No abstract

Today, active compensation systems have become more preferable compared with passive compensation systems. Performance of an active compensation system depends on current control method of the compensator as well as how this current is generated. In this study, a Discrete Wavelet Packets Transform (DWPT) based method was proposed for calculating reference current of active compensation systems and the results were compared with the currents produced from classical p-q theory. According to the obtained results, the proposed method can be used in active compensation systems without using an additional signal processing and filtering method. Ill. 12, bibl. 21, tabl. 1 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.146

The majority of power system faults occur in transmission lines. The classification of these faults in power systems is an important issue. In this paper, the real parameters of a 28 km, 154 kV transmission line between Simav and Demirci in Turkey's electricity transmission network is simulated in MATLAB/Simulink. Wavelet packet transform (WPT) is applied to instantaneous voltage signals. Instantaneous active power components are obtained by multiplying instantaneous currents obtained from a voltage source side with these WPT-based voltage signal components. A new feature vector extraction scheme is employed by calculating the energies of instantaneous active power components.Constructed feature vectors are treated with a classifier for short-circuit faults that occurred in high-voltage energy transmission lines; this is known as the common vector approach (CVA). This is the first implementation of CVA in the classification of short-circuit faults that occurred in high-voltage energy transmission lines. Furthermore, the same feature vector is applied to a support vector machine and artificial neural network for a comparison with the CVA method regarding classification performance and testing duration issues. Additionally, a graphical user interface is designed in MATLAB/GUI. Various noise levels, source frequencies, fault distances, fault inception angles, and fault exposure durations can be investigated with this interface. Classification of short-circuit faults in high-voltage transmission line is achieved by using an offline monitoring methodology. It is concluded that a combination of the proposed feature extraction scheme with the CVA classifier gives substantially high performance for the classification of short circuit faults in transmission line.

Electromyography (EMG) signals are an important technique in the control applications of prostatic hand. These signals, which are measured from the skin surface, are used to perform movements such as wrist flexion / extension, forearm supination / pronation and hand opening / closing of prosthetic devices. In this study, root mean square, waveform length and kurtosis methods were applied to extracted EMG signals from flexor carpi radialis and extensor carpi radialis muscles by using two channel surface electrodes. A fuzzy logic based classification method has been applied to classify the extracted signal features. With this method, classification for different gripping movements has been successfully accomplished.

Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

With arc welding machines, welding is only performed at optimum operating points. Determination of optimum operating points is important so as for welding machines which will be produced in future to be developed in a manner to operate in such parts. In this study, an Artificial Neutral Networks method was used in order to determine the optimum operating points of Electric Arc welding machine. For this purpose, a measurement system used to get the current measurements during the welding operation. A welding process includes some stages like initial case; transient case and operation case respectively. So as to use ANN model, a data set was established via time series. ANN is trained with 90% of data set and tested with 10% thereof. At the end of the test, a prediction of 97.49% was made according to the regression value. And according to the MSE value, it was understood that a successful prediction was made with an error of 0.00353075 values.

Dünya tarihinde önemli bir yere sahip olan Büyük Selçukluların tarihi, yayıldığı topraklar üzerinde birçok devletin ortak geçmişini oluşturmaktadır. Bu önemli devleti, tarihî, sosyal, kültürel ve etnik açıdan en iyi ifade edenlerden biri de şair Mu'izzî (öl. 518-521/1124-1127) olmuştur. Mu'izzî'nin şiirlerinde Selçuklu geleneğinin, Türk kimliğinin ve onların dinî tercihlerinin izleri görülmektedir. Devletle ve siyasetle iç içe olan Mu'izzî, Türk unsurunu ve Selçukluların öz kimliklerini ön plana çıkaran şiirlerinde Türk boyları hakkında birçok malumata yer vermekte, ayrıca Dîvân'ında Âd kavmi, Arap, Acem, Deylem, Deyyâr, Ermeni, Hint, Çin ve Rûm boyları hakkında da dikkate değer bilgiler beyan etmektedir. Mu'izzî'nin, Türk boyları dışında şiirlerinde örneklere yer verip görüşler ileri sürdüğü bu kavimlere nasıl bir bakış açısıyla yaklaştığı hususu birçok açıdan anlam yüklüdür. Bir yandan Mu'izzî'nin Dîvânı'nda ve düşünce dünyasında bu boyların ne şekilde yer aldığının tespiti hakkında bilgilere ulaşılabilecek; öte yandan bu kavimlerin edebî ve tarihî metinlerdeki varlığı hakkında bilgiler elde edilebilecektir. Bu makalede Mu'izzî'nin Dîvânı'nda geçen bu boylarla ilgili örneklere ve ifadelere yer verilmektedir.

Advanced driver assistance functions help us prevent the human-based accidents and reduce the damage and costs. One of the most important functions is the lane keeping assist which keeps the car safely in its lane by preventing careless lane changes. Therefore, many researches focused on the lane detection using an onboard camera on the car as a cost-effective sensor solution and used conventional computer vision techniques. Even though these techniques provided successful outputs regarding lane detection, they were time-consuming and required hand-crafted stuff in scenario-based parameter tuning. Deep learning-based techniques have been used in lane detection in the last decade. More successful results were obtained with fewer parameter tuning and hand-crafted things. The most popular deep learning method for lane detection is convolutional neural networks (CNN). In this study, some reputed CNN architectures were used as a basis for developing a deep neural network. This network outputs were the lane line coefficients to fit a second order polynomial. In the experiments, the developed network was investigated by comparing the performance of the CNN architectures. The results showed that the deeper architectures with bigger batch size are stronger than the shallow ones.