High-order image subsampling using feedforward artificial neural networks

The increase of the world population, especially in the global competition, together with the increasing use of fossil fuel resources to meet energy needs, leads to more greenhouse gases (more than one CO2, methane etc.) emissions and the global climate crisis. In this process, changes in meteorological events such as temperature, precipitation, and wind, attract attention moreover but when considered as a whole, we know that these negative changes in the ecosystem negatively affect many living groups. Sea Surface Temperature (SST) as measured meteorologically is the most important environmental parameter where these changes are monitored and observed. It draws attention to the fact that changes in SST are not limited to living organisms as habitats, but also catalyze many chain reactions, especially socio-economic impacts. Therefore, much of the work is devoted to forecasting studies to adapt to changing habitats and take the necessary precautions against potential risks. Feed-forward artificial neural networks have been commonly used to address these emerging needs. Artificial neural networks, which are a simple imitation of the human neurological system, have been used as an artificial intelligence method in forecasting problems due to their superior performance and not having the limitations of classical time series. In this study, the forecasting of the time series of monthly mean SST temperature obtained from Rize station between the years 2010 and 2020 is performed by using feed-forward artificial neural networks, and the forecasting performance of the corresponding time series is compared with many forecasting methods with different characteristics. The comparison of the methods used the mean square error and mean absolute percentage error criteria, which are commonly used in the forecasting literature. The analysis results showed that the analysis results obtained with the feed-forward artificial neural networks have the best prediction performance. As a result, it can be stated that the sea surface temperature can be forecasted with a very high accuracy using the feed-forward artificial neural networks.

<p>This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6- PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.</p>

A feedforward artificial neural network has been realized and trained to individualize subharmonics frequencies in an electric network. The adopted sampling window is fixed in 20 ms correspondent to a period of the fundamental frequency of 50 Hz, nevertheless the net is able to identify the subharmonics frequencies even if their period is not completed. The particular binary successive approximation structure realized for the ANN forecasts the scalability of the same net so allowing to recognize the subharmonics with a prefixed resolution. The net has been widely tested on synthesized signals.

The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.

On the Circuit Complexity of Sigmoid Feedforward Neural Networks

The authors examine the usefulness of the feedforward neural network as a controller. In particular, the authors consider as an example the problem of design of a power system stabilizer that has the structure of a classical feedforward artificial neural network. In this case, the controller is developed for a class of problems associated with linear plants. Certain observations are made regarding the generic behavior of systems with this type of controller structure. The authors incorporate a robustness criterion into the design as well as a conventional dynamic performance criterion.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Weight initialization of sigmoidal feed forward artificial neural network (SFFANN) and the Convolutional neural networks (CNN) has been a known factor which affects the learning abilities of the neural network. The uniform random weight initialization approach has been quite often used as the conventional network weight initial technique, due to its simplicity. However, various researches have shown that the random technique may not be the ideal choice of weight initialization for these neural networks. In this work, we analyze two separate chaotic functions and explore the possibilities of these being used as the weight initialization methods against the conventional random initialization technique for SFANNs as well as for the CNNs. For the SFFANNs, this analysis were done over 8 function approximation problems chosen for experimentation. The mean test error values along with a two sample t-test results strongly suggest that the Chebyshev chaotic map based weight initialization technique outperforms the conventional random initialization technique for most of the problems under consideration and hence may be used as an alternative weight initialization technique for the SFFANNs. For the CNN experiment, the MNIST dataset was used for analyzing the performance of the random and the Chebyshev based initialization scheme. Results strongly support the use of the Chebyshev chaotic map based initialization scheme as an alternate to the conventional random initialization.

Accommodating learning styles in adaptive educational hypermedia systems (AEHS) improves students' learning performance in web-based learning. Hence, in implementing the systems, the students' learning style identification process is important. Most of today's AEHS rely on a traditional technique of identifying students' learning styles, which is using questionnaires. However, using questionnaires for this purpose is less efficient, cumbersome and may not be that feasible. This study proposes a real-time learning style identification technique by recording students' browsing behaviors and analyzing them by using multi-layer feed forward artificial neural network (MLFF). The result suggests that there is a relationship between the frequencies of students' click on learning components with their staying time on those components. It also indicates that the proposed identification technique performs well in identifying students' learning styles.

Due to light scattering by air particles, images taken under bad lighting conditions (such as haze, fog, mist, or smog) have a reduced level of visibility. One blurry image can be made visible again using single image dehazing techniques. Due to the ill-posedness of the Single image dehazing problem, it is difficult to solve. Even though, the Dark channel prior (DCP) has been the most prevalent method for image dehazing algorithms, but it suffers from huge computational time and picture quality. The accurate transmission map estimation is one of the best preferred ways to achieve the least computational time and high picture quality in DCP. Hence, this work is focused on constructing transmission map of a hazy image based on feed forward artificial neural network. The recommended method uses a feed forward ANN to conduct the transmission map straight from the minimum channel and a normalizing procedure to increase the re-covered image information. With a training set of eighty images, the network is trained by means of mean square error (MSE). The proposed method utilizing peak signal to noise ratio (PSNR) and structural similarity (SSIM) index measures are used to assess the restoration quality. The investigational conclusions have demonstrated that, the suggested method outperforms the dehazing of an input image without degrading visible quality (PSNR = 69.16, SSIM index = 0.8913). In addition, the proposed method is suited for real-time applications given the average computing time it achieves is 1.03 s.

In this paper, we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANNs), the method is called the Interactive FFANN Procedure. In the procedure, the decision maker articulates preference information over representative samples from the nondominated set either by assigning preference “values” to the sample solutions or by making pairwise comparisons in a fashion similar to that in the Analytic Hierarchy Process. With this information, a FFANN is trained to represent the decision maker's preference structure. Then, using the FFANN, an optimization problem is solved to search for improved solutions. An example is given to illustrate the Interactive FFANN Procedure. Also, the procedure is compared computationally with the Tchebycheff Method (Steuer and Choo [Steuer, R. E., E.-U. Choo. 1983. An interactive weighted Tchebycheff procedure for multiple objective programming. Math. Programming 26(1) 326–344.]). The computational results indicate that the Interactive FFANN Procedure produces good solutions and is robust with regard to the neural network architecture.

The feedforward artificial neural network (FFANN) based automatic voltage regulator (AVR) controller for excitation system using Matlab/Simulink approach is proposed in this paper.The proposed AVR controller investigates and demonstrates the application of radial basis function (RBF) and multiplayer perceptron (MLP) architectures of FFANNs and compares the suitability of both architectures. The simulation results of suggested AVR controller not only show the encouraging responses for the application but also show the improvement in the transient responses of synchronous machine. The responses of developed RBF and MLP networks have also been compared with conventional proportional integral and derivative (PID) excitation controller of synchronous machine. Investigations and results prove that FFANN AVR controllers are very simple and accurate than conventional AVR and also enhance the stability performance of synchronous machine in an efficient manner. This research also suggests that RBF network is more simple, accurate, fast and robust controller than MLP architecture. Huge numbers of research papers have been written and published on the different types of excitation controller, but the proposed controller for excitation control system is relatively most simple and suitable for software demonstration and practical implementation.

BACKGROUNDTraditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional approaches are unable to take advantage of more complex data structures such as repeated measurements. Deep learning methods have the potential ability to automatically find and learn complex, hidden relationships between predictive markers and outcomes, but their application to clinical prediction in CD and IBD has not been explored previously.AIMTo determine and compare the utility of deep learning with conventional algorithms in predicting response to anti-tumor necrosis factor (anti-TNF) therapy in CD.METHODSThis was a retrospective single-center cohort study of all CD patients who commenced anti-TNF therapy (either adalimumab or infliximab) from January 1, 2010 to December 31, 2015. Remission was defined as a C-reactive protein (CRP) < 5 mg/L at 12 mo after anti-TNF commencement. Three supervised learning algorithms were compared: (1) A conventional statistical learning algorithm using multivariable logistic regression on baseline data only; (2) A deep learning algorithm using a feed-forward artificial neural network on baseline data only; and (3) A deep learning algorithm using a recurrent neural network on repeated data. Predictive performance was assessed using area under the receiver operator characteristic curve (AUC) after 10× repeated 5-fold cross-validation.RESULTSA total of 146 patients were included (median age 36 years, 48% male). Concomitant therapy at anti-TNF commencement included thiopurines (68%), methotrexate (18%), corticosteroids (44%) and aminosalicylates (33%). After 12 mo, 64% had CRP < 5 mg/L. The conventional learning algorithm selected the following baseline variables for the predictive model: Complex disease behavior, albumin, monocytes, lymphocytes, mean corpuscular hemoglobin concentration and gamma-glutamyl transferase, and had a cross-validated AUC of 0.659, 95% confidence interval (CI): 0.562-0.756. A feed-forward artificial neural network using only baseline data demonstrated an AUC of 0.710 (95%CI: 0.622-0.799; P = 0.25 vs conventional). A recurrent neural network using repeated biomarker measurements demonstrated significantly higher AUC compared to the conventional algorithm (0.754, 95%CI: 0.674-0.834; P = 0.036).CONCLUSIONDeep learning methods are feasible and have the potential for stronger predictive performance compared to conventional model building methods when applied to predicting remission after anti-TNF therapy in CD.

Recently, membrane technology has become more attractive particularly in solid-liquid separation process. Membrane bioreactor (MBR) has found to be a reliable technology to replace the conventional activated sludge (CAS) process for water and wastewater treatment by adopting membrane filtration technology and bioreactor. However, numerous drawbacks arise when using membrane which includes high maintenance cost and fouling problem. An optimal MBR plant operation is needed to be determined in order to reduce fouling and at the same time reduce the cost of running the MBR. It is crucial to have a reliable MBR filtration prediction that can measure and predict the filtration dynamic performance especially the effect of fouling to the filtration and cleaning operations. With this prediction tool, suitable action can be taken to improve the operation in order to find the optimum setting of the filtration process. This paper presents the permeate flux measurement and prediction development for submerged membrane filtration process. Three input filtration parameters were used to predict the permeate flux in the filtration process. This work employed feed forward artificial neural network (FFNN) and radial basis function neural network (RBFNN) for the prediction purpose. The permeate flux prediction method was developed using operation settings such as aeration airflow, suction pump voltage and transmembrane pressure (TMP) under schedule relaxation condition. The result shows that FFNN method gives better performance compared with RBFNN method in terms of accuracy and reliability.

This paper presents a method to estimate a transformer health condition based on diagnostic tests. A feed forward artificial neural network (FFANN) is used to find the health index of the transformer. The health index is used to find the health condition of the transformer. The training of the FFANN is done using real measurements of 59 working transformers. The testing of the trained neural network performance is done using real data for 29 working transformers. The performance evaluation of the trained FFANN shows that the trained neural network is reliable in finding the health condition of any working transformer.

This paper presents a study on the weld quality obtained by different values of the input parameters. The weld quality is characterized by two categories of parameters: geometrical parameters and mechanical parameters. They are dependent on the following process parameters: electric arc voltage, electric current intensity, welding speed, the feed wire velocity. Because the dependence between inputs and outputs is a nonlinear one was used an artificial feed forward neural network (ANN). The ANN was trained with the backpropagation algorithm, using as training patterns data measured from the mechanical process. This ANN can be used to estimate some parameters from future experiments of the mechanical process.