Person: YILMAZ, ERSEN
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YILMAZ
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ERSEN
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Publication Compact Cramer-Rao bound expressions for frequency estimation of sinusoidal signals in unknown colored noise(IEEE, 2004-01-01) Yılmaz, Ersen; Dilaveroğlu, Erdoğan; IEEE; YILMAZ, ERSEN; DİLAVEROĞLU, ERDOĞAN; Elektrik Mühendisliği Bölümü; 0000-0002-6620-655X; G-3554-2013; EWI-6920-2022Compact expressions of the Cramer-Rao bound matrix for estimating the frequencies of a number of superimposed sinusoidal signals in colored Gaussian noise are presented for both complex and real data cases. The expressions are given for the most general case in which all the signal parameters (the frequencies, amplitudes, and phases of the sinusoids) are unknown and the covariance function of the noise is parameterized in some unspecified way. We then specialize our results for the important case where the noise samples come from an autoregressive noise process.Publication A driver safety support system which detects traffic lights(Gazi Üniversitesi, 2018-06-01) Kılıkcıer, Çağlar; Yılmaz, Ersen; KILIKÇIER, ÇAĞLAR; YILMAZ, ERSEN; Mühendislik Fakültesi; Elektrik Elektronik Mühendisliği Bölümü; 0000-0001-7933-1643; 0000-0002-6620-655X; AAH-3031-2021; G-3554-2013The number of traffic accidents can be decreased through driver safety support systems (DSSS). In this study, a driver safety support system is proposed in which the driver is warned by finding traffic lights. The proposed DSSS works on only visual information and detects traffic lights. The system primarily transforms the received images into gray scale images and subject them to multi-level thresholding with Otsu criteria. The regions of interest which can be traffic lights are found for the thresholded images by using connected component analysis and blob analysis, respectively. Feature vectors including the color information are extracted from the founded regions. Finally, it is decided if the regions of interest are traffic lights by using support vector machines (SVM). The performance of the proposed DSSS is examined onPublication A method based on an autoencoder for anomaly detection in dc motor body temperature(Mdpi, 2023-08-01) Demircioglu, Emine Hümeyra; Yılmaz, Ersen; YILMAZ, ERSEN; Mühendislik Fakültesi; Elektrik ve Elektronik Mühendisliği Bölümü; 0000-0002-6620-655X; G-3554-2013Anomaly detection has an important role in industrial systems. Abnormal situations occurring in a system cause anomalies, and the anomalies reduce system performance over time, and may also make the system malfunction. Therefore, the correct and timely detection of anomalies is of critical importance for predictive maintenance. In this study, an autoencoder-based method is proposed for anomaly detection in DC motor body temperature. The performance of the method was examined on a dataset that was created specifically for this study. In the experiments, the three-sigma outlier method was also applied on the same dataset for the same purpose and its performance results are used for comparison. The performance results of both methods are represented in terms of three measures, namely, accuracy, recall, and precision. The experimental study showed that the proposed method achieved over 96% ratios for all three measures, and it can be successfully used for anomaly detection in DC motor body temperature. Additionally, it can be concluded that the proposed system can be preferred for anomaly detection in time series data collected from different types of sensors when the performance results are taken into consideration.