In this study Proportional-Integral-Derivative (PID) control of brushed DC Motor is analyzed. The parameters of the PID controller are tuned with two different approaches, namely Ziegler-Nichols (ZN) and Particle Swarm Optimization (PSO). The system is tested under sinusoidal disturbance of varying frequencies in order to evaluate and compare disturbance rejection performances. It is shown that PSO approach has clearly higher performance compared with ZN approach for all disturbance frequencies. Simulations are done using Python programming language with trapezoid rule for differentiation and integration. Results are given in both figures and tables. Comments are done on results and future study is planned.

Summary Triboelectric nanogenerators (TENGs) are promising new generation systems with their basic motion‐based working principle using both triboelectric and electrostatic effects. Today, the energy densities of TENGs are insufficient for many electronic devices and new strategies are needed to increase their power conversion efficiency. In this study, two different Perylene‐based organic structures were added to the triboelectric layers as well as the electrochemical properties of these structures, and the device parameters related to these properties were investigated. A large variety of instrumental analyses, including cyclic voltammetry, contact angle, scanning electron microscopy, atomic force microscopy, and so on, have been used to identify the relationship between doped molecules, their doping ratios, and obtained fiber structures. Depending on molecular structure and even any small variations in side groups of molecules, different doping rates brought about various device outputs. Compared with undoped layers, doping of small molecules led to a ~3.3 times increase in the maximum power of the best‐performed devices, and a very high voltage value of 500 V was obtained. The analysis of doping with small molecules undertaken here has extended our knowledge of how material design improves the electrical output and contributes to the device performance in TENGs.

An advanced controller architecture and design for quadcopter control implementation is proposed in this study. Instead of using only the error information as input to the controller, reference and measured outputs are used separately independent from each other. This enhances the performance of the controller of quadcopter being a highly non-linear platform. In this study single layer neural network is directly used as a controller. A complex controller is grown from an initially simple PID controller. This elevates the need for time consuming search in huge parameter space due to very high dimensions. About ten percent improvement over state-of-the-art controllers is observed and results are reported both numerically and graphically. Promising results encourage to use the type of controller proposed for various real applications.

In this study, we propose a novel controller architecture and design for the automatic control of agricultural mobile robots to be used in farms and greenhouses. There are two novelties of this study. The first novelty is a completely new type of controller architecture proposed in which reference inputs and measured outputs are fed separately independent from each other to the controller. The controller architecture currently used in the literature uses only the difference between reference and measurement which is the error signal. The proposed architecture in this study is completely novel in the sense that not only the error information is used in the controller but also the information in reference inputs and information in measured outputs are used separately. This means a completely new type of look to control system by utilizing the information maximally in order to achieve superior performance. This performance boost is shown in the paper where the proposed architecture achieves up to 2000% better performance compared with state-of-the-art controllers. Second, controller architecture is grown to a complex structure from an initially simple PID structure. Using the maximal information comes with the cost of computational complexity to design the controller. The second novelty of growing the controller from initially simple PID equivalent controller tackles this difficulty by making the problem tractable and efficient to compute. This way the proposed novel controller can be designed within minutes in a commercially available laptop computer. The proposed controller is tested on a simulated agricultural mobile robot and results are compared with a previous state-of-the-art optimal controller. It is believed that the proposed architecture will be dominant in future automatic controllers and make current state-of-the-art controllers obsolete. This is because of the full utilization of information in controller design which results in robust disturbance rejection performance.

Bu çalışmada nesne tespitinde ve nesne tanımada literatürde sıklıkla kullanılan iki popüler özniteliğin sınıflandırma performansı tahmini karşılaştırılması yapılmıştır. Birinci öznitelik, orijinal ismiyle Histogram of Oriented Gaussians (HOG), veya Türkçe’deki karşılığı ile Yönlendirilmiş Gradyanların Histogramları, nesne tespitinde ve nesne tanımada en sık kullanılan özniteliklerden birisidir. İkinci öznitelik, orijinal ismiyle Scale Invariant Feature Transform (SIFT), veya Türkçe’deki karşılığı ile Ölçek Değişmez Unsur Dönüşümü, yine nesne tespitinde ve nesne tanımada çok sık kullanılan bir başka özniteliktir. Bu iki öznitelikten birisinin çıktısını herhangi bir sınıflandırıcıya girerek oldukça başarılı sonuçlar almak mümkündür. Peki sınıflandırıcıdan bağımsız olarak hangi öznitelik daha iyi sınıflandırma performansı vermeye yatkındır? Bu çalışmada bu soru cevaplanmaya çalışılmıştır. Veri olarak VisDrone veri setinden araba ve yaya sınıflarından 10’ar tane görüntü kullanılmıştır. Bu iki sınıftan örnek görüntülerin sınıf içi ve sınıflar arası ortalama uzaklıkları hesaplanmış ve sonuçlar raporlanmıştır. Fisher’in Ayırtacına benzer bir mantık ile bir performans metriği hesaplanmıştır. Elde edilen sonuçlardan HOG özniteliğinin sınıflandırıcıdan bağımsız olarak bu örnek veri setinde sınıflandırma için daha uygun bir öznitelik olduğu tahminine varılmıştır.