Neural Mechanisms of Human Perceptual Learning: Electrophysiological Evidence for a Two-Stage Process

Artículo de publicación ISIBackground: Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed. Methodology/Principal Findings: We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30-60 Hz) and alpha (8-14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing.This research was supported by CONICYT doctoral grant to C.M.H. and by an ECOS-Sud/CONICYT grant C08S02 and FONDECYT 1090612 grant to D.C. and F.A

Neural network analysis of head-flow curves in deep well pumps

In impellers with splitter blades, the difficulty in calculation of the flow area of the impeller is because of the unknown flow rate occurring in the two separate areas when the splitter blades are added. Experimental studies were made to investigate the effects of splitter blade length on deep well pump performance for different numbers of blades. Head-flow curves of deep well pump impellers with splitter blades were investigated using artificial neural networks (ANNs). Gradient descent (GD), Gradient descent with momentum (GDM) and Levenberg-Marquardt (LM) learning algorithms were used in the networks. Experimental studies were completed to obtain training and test data. Blade number (z), non-dimensional splitter blade length ((L) over bar) and flow rate (Q) were used as the input layer, while the output is head (H-m). For the testing data, the root mean squared error (RMSE), fraction of variance (R-2) and mean absolute percentage error (MAPE) were found to be 0.1285, 0.9999 and 1.6821%, respectively. With these results, we believe that the ANN can be used for prediction of head-flow curves as an appropriate method in deep well pump impellers with splitter blades. (c) 2005 Elsevier Ltd. All rights reserved

of deep well pumps with splitter blade

Experimental studies were made to investigate the effects of splitter blade length (25%, 35%, 50%, 60% and 80% of the main blade length) on the pump characteristics of deep well Pumps for different blade numbers (z = 3, 4, 5, 6 and 7). In this study, an artificial neural network (ANN) was used for modeling the performance of deep well pumps with splitter blades. Two hundred and ten experimental results were used to train and test. Forty-two patterns have been randomly selected and used as the test data. The main parameters for the experiments are the blade number (z), non-dimensional splitter blade length ((L) over bar), flow rate (Q, 1/s), head (H-m, m), efficiency (eta, %) and power (P,, W). z, (L) over bar and Q have been used as the input layer, and H-m and eta have also been used as the output layer. The best training algorithm and number of neurons were obtained. Training of the network was performed using the Levenberg-Marquardt (LM) algorithm. To determine the effect of the transfer function, different ANN models are trained, and the results of these ANN models are compared. Some statistical methods; fraction of variance (R 2) and root mean squared error (RMSE) values, have been used for comparison. (c) 2006 Elsevier Ltd. All rightsC1 Pamukkale Univ, Tech Educ Fac, Dept Mech Educ, TR-20017 Kinikli, Denizli, Turkey

ENERGY SAVING BY USING AN AXIAL FLOW DEEP WELL PUMP: AN APPLICATION IN

Turkey is home to a large number of lakes, dam lakes and small lakes which have a great potential for growing fishery products. Growing trout in net frame systems manufactured in dam lakes is very common and economically very promising application. For growing trout, certain conditions such as certain water temperature range and minimum dissolved oxygen (DO) level must be satisfied. The water temperature required should be at 17-20 degrees C to grow trout. Also, the amount of dissolved oxygen (pc) should never fall below 6-7 mg/L. As the temperature rises during the months of June-September, water temperature rises above the acceptable limits to grow trout. Producers usually use different types of internal combustion engine-pump (ICE-P) systems to provide the circulation of the colder water in the deeper parts of the lake to lake surface to grow trout in these months. But, this method is not economically feasible. In this study, as an alternative to ICE-P system, an axial flow deep well pump has been proposed for energy saving purposes. To validate the feasibility of the system, total of 40 net frame systems each has dimensions of 5x5x8 meters have been installed to grow trout in net frames in Lake Karacaoren 2 in Burdur, Turkey. To grow trout in the hot seasons, it is necessary to circulate the colder water in the deeper parts of the lake to lake surface economically. For this aim, total of 10 an axial flow deep well pumps (AFDWPs) having a capacity of 300 m(3)/h, head of 4 m and running at 2850 rpm which is driven by 5.5 kW deep well motor (DWM) have been specially designed and manufactured. To compare the classical water circulation method with ICE-P and newly proposed AFDWP, after every two pumps (ICE/AFDWP) are installed, mean water temperatures were measured along the water column in net frame with the depth of 8 meters and also energy consumptions have been compared during the months of June to September. AFDWP and ICE-P were used only in these months where the water has to be circulated. According to the results energy consumption by using AFDWP was about 90 MWh/year, on the other hand, energy consumed by ICE-P was about 1949 MWh/year during the months of June-September. As a result, significant energy saving of 95.3 % (1906.8 MWh/year) can be obtained by using proposed AFDWP instead of the classical ICE-P during these months for total of 40 net frames

ENERGY SAVING BY USING AN AXIAL FLOW DEEP WELL PUMP: AN APPLICATION IN

Turkey is home to a large number of lakes, dam lakes and small lakes which have a great potential for growing fishery products. Growing trout in net frame systems manufactured in dam lakes is very common and economically very promising application. For growing trout, certain conditions such as certain water temperature range and minimum dissolved oxygen (DO) level must be satisfied. The water temperature required should be at 17-20 degrees C to grow trout. Also, the amount of dissolved oxygen (pc) should never fall below 6-7 mg/L. As the temperature rises during the months of June-September, water temperature rises above the acceptable limits to grow trout. Producers usually use different types of internal combustion engine-pump (ICE-P) systems to provide the circulation of the colder water in the deeper parts of the lake to lake surface to grow trout in these months. But, this method is not economically feasible. In this study, as an alternative to ICE-P system, an axial flow deep well pump has been proposed for energy saving purposes. To validate the feasibility of the system, total of 40 net frame systems each has dimensions of 5x5x8 meters have been installed to grow trout in net frames in Lake Karacaoren 2 in Burdur, Turkey. To grow trout in the hot seasons, it is necessary to circulate the colder water in the deeper parts of the lake to lake surface economically. For this aim, total of 10 an axial flow deep well pumps (AFDWPs) having a capacity of 300 m(3)/h, head of 4 m and running at 2850 rpm which is driven by 5.5 kW deep well motor (DWM) have been specially designed and manufactured. To compare the classical water circulation method with ICE-P and newly proposed AFDWP, after every two pumps (ICE/AFDWP) are installed, mean water temperatures were measured along the water column in net frame with the depth of 8 meters and also energy consumptions have been compared during the months of June to September. AFDWP and ICE-P were used only in these months where the water has to be circulated. According to the results energy consumption by using AFDWP was about 90 MWh/year, on the other hand, energy consumed by ICE-P was about 1949 MWh/year during the months of June-September. As a result, significant energy saving of 95.3 % (1906.8 MWh/year) can be obtained by using proposed AFDWP instead of the classical ICE-P during these months for total of 40 net frames

network method: A case study of Turkey

The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict daily mean ambient temperatures in Denizli. south-western Turkey. In order to train the model, temperature values, measured by The Turkish State Meteorological Service over three years (2003-2005) were used as training data and the values of 2006 were used as testing data.In order to determine the optimal network architecture, various network architectures were designed; different training algorithms were used; the number of neuron and hidden layer and transfer functions in the hidden layer/output layer were changed. The predictions were performed by taking different number of hidden layer neurons between 3 and 30. The best result was obtained when the number of the neurons is 6. The selected ANN model of a multi-layer consists of 3 inputs, 6 hidden neurons and 1 output. Training of the network was performed by using Levenberg-Marquardt (LM) feed-forward backpropagation algorithms. A computer program was performed under Matlab 6.5 software. For each network, fraction of variance (R(2)) and root-mean squared error (RMSE) values were calculated and compared. The results show that the ANN approach is a reliable model for ambient temperature prediction. (c) 2008 Elsevier Ltd. All rights reserved

Effects of splitter blades on deep well pump performance

Impellers with splitter blades have been used in turbomachinery design for both pumps and compressors. Increasing the number of blades increases the head of the pump, however it causes a decrease in efficiency due to the blockage effect of the blade thickness and firiction. The impellers with splitter blades between two long blades can be used to alleviate the serious clogging at the inlet of the impeller caused by more blades. In this study, impellers having a different number of blades (z=3, 4, 5, 6, and 7) with and without splitter blades (25, 35, 50, 60, and 80% of the main blade length) were tested in a deep well pump. The effects of the main blade number and lengths of splitter blades oil the pump performance have been investigated. While the number of main blades and the lengths of the splitter blades of a principal impeller were changed, the other parameters such as pump casing, blade inlet and outlet angles, blade thickness, impeller inlet and outlet diameters, were kept the same

EFFECT OF ADDING BUTANOL ON THE PERFORMANCE AND EMISSIONS OF A

Butanol is one of the fuel which is seen as alternative to petroleum based on fuels in internal combustion engines. Butanol, can be produced via fermentation from biomass (biobutanol) and it can be used as pure or mixed with gasoline in specific ratios in spark ignition engines without a significant change. In this study, the effects on the performance and exhaust emissions of n-butanol addition 10% and 20% by volume into the gasoline, on single-cylinder spark ignition engine was investigated at engine speed 2400 1/min and engine load 20%, 40%, 60%, 80% and 100%. According to the results obtained from the study, n-butanol addition to gasoline increases brake specific fuel consumption, CO2, NOx emissions, and exhaust gas temperature and reduces CO and HC emissions. Moreover, the result of adding n-butanol inside of gasoline does not cause phase seperation and any negativeness is seen while using in engine. Therefore it can be signified that n-butonal can be used as additive for reducing fosil based fuel emission

Energy saving in a deep well pump with splitter blade

Design parameters, like blade number, blade outlet angle and impeller outlet diameter, affect pump performance and energy consumption. Deep well pumps with splitter blades (DWPwsb) are manufactured to achieve energy saving and improve efficiency. Splitter blades are generally located at the centerline of the main blades. Blade number and blade discharge angle should be conveniently determined when splitter blades are used on the impellers.In this study, impellers having different numbers of blades (z = 5, 6, 7) with and without splitter blades (35%, 60% and 80% of the main blade length) were tested in a deep well pump. Tests have been conducted on a total of 12 impellers, and the characteristics of deep well pumps without splitter blade (DWPwosb) and DWPwsb were obtained experimentally. These results show that splitter blades cause negative effects on pump performance in impellers with blade numbers of 6 and 7. When the splitter blade is added to the impeller with the blade number of 5, the efficiency increases with flow up to 10 l/s flow rate, after which it decreases as the splitter blade length increases. The highest efficiency and the lowest energy consumption were obtained in DWPwsb with 80% of the main blade length. At the best efficiency point (b.e.p), an energy saving of 6.6% and an improvement of 1.14% in efficiency were achieved. An analysis of the additional cost of the splitter blade and the application in an agricultural area were performed. (c) 2005 Elsevier Ltd. All rights reserved.C1 Pamukkale Univ, Dept Mech Educ, Denizli, Turkey.Osman Gazi Univ, Dept Mech Engn, TR-26480 Eskisehir, Turkey.Zonguldak Karaelmas Univ, Karabuk Vocat Coll, TR-78100 Karabuk, Turkey