Yazar "Gezer, Gulsum" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    ANN-Based Control of a Multiboat Group for the Deployment of an Underwater Sensor Network
    (Hindawi Ltd, 2014) Michailidis, Emmanouel T.; Tuna, Gurkan; Gezer, Gulsum; Potirakis, Stelios M.; Gulez, Kayhan
    Underwater sensor networks (USNs) can be used for several types of commercial and noncommercial applications. However, some constraints resulting from the nature of aquatic environments severely limit their use. Due to constraints such as large propagation latency, low-bandwidth capacity, and short-distance communications, a large number of USN nodes are deployed to provide reliability in most applications. In this study, an unattended deployment approach based on the use of an autonomous boat group is proposed. A map of the deployment zone and optimal locations of USN nodes are fed into the onboard computers of the boat group. After processing these data and determining paths to be followed, the boat group deploys sensor nodes at predetermined locations. During the deployment, the boat group is controlled by an artificial neural network-(ANN-) based control system for reducing path errors. A set of performance evaluations is given to prove efficiency of the proposed control system. Performance results show that the boat group can successfully follow a predefined path set and deploy USN nodes. The tradeoffs between energy consumptions, end-to-end delay, and number of hops between underwater relay nodes of energy-efficient USN are also examined. The results indicate that increasing the number of hops reduces the total energy consumption and the end-to-end delay.
  • Küçük Resim Yok
    Öğe
    ANN-Based Estimation of Groundwater Quality Using a Wireless Water Quality Network
    (Hindawi Ltd, 2014) Kilicaslan, Yilmaz; Tuna, Gurkan; Gezer, Gulsum; Gulez, Kayhan; Arkoc, Orhan; Potirakis, Stelios M.
    Water is essential for life. Considering its importance for humans, it must be periodically analyzed to ensure its quality. In this study, a wireless water quality network is deployed to collect water quality parameters periodically and an artificial neural network-based estimation method is proposed to estimate groundwater quality. Estimating groundwater quality enables the authorities to take immediate actions for ensuring water quality. Compared to traditional water quality analysis methods, the proposed method has the advantage of letting the authorities know the quality of their water resources beforehand. A set of simulation studies given in this paper proves the efficiency and accuracy of the proposed method.
  • Küçük Resim Yok
    Öğe
    PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids
    (IEEE, 2015) Gezer, Gulsum; Tuna, Gurkan; Kogias, Dimitris; Gulez, Kayhan; Gungor, V. Cagri
    Although Smart Grid (SG) transformation brings many advantages to electric utilities, the longstanding challenge for all them is to supply electricity at the lowest cost. In addition, currently, the electric utilities must comply with new expectations for their operations, and address new challenges such as energy efficiency regulations and guidelines, possibility of economic recessions, volatility of fuel prices, new user profiles and demands of regulators. In order to meet all these emerging economic and regulatory realities, the electric utilities operating SGs must be able to determine and meet load, implement new technologies that can effect energy sales and interact with their customers for their purchases of electricity. In this respect, load forecasting which has traditionally been done mostly at city or country level can address such issues vital to the electric utilities. In this paper, an artificial neural network based energy consumption forecasting system is proposed and the efficiency of the proposed system is shown with the results of a set of simulation studies. The proposed system can provide valuable inputs to smart grid applications.