An Optimal-Pruned Extreme Learning Machine based modelling of surface roughness

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah

doi:10.1109/icimu.2014.7066644

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The OPELM method is based on the ELM algorithm using SLFN [5], [15], [16]. The translation steps of the OPELM algorithm are shown in the figure below.…”

Section: Opelm Modelmentioning

confidence: 99%

Short-Term Electrical Load Forecasting of 150 kV Ternate System Using Optimally Pruned Extreme Learning Machine (OPELM)

Andi Muhammad Ilyas,

Fahrizal Djohar,

Muhammad Natsir Rahman

et al. 2023

Technium

View full text Add to dashboard Cite

Short-term electrical loads forecasting is one of the most important factors in the design and operation of electrical systems. The purpose of electric load forecasting is to balance electricity demand and electricity supply. The load characteristics of Ternate City vary, so this study uses the Optimally Pruned Extreme Learning Machine (OPELM) method to predict electrical loads. The advantages of OPELM are the fast-learning speed and the selection of the right model, even though the data has a non-linear pattern. The accuracy of the OPELM method can be determined using a comparison method, namely the ELM method. Mean Absolute Percentage Error (MAPE) is used as the accuracy criterion. The results of the comparison of accuracy criteria show that the predictive performance of OPELM is better than that of ELM. The minimum error average of the OPELM forecast test results shows a MAPE of 5,2557%, for Saturday's forecast, while the ELM method gives a MAPE of 6.4278% on the same day.

show abstract

“…The OPELM method is based on the ELM algorithm using SLFN [5], [15], [16]. The translation steps of the OPELM algorithm are shown in the figure below.…”

Section: Opelm Modelmentioning

confidence: 99%

Short-Term Electrical Load Forecasting of 150 kV Ternate System Using Optimally Pruned Extreme Learning Machine (OPELM)

Andi Muhammad Ilyas,

Fahrizal Djohar,

Muhammad Natsir Rahman

et al. 2023

Technium

View full text Add to dashboard Cite

show abstract

“…Therefore, many scholars have tried to construct models of machining parameters and surface roughness of machined parts to accomplish the prediction of surface roughness. [17][18][19] However, surface roughness was influenced not only by controllable factors (machining parameters), but also by uncontrollable factors (e.g., machine vibration, tool wear). 20 Hence, the prediction of such roughness was hard.…”

Section: Introductionmentioning

confidence: 99%

Roughness detection method based on image multi-features

Pan

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.

show abstract

Condition Monitoring for Predictive Maintenance of Machines and Processes in ARTC Model Factory

Bahador

et al. 2021

Intelligent Systems Reference Library

View full text Add to dashboard Cite

An Optimal-Pruned Extreme Learning Machine based modelling of surface roughness

Cited by 4 publications

References 11 publications

Short-Term Electrical Load Forecasting of 150 kV Ternate System Using Optimally Pruned Extreme Learning Machine (OPELM)

Short-Term Electrical Load Forecasting of 150 kV Ternate System Using Optimally Pruned Extreme Learning Machine (OPELM)

Roughness detection method based on image multi-features

Condition Monitoring for Predictive Maintenance of Machines and Processes in ARTC Model Factory

Contact Info

Product

Resources

About