89 research outputs found
Withdrawn article: A Quarter Active Suspension System Based Ground-Hook Controller
No full textWithdrawn article: This paper has been formally withdrawn. It should not be cited or referred to in the future. Request approved by the Authors, the Editors and the Publisher on October 31, 2017
Development of Online Ticket Booking Application for Ferry Crossing Website Based in Toba Lake Area
No full textThe Performance of EEG-P300 Classification using Backpropagation Neural Networks
Get PDFElectroencephalogram (EEG) recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate) with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA). Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks
Design of intelligent sprayer control for an autonomous farming drone using a multiclass support vector machine
No full textThe increasing need for agricultural commodities poses a serious threat to the cultivating ability of agricultural supplies. The use of autonomous farming drones to support the cultivation process is a rapidly growing trend. In an effort to improve efficiency and accuracy in watering using drones, this research strived to propose a control design for the drones using the multiclass support vector machine (MSVM) method. The proposed system was an improvement compared to the common approach of using constant watering strength in all drone conditions. By obtaining suitable watering strength based on the drone’s altitude, wind speed, and speed sensor data as the input, the optimal solution between water usage and water efficiency was expected to be achieved. An experimental trial that consisted of 12 flights was conducted to acquire a data set with 3,750 data. The results of classification with MSVM obtained an accuracy of 90.82%. The efficiency of using the water resource and the accuracy of delivering the correct amount of water based on the drone condition were achieved. These results show that the proposed technology has great potential for using drones as an automatic watering system in agriculture
Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification
Get PDFBrain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%
Artefacts Removal of EEG Signals with Wavelet Denoising
Get PDFThe recording of EEG signals often still contains many contaminants electrical signals that originate from non-cerebral origin such as ocular muscle activity called artefacts. The amplitude of artefacts can be quite large relative to the size of amplitude of the cortical signals of interest. In this paper, an application of wavelet denoising method for artefacts removal of EEG signals is proposed. The experiment result shows that contaminant artifact of EEG signals can significantly removed
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