKRVM model proposed in this paper was reduced by 42%, 7%, 14%, and 35%; root mean square error (RMSE) is reduced by 28%, 10%, 12%, and 21%; squares due to error (SSE) is reduced by 51%, 24%, 27%, and 41%; Theils inequality coefficient (TIC) is reduced by 27%, 17%, 21%, and 19%; [Formula: see text] is improved by 64%, 18%, 39%, and 91%; and Index of agreement (IA) is improved by 28%, 9%, 22%, and 13%. The multi-MKRVM model proposed in this paper has higher accuracy, better generalization ability and stability, and provides an effective method for f-CaO content prediction under complex multiple working conditions.

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“…At first, the research life cycle and the data life cycle were of interest, where information was presented in a semantic way (Chartier, Mongeau & Saint-Charles, 2019;Zhang, Ye, Gan & Zhang, 2019). For this, such cognitive and semiotic approaches as hard and soft system analysis (Khan & Finkelstein, 2013;Feng, Akram, Davvaz & Leoreanu-Fotea, 2014) were used. P.Chekland (Checkland, 1981) could not take into account the cognitive processes generated by the understanding and memorization of knowledge by the human intellect.…”

Section: Introductionmentioning

confidence: 99%

Obtaining the Dynamic Coefficients of Structuredness for Assessing a Domain

Popova

Popov

Karandey

et al. 2022

International Journal of Cognitive Informatics and Natural Intelligence

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Today, effective management of information requires an in-depth study of its internal organization. The structural organization of information affects the efficiency of choosing a method for solving the problem and the qualitative presentation of information about the subject area. Therefore, the article proposes a new semiotic structural approach to assessing the structuredness of information in a subject area, as well as theoretical, practical, and general logical methods for studying the process of search research as a single system. The authors proposed and investigated the structured information coeffi-cient, which the authors propose to consider in several aspects - with respect to the search research model presented by the traditional algorithm, and the structured subject area. The article presents theo-retical positions, derives the formula of coefficients for different cases, carries out calculations on the example of the subject area “optimization methods”, constructs graphs based on the calculated data, and draws conclusions.

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Mathematical modelling in the analysis and design of hard and soft measurement systems

Cited by 7 publications

References 26 publications

Management and Production Engineering Review

Management and Production Engineering Review

A multi-model fusion soft measurement method for cement clinker f-CaO content based on K-means ++ and EMD-MKRVM

Obtaining the Dynamic Coefficients of Structuredness for Assessing a Domain

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