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    A study on the monitoring of weld quality using XGBoost with Particle Swarm Optimization
    (Elsevier, 2024) Avci, Adem; Kocakulak, Mustafa; Acir, Nurettin; Gunes, Emrah; Turan, Sertan
    Gas Metal Arc Welding is a joining technique with numerous uses in manufacturing. Since welding process parameters have a considerable impact on the welding quality, online monitoring systems are utilized on production lines to achieve standard welding quality with minimum welding faults. This study presents preliminary work to develop a monitoring system for defect analysis in a Gas Metal Arc Welding process. This study aims to eliminate the need for laboratory tests with a model to reduce welding quality control costs. In this study, welding data such as voltage, current, and wire feeding rate were collected during the welding process in realtime. New features were derived from the gathered data at the preprocessing stage using statistical approaches. The determination of whether the welding process is defective or flawless was made using the Extreme Gradient Boosting Algorithm. The hyperparameters of the algorithm were optimized with Particle Swarm Optimization. The accuracy value was obtained as 93.15% after repeating the conducted experiments ten times. The recall, precision, specificity, and F1 -Score values in these experiments were calculated as 97.22%, 94.75%, 72.35%, and 95.94%, respectively. The mean current value was found to be the most relevant and meaningful feature that describes the welding quality based on intensive experiments. In addition to the proposed algorithm, some other machine -learning algorithms were tested on the welding dataset. With this study, the significance of feature derivation from the acquired welding current data has been discovered to analyze welding quality.
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    Automated vein verification using self-attention-based convolutional neural networks
    (Pergamon-Elsevier Science Ltd, 2023) Kocakulak, Mustafa; Avci, Adem; Acir, Nurettin
    Vein-based biometric traits have been regarded as trustworthy for biometric applications. With technical advances in deep learning, verification performance has started to be improved in these applications to increase trust level in daily life by providing usage convenience and user satisfaction. In this study, the effect of self-attention mechanism on convolutional neural networks for the performance of finger-vein and hand dorsal vein verification was investigated using an open-set protocol. To provide generalizability to the trained model, self-attention-based convolutional neural networks were used rather than existing architectures and pre-trained models. With the architecture that uses residual blocks and self-attention mechanism, a fair verification performance was suggested. Verification performance was assessed on DHVI-DB and Bosphorus hand dorsal vein datasets and SDUMLA and PolyU-F finger-vein datasets in terms of equal error rate using the distance between feature vectors through the existing and the proposed distance measures. The obtained equal error rates for hand dorsal vein datasets DHVI-DB1, DHVI-DB2, and Bosphorus are 2.17, 2.21, and 18.33, respectively and for finger-vein datasets SDUMLA and PolyU-F, are 1.65 and 10.64, respectively. Moreover, 4 different loss functions were used throughout the conducted experiments to see the discriminative ability of the proposed network for vein verification. The experimental results on these datasets indicate the potential effectiveness of the self-attention mechanism on automated vein verification.
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    Estimation of the coverage area with ResNet-based Conditional Variational Autoencoder
    (Ieee, 2025) Erbas, Ugur; Avci, Adem; Tabakcioglu, Mehmet Baris
    The rapid expansion of wireless communication networks, especially with 5G, has made the accurate and fast determination of base station locations critical. With the increase in network traffic and the number of users, it has become important to optimize the network infrastructure. This process is complex, taking into account technical and environmental factors. The study investigates machine learning (ML) and deep learning (DL) techniques to optimize base station placement, emphasizing that traditional methods require manual calculations and field measurements, whereas ML and DL-based approaches are more efficient and faster. Furthermore, a ResNet-based Conditional Variational Autocoder (ResNet-CVO) model for coverage map estimation is proposed and its performance is evaluated. With the proposed generative model, a more efficient approach for coverage map estimation is presented.