scholarly journals KLASIFIKASI JENIS KENDARAAN RODA EMPAT MENGGUNAKAN EXTREME LEARNING MACHINE

2021 ◽  
Vol 3 (2) ◽  
pp. 199-206
Author(s):  
Ina Najiyah ◽  
Salman Topiq
Keyword(s):  
Image Processing ◽  
Extreme Learning Machine ◽  
Learning Machine

Kendaraan merupakan sebuah objek yang menjadi alat transportasi penduduk khususnya di Indonesia. Kendaraan roda empat saat ini sudah beranekaragam jenisnya mulai dari kendaraan kecil, sedang sampai kendaraan besar. Tujuan penelitian ini melakukan klasifikasi jenis kendaraan roda empat dengan bidang Image Processing. Metode yang dipilih adalah metode Extreme learning machine, dimana metode ini cukup baik dalam melakukan pemroresan gambar dan untuk klasifikasi. Penelitian ini mengklasifikasikan kedalam empat class yaitu class sedan, MVP, truck dan Bus dengan total masing-masing dataset 100 gambar. Hasil dari penelitian ini membuktikan bahwa metode Extreme learning machine baik untuk mengklasifikasikan kendaraan roda empat dengan akurasi baik yaitu 86% dan nilai precisionnya 82%. 

Bubble Detection in Lithium-ion Polymer Cell Sheet Using Extreme Learning Machine

2019 ◽  
Vol 13 (1) ◽  
pp. 75-82
Author(s):  
Liyong Ma ◽  
Chengkuan Ma ◽  
Lidan Tang
Keyword(s):  
Image Processing ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Cell Sheet ◽  
Lithium Ion ◽  
Cell Sheets ◽  
Bubble Detection ◽  
Lithium Ion Polymer Battery ◽  
Learning Machine ◽  
Polymer Battery

Background: As lithium-ion polymer battery has high energy density and it is easy to be manufactured into different shapes, it arouses more interests of both technology and application recently. The quality of the lithium-ion polymer battery is essential to all the applications, and the detection of bubble defect in cell sheets is critical to the quality control of batteries. Recent patents on flaw detection in cell sheet are reviewed. Method: A novel application is developed to detect bubble defect in cell sheets of lithium-ion polymer battery by using extreme learning machine. The image processing methods and the selected features for bubble detection are detailed. Gaussian mixture model density estimation for extreme learning machine is developed to solve the problem of lack of enough flaw samples for classification learning. Results: The comparison of classification correction rate of different methods showed that the classification accuracy of the proposed method was between 99% and 100%. The proposed method was able to keep the superior performance of accuracy with the different sample numbers, and it had most satisfactory performance with varies of sample number. Experimental results also showed that the number of nodes in the hidden layer had little influence on the classification accuracy in the proposed method. Conclusion: All these experiments have shown that the proposed method has the best performance and the proposed bubble detection method is more efficient than other learning-based methods, and the proposed method has the potential to defect detection in other image processing applications.

Sistema Híbrido Baseado Em Extreme Learning Machine E Harmony Search

2016 ◽  
Author(s):  
Edgar Wellington Marques de Almeida ◽  
Mêuser Jorge da Silva Valença
Keyword(s):  
Extreme Learning Machine ◽  
Harmony Search ◽  
Learning Machine

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

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