scholarly journals MACHINE LEARNING IN SMED

2018 ◽  
Vol 18 (2) ◽  
pp. 31-40 ◽  
Author(s):  
Izabela KUTSCHENREITER-PRASZKIEWICZ
Keyword(s):  
Machine Learning ◽  
Manufacturing Process ◽  
Time Measurement ◽  
Manufacturing Cost ◽  
Paper Machine ◽  
Linear Regression Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Time And Motion ◽  
Make To Order

The paper discusses Single Minute Exchange of Die (SMED) and machine learning methods, such as neural networks and a decision tree. SMED is one of lean production methods for reducing waste in the manufacturing process, which helps to reorganize a conversion of the manufacturing process from current to the next product. SMED needs set-up activity analyses, which include activity classification, working time measurement and work improvement. The analyses presented in the article are focused on selecting the time measurement method useful from the SMED perspective. Time measurement methods and their comparison are presented in the paper. Machine learning methods are used to suggest the method of time measurement which should be applied in a particular case of workstation reorganization. A training set is developed and an example of classification is presented. Time and motion study is one of important methods of estimating machine changeover time. In the field of time study, researchers present the obtained results by using (linear) multi-linear regression models (MLR), and (non-linear) multi-layer perceptrons (MLP). The presented approach is particularly important for the enterprises which offer make-to-order products. Development of the SMED method can influence manufacturing cost reduction of customized products. In variety oriented manufacturing, SMED supports flexibility and adaptability of the manufacturing system.

scholarly journals Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2574
Author(s):  
Jenny von Platten ◽  
Claes Sandels ◽  
Kajsa Jörgensson ◽  
Viktor Karlsson ◽  
Mikael Mangold ◽  
...  
Keyword(s):  
Machine Learning ◽  
Specific Energy ◽  
Energy Savings ◽  
Energy Performance ◽  
Paper Machine ◽  
Building Stock ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Building Characteristics ◽  
Energy Conservation Measures

Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristics could be predicted with an accuracy of 88.9% and 72.5% respectively. It could be concluded that machine learning methods show promising potential to enrich building databases with building characteristics relevant for energy retrofitting, which in turn can improve estimations of national energy savings potential.

scholarly journals Improving CAT bond pricing models via machine learning

2020 ◽  
Vol 21 (5) ◽  
pp. 428-446
Author(s):  
Tobias Götze ◽  
Marc Gürtler ◽  
Eileen Witowski
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Linear Regression ◽  
Random Forests ◽  
Regression Models ◽  
Linear Regression Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Forecasting Performance ◽  
Asset Classes

Abstract Enhanced machine learning methods provide an encouraging alternative to forecast asset prices by extending or generalizing the possible model specifications compared to conventional linear regression methods. Even if enhanced methods of machine learning in the literature often lead to better forecasting quality, this is not clear for small asset classes, because in small asset classes enhanced machine learning methods may potentially over-fit the in-sample data. Against this background, we compare the forecasting performance of linear regression models and enhanced machine learning methods in the market for catastrophe (CAT) bonds. We use linear regression with variable selection, penalization methods, random forests and neural networks to forecast CAT bond premia. Among the considered models, random forests exhibit the highest forecasting performance, followed by linear regression models and neural networks.

scholarly journals Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2777
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski ◽  
Anna Hoła ◽  
Jan Sikora ◽  
Tomasz Wołowiec ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Regression Models ◽  
Mathematical Methods ◽  
Electrical Tomography ◽  
Learning Methods ◽  
Historical Building ◽  
Machine Learning Methods

This paper presents the results of research on the use of machine learning algorithms and electrical tomography in detecting humidity inside the walls of old buildings and structures. The object of research was a historical building in Wrocław, Poland, built in the first decade of the 19th century. Using the prototype of an electric tomograph of our own design, a number of voltage measurements were made on selected parts of the building. Many algorithmic methods have been preliminarily analyzed. Ultimately, the three models based on machine learning were selected: linear regression with SVM (support vector machine) learner, linear regression with least squares learner, and a multilayer perceptron neural network. The classical Gauss–Newton model was also used in the comparison. Both the experiments based on real measurements and simulation data showed a higher efficiency of machine learning methods than the Gauss–Newton method. The tomographic methods surpassed the point methods in measuring the dampness in the walls because they show a spatial image of the interior and not separate points of the examined cross-section. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. Machine learning has a greater potential to create correct tomographic reconstructions than traditional mathematical methods. In this research, linear regression models performed slightly worse than neural networks.

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