Scopus Kapalı Erişimli Yayınlar

Permanent URI for this collectionhttps://hdl.handle.net/11727/10761

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Author: search.filters.author.Altiparmak, F.

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    A hybrid ant colony optimization approach for the design of reliable networks
    (2007) Dengiz, B.; Altiparmak, F.; Belgin, O.; 0000-0003-1730-4214; 0000-0001-6702-2608; AAF-7020-2021; K-1080-2019
    This paper presents a new solution approach, which is a hybridization of ant colony optimization (ACO) and simulated annealing (SA), called (h_ACO) to design of communication networks. The design problem is to find the optimal network topology where total cost is minimum and all-terminal reliability is not less-than a given level of reliability. The effectiveness of the h_ACO is investigated comparing its results with those obtained by SA and ACO, which are basic forms of the h_ACO, and also GAs given in the literature for the design problem. Computational results show that the h_ACO is an effective heuristic approach to design of reliable networks.
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    Applications of Deep Learning Techniques to Wood Anomaly Detection
    (2022) Celik, Yaren; Guney, Selda; Dengiz, Berna; Xu, J; Altiparmak, F.; Hassan, MHA; Marquez, FPG
    Wood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. Some difficulty and complexity of production processes result in high variability of raw materials such as a wide range of visible structural defects that must be checked by specialists on line or of line. These issues are not only difficult and biased in manual processes, but also less effective and misleading. To overcome the drawbacks of the manual quality control processes, machine vision-based inspection systems are in great of interest recently for quality control applications. In this study, the wood anomaly has been detected by using deep learning. As it will be a distinction-based method on image processing, the Convolution Neural Network (CNN), which is one of the most suitable methods, has been used for anomaly detection. In addition, it will be tried to obtain the most suitable one among different CNN architectures such as ShuffleNet, AlexNet, GoogleNet for the problem. MobileNet, SqueezeNet, GoogleNet, ShuffleNet among considered methods show promising results in classifying normal and abnormal wood products.