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Öğe Privileged information learning with weak labels(Elsevier, 2023) Xiao, Yanshan; Ye, Zexin; Zhao, Liang; Kong, Xiangjun; Liu, Bo; Polat, Kemal; Alhudhaif, AdiPrivileged information learning is proposed to construct the classifier by incorporating privileged knowledge. At present, most of the privileged information learning methods assume that the instance is accurately labeled. However, in real-world applications, an instance may be weakly labeled. In this paper, we propose a novel privileged information learning method with weak labels (PLWB). The hypothesis of our work is that an instance may be annotated by a number of labelers and different labelers may give different labels to this instance due to distinct professional knowledge and subjective factors. It leads to ambiguous labels of instances, namely weak labels. To solve this problem, our methodology is to give each labeler a weight and incorporate these weights into a privileged information learning model. Our technique is to employ a heuristic framework to optimize the labeler weights and the privileged information learning model jointly. The existing privileged information learning methods do not consider the weak label problem, and assign an equal or random weight to each labeler. Our work is different from these methods. The novelty and theoretical contribution is that this is the first work to deal with the weak label problem in privileged information learning. The merit is that we assign an unknown weight to each labeler and solve the optimal values of these weights in the optimization process, such that the performance of the learning model can be improved with the optimal labeler weights. In the experiments, the tool that we use is MATLAB, in which we implement our algorithm. The experimental datasets include one handwritten categorization dataset, two image classification datasets (i.e., Animals-with-Attributes dataset and Caltech-101 dataset), and one disease diagnosis dataset (i.e., Alzheimer's Disease Neuroimaging Initiative dataset), in which the number of instances used is 2000, 6180, 8677 and 202, respectively. The obtained results are that: (1) by optimizing the labeler weights, the proposed PLWB method obtains explicitly higher classification accuracy than the existing privileged information learning methods; (2) PLWB has relatively higher training time since it needs to solve the labeler weights in the optimization process.& COPY; 2023 Elsevier B.V. All rights reserved.Öğe Classification of freezing of gait in Parkinson's disease using machine learning algorithms(Institute of Electrical and Electronics Engineers Inc., 2023) Önder, Mithat; Şentürk, Ümit; Polat, Kemal; Paulraj, D.Freezing of gait (FoG) is a prevalent and incapacitating symptom that affects individuals diagnosed with Parkinson's disease (PD) and other movement disorders. Detecting FoG is crucial for accurate diagnosis, fall prevention, and providing objective measurements, all of which are essential for optimizing treatment strategies and improving the quality of life for individuals with FoG. In this study, FoG has been detected using three different classification algorithms: Medium Gaussian Support Vector Machine (SVM), Medium K-Nearest Neighbor (KNN), and Boosted Trees. The process starts with data segmentation, where the dataset is divided into smaller segments. Then, feature extraction is performed on each segment to obtain various statistical measures such as mean, root mean square, maximum, standard deviation, kurtosis, skewness, and peak of root mean square. To ensure a robust and reliable analysis, the dataset is resampled using bootstrapping, a statistical technique that involves drawing random samples from the dataset with replacement. This leads to a more representative sample and reduces the impact of outliers or imbalanced data. The next step is to split the resampled dataset into three different approaches for the classification algorithm: In 5-FCV, the dataset is divided into five equal-sized subsets. Similarly, 10-FCV splits the dataset into ten subsets and follows the same process. Finally, the Medium Gaussian SVM, Medium KNN, and Boosted Trees classification algorithms are applied to the FoG dataset. The classification accuracy achieved is 86.9%, 87.6%, and 92.7% with 10-fold cross-validation, indicating that these algorithms are effective in accurately classifying FoG. © 2023 IEEE.Öğe Message from the general chair(Institute of Electrical and Electronics Engineers Inc., 2023) Polat, KemalThese proceedings contain papers presented at 2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI 2023) was taken place in Prague, Czech Republic on April 17-19, 2023. ACEDPI 2023 aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of electronics, data processing and informatics.Öğe Detection of Alzheimer's disease from EEG signals using explainable artificial intelligence analysis(Institute of Electrical and Electronics Engineers Inc., 2024) Arabacı, Bahadır; Öcal, Hakan; Polat, KemalIn this study, the evaluation of classification models with frequency and chaotic features was aimed for the classification of healthy individuals and Alzheimer's patients using EEG signals. Morlet wavelet transform was employed for calculating EEG features to determine the characteristics in the frequency domain. Additionally, Lyapunov exponents were utilized for the analysis of chaotic features, and significant EEG channels were identified from the obtained results of the wavelet transform. Using permutation importance, the impact of each feature on the performance of the classification model was assessed. In this evaluation, the Random Forest model stood out in overall performance, showing the highest accuracy (0.7614), precision (0.7546), and F1 score (0.793) compared to other models. Furthermore, the Naive Bayes model achieved the highest sensitivity (0.8662) in detecting positive instances. © 2024 IEEE.Öğe The investigation of CMOS inverter based comparator circuits(Institute of Electrical and Electronics Engineers Inc., 2018) Keleş, Fatmanur; Aytar, OktayIn this study, the performance of the CMOS inverter circuit with active load, which can be used as a comparator structure in analogue digital converter circuits, is investigated with respect to other CMOS inverter circuits by using 0.25?m CMOS technology library in Cadence Virtuoso 6.13 design program. As a result of the analyzes made, it is seen that the proposed structure only consumes 1,041 uW and the delay time is 36.11 ps. The power dissipation, which the proposed design consumes, is significantly lower than the other designed CMOS inverter circuits. Therefore, when the proposed architecture is used in the comparator block of high-speed parallel analogue digital converters, power consumption is likely to decrease. © 2018 IEEE.Öğe Improving fault ride through capability of DFIG with fuzzy logic controlled crowbar protection(Institute of Electrical and Electronics Engineers Inc., 2022) Bekiroğlu, Erdal; Yazar, Muhammed DuranDoubly fed induction generators (DFIG) are sensitive to intense disturbances in the grid. Various methods such as crowbar protection circuits are used to increase the fault ride-through (FRT) capability of DFIGs. Crowbar is one of the most important protection methods used to protect the rotor side converter, rotor windings, and capacitors in the wind energy conversion system (WECS) from the damage of overcurrent/overvoltage. Setting the timing of the crowbar switch excellently is significant for the stability of the grid and the efficiency of the WECS. In this study, a Fuzzy logic controller (FLC) block has been added to control the crowbar switch to improve the timing of the crowbar and improve the FRT capability of the WECS. The performance of DFIG-based WECS with FLC under symmetric voltage dip was investigated by using Matlab/Simulink environment at a constant wind speed of 12 m/s. DC bus voltage, crowbar current, the terminal voltage of DFIG, and electromagnetic torque results were obtained. The results of the crowbar with the proposed controller and the results of the crowbar without FLC were compared. The results revealed that the proposed system is reliable, proper, and prevailing for the grid-connected DFIG under grid fault conditions. © 2022 IEEE.Öğe Performance analysis of machine Learning algorithms on power quality disturbances classification(Institute of Electrical and Electronics Engineers Inc., 2024) Gümüş, Birsen; Çoban, Melih; Tezcan, Süleyman SungurThe importance of the concept of power quality in electrical power systems is increasing. This situation causes the acceleration of work on detecting and eliminating power quality events. It is possible to automatically detect and classify these events using signal processing techniques and machine learning systems. In this study, the performances of Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naive Bayes (NB) classification algorithms were compared among themselves and with the literature. Signals containing 15 single and multiple power quality events were used for classification. Discrete Wavelet Transform (DWT) technique was preferred to extract the features of the signals. The data sets obtained by feature extraction were divided into test data and training data with a ratio of 20%, 30%, 40%. Thus, the classifiers were trained and tested with data sets of different sizes and their performances were evaluated. The highest accuracy rate was obtained as 99.75% with DT when 20% of the data set of noiseless signals was used as test data. The lowest accuracy rate was obtained as 30.6% with KNN when 30% of the data set of signals with 10dB noise was used as test data. In addition, the performances of the classifiers were examined by performing a 5-fold cross validation test and the average accuracy rates obtained from this were compared with other studies in the literature. © 2024 IEEE.Öğe Residual voltage tests of 4.5 kV metal oxide surge arrester(Institute of Electrical and Electronics Engineers Inc., 2023) Tunç, Emre; Fidan, MuratOvervoltages can cause various malfunctions in electrical power systems and high voltage facilities. Metal oxide surge arresters (MOSAs) are used to prevent overvoltage-related malfunctions in electrical power systems and high voltage facilities. MOSAs protect the system from high amplitude currents by conducting overvoltage-induced discharge currents through themselves to ground in the network to which they are connected. Residual voltage tests of MOSAs are carried out with lightning impulse current in 8/20 ?s waveform according to IEC 60099-4 standards. In this study, residual voltage tests of a 4.5 kV metal oxide surge arrester (MOSA) were carried out by producing 2.5 kA, 5 kA and 10 kA impulse currents with an impulse current generator (ICG) with 24 kV and 2.9 kJ label values. As a result of the experiments carried out according to IEC 60099-4 standards, it was concluded that the residual voltage of the MOSA is within the limits guaranteed by the manufacturer. © 2023 IEEE.Öğe Real time heart rate detection using non-contact photoplethysmography signals(IEEE Computer Society, 2014) Kavsaoğlu, Ahmet Reşit; Polat, Kemal; Bozkurt, Mehmet RecepHeart is contracted rhythmically so as to drive nutrients and oxygen necessary for life through our organs with blood arteries. The frequency for the rhythmic contraction of heart just as a pump is called heart rate (HR). Heart rate variation (HRV) is a measure of a fluctuation of time interval between heart beats. HRV is calculated considering electrodiagram (ECG) signals, arterial blood pressure signals or photoplethysmography (PPG) signals-derived time series of in-between heart beats. HRV is used as a significant indicator for the detection of healthiness and sickness state. Such pathological cases as high blood pressure, heart failure, and septic shock can be diagnosed using HRV. Therefore, accurate and rapid detection of HR is essential to correct diagnosis. In this study, real-time heart rate detection was derived from contactless PPG signals. PPG calling for contact with skin becomes useless in case of tissue scars or burns. In such cases, the use of contactless PPG is superior. Contactless PPG consists of a light source and a camera that senses reflection or transmittance of the light source. Camera images obtained were processed through an interface prepared in the MATLABTM GUI setting, and real-time heart rate detection was carried out. © 2014 IEEE.Öğe Residual block fully connected DCNN with categorical generalized focal dice loss and its application to Alzheimer's disease severity detection(PeerJ Inc, 2023) Alhudhaif, Adi; Polat, KemalBackground. Alzheimer's disease (AD) is a disease that manifests itself with a deteriora-tion in all mental activities, daily activities, and behaviors, especially memory, due to the constantly increasing damage to some parts of the brain as people age. Detecting AD at an early stage is a significant challenge. Various diagnostic devices are used to diagnose AD. Magnetic Resonance Images (MRI) devices are widely used to analyze and classify the stages of AD. However, the time-consuming process of recording the affected areas of the brain in the images obtained from these devices is another challenge. Therefore, conventional techniques cannot detect the early stage of AD.Methods. In this study, we proposed a deep learning model supported by a fusion loss model that includes fully connected layers and residual blocks to solve the above -mentioned challenges. The proposed model has been trained and tested on the publicly available T1-weighted MRI-based KAGGLE dataset. Data augmentation techniques were used after various preliminary operations were applied to the data set.Results. The proposed model effectively classified four AD classes in the KAGGLE dataset. The proposed model reached the test accuracy of 0.973 in binary classification and 0.982 in multi-class classification thanks to experimental studies and provided a superior classification performance than other studies in the literature. The proposed method can be used online to detect AD and has the feature of a system that will help doctors in the decision-making process.Öğe A novel adaptive time delay identification technique(Elsevier Science Inc, 2023) Bayrak, Alper; Tatlıcıoğlu, EnverIn this work, a novel online adaptive time delay identification method is proposed for a class of signal processing and communication applications, where the received signal is a combination of the transmitted signal and its delayed forms with the delay values being uncertain and required to be estimated. The design relies on a filtered form of a prediction error-like term which is then used in the design of the novel nonlinear adaptive update law. The stability of the identification algorithm is then investigated via novel Lyapunov based tools and the time delay identification is proven to be globally uniformly ultimately bounded. Several numerical simulations are conducted to evaluate the performance of the proposed identifier where constant, slowly varying and suddenly changing delays are successfully identified even in the presence of additive noise.& COPY; 2023 Published by Elsevier Ltd on behalf of ISA.Öğe Investigation of energy generation potential of solar panels placed as shutters for windows in residential(Taylor & Francis Inc, 2023) Fidan, MuratThis study investigated the annual potential to generate electricity from 260 W solar panels placed on windows as a shutter of a sample flat in a mass housing in Bolu, Turkiye, which has adverse conditions such as shadowing and limited sunshine duration. Results showed that 4.36% of the annual energy consumed by the sample flat from the network can be produced by solar panels placed on the windows as shutter. It is predicted that the residential windows in the 500 flats mass housing where the sample flat is located have the potential to generate at least 53.73 MWh of electrical energy per year. This generated energy can prevent the emission of 14,010,632 tons of CO2 annually. It has been shown that the electrical energy produced from the residential windows can be used with 94.55% efficiency with the DC installation proposed within the scope of the study. It is shown that spread lighting provided by LED strips may be an alternative for Light Fidelity communication. This is the first study for Bolu in which measurements were recorded for one year.Öğe Improving energy efficiency in climatic test chambers with deep learning and absolute humidity methods(IEEE, 2023) Bekiroğlu, Erdal; Karaca, HakanClimatic test chambers are devices used to simulate environmental conditions for the testing and verification of products in various industries. However, these chambers can consume significant amounts of energy, resulting in high operating costs and environmental impacts. Therefore, the need to optimize the energy efficiency of climatic test chambers while maintaining their performance is becoming increasingly important. In this paper, we will discuss the control method for humidity testing by calculating the use of the LSTM algorithm instead of the classical control method PID to control climatic test chambers to improve energy efficiency and the control method based on absolute humidity instead of relative humidity. In particular, we harness the power of artificial neural networks to reduce energy consumption and improve control of climatic test chambers based on various input parameters such as temperature, humidity, and test duration. By changing the control methods, we aim to increase efficiency and make it more suitable and efficient for smart grid systems.Öğe Design and magnetic analysis of a grounding transformer compatible with wind power system(IEEE, 2023) Demiral, Elif; Bekiroğlu, Erdal; Yazar, Muhammed DuranThe aim of this study is to design and magnetic analysis of a grounding transformer in order to prevent material damage caused by phase-ground faults that may occur in wind energy systems. A zig-zag transformer is designed with a new winding design technique, different from the traditional zig-zag transformer design. Due to the lack of analytical formulas, finite element analysis (FEA) is used to calculate leakage reactance of designed transformer. The designed zig-zag transformer is analyzed in the ANSYS Maxwell environment. The proposed grounding transformer for wind power systems is investigated in terms of 3-phase 3D leakage reactance, 1-phase 3D leakage reactance, core losses of 3D, core losses of 2D, and flux density. The obtained results showed that the designed zig-zag transformer is compatible with wind power systems.Öğe Stable measurement system for platinum resistance temperature detector(Maik Nauka/Interperiodica/Springer, 2023) Altınkaya, Serdar; Bayrak, Alper; Daldal, Nihat; Özdil, Osman Eren-Stable and precise temperature measurement is crucial for thermodynamic applications. The stability and precision of the measurement depend on the sensor type and measurement method. In industrial applications, platinum resistance temperature detectors (RTD) are widely preferred. In this study, stable and precise temperature measurement methods by using the Platinum RTD PT1000 sensor are investigated. Measurement methods are considered as measurement circuits and digital filter applications, separately. Experimental studies were evaluated on a household-type oven and comparative results are presented.Öğe Tinba: Incremental partitioning for efficient trajectory analytics(Elsevier Sci LTD, 2023) Tian, Ruijie; Zhang, Weishi; Wang, Fei; Polat, Kemal; Alenezi, FayadhApplications with mobile and sensing devices have already become ubiquitous. In most of these applications, trajectory data is continuously growing to huge volumes. Existing systems for big trajectory data organize trajectories at distributed block storage systems. Systems like DITA that use block storage (e.g., 128 MB each) are more efficient for analytical queries, but they cannot incrementally maintain the partitioned data and do not support delete operations, resulting in difficulties in trajectory analytics. In this paper, we propose an incremental trajectory partitioning framework Tinba that enables distributed block storage systems to efficiently maintain optimized partitions under incremental updates of trajectories. We employ a data flushing technique to bulk ingest trajectory data for random writing in distributed file system (DFS). We recast the incremental partitioning problem as an optimal partitioning problem and prove its NP-hardness. A cost- benefit model is proposed to address the optimal partitioning problem. Moreover, Tinba supports most of the existing similarity measures to quantify the similarity between trajectories. A heuristic is developed to instantiate the Tinba framework. Comprehensive experiments on real-world and synthetic datasets demonstrate the advancements in ingestion performance and partition quality, as opposed to other trajectory partition methods.Öğe An ensemble learning approach for resampling forgery detection using Markov process(Elsevier, 2023) Mehta, Rachna; Kumar, Karan; Alhudhaif, Adi; Alenezi, Fayadh; Polat, KemalResampling is an extremely well-known technique performed for Image forgery detection. It includes the changes in the content of a picture in terms of rotation, stretching/zooming, and shrinking, to frame a forged picture that is a localized forgery in comparison to the original picture. With the wrong intention, resampling forgery has been increased day by day, and its negative impact has been increased in criminology, law enforcement, forensics, research etc. Accordingly, the interest in the algorithm of image resampling forgery detection is significantly developed in image forensics. In this paper, a novel image resampling forgery detection technique has been proposed. In the proposed technique, two types of Markov feature with spatial and Discrete Cosine Transform domains have been extracted to recognize the resampling operation. The spatial domain gives the information for the distribution of the pixels and DCT gives the edge information. Further, these Markov features are consolidated. Due to high dimensionality hard thresholding technique is used for reducing the dimensionality. Then, these Markov features are applied to the set of models of different classifiers. With the utilization of classifiers, weighted majority voting values have been calculated during the ensemble classification. Unlike the other techniques, these weighted voting boundaries have been consequently balanced during the training process until the best accuracy has been obtained. However, it is very difficult to get best accuracy so for getting best accuracy this research needs to do lots of iterations and trained the dataset. For the comparative study very few research has been found for this resampling forgery technique with different interpolation techniques and classifier. Still, comparison has been done with some latest research work. The comparative analysis shows that the proposed ensemble learning-based algorithm provides the best outcomes with the accuracy of 99.12% for bicubic, 98.89% for bilinear, and 98.23% for lanczos3 kernel with considerably less complexity and high speed in comparison to prior techniques which are using single support vector machine for classification. Moreover, the proposed algorithm also detects a very low probability of error of 0.44% and detects the type of interpolation kernel, size of the forgery, and the type of resampling, whether it is up sampling and down sampling, using Graphical User Interface which has not been detected previously with multiple forgery detection.& COPY; 2023 Elsevier B.V. All rights reserved.Öğe Annealing-induced modifications on structural, surface chemical bonding, and electrical characteristics of p-NiO/n-TiO2 heterostructure(Springer, 2023) Kaya, Şenol; Soykan, Uğur; Sunkar, Mustafa; Karaboğa, Seda; Doğan, Muhsin Uğur; Terzioğlu, Rıfkı; Yıldırım, Gürcan; Terzioğlu, CabirThe influences of annealing temperatures on the electrical characteristics of a p- NiO/n-TiO2 heterojunction diode were thoroughly investigated, taking into account changes in microstructure, morphology, and surface chemistry of the p-NiO/n-TiO2 films, which were deposited on an insulating SiO2/ Si layer. During different annealing processes, considerable stress variations were observed in the p-NiO/n-TiO2 films due to the crystalline evolution of p-NiO and n-TiO2. Notably, the crystallization of the TiO2 layer, which serves as the intermediary between the back contact materials and NiO, led to the evident formation of grain structures. As the annealing temperature increased, the surface roughness also grew from 5.4 to 8.7 nm. At an annealing temperature of 500 degrees C, the formation of a parasitic NiTiOx phase was observed, particularly at the interface between NiO and TiO2. Conversely, the study also revealed that annealing temperature played a significant role in the rectifying behavior, barrier potential, and ideality factor of the diode. Among the various annealing processes, the most favorable results were achieved after annealing at 400 degrees C. At this temperature, the diode demonstrated the lowest ideality factor of 1.89, accompanied by superior rectifying behavior and a barrier potential of 0.70 eV. The findings clearly indicate that any alterations in the surface chemistry and microstructure of the film directly impact the diode's characteristics. Thus, optimizing the annealing temperature becomes crucial for enhancing the performance of the p-NiO/n-TiO2 heterojunction diode.Öğe Technical feasibility of offshore wind power plant for Gokceada region in Turkiye(IEEE, 2023) Akyol, Burak; Yazar, Muhammed Duran; Bekiroğlu, ErdalThe aim of this study is technical feasibility of the offshore wind power plant (OWPP) installation in Gokceada/Turkiye. First of all, the site selection process for OWPP has been executed. Then, the suitable wind turbine model for the wind speed profile of the region is determined. The layout of the turbines is made according to the main wind direction of the region. The designed system in the Homer PRO environment has been analyzed under actual wind speed. The ratio of the power produced by the system to meet the actual electricity demand of Canakkale and Turkish power system has been analyzed. In addition, the designed system is also analyzed under actual wind speed in the WindSim environment. Annual power generation, capacity factor, and full power generation time results are obtained. The applicability of the proposed system has been validated with the help of simulation environments. The obtained results showed that the OWPP in Gokceada/Turkiye is prevailing, reliable, and efficient.Öğe Design and double-stage optimization of synchronous reluctance motor for electric vehicles(Taylor & Francis Inc, 2023) Bekiroğlu, Erdal; Esmer, SadullahIn this study, a high-power synchronous reluctance motor (SynRM) was designed for the traction motor of electric vehicle (EV) and its double-stage optimization was performed. Genetic algorithm and sensitivity analysis methods were used to obtain the best design parameters. Double-stage optimization was carried out to minimize the torque ripple and obtain the targeted torque, speed, and power values of the SynRM. In the first stage, the genetic algorithm method was used to improve the design parameters of the stator and rotor. With the improved design parameters, it was observed that the torque ripple decreased. In the second stage, the sensitivity analysis method was used. In this method, the effect of changing the skew angle of the stator on the torque ripple was investigated. The performance of the designed motor was examined in the optimization process. It was observed that the targeted torque, power, speed, efficiency, and torque ripple minimization values are successfully achieved with the best stator and rotor parameters. The results showed that SynRM produces high torque and high power with high efficiency and low torque ripple over wide speed range. It is quite proper to use the designed SynRM as a traction motor of new generation electric vehicles.
