TR-Dizin İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.yasar.edu.tr/handle/123456789/11291

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Browsing TR-Dizin İndeksli Yayınlar Koleksiyonu by Journal "Academic Platform Journal of Engineering and Smart Systems"

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    Article
    A Novel Clustering-based Forecast Framework: The Clusters with Competing Configurations Approach
    (2023) Gökhan Demirkıran; Miray Alp; Alp, Miray; Demirkıran, Gökhan
    Accurate aggregate (total) short-term load forecasting of Smart Homes (SHs) is essential in planning and management of power utilities. The baseline approach consists of simply designing and training predictors for the aggregated consumption data. Nevertheless better performance can be achieved by using a clustering-based forecasting strategy. In such strategy the SHs are grouped according to some metric and the forecast of each group's total consumption are summed to reach the forecast of aggregate consumption of all SHs. Although the idea is simple its implementation requires fine-detailed steps. This paper proposes a novel clustering-based aggregate-level forecast framework so called Clusters with Competing Configurations (CwCC) approach and then compares its performance to the baseline strategy namely Clusters with the Same Configurations (CwSC) approach. The Configurations in the name refers to the configurations of ARIMA Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) forecasting methods which the CwCC approach uses. We test the CwCC approach on Smart Grid Smart City Dataset. The results show that better performance can be achieved using the CwCC approach for each of the three forecast methods and LSTM outperforms other methods in each scenario.
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    Article
    Evaluation of Convolutional Networks for Event Camera Face Pose Alignment
    (2025) Arman Savran; Burhan Burak Oral; Alptuğ Çakıcı; Oral, Burhan Burak; Çakıcı, Alptuğ; Savran, Arman
    Event camera offers substantial advantages over conventional video cameras with their efficiency extremely high temporal resolutions low latency and high dynamic range. These benefits have led to applications in various vision domains. Recently they have been applied in facial recognition tasks as well. However while significant advantages of event cameras in some facial processing tasks have been demonstrated the initial stage in almost any task i.e. face alignment is not at par with the conventional cameras. This study investigates the use of face alignment convolutional networks regarding both performance and complexity for event camera processing. Our aim is event camera face pose alignment that can be used as an efficient preprocessor for facial tasks. Therefore we comparatively evaluate simple convolutional coordinate regression with a hybrid of coordinate and heatmap regression known as pixel-in-pixel regression. Our experimental results reveal the superior performance of the hybrid method. However we also show that if there is a computation bottleneck simple convolutional coordinate regression is preferable for their low resource requirements though at the expense of some performance loss.