Yazar "Sindi, Hatem" seçeneğine göre listele

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    An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events
    (Pergamon-Elsevier Science Ltd, 2021) Sindi, Hatem; Nour, Majid; Rawa, Muhyaddin; Öztürk, Şaban; Polat, Kemal
    Distributed generation (DG) sources are preferred to meet today's energy needs effectively. The addition of many different types of renewable energy sources to the grid causes various problems in signal quality. Detection and classification of these problems increase efficiency by both the producer and the consumer. In the literature, incredibly singular and some composite power quality disturbance (PQD) detection is performed effectively. However, the multitude of composite PQD variations degrades the performance of existing algorithms. In this study, the classification of all PQD variations that may occur is performed by using singular PQD and some composite PQD signals. A different number of subcomponents representing the signal are created according to each signal characteristic. Instantaneous energies from these subcomponents are used as deep learning (DL) input. Deep learning cycles are created as much as the instantaneous energy number of each signal. Each cycle has specific features of defining a single event. Therefore, the proposed approach is able to classify composite PQD signals that it has not encountered before. The proposed method's performance is first evaluated with the known PQD events and compared with the current state-of-the-art methods in the literature. Then, a dataset containing the combinations of different events not encountered during the training is created, and the performance is evaluated on this dataset. In the experiments performed, it is revealed that the proposed framework produces higher performance than other state-of-the-art methods
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    A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity
    (Elsevier Ireland Ltd, 2020) Nour, Majid; Sindi, Hatem; Abozinadah, Ehab; Öztürk, Şaban; Polat, Kemal
    Objective: Hospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation of subjective expert thoughts in the process. Method: As a result of budget cuts in health expenditures worldwide, the necessity of using hospital resources most efficiently emerges. The most effective way to do this is to determine the evaluation criteria effectively. Machine learning (ML) is the current method to determine these criteria, determined by consulting with experts in the past. ML methods, which can remain utterly objective concerning all indicators, offer fair and reliable results quickly and automatically. Based on this idea, this study provides an automated healthcare system evaluation framework by automatically assigning weights to specific indicators. First, the ability of hands to be used as input and output is measured. Results: As a result of this measurement, indicators are divided into only input group (group A) and both input and output group (group B). In the second step, the total effect of each input on the output is calculated by using the indicators in group B as output sequentially using the random forest of the regression tree model. Conclusion: Finally, the total effect of each indicator on the healthcare system is determined. Thus, the whole system is evaluated objectively instead of a subjective evaluation based on a single output.
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    A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification
    (PERGAMON-ELSEVIER SCIENCE LTD, 2021) Sindi, Hatem; Nour, Majid; Rawa, Muhyaddin; Öztürk, Şaban; Polat, Kemal
    As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total efficiency. The proposed hybrid convolutional neural network method consists of a 1D convolutional neural network structure and a 2D convolutional neural network structure. The features acquired by these two convolutional neural network architectures are classified using the fully connected layer, which is traditionally used as the classifier of convolutional neural network architectures. Power signals are processed using a 1D convolutional neural network in their original form. Then these signals are converted into images and processed using a 2D convolutional neural network. Then, feature vectors generated by 1D and 2D convolutional neural networks are combined. Finally, this combined vector is classified by a fully connected layer. The proposed method is well suited to the nature of signal processing. It is a novel approach that covers the steps of an expert examining a signal. The proposed framework is compared with other state-of-the-art power quality disturbance classification methods in the literature. While the proposed method's classification performance is relatively high compared to other methods, the computational complexity is almost the same.
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    A novel tilt and acceleration measurement system based on hall-effect sensors using neural networks
    (Hindawi Ltd, 2022) Nour, Majid; Daldal, Nihat; Kahraman, Mehmet Fatih; Sindi, Hatem; Alhudhaif, Adi; Polat, Kemal
    A tilt sensor is a device used to measure the tilt on many axes of a reference point. Tilt sensors measure the bending position according to gravity and are used in many applications. Slope sensors allow easy detection of direction or slope in the air. These tilt gauges have become increasingly popular and are being adapted for a growing number of high-end applications. As an example of practical application, the tilt sensor provides valuable information about an aircraft's vertical and horizontal tilt. This information also helps the pilot understand how to deal with obstacles during flight. In this paper, Hall-effect effective inclination and acceleration sensor design, which makes a real-time measurement, have been realized. 6 Hall-effect sensors with analog output (UGN-3503) have been used in the sensor structure. These sensors are placed in a machine, and the hall sensor outputs are continuously read according to the movement speed and direction of the sphere magnet placed in the assembly. Hall sensor outputs produce 0-5 Volt analog voltage according to the position of the magnet sphere to the sensor. It is clear that the sphere magnet moves according to the inclination of the mechanism when the mechanism is moved angularly, and the speed of movement from one point to the other changes according to the movement speed. Here, the sphere magnet moves between the hall sensors in the setup according to the ambient inclination and motion acceleration. Each sensor produces analog output values in the range of 0-5 V instantaneous according to the position of the spheroid. Generally defined, according to the sphere magnet position and movement speed, the data received from the hall sensors by the microcontroller have been sent to the computer or microcomputer unit as UART. In the next stage, the actual sensor has been removed. The angle and acceleration values have been continuously produced according to the mechanism's movement and output as UART. Thanks to the fact that the magnet is not left idle and is fixed with springs, problems such as vibration noises and wrong movements and the magnet leaning to the very edge and being out of position even at a slight inclination are prevented. In addition, the Hall-effect sensor outputs are given to an artificial neural network (ANN), and the slope and acceleration information is estimated in the ANN by training with the data obtained from the real-time slope and accelerometer sensor.
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    Random fully connected layered 1D CNN for solving the Z-bus loss allocation problem
    (Elsevier Sci Ltd, 2021) Sindi, Hatem; Nour, Majid; Rawa, Muhyaddin; Öztürk, Şaban; Polat, Kemal
    Power loss allocation methods should be efficient enough to meet the needs of the customers on the bus and effectively calculate the losses from generators and consumers. In order to perform these tasks, a highly robust model is essential to distinguish between the effects of multi-consumers. This study presents a novel convolutional neural network (CNN) architecture that is highly effective for z-bus loss allocation. The proposed CNN architecture that uses the Z-bus matrix as input is 1D. Unlike traditional 1D CNN architectures in the literature, the fully connected layer (FCL) of the proposed method is randomized. Unlike Traditional FCL layers, randomized FCL's input weights and biases are not needed to be tuned. This makes the proposed 'Randomized Fully Connected Layered 1D CNN' architecture relatively fast and straightforward. Proposed Randomized Fully Connected Layered 1D CNN is trained in an end-to-end manner with a regression task for robust loss allocation. The performance of it is higher than other state-of-the-art methods. In addition to the fact that the proposed method's regression performance is very promising, the classifier performance is quite satisfactory thanks to the changes to be made in its output.