Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Distinction of males of the Lutzomyia intermedia (Lutz & Neiva, 1912) species complex by ratios between dimensions and by an artificial neural network (Diptera: Psychodidae, Phlebotominae)

The females of the two species of the Lutzomyia intermedia complex can be easily distinguished, but the males of each species are quite similar. The ratios between the extra-genital and the genital structures of L. neivai are larger than those of L. intermedia s. s., according to ANOVA. An artificial neural network was trained with a set of 300 examples, randomly taken from a sample of 358 individuals. The input vectors consisted of several ratios between some structures of each insect. The model was tested on the remaining 58 insects, 56 of which (96.6%) were correctly identified. This ratio of success can be considered remarkable if one takes into account the difficulty of attaining comparable results using traditional statistical techniques.

Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

Volatility smile extrapolation with an artificial neural network

I use a multi-layer feedforward perceptron, with backpropagation learning implemented via stochastic gradient descent, to extrapolate the volatility smile of Euribor derivatives over low-strikes by training the network on parametric prices.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R² = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.

Artificial neural network modeling of phenol adsorption onto barley husks activated carbon in an airlift reactor.

Tesis (Doctor en Ciencias con Orientación en Procesos Sustentables) UANL, 2013.

Tunnel-Based Artificial Neural Network Technique to Calculate the Resonant Frequency of a Thick-Substrate Microstrip Antenna

The mathematical formulation of empirically developed formulas Jirr the calculation of the resonant frequency of a thick-substrate (h s 0.08151 A,,) microstrip antenna has been analyzed. With the use qt' tunnel-based artificial neural networks (ANNs), the resonant frequency of antennas with h satisfying the thick-substrate condition are calculated and compared with the existing experimental results and also with the simulation results obtained with the use of an IE3D software package. The artificial neural network results are in very good agreement with the experimental results

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components