Sürdürülebilir Kalkınma Amaçları
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- MakaleYalnız künye2026Scopus SJRQ1Modeling stack voltage and remaining useful life of proton exchange membrane fuel cells using deep neural networks
Dogan, Gurkan; Imak, Andaç
Currently, various energy conversion systems are being developed to reduce environmental impacts. Proton exchange membrane fuel cells (PEMFCs), which stand out with their high efficiency characteristics, have emerged as an important alternative. However, these advantages, along with high costs and uncertainties regarding remaining useful life (RUL) estimation, restrict their further commercialization, especially in the automotive industry. This study presents a forecasting framework using a long short-term memory (LSTM) architecture enhanced with the Bahdanau attention mechanism for predicting RUL and long-term degradation trends. The model thus exhibits a performance that can more effectively capture dependencies in time series and adaptively weight significant time steps. Furthermore, the tree-structured parzen estimator (TPE) was chosen for the appropriate selection of hyperparameters in the developed forecasting framework. The validity, stability, and performance of the proposed model under different operating conditions were examined on the FC1 and FC2 datasets. Experimental studies have shown that the proposed method is the most successful among the best methods, achieving RE metric values of 1.628 × 10−3, 0.003 × 10−1, 99.16 × 10−2, and 0.044 × 10−1 respectively in estimating the best RMSE, MAPE, R2, and RUL.
- MakaleAçık Erişim2025Scopus SJRQ1A new lightweight convolutional neural network model for detecting drivable road regions
Doğan, Gürkan; Uyanık, Hakan; Ergen, Burhan
Nowadays, due to the rapid increase in the number of autonomous vehicles on the market, the safe navigation of these vehicles in drivable road areas has become extremely important. One of the most crucial factors in ensuring safe navigation is addressing the detection of drivable road areas as a task of semantic segmentation. Considering that autonomous vehicles are modular, the algorithm to perform this task must have the optimum trade-off in terms of lightweight, computational complexity, and segmentation accuracy. In this study, RoNet, a new model based on convolutional neural networks that provides an optimum trade-off for the detection of drivable road regions, was designed and proposed. The standard convolution types for the encoder and decoder bottleneck module of the RoNet model, as well as the spatial edge attention mechanism, have been optimized by developing asymmetric convolution types using asymmetric atrous convolution, asymmetric convolution types using Prewitt and Sobel kernels. Spatial edge attention mechanism is designed to reduce the loss of detailed information in small-resolution feature maps. In experimental tests performed with CamVid and FUVid datasets, RoNet achieved a better trade-off in terms of segmentation accuracy, number of parameters, and computational complexity compared to other state-of-the-art methods.
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İncele - BildiriYalnız künye2024Performance Analysis of Attention Mechanisms for Melanoma Cancer Detection
Dogan, Gurkan
The deadliest type of skin cancer worldwide is melanoma. If melanoma skin cancer is detected and treated at an early stage, the mortality rate can be significantly reduced. In particular, convolutional neural networks, one of the deep learning techniques that have achieved remarkable success in recent years in areas such as computer vision, natural language processing, signal processing, etc., can detect this type of cancer automatically, quickly and with high accuracy. Moreover, the accuracy performance of the neural network can be further increased by using attention mechanisms together with convolutional neural networks. However, attention mechanisms have not been used correctly and effectively in the neural network models proposed so far for the detection of melanoma skin cancer. This study examines the effect of attention mechanisms on the accuracy performance of a convolutional neural network and guides researchers to use these mechanisms correctly and effectively. To achieve this, a base model based on a convolutional neural network was built. Then, seven different attention mechanisms were integrated separately into this basic model and the results of the experimental tests of each were presented.
AtıfScopus1OpenAlex1İncele - MakaleAçık Erişim2024Scopus SJRQ2A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood
Şener, Abdullah; Doğan, Gürkan; Ergen, Burhan
Natural disasters are sudden and unexpected events that occur as a result of natural processes and often have a significant impact on people, animals, and plants, often resulting in material and emotional losses. Two of the most devastating disasters are floods and tsunamis. After these disasters, search and rescue operations to identify flooded areas are very important to save the lives of affected people and to ensure the health and safety of rescue workers. In this study, a novel semantic segmentation model called Flood Area Segmentation Network (FASegNet) was proposed to guide search and rescue teams in disaster areas to speed up search and rescue operations after natural disasters such as floods, high tides, and tsunamis. The obtained results were compared with common image segmentation models used in various fields. It was found that the developed model achieved higher accuracy rates with fewer parameters. When tested with the "Flood Area" and "Water Body" datasets without pretraining and data augmentation, FASegNet achieved mIoU accuracy of 84.3% and 84.5% respectively.
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