TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/4

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Asenkron Motorun Çalışma Parametrelerinin Scada ile İzlenmesinin Tasarımı
(2019) Terzioğlu, Hakan; Ağaçayak, Abdullah Cem; Yalçın, Gökhan; Neşeli, Süleyman
Asenkron motorlar; doğrudan bir fazlı ya da üç fazlı alternatif akım şebekesinden beslenebilmesi, dayanıklı, bakım gerektirmeyen yapısı ve düşük maliyetleri nedeniyle, hem sanayide hem de ev aletlerinde en çok kullanılan motor türü haline gelmiştir. Asenkron makinelerin, senkron makinelerinden en büyük farkı dönme hızının sabit olmayışıdır. Motor olarak çalışan bir asenkron motorda bu hız, senkron hızdan küçüktür. Makine bu özelliğinden dolayı, asenkron makine adını almıştır. Dünyada üretilen enerjinin %70 civarındaki kısmının asenkron motorlarda tüketiliyor olması bu motorların kullanım sıklığını ve önemini göstermektedir. Yapılarının basit, ekonomik ve sağlam olmaları, bakım gerektirmemeleri ve her türlü ortam koşullarında çalışabilmeleri gibi üstün özellikleri nedeniyle asenkron motorlar, endüstride yaygın olarak kullanılmaktadır. Yaygın olarak kullanılan asenkron motorların kontrolünde çeşitli sürme teknikleri kullanılmaktadır. Bu sürme tekniklerine göre asenkron motorların matematiksel modellemesinin gerçekleştirilmesi de önemli bir çalışma konusudur. Asenkron motorlarının dinamik performanslarının incelenmesi ve matematiksel modellerinin çıkartılabilmesi için parametrelerinin doğru olarak hesaplanması gerekmektedir. Bu çalışmada asenkron motorların en büyük sorunlarından birisi olan parametrelerinin doğru olarak belirlenmesi amaçlanmıştır. Bilgisayar ve mikroişlemci teknolojisinin gelişmesine bağlı olarak asenkron motor parametrelerinin tespiti hem kolaylaşmış hem de daha önem kazanmıştır. Bu çalışmayla yaygın olarak kullanılan asenkron motorların çalışma parametreleri olan akım, gerilim, cos ?, tork, güç değerlerinin operatör panelinde okunmasının sağlanacağı bir SCADA sisteminin tasarımı gerçekleştirilmiştir. Bu sistemde PLC, HMI ve hız kontrol cihazı haberleştirilerek asenkron motorun çalışma parametreleri HMI ekranında görüntülenebilecektir. Ekranda görüntülenen veriler kullanılarak asenkron motorun istenilen değerlerine hesaplama yöntemleri kullanılarak ulaşılabilecektir. SCADA programında asenkron motor parametrelerinin en doğru ve en hızlı şekilde hesaplanacağı bir algoritma tasarlanacaktır. Parametre değerlerinin doğru olarak hesaplanması motorun dinamik etkilerinin belirlenmesinde ve sürme devrelerinin tasarlanmasında büyük bir gelişme sağlayacaktır.
Citation - WoS: 6
Citation - Scopus: 7
Automated Elimination of Eog Artifacts in Sleep Eeg Using Regression Method
(2019) Dursun, Mehmet; Özşen, Seral; Güneş, Salih; Akdemir, Bayram; Yosunkaya, Şebnem
Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%– 1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.
Ball Throwing Machine Design To Develop Footballers’ Technical Attributes
(2021) Arslan, Cemile; Arslan, Mustafa; Yalçın, Gökhan; Kaplan, Turgut; Örnek, Humar Kahramanlı
For a football player to perform well in football, it is necessary to improve his technical and tactical skills. Improving these skills is enabled with the repetition of the same positions that the football player has the ball. The repetitions of these same positions can be performed with the aid of a machine. In this study, a football throwing machine design that can provide direction and velocity for the ball in a repeatable and controllable manner is generated for full educational evaluation. Ball loading canister on the ball throwing machine enabled to use of many balls. There are a couple of ball throwing wheels both are made of polyurethane material and have a concave surface. These wheels are mounted on a body for axial rotation on common ground. Each wheel’s rotation speed can be adjusted individually. To determine the horizontal-vertical direction of movement of the ball, two linear actuators are used. Ball’s velocity, direction, orbit, and throwing laps are controlled electronically. All controls concerning ball throwing are carried out via Delta PLC (Programmable Logic Controller) and HMI (Human Machine Interface) panel. A user interface is developed for controls made via PLC. Owing to the interface, different training plans are designed by handler or trainer via operator panel, and footballer is provided to train in various densities. To prevent toppling tripod system, and to carry easily a towing arm is used. The machine can work with an accumulator or feed directly from the grid circuit.
BİR HİDROLİK DERİN ÇEKME PRES MAKİNESİNİN PLC TABANLI BULANIK MANTIK KONTROLÜ VE ENDÜSTRİ 4.0 UYGULAMASI
(Konya Technical University, 2019) Aydoğdu, Ömer; Çatkafa, Ahmet
Bu makalede bir derin çekme pres makinesinin PLCtabanlı bulanık mantık kontrolü gerçekleştirilmiştir. PLC ortamında BulanıkMantık Denetleyiciler için bulanıklaştırma, çıkarım işlemi ve durulaştırmaadımları ayrı bloklar olarak gerçekleştirilmiş, blokların program dâhilindeyürütülmesi ile kontrol işlemi gerçekleştirilmiştir. PLC ortamında gerçekleştirilenbulanık mantık kontrol programı, derin çekme pres makinasında uygulanmış vekarşılaştırma amaçlı sonuçlar elde edilmiştir. Ayrıca hidrolik derin çekme presmakineleri için Endüstri 4.0 kavramı incelenmiş, mevcut sistemlerlekarşılaştırılması yapılmış ve mevcut sistemlerin Endüstri 4.0’a uyumları elealınmıştır. Çalışmada, proje kapsamında iki farklı hidrolik derin çekme presmakinesi ele alınmıştır. Bunlardan biri klasik olarak kontrol edilen AC motortahrikli sabit devir ve debide olan hidrolik güç ünitesi ile çalışan birprestir. Diğer makine ise servo motor tahrikli değişken debili hidrolikpompanın bulunduğu hidrolik güç ünitesi ile çalışan sistemdir. Bu iki sistemarasında belirli özellikler için kıyaslama yapılarak Endüstri 4.0 uyumu veüstünlüğü somutlaştırılmaya çalışılmıştır.
Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data
(2018) Aslan, Muhammet Fatih; Çelik, Yunus; Sabancı, Kadir; Durdu, Akif
Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective these methods are for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.
Citation - Scopus: 7
Classification of 3b alzheimer's mr images using voxel values in volumetric loss regions
(TUBITAK, 2020) Öziç, Muhammet Üsame; Özşen, Seral
Alzheimer's Disease is a deadly neurological disease that begins with cognitive disorders and forgetfulness. Volumetric changes caused by the disease in the brain can be monitored with high resolution magnetic resonance images. In this study, volumetric losses occurring in gray matter and white matter regions were mapped by voxel-based morphometry method using 3D T1-weighted magnetic resonance images taken form OASIS database, and a decision support system was designed that classifies alzheimer's and normal magnetic resonance images with significant voxel values in these regions. SPM8, MRIcro programs and VBM8 library were used for inter-group voxel-based morphometry on magnetic resonance images. Gray matter and white matter regions were masked with binary masks obtained from volumetric loss maps. Significant data sets were created with voxel values corresponding to the same coordinate points from the areas under the mask in each gray matter and white matter image. With feature ranking methods, the data were ranked from the most meaningful feature to the most meaningless feature. The ranked features were given as input to the support vector machine using linear and rbf kernel with 10 fold cross validation. As a result of the experiments, the highest accuracy rates were found as 92.857% in gray matter classification and 79.286% in white matter classification with linear support vector machines based on t-test feature ranking. © 2020, TUBITAK. All rights reserved.
Comparison Global Brain Volume Ratios on Alzheimer’s Disease Using 3d T1 Weighted Mr Images
(2020) Öziç, Muhammet Üsame; Özşen, Seral
Alzheimer's Disease is a cause of dementia that starts with the loss of cognitive functions. The degeneration that starts in memoryrelated areas in the brain spreads to other regions as the disease progresses. Volumetric losses occurring in the brain can be monitored with high resolution 3D T1-weighted magnetic resonance images. The interpretation of these images is carried out by radiologists in hospitals. However, since the voxel intensity transitions of the brain regions are not clear in magnetic resonance images, computeraided numerical methods are needed. These methods can perform pre-processing, post-processing, segmentation and volume calculation on magnetic resonance images. In this study, gray matter, white matter, cerebrospinal fluid, total intracranial volume, parenchyma, and lateral ventricle global volumes were calculated for 70 Alzheimer Patients and 70 Normal Control 3D T1-weighted magnetic resonance images taken from Open Access Series of Imaging Studies database. SPM8 and MRIcro programs, ALVIN and VBM8 libraries were used. Since the numerical methods used are found in different programs and libraries, a model is proposed which combinations should be used. Volumetric results are relative due to the different head sizes in each person. Therefore, the problem of relativity should be eliminated by proportioning each volume value with another volume value. Twenty different metrics of the brain were obtained by summing and dividing the six global volume regions obtained in different combinations. Using these values, it was determined whether there was a statistically significant difference between two groups by independent samples t-test. The performance of the numerical methods and the statistical results of twenty metrics obtained from global brain volumes were discussed. After measurements and evaluations, it was observed that the ratio of cerebrospinal fluid volume to gray matter volume was an important marker in the differential diagnosis of the disease.
Comparison of Contourlet and Time-Invariant Contourlet Transform Performance for Different Types of Noises and Images
(2019) Aslan, Muhammet Fatih; Sabancı, Kadir; Durdu, Akif
A noiseless image is desirable for many applications. However, this is not possible. Generally, wavelet-based methods are used to noise reduction. However, due to insufficient performance of wavelet transforms (WT) on images, different multi-resolution analysis methods have been proposed. In this study, one of them is Contourlet Transform (CT) and the Translation-Invariant Contourlet Transform (TICT) which is an improved version of CT is compared using different noises. The fundus images are taken from the DRIVE dataset and benchmark images are used. Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Structural Similarity (MSSIM) and Feature Similarity Index (FSIM) are used as comparison criteria. The results showed that TICT is better in Gaussian noisy images.
Citation - Scopus: 1
Comparison of Ml Algorithms To Distinguish Between Human or Human-Like Targets Using the Hog Features of Range-Time and Range-Doppler Images in Through-The Applications
(Scientific and Technological Research Council Turkey, 2022) Acar, Yunus Emre; Saritas, İsmail; Yaldız, Ercan
When detecting the human targets behind walls, false detections occur for many systematic and environmental reasons. Identifying and eliminating these false detections is of great importance for many applications. This study investigates the potential of machine learning (ML) algorithms to distinguish between the human and human-like targets behind walls. For this purpose, a stepped-frequency continuous-wave (SFCW) radar has been set up. Experiments have been carried out with real human targets and moving plates imitating a regular breath of a healthy human. Unlike conventional methods, human and human-like returns are classified using range-Doppler images containing range and Doppler information. Then, the histogram of oriented gradients (HOG) features of the range-Doppler images are extracted, and the number of these features is reduced by principal component analysis (PCA). Finally, popular ML algorithms are executed to distinguish the human and human-like returns. The performances of the ML algorithms are compared for both range-time and range-Doppler images with or without HOG features. Experiments have indicated that the HOG features of the range-Doppler profiles provide the best results with the support vector machine (SVM) classifier with an accuracy of 93.57%.
Citation - WoS: 1
Deep Learning Based Super Resolution Application for a New Data Set Consisting of Thermal Facial Images
(Gazi Univ, 2022) Şenalp, Fatih Mehmet; Ceylan, Murat
Although thermal camera systems can be used in any application that requires the detection of temperature change, thermal imaging systems are highly costly systems and this situation makes difficult the common use of thermal systems. In addition, blurry images of low quality can occur when thermal images are obtained. In this article, super resolution application has been carried out on a data set consisting of thermal face images obtained from two different thermal cameras. The specified data set was created differently from traditional methods, low resolution (LR) thermal images were obtained from a 160x120 thermal resolution camera, while high resolution (reference) images were obtained from a camera with a thermal resolution of 640x480. Later, unnecessary parts of these images were cropped and another study was carried out by focusing only on the face area. A deep learning model based on adversarial generative networks (GAN) has been developed for these applications. The success performance of the results was evaluated by the image quality metrics PSNR (peak signal to noise ratio) and SSIM (structural similarity index). It has been observed that the results of the application performed by focusing only on the facial areas are better than the results of the application with original images. In addition, this study gave positive results in terms of approximating the resolution of the thermal images obtained by the less costly thermal camera to the resolution of the thermal camera, which has a high cost and can obtain high quality images, especially visually.
Deep Learning Methods in Unmanned Underwater Vehicles
(2020) Ataner, Ercan; Özdeş, Büşra; Öztürk, Gamze; Çelik, Taha Yasin Can; Durdu, Akif; Terzioğlu, Hakan
Unmanned underwater vehicles (ROV/AUV) are robotic systems that can float underwater, are autonomous and remotely controlled. Nowadays, the Navy has focused on the operational use of unmanned underwater vehicles in the defense industry and in many areas, and has increased interest in this issue. Unmanned underwater vehicles. Unmanned underwater vehicles are carried out in civilian and military applications for different and varied purposes like protection of national sources, protection of environmental sources and researchs about that, miscellaneous construction activities, police of coastal and country. Also they can use civil and military applications and they helped they have helped with much of the academic and industrial research done in recent years. To sum up they are remotely controlled vehicles with observation and exploration features. This article discusses image processing and deep learning techniques in unmanned underwater vehicles. Also it presents an in-depth review of the artificial intelligence technique and aims to contribute to our country's defense industry. The options that will enable the vehicle to succeed in autonomous missions are mentioned. The Raspberry Pi 3 microprocessor was used in autonomous missions. The Raspberry Pi Camera Module, which is compatible with the Raspberry Pi 3, is preferred. Python was used as a programming language during software process. Objects in the images taken from the camera have been identified using the OpenCV library and deep learning. The TensorFlow library which deep learning library, was used for object detection and tracking. At the beginning The Faster-RCNN-Inception-V2 model was used as the Model. However, Faster-RCNN-Inception-V2 model and Raspberry Pi 3 FPS cooperation working did not show a good performance. For this reason, the SSDLite-MobileNet-V2 model, which is fast enough for most real-time object detection applications, is preferred.
Citation - Scopus: 7
Deep Transfer Learning and Majority Voting Approaches for Osteoporosis Classification
(Ismail Saritas, 2021) Ashames, M.M.; Ceylan, Murat; Jennane, R.
Osteoporosis is a systemic skeletal disease characterized by low bone mass density and deterioration of the micro-architectural structure of the bone tissue, increasing bone fragility, and the probability of fracture. In this study, we propose a non-invasive method for osteoporosis classification using X-ray images (plain radiographs) of the ankle. Convolutional Neural Networks along with Data Augmentation techniques and Deep Transfer Learning Architectures are combined to classify X-ray images of healthy and osteoporotic patients. The proposed approach achieved an accuracy of 99% using ResNet50, and 100% with GoogleNet. © 2021, Ismail Saritas. All rights reserved.
Design and Implementation of a Quasi-Z Inverter
(Pamukkale Univ, 2022) Endiz, Mustafa Sacid; Akkaya, Ramazan
In this study, single phase quasi-Z-source inverter (QZSI) circuit was designed and realized which is an improved version of ZSI and offers a unique power conversion concept by eliminating the conceptual and theoretical limitations of the conventional current and voltage source inverters. Simple boost PWM control technique has been employed to the switches using NUCLEO-F411RE development board since this technique doesn't involve low-frequency ripples on the passive components of the impedance network and has lower distortions at the output. It has been shown that the developed QZSI circuit can work as a buck-boost converter at different shoot-through duty ratios. At the output of the circuit up to 300W, the AC output voltage is obtained with 85% efficiency. It has been observed that simulation and experimental results carried out in the laboratory environment are compatible.
Design of Communication and Power Systems in Unmanned Underwater Vehicles
(2020) Ataner, Ercan; Özdeş, Büşra; Öztürk, Gamze; Çelik, Taha Yasin Can; Durdu, Akif; Terzioğlu, Hakan
Unmanned underwater vehicles (ROV/AUV) are robotic systems that can float underwater, are autonomous and remotely controlled. The first unmanned underwater vehicle on record was designed by Luppis Whitehead Automobile in the form of a torpedo in 1864. The first vehicle designed in the same sense used today was designed by Dimitri Rebikoff in 1953. Today, unmanned underwater vehicles are used in a wide range of areas such as underwater search and rescue operations, ship underwater maintenance and repair operations, taking images from dangerous environments where divers cannot enter, military use, inspection of wrecks and underwater cleaning. The design stages of underwater vehicle control system are given in this study. The system consists of control cards, communication modules, sensors, lighting and power electronics elements. The basic philosophies followed for the design of the system are modularity and safety. This situation provides ease in the organization of the components in the underwater vehicle as well as the modular structure, the test and repair stages are easily carried out. To ensure modularity, the system is divided into two subcomponents as power and control units. In addition, a computer interface is used to control the underwater vehicle. With this interface, data is exchanged with underwater vehicles so that the depth, water temperature and temperature of the sealed tube containing the electronic components can be monitored. Another task of the computer interface is to transfer the camera image taken from underwater to the user. In this study, the remote control of unmanned underwater vehicles, the power system, communication infrastructure, the design of the structure that provides the transmission of the image and sensor information taken from underwater is mentioned.
Citation - WoS: 1
Citation - Scopus: 1
Detection of Vortex Cavitation With the Method Adaptive Neural Fuzzy Networks in the Deep Well Pumps
(Univ Namik Kemal, 2021) Durdu, Akif; Çeltek, Seyit Alperen; Orhan, Nuri
Nowadays submersible deep well pumps are the most used irrigation systems in agriculture field. Efficient operation and economical life of pumps is an important issue. One of the most important parameters affecting pump efficiency and life is cavitation The cavitation is one of the problems frequently faced in the pump systems that widely used in the agriculture field. The cavitation could cause more undesired effects such as loss of hydraulic performance, erosion, vibration and noise. This paper presents a novel model for the detection of vortex cavitation in the deep well pump used in the agriculture system using adaptive neural fuzzy networks. The data submergence, flow rate, power consumption, pressure values, and noise values used for training the ANFIS (Adaptive-Network Based Fuzzy Inference Systems) network are acquired from an experimental pump. In this study, we use to the sixty-seven data for training process, while the fifteen data have used for testing of our model. The average percentage error (APE) has obtained as 0.08 % and as 0.34 % respectively for 67 training data and for 15 test data. The performance of the implemented model shows the advantages of ANFIS. The result of this study shows that ANFIS can be successfully used to detect vortex cavitation. This paper has two novel contributions which are the usage of noise value on cavitation detection and find out cavitation by using adaptive neural fuzzy networks. During the cavitation, the pump parameters must change by controller for prevent unwanted pump errors. The strategy proposed could be preliminary study of automatic pump control. Also proposed novel control strategy can be used for cavitation control in agriculture irrigation pumps, because of easy set up and no need extra cost. The ANFIS based model has real-time applicable thanks to rapid and easy control. It is possible to set safe boundaries in submergence in this model. Thus, users by adjusting controllable parameters can prevent cavitation and increase pump efficiency.
Dijkstra Algorithm Using Uav Path Planning
(Konya Technical University, 2020) Dhulkefl, Elaf; Durdu, Akif; Terzioğlu, Hakan
The use of unmanned aerial vehicles (UAV) is increasing today. UAVs can be divided into two parts, which are remote controlled and can travel automatically due to a certain battery problem. Recent research has also focused on the development and application of new algorithms to autonomously control these vehicles and determine the shortest flight paths. Together with these researches, UAVs are used in many civil activities such as weather forecasts, environmental studies and traffic control. Three-dimensional (3D) path planning is an important issue for autonomously moving UAVs. The shortest path for Unmanned Aerial Vehicles (UAV) is determined by using two-dimensional (2D) path planning algorithms using the obstacles in the environment, and allows UAVs to perform their environmental tasks as soon as possible. The purpose of this study is to determine the shortest path to the target point and avoiding obstacles for UAVs using the Dijkstra algorithm. It was developed to evaluate the arrival time of the UAVs in the path planning algorithm with the simulation performed in the MATLAB program. In this study, the obstacles were defined for the purpose of the building with different heights and different widths and 2D and 3D models were carried out, assuming that the UAV flies at certain heights. In addition, the flight of the UAVs in the route planning determined in the real applications was carried out and the data such as battery consumption, amount of battery spent, speed, amount of travel were examined.
Citation - WoS: 2
Dimension Optimization of Multi-Band Microstrip Antennas Using Deep Learning Methods
(2021) Özkaya, Umut; Seyfi, Leventl; Öztürk, Şaban
The electromagnetic frequency spectrum is divided into different sub frequency bands. These sub-frequency bands are allocated for different applications. In these days, devices operating in multiple sub-frequency bands provide significant advantages. Devices require antenna structures to operate in multiple frequency bands. Microstrip antennas have become prominent antenna structures with their small size, portable structures and easy integration into other systems. In this study, microstrip antenna structure which can work in multi frequency bands is designed. At the same time, it was used with deep learning methods in optimization of antenna sizes to ensure the optimization of the designed antenna in a shorter time. The operating frequencies of designed antenna structure work in the C and X band as seen in the obtained results. According to IEEE standards, C band is determined between 4 GHz and 8 GHz; X band determined as in 8 GHz and 12 GHz frequency range. In the proposed antenna structure, the ability to operate in multi-band structures was achieved by means of a C-shaped antenna array. In the deep learning methods that will be used in the optimization process, five different Long Short Term Memory (LSTM) models are used. The most important advantage of deep learning methods is that it can achieve satisfactory results by identifying the necessary features for solving difficult and time consuming problems with its own learning ability. In this context, 52 pieces of antenna data were produced. 40 pieces of data were used in the training process and 12 pieces of data were used in the test stage. The lowest root mean square error (RMSE) performance obtained in the test data was determined as LSTM-1 + Dropout layer-1 + LSTM -2 + Dropout layer-2 and 1.0161 error value. The obtained results by proposed method were evaluated in High Frequency Simulation Software (HFSS) program. In experimental results, it was observed that the results produced by the deep learning model and the test data were very close to each other.
DÜŞÜK MALİYETLİ SÜREKLİ DALGA DOPPLER RADARI İLE TEMASSIZ YAŞAMSAL BELİRTİ ÖLÇÜMÜ
(2020) Şeflek, İbrahim; Yaldız, Ercan
Hayati sinyallerin temassız olarak uzaktan algılanması birçok uygulama açısından önem arz etmektedir. Bu algılamayı gerçekleştiren radarlar biyoradar olarak adlandırılmaktadır. Biyoradar kişinin solunum ve kalp atışından kaynaklanan göğüs duvarı hareketinin değişimiyle Doppler prensibini kullanarak hayati sinyallerin doğru bir şekilde ölçülmesini sağlamaktadır. Bu çalışmada, 24 GHz çalışma frekansına sahip düşük maliyetli sürekli dalga (CW) Doppler radarı kullanılarak insan denekten temassız bir şekilde yaşamsal belirti (solunum, kalp atış hızı) ölçümleri gerçekleştirilmiştir. Ölçümlerden elde edilen sinyallerin işlenmesinde iki farklı yöntem kullanılmıştır. İlk yöntem Hızlı Fourier Dönüşümünü (FFT) esas alırken ikinci yöntemde Dalgacık yöntemine dayalı Çoklu Çözünürlük Analizi (MRA) yöntemi kullanılmaktadır. Solunum hızında birinci ve ikinci yöntem için elde edilen sonuçlar %3.75 ve %0’ hata oranlıdır. Kalp atışı için sırasıyla %9.35 ve %8.45 hata oranlı değerler elde edilmiştir. Bu sonuçlar özellikle radarların tıbbi uygulamalar için başarıyla kullanılabileceğini göstermektedir.
The Effect of Increases in User Weight and Road Slope on Energy Consumption in Disabled Vehicle Driven With Pmsm
(2021) Kazan, Fatih Alpaslan; Akkaya, Ramazan
In this study, the effect of increases in user weight and road slope on energy consumption (Wh/km) value of a disabled vehicle driven with permanent magnet synchronous motor (PMSM) was investigated. In order to digitize this effect, a test system consisting of a data reading card and an interface program prepared in Visual C # was developed. In this way, information about the disabled vehicle and the road was collected instantly during the test process and visualized in the interface. Then experimental studies were carried out on two roads with different slopes with users of different weights. Finally, by using the obtained results, the effect of increases in road slope and user weight on the energy consumption of the vehicle was demonstrated by numerical data. By using these results, the numerical simulations of battery-operated disabled vehicles can be calibrated and much more realistic simulation results can be obtained in future studies.
Citation - WoS: 6
Citation - Scopus: 7
The Effect of Snowfall and Icing on the Sustainability of the Power Output of a Grid-Connected Photovoltaic System in Konya, Turkey
(2019) Anadol, Mehmet Ali; Erhan, Erman
When the module surface is covered with various factors such as snow and icing which prevent the solar irradiance from reaching the photovoltaic cells, the power production of the system and its performance decrease. The purpose of this study is to determine the energy production losses of a grid-connected photovoltaic plant due to snowfall and icing. The effect of snowfall and icing has been examined on a photovoltaic system consisting of a hybrid inverter with two separate maximum power point tracking inputs and 36 monocrystalline modules, which are mounted on the supporting system horizontally in the south direction and at a constant tilt angle of 30 ? . The plant area is located in a priority and snowy region (Konya,Turkey) where large-scale photovoltaic system installations are carried out. In order to evaluate the effect of snowfall, the minute resolution data of the hybrid inverter which provides connection to the grid is used. The change over time of the power generated by the two arrays of the plant was examined comparatively. For comparison, one of the arrays was continuously cleared. The recorded data was used to determine the expected energy output of the array covered with snow. Besides, the solar irradiance and ambient temperature data obtained from the meteorological station were used to accurately identify and evaluate the effects of snowfall with digital images recorded in the site area. The results showed that surface clearing of modules had a significant positive effect on the power output of the system. In the array entirely covered with snow, the daily energy loss exceeds 93%. In months of heavy snowfall, the monthly energy loss is 18% depending on time of being covered with snow of the modules. When the production data of 2017 and 2018 is evaluated, it is seen that the total energy loss of the plant varies between 1% and 2%.

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