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Articles 1 - 10 of 10
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Intelligent Protection Scheme Using Combined Stockwell-Transform And Deep Learning-Based Fault Diagnosis For The Active Distribution System, Latha Maheswari Kandasamy, Kanakaraj Jaganathan
Intelligent Protection Scheme Using Combined Stockwell-Transform And Deep Learning-Based Fault Diagnosis For The Active Distribution System, Latha Maheswari Kandasamy, Kanakaraj Jaganathan
Turkish Journal of Electrical Engineering and Computer Sciences
This study aims to perform fast fault diagnosis and intelligent protection in an active distribution network (ADN) with high renewable energy penetration. Several time-domain simulations are carried out in EMTP-RV to extract time-synchronized current and voltage data. The Stockwell transform (ST) was used in MATLAB/SIMULINK to preprocess these input datasets to train the adaptive fault diagnosis deep convolutional neural network (AFDDCNN) for fault location identification, fault type identification, and fault phase-detection for different penetration levels. Based on the AFDDCNN output, the intelligent protection scheme (IDOCPS) generates the signal for isolating a faulty section of the ADN. An intelligent fault diagnosis …
Extending The M3-Competition: Category And Interval-Specific Time Series Forecasting, Will Sherman, Kati Schuerger, Randy Kim, Bivin Sadler
Extending The M3-Competition: Category And Interval-Specific Time Series Forecasting, Will Sherman, Kati Schuerger, Randy Kim, Bivin Sadler
SMU Data Science Review
The M3-Competition found that simple models outperform more complex ones for time series forecasting. As part of these competitions, several claims were made that statistical models exceeded machine learning (ML) techniques, such as recurrent neural networks (RNN), in prediction performance. These findings may over-generalize the capabilities of statistical models since the analysis measured the total forecasting accuracy across a wide range of industries and fields and with different interval lengths. This investigation aimed to assess how statistical and ML methods compared when individuating series by category and time interval. Utilizing the M3 data and building individual models using Facebook© Prophet …
Evaluation Of Artificial Neural Network Methods To Forecast Short-Term Solar Power Generation: A Case Study In Eastern Mediterranean Region, Heli̇n Bozkurt, Ramazan Maci̇t, Özgür Çeli̇k, Ahmet Teke
Evaluation Of Artificial Neural Network Methods To Forecast Short-Term Solar Power Generation: A Case Study In Eastern Mediterranean Region, Heli̇n Bozkurt, Ramazan Maci̇t, Özgür Çeli̇k, Ahmet Teke
Turkish Journal of Electrical Engineering and Computer Sciences
Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, …
Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan
Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan
Future Computing and Informatics Journal
In this modern world of ever-increasing one-click purchases, movie bookings, music, health- care, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. …
A Method For Classifying Ecg Signals With Different Possible States On A Multilayer Perceptron, Sherzod Nematov, Y Talatov
A Method For Classifying Ecg Signals With Different Possible States On A Multilayer Perceptron, Sherzod Nematov, Y Talatov
Technical science and innovation
To automatically determine the state of the cardiovascular system based on the recorded ECG signals, an artificial neural network is trained to classify signals into various possible states. At the same time, the parameters of heart rate variability (HRV) were extracted from the ECG signals and used as input functions for the neural network. HRV is the fluctuation in the time intervals between adjacent heartbeats. For this, the architecture of a neural network based on a multilayer perceptron and a method for obtaining the necessary parameters in the learning process have been developed, and the classification efficiency has been checked …
Prediction Of Gross Calorific Value Of Coal Based On Proximate Analysis Using Multiple Linear Regression And Artificial Neural Networks, Mustafa Açikkar, Osman Si̇vri̇kaya
Prediction Of Gross Calorific Value Of Coal Based On Proximate Analysis Using Multiple Linear Regression And Artificial Neural Networks, Mustafa Açikkar, Osman Si̇vri̇kaya
Turkish Journal of Electrical Engineering and Computer Sciences
Gross calorific value (GCV) of coal was predicted by using as-received basis proximate analysis data. Two main objectives of the study were to develop prediction models for GCV using proximate analysis variables and to reveal the distinct predictors of GCV. Multiple linear regression (MLR) and artifcial neural network (ANN) (multilayer perceptron MLP, general regression neural network GRNN, and radial basis function neural network RBFNN) methods were applied to the developed 11 models created by different combinations of the predictor variables. By conducting 10-fold cross-validation, the prediction accuracy of the models has been tested by using $ R^2 $, $ RMSE …
Short-Term Load Forecasting Using Mixed Lazy Learning Method, Seyed-Masoud Barakati, Ali Akbar Gharaveisi, Seyed-Mohammad Reza Rafiei
Short-Term Load Forecasting Using Mixed Lazy Learning Method, Seyed-Masoud Barakati, Ali Akbar Gharaveisi, Seyed-Mohammad Reza Rafiei
Turkish Journal of Electrical Engineering and Computer Sciences
A novel short-term load forecasting method based on the lazy learning (LL) algorithm is proposed. The LL algorithm's input data are electrical load information, daily electricity consumption patterns, and temperatures in a specified region. In order to verify the ability of the proposed method, a load forecasting problem, using the Pennsylvania-New Jersey-Maryland Interconnection electrical load data, is carried out. Three LL models are proposed: constant, linear, and mixed models. First, the performances of the 3 developed models are compared using the root mean square error technique. The best technique is then selected to compete with the state-of-the-art neural network (NN) …
A Comparative Performance Evaluation Of Various Approaches For Liver Segmentation From Spir Images, Evgi̇n Göçeri̇, Mehmet Zübeyi̇r Ünlü, Oğuz Di̇cle
A Comparative Performance Evaluation Of Various Approaches For Liver Segmentation From Spir Images, Evgi̇n Göçeri̇, Mehmet Zübeyi̇r Ünlü, Oğuz Di̇cle
Turkish Journal of Electrical Engineering and Computer Sciences
Developing a robust method for liver segmentation from magnetic resonance images is a challenging task because of the similar intensity values between adjacent organs, the geometrically complex liver structure, and injection of contrast media. Most importantly, a high anatomical variability of a healthy or diseased liver is a major challenge in defining the exact boundaries of the liver. Several artifacts of pulsation, motion, and partial volume effects are also among the variety of factors that make automatic liver segmentation difficult. In this paper, we present an overview of liver segmentation methods in magnetic resonance images and show comparative results of …
Artificial Neural Network Analysis For Prediction Of Headache Prognosis In Elderly Patients, Bahar Taşdelen, Sema Helvaci, Hakan Kaleağasi, Aynur Özge
Artificial Neural Network Analysis For Prediction Of Headache Prognosis In Elderly Patients, Bahar Taşdelen, Sema Helvaci, Hakan Kaleağasi, Aynur Özge
Turkish Journal of Medical Sciences
Aim: To investigate the ability of neural networks to detect and classify the complete improvement of headache in elderly patients during the follow- up period. Materials and Methods: The multilayer perceptron (MLP), which is the most common neural network, was used to predict prognosis of headache in elderly patients. The data set was divided into training and test sets, and back-propagation algorithm was used as the learning algorithm. The accuracies of the models to predict completely improved patients at the end of 20, 40, and 60 months of follow-up were evaluated by means of the areas under the receiver operating …
Using Connectionist Models To Evaluate Examinees’ Response Patterns To Achievement Tests, Mark J. Gierl, Ying Cui, Steve Hunka
Using Connectionist Models To Evaluate Examinees’ Response Patterns To Achievement Tests, Mark J. Gierl, Ying Cui, Steve Hunka
Journal of Modern Applied Statistical Methods
The attribute hierarchy method (AHM) applied to assessment engineering is described. It is a psychometric method for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated for each examinee thereby providing specific information about the examinee’s attribute-mastery level. The pattern recognition approach described in this study relies on an explicit cognitive model to produce the expected response patterns. The expected response patterns serve as the input to the neural network. The model also yields the cognitive …