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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Abadal-Salam T. Hussain
Al-Kitab University
Computer Science & IT Engineering

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Early fault identification for operating circuit breaker based on classifier model system Abadal-Salam T. Hussain; Shouket A. Ahmed; Taha A. Taha
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp699-706

Abstract

One of the most important switchgear in a substation is the circuit breaker (CB); it is used either in the transmission or distribution sections. Currently, the maintenance of a CB is done by capturing the trip time using a handheld device; the trip time is the time from trip initiation to the moment of current flow cessation in the load side of the CB. For the maintenance staff of the Iraqi National Power Board (INPB), their decision is mainly aimed to pinpoint the specific problem of the CB, the breaker parameters, such as latch, buffer, mcon, acon, and end which can be analysed using data mining methods such as K-means clustering and Sammon mapping (KCSM). The advantages of this approach include early identification of faults and saving more cost and time of repairing and replacing damaged CBs as the number of damaged CBs can be decreased. The problem with this method is the prolonged time of testing the conventional trip as it requires removing the CBs from service and planned outage. Furthermore, the CB may not capture the crucial information that causes slow tripping. Hence, the main objectives of this work are to analyse CB trip coil current data and study the effect and relationship between two different analytical approaches to analyse the data. The result of this technique showed excellent identification of the switch faults.
Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application Shouket Abdulrahman Ahmed; Hazry Desa; Abadal-Salam T. Hussain
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp641-647

Abstract

The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.
Aerial image semantic segmentation based on 3D fits a small dataset of 1D Shouket Abdulrahman Ahmed; Hazry Desa; Abadal-Salam T. Hussain
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp2048-2054

Abstract

Time restrictions and lack of precision demand that the initial technique be abandoned. Even though the remaining datasets had fewer identified classes than initially planned for the study, the labels were more accurate. Because of the need for additional data, a single network cannot categorize all the essential elements in a picture, including bodies of water, roads, trees, buildings, and crops. However, the final network gains some invariance in detecting these classes with environmental changes due to the different geographic positions of roads and buildings discovered in the final datasets, which could be valuable in future navigation research. At the moment, binary classifications of a single class are the only datasets that can be used for the semantic segmentation of aerial images. Even though some pictures have more than one classification, images of roads and buildings were only found in a significant number of samples. Then, the building datasets were pooled to produce a larger dataset and for the constructed models to gain some invariance on image location. Because of the massive disparity in sample size, road datasets needed to be integrated.
Co-Authors Hazry Desa Shouket A. Ahmed Shouket Abdulrahman Ahmed Taha A. Taha