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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Siti Khadijah Ali
Universiti Putra Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Control design of a de-weighting upper-limb exoskeleton: extended-based fuzzy Siti Khadijah Ali; M. Osman Tokhi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 1: March 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (643.394 KB) | DOI: 10.52549/ijeei.v7i1.938

Abstract

One of the most common issues to human is fatigue. A technology known as exoskeleton has been identified as one of the solutions to address this issue. However, there are two issues that need to be solved. One of them is the control approach. Hence, the main aim of this work, is to investigate the control design for upper-limb exoskeleton. An extended based fuzzy control is proposed to observe the effectiveness of the exoskeleton in dealing with human with different strength. Three conditions of human strength were applied. PID was used for a comparison purpose. It is shown that with the proposed control approach, the exoskeleton can assist human to achieve the desired trajectory accurately with a minimal amount of torque required.
Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN Nur Sholehah Mat Said; Hizmawati Madzin; Siti Khadijah Ali; Ng Seng Beng
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1523-1533

Abstract

In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research paper. Four types of color feature extraction methods which are color histogram, color moment, hue, saturation, and value (HSV) histogram and color auto correlogram are experimented to determine the best method for banana leaf diseases classification. While for the classifiers, support vector machine (SVM) and k-Nearest neighbors (k-NN) are used to evaluate the performance and accuracy of each color feature extraction methods. There are also preliminary experiments to identify accurate parameters to use during classification for both classifiers. Our experimental result express that HSV histogram is the best method to classify banana leaf diseases with 83.33% of accuracy and SVM classifier perform better compared to k-NN.
Co-Authors Hizmawati Madzin M. Osman Tokhi Ng Seng Beng Nur Sholehah Mat Said