SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

International Nuclear Information System (INIS)

Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa

2014-01-01

Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation

DESIGN OF A VISUAL INTERFACE FOR ANN BASED SYSTEMS

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Ramazan BAYINDIR

2008-01-01

Full Text Available Artificial intelligence application methods have been used for control of many systems with parallel of technological development besides conventional control techniques. Increasing of artificial intelligence applications have required to education in this area. In this paper, computer based an artificial neural network (ANN software has been presented to learning and understanding of artificial neural networks. By means of the developed software, the training of the artificial neural network according to the inputs provided and a test action can be performed by changing the components such as iteration number, momentum factor, learning ratio, and efficiency function of the artificial neural networks. As a result of the study a visual education set has been obtained that can easily be adapted to the real time application.

IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN

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K. Prasada Rao

2017-09-01

Full Text Available Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility. This study investigates the performance and emission characteristics of single cylinder four stroke indirect diesel injection (IDI engine fueled with Rice Bran Methyl Ester (RBME with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN modeling. The study used IDI engine experimental data to evaluate nine engine performance and emission parameters including Exhaust Gas Temperature (E.G.T, Brake Specific Fuel Consumption (BSFC, Brake Thermal Efficiency (B.The and various emissions like Hydrocarbons (HC, Carbon monoxide (CO, Carbon dioxide (CO2, Oxygen (O2, Nitrogen oxides (NOX and smoke. For the ANN modeling standard back propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception (MLP network was used for non-linear mapping between the input and output parameters. It was found that ANN was able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.995, 0.980, 0.999, 0.985, 0.999, 0.999, 0.980, 0.999, and 0.999 for E.G.T, BSFC, B.The, HC, O2, CO2, CO, NOX, smoke respectively.

USING ARTIFICIAL NEURAL NETWORKS (ANNs FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH

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Vahid Nourani

2009-01-01

Full Text Available Without a doubt the carried sediment load by a river is the most important factor in creating and formation of the related Delta in the river mouth. Therefore, accurate forecasting of the river sediment load can play a significant role for study on the river Delta. However considering the complexity and non-linearity of the phenomenon, the classic experimental or physical-based approaches usually could not handle the problem so well. In this paper, Artificial Neural Network (ANN as a non-linear black box interpolator tool is used for modeling suspended sediment load which discharges to the Talkherood river mouth, located in northern west Iran. For this purpose, observed time series of water discharge at current and previous time steps are used as the model input neurons and the model output neuron will be the forecasted sediment load at the current time step. In this way, various schemes of the ANN approach are examined in order to achieve the best network as well as the best architecture of the model. The obtained results are also compared with the results of two other classic methods (i.e., linear regression and rating curve methods in order to approve the efficiency and ability of the proposed method.

Comparison of the accuracy of SST estimates by artificial neural networks (ANN) and other quantitative methods using radiolarian data from the Antarctic and Pacific Oceans

Digital Repository Service at National Institute of Oceanography (India)

Gupta, S.M.; Malmgren, B.A.

) regression, the maximum likelihood (ML) method, and artificial neural networks (ANNs), based on radiolarian faunal abundance data from surface sediments from the Antarctic and Pacific Oceans. Recent studies have suggested that ANNs may represent one...

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

Science.gov (United States)

Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

2018-01-05

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Anne K. Bang: Islamic Sufi Networks in the Western Indian Ocean (c. 1880-1940. Ripples of Reform.

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Angelika Brodersen

2015-03-01

Full Text Available This contribution offers a review of Anne K. Bang's book: Islamic Sufi Networks in the Western Indian Ocean (c. 1880-1940. Ripples of Reform. Islam in Africa, Volume 16. Leiden: Brill 2014. xiv + 227 pages, € 104.00, ISBN 978-900-425-1342.

Comparative study of landslides susceptibility mapping methods: Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN)

Science.gov (United States)

Salleh, S. A.; Rahman, A. S. A. Abd; Othman, A. N.; Mohd, W. M. N. Wan

2018-02-01

As different approach produces different results, it is crucial to determine the methods that are accurate in order to perform analysis towards the event. This research aim is to compare the Rank Reciprocal (MCDM) and Artificial Neural Network (ANN) analysis techniques in determining susceptible zones of landslide hazard. The study is based on data obtained from various sources such as local authority; Dewan Bandaraya Kuala Lumpur (DBKL), Jabatan Kerja Raya (JKR) and other agencies. The data were analysed and processed using Arc GIS. The results were compared by quantifying the risk ranking and area differential. It was also compared with the zonation map classified by DBKL. The results suggested that ANN method gives better accuracy compared to MCDM with 18.18% higher accuracy assessment of the MCDM approach. This indicated that ANN provides more reliable results and it is probably due to its ability to learn from the environment thus portraying realistic and accurate result.

Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India.

Science.gov (United States)

Sau, Arkaprabha; Bhakta, Ishita

2017-05-01

Depression is one of the most important causes of mortality and morbidity among the geriatric population. Although, the aging brain is more vulnerable to depression, it cannot be considered as physiological and an inevitable part of ageing. Various sociodemographic and morbidity factors are responsible for the depression among them. Using Artificial Neural Network (ANN) model depression can be predicted from various sociodemographic variables and co morbid conditions even at community level by the grass root level health care workers. To predict depression among geriatric population from sociodemographic and morbidity attributes using ANN. An observational descriptive study with cross-sectional design was carried out at a slum under the service area of Bagbazar Urban Health and Training Centre (UHTC) in Kolkata. Among 126 elderlies under Bagbazar UHTC, 105 were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale. WEKA 3.8.0 was used to develop the ANN model and test its performance. Prevalence of depression among the study population was 45.7%. Various sociodemographic variables like age, gender, literacy, living spouse, working status, personal income, family type, substance abuse and co morbid conditions like visual problem, mobility problem, hearing problem and sleeping problem were taken into consideration to develop the model. Prediction accuracy of this ANN model was 97.2%. Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes.

Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN)

Energy Technology Data Exchange (ETDEWEB)

Delnavaz, M. [Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, Tehran (Iran, Islamic Republic of); Ayati, B., E-mail: ayati_bi@modares.ac.ir [Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, Tehran (Iran, Islamic Republic of); Ganjidoust, H. [Tarbiat Modares University, Civil Engineering Department, Environmental Engineering Division, Tehran (Iran, Islamic Republic of)

2010-07-15

In this study, the results of 1-year efficiency forecasting using artificial neural networks (ANN) models of a moving bed biofilm reactor (MBBR) for a toxic and hard biodegradable aniline removal were investigated. The reactor was operated in an aerobic batch and continuous condition with 50% by volume which was filled with light expanded clay aggregate (LECA) as carrier. Efficiency evaluation of the reactors was obtained at different retention time (RT) of 8, 24, 48 and 72 h with an influent COD from 100 to 4000 mg/L. Exploratory data analysis was used to detect relationships between the data and dependent evaluated one. The appropriate architecture of the neural network models was determined using several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting MBBR performance for treating aromatic amine compounds.

Applying of the Artificial Neural Networks (ANN) to Identify and Characterize Sweet Spots in Shale Gas Formations

Science.gov (United States)

Puskarczyk, Edyta

2018-03-01

The main goal of the study was to enhance and improve information about the Ordovician and Silurian gas-saturated shale formations. Author focused on: firstly, identification of the shale gas formations, especially the sweet spots horizons, secondly, classification and thirdly, the accurate characterization of divisional intervals. Data set comprised of standard well logs from the selected well. Shale formations are represented mainly by claystones, siltstones, and mudstones. The formations are also partially rich in organic matter. During the calculations, information about lithology of stratigraphy weren't taken into account. In the analysis, selforganizing neural network - Kohonen Algorithm (ANN) was used for sweet spots identification. Different networks and different software were tested and the best network was used for application and interpretation. As a results of Kohonen networks, groups corresponding to the gas-bearing intervals were found. The analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in sweet spots is present. Kohonen algorithm was also used for geological interpretation of well log data and electrofacies prediction. Reliable characteristic into groups shows that Ja Mb and Sa Fm which are usually treated as potential sweet spots only partially have good reservoir conditions. It is concluded that ANN appears to be useful and quick tool for preliminary classification of members and sweet spots identification.

A novel framework for intelligent signal detection via artificial neural networks for cyclic voltammetry in pyroprocessing technology

International Nuclear Information System (INIS)

Rakhshan Pouri, Samaneh; Manic, Milos; Phongikaroon, Supathorn

2018-01-01

Highlights: •First time ANN implementation toward pyroprocessing safeguards. •Real time monitoring in terms of intelligent materials detection and accountability. •CV simulation via ANN showing a high accuracy of prediction for the unseen situation. •Elimination of trial and error approach to avoid overfitting in learning. -- Abstract: Electrorefiner (ER) is the heart of pyroprocessing technology which contains different fission, rare-earth, and transuranic chloride compositions during the operation. This is still a developing technology that needs to be advanced for the commercial reprocessing design of used nuclear fuel (UNF) in terms of intelligent materials detection and accountability towards safeguards. A novel signal detection, artificial neural network (ANN), has been proposed in this study to apply on massive ER systemic parameters to simulate cyclic voltammetry (CV) graphs for the unseen situation. ANN could be trained to mimic the system by driving the data sets interrelation between variables to provide current and potential simulated data sets with a high accuracy of prediction. For this purpose, over 230,000 experimental data points reported in literature have been explored—0.5–5 wt% of zirconium chloride (ZrCl 4 ) in LiCl-KCl molten salt with different scan rates at 773 K. This study has illustrated a new framework of ANN implementation to eliminate trial and error approach by comparing the average error of one to three hidden layers with different number of neurons. In addition, this framework results in finding a preferable balance between underfitting and overfitting in deep learning. Furthermore, simulated CV graphs were compared with the experimental data and illustrated a reasonable prediction. The results reveal two structures with three hidden layers providing a good prediction with a low average error. The outcomes indicate that ANN has a strong potential in applying toward safeguards for pyroprocessing technology.

Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells

Energy Technology Data Exchange (ETDEWEB)

Parthiban, Thirumalai; Ravi, R.; Kalaiselvi, N. [Central Electrochemical Research Institute (CECRI), Karaikudi 630006 (India)

2007-12-31

CoO anode, as an alternate to the carbonaceous anodes of lithium-ion cells has been prepared and investigated for electrochemical charge-discharge characteristics for about 50 cycles. Artificial neural networks (ANNs), which are useful in estimating battery performance, has been deployed for the first time to forecast and to verify the charge-discharge behavior of lithium-ion cells containing CoO anode for a total of 50 cycles. In this novel approach, ANN that has one input layer with one neuron corresponding to one input variable, viz., cycles [charge-discharge cycles] and a hidden layer consisting of three neurons to produce their outputs to the output layer through a sigmoid function has been selected for the present investigation. The output layer consists of two neurons, representing the charge and discharge capacity, whose activation function is also the sigmoid transfer function. In this ever first attempt to exploit ANN as an effective theoretical tool to understand the charge-discharge characteristics of lithium-ion cells, an excellent agreement between the calculated and observed capacity values was found with CoO anodes with the best fit values corresponding to an error factor of <1%, which is the highlight of the present study. (author)

Feature Selection and ANN Solar Power Prediction

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Daniel O’Leary

2017-01-01

Full Text Available A novel method of solar power forecasting for individuals and small businesses is developed in this paper based on machine learning, image processing, and acoustic classification techniques. Increases in the production of solar power at the consumer level require automated forecasting systems to minimize loss, cost, and environmental impact for homes and businesses that produce and consume power (prosumers. These new participants in the energy market, prosumers, require new artificial neural network (ANN performance tuning techniques to create accurate ANN forecasts. Input masking, an ANN tuning technique developed for acoustic signal classification and image edge detection, is applied to prosumer solar data to improve prosumer forecast accuracy over traditional macrogrid ANN performance tuning techniques. ANN inputs tailor time-of-day masking based on error clustering in the time domain. Results show an improvement in prediction to target correlation, the R2 value, lowering inaccuracy of sample predictions by 14.4%, with corresponding drops in mean average error of 5.37% and root mean squared error of 6.83%.

Prediction by Artificial Neural Networks (ANN of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius

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Julio Rojas Naccha

2012-09-01

Full Text Available The predictive ability of Artificial Neural Network (ANN on the effect of the concentration (30, 40, 50 y 60 % w/w and temperature (30, 40 y 50°C of fructooligosaccharides solution, in the mass, moisture, volume and solids of osmodehydrated yacon cubes, and in the coefficients of the water means effective diffusivity with and without shrinkage was evaluated. The Feedforward type ANN with the Backpropagation training algorithms and the Levenberg-Marquardt weight adjustment was applied, using the following topology: 10-5 goal error, 0.01 learning rate, 0.5 moment coefficient, 2 input neurons, 6 output neurons, one hidden layer with 18 neurons, 15 training stages and logsig-pureline transfer functions. The overall average error achieved by the ANN was 3.44% and correlation coefficients were bigger than 0.9. No significant differences were found between the experimental values and the predicted values achieved by the ANN and with the predicted values achieved by a statistical model of second-order polynomial regression (p > 0.95.

Artificial Neural Network (ANN) design for Hg-Se interactions and their effect on reduction of Hg uptake by radish plant

International Nuclear Information System (INIS)

Kumar Rohit Raj; Abhishek Kardam; Shalini Srivastava; Jyoti Kumar Arora

2010-01-01

The tendency of selenium to interact with heavy metals in presence of naturally occurring species has been exploited for the development of green bioremediation of toxic metals from soil using Artificial Neural Network (ANN) modeling. The cross validation of the data for the reduction in uptake of Hg(II) ions in the plant R. sativus grown in soil and sand culture in presence of selenium has been used for ANN modeling. ANN model based on the combination of back propagation and principal component analysis was able to predict the reduction in Hg uptake with a sigmoid axon transfer function. The data of fifty laboratory experimental sets were used for structuring single layer ANN model. Series of experiments resulted into the performance evaluation based on considering 20% data for testing and 20% data for cross validation at 1,500 Epoch with 0.70 momentums The Levenberg-Marquardt algorithm (LMA) was found as the best of BP algorithms with a minimum mean squared error at the eighth place of the decimal for training (MSE) and cross validation. (author)

Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices

International Nuclear Information System (INIS)

Taghavifar, Hamid; Mardani, Aref

2014-01-01

This paper examines the prediction of energy efficiency indices of driven wheels (i.e. traction coefficient and tractive power efficiency) as affected by wheel load, slippage and forward velocity at three different levels with three replicates to form a total of 162 data points. The pertinent experiments were carried out in the soil bin testing facility. A feed-forward ANN (artificial neural network) with standard BP (back propagation) algorithm was practiced to construct a supervised representation to predict the energy efficiency indices of driven wheels. It was deduced, in view of the statistical performance criteria (i.e. MSE (mean squared error) and R 2 ), that a supervised ANN with 3-8-10-2 topology and Levenberg–Marquardt training algorithm represented the optimal model. Modeling implementations indicated that ANN is a powerful technique to prognosticate the stochastic energy efficiency indices as affected by soil-wheel interactions with MSE of 0.001194 and R 2 of 0.987 and 0.9772 for traction coefficient and tractive power efficiency. It was found that traction coefficient and tractive power efficiency increase with increased slippage. A similar trend is valid for the influence of wheel load on the objective parameters. Wherein increase of velocity led to an increment of tractive power efficiency, velocity had no significant effect on traction coefficient. - Highlights: • Energy efficiency indexes were assessed as affected by tire parameters. • ANN was applied for prognostication of the objective parameters. • A 3-8-10-2 ANN with MSE of 0.001194 and R 2 of 0.987 and 0.9772 was designated as optimal model. • Optimal values of learning rate and momentum were found 0.9 and 0.5, respectively

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

Science.gov (United States)

Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

2017-11-01

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Optimising training data for ANNs with Genetic Algorithms

OpenAIRE

Kamp , R. G.; Savenije , H. H. G.

2006-01-01

International audience; Artificial Neural Networks (ANNs) have proved to be good modelling tools in hydrology for rainfall-runoff modelling and hydraulic flow modelling. Representative datasets are necessary for the training phase in which the ANN learns the model's input-output relations. Good and representative training data is not always available. In this publication Genetic Algorithms (GA) are used to optimise training datasets. The approach is tested with an existing hydraulic model in ...

Optimising training data for ANNs with Genetic Algorithms

OpenAIRE

R. G. Kamp; R. G. Kamp; H. H. G. Savenije

2006-01-01

Artificial Neural Networks (ANNs) have proved to be good modelling tools in hydrology for rainfall-runoff modelling and hydraulic flow modelling. Representative datasets are necessary for the training phase in which the ANN learns the model's input-output relations. Good and representative training data is not always available. In this publication Genetic Algorithms (GA) are used to optimise training datasets. The approach is tested with an existing hydraulic model in The Netherlands. An...

Biogas engine performance estimation using ANN

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Yusuf Kurtgoz

2017-12-01

Full Text Available Artificial neural network (ANN method was used to estimate the thermal efficiency (TE, brake specific fuel consumption (BSFC and volumetric efficiency (VE values of a biogas engine with spark ignition at different methane (CH4 ratios and engine load values. For this purpose, the biogas used in the biogas engine was produced by the anaerobic fermentation method from bovine manure and different CH4 contents (51%, 57%, 87% were obtained by purification of CO2 and H2S. The data used in the ANN models were obtained experimentally from a 4-stroke four-cylinder, spark ignition engine, at constant speed for different load and CH4 ratios. Using some of the obtained experimental data, ANN models were developed, and the rest was used to test the developed models. In the ANN models, the CH4 ratio of the fuel, engine load, inlet air temperature (Tin, air fuel ratio and the maximum cylinder pressure are chosen as the input parameters. TE, BSFC and VE are used as the output parameters. Root mean square error (RMSE, mean absolute percentage error (MAPE and correlation coefficient (R performance indicators are used to compare measured and predicted values. It has been shown that ANN models give good results in spark ignition biogas engines with high correlation and low error rates for TE, BSFC and VE values.

Flow forecast by SWAT model and ANN in Pracana basin, Portugal

NARCIS (Netherlands)

Demirel, M.C.; Venancio, Anabela; Kahya, Ercan

2009-01-01

This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the

Anne-Ly Võlli: Iga inimene ja asutus vajab omamoodi lähenemist / Anne-Ly Võlli ; intervjueerinud Jaanika Kressa

Index Scriptorium Estoniae

Võlli, Anne-Ly, 1976-

2009-01-01

MTÜ Jõgevamaa Omavalitsuste Aktiviseerimiskeskus kinnitas avaliku konkursi tulemusel juhatuse liikmeks Anne-Ly Võlli, kelle ülesandeks on keskuse tegevuse juhtimine ja koostöö arendamine partneromavalitsuste ja teiste koostööpartnerite vahel

Research on artificial neural network applications for nuclear power plants

International Nuclear Information System (INIS)

Chang, Soon-Heung; Cheon, Se-Woo

1992-01-01

Artificial neural networks (ANNs) are an emerging computational technology which can significantly enhance a number of applications. These consist of many interconnected processing elements that exhibit human-like performance, i.e., learning, pattern recognition and associative memory skills. Several application studies on ANNs devoted to nuclear power plants have been carried out at the Korea Advanced Institute of Science and Technology since 1989. These studies include the feasibility of using ANNs for the following tasks: (1) thermal power prediction, (2) transient identification, (3) multiple alarm processing and diagnosis, (4) core thermal margin prediction, and (5) prediction of core parameters for fuel reloading. This paper introduces the back-propagation network (BPN) model which is the most commonly used algorithm, and summarizes each of the studies briefly. (author)

Prediction of scour below submerged pipeline crossing a river using ANN.

Science.gov (United States)

Azamathulla, H M; Zakaria, Nor Azazi

2011-01-01

The process involved in the local scour below pipelines is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published works and used to train the network or evolve the program. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.

ANN based optimization of a solar assisted hybrid cooling system in Turkey

Energy Technology Data Exchange (ETDEWEB)

Ozgur, Arif; Yetik, Ozge; Arslan, Oguz [Mechanical Eng. Dept., Engineering Faculty, Dumlupinar University (Turkey)], email: maozgur@dpu.edu.tr, email: ozgeyetik@dpu.edu.tr, email: oarslan@dpu.edu.tr

2011-07-01

This study achieved optimization of a solar assisted hybrid cooling system with refrigerants such as R717, R141b, R134a and R123 using an artificial neural network (ANN) model based on average total solar radiation, ambient temperature, generator temperature, condenser temperature, intercooler temperature and fluid types. ANN is a new tool; it works rapidly and can thus be a solution for design and optimization of complex power cycles. A unique flexible ANN algorithm was introduced to evaluate the solar ejector cooling systems because of the nonlinearity of neural networks. The conclusion was that the best COPs value obtained with the ANN is 1.35 and COPc is 3.03 when the average total solar radiation, ambient temperature, generator temperature, condenser temperature, intercooler temperature and algorithm are respectively 674.72 W/m2, 17.9, 80, 15 and 13 degree celsius and LM with 14 neurons in single hidden layer, for R717.

Identification of drought in Dhalai river watershed using MCDM and ANN models

Science.gov (United States)

Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

2017-03-01

An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

Directory of Open Access Journals (Sweden)

Maria Grazia De Giorgi

2014-08-01

Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

A Hybrid FEM-ANN Approach for Slope Instability Prediction

Science.gov (United States)

Verma, A. K.; Singh, T. N.; Chauhan, Nikhil Kumar; Sarkar, K.

2016-09-01

Assessment of slope stability is one of the most critical aspects for the life of a slope. In any slope vulnerability appraisal, Factor Of Safety (FOS) is the widely accepted index to understand, how close or far a slope from the failure. In this work, an attempt has been made to simulate a road cut slope in a landslide prone area in Rudrapryag, Uttarakhand, India which lies near Himalayan geodynamic mountain belt. A combination of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been adopted to predict FOS of the slope. In ANN, a three layer, feed- forward back-propagation neural network with one input layer and one hidden layer with three neurons and one output layer has been considered and trained using datasets generated from numerical analysis of the slope and validated with new set of field slope data. Mean absolute percentage error estimated as 1.04 with coefficient of correlation between the FOS of FEM and ANN as 0.973, which indicates that the system is very vigorous and fast to predict FOS for any slope.

A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

Science.gov (United States)

Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

2014-03-01

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

Science.gov (United States)

Yeh, Wei-Chang

Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

Application of ANN-SCE model on the evaluation of automatic generation control performance

Energy Technology Data Exchange (ETDEWEB)

Chang-Chien, L.R.; Lo, C.S.; Lee, K.S. [National Cheng Kung Univ., Tainan, Taiwan (China)

2005-07-01

An accurate evaluation of load frequency control (LFC) performance is needed to balance minute-to-minute electricity generation and demand. In this study, an artificial neural network-based system control error (ANN-SCE) model was used to assess the performance of automatic generation controls (AGC). The model was used to identify system dynamics for control references in supplementing AGC logic. The artificial neural network control error model was used to track a single area's LFC dynamics in Taiwan. The model was used to gauge the impacts of regulation control. Results of the training, evaluating, and projecting processes showed that the ANN-SCE model could be algebraically decomposed into components corresponding to different impact factors. The SCE information obtained from testing of various AGC gains provided data for the creation of a new control approach. The ANN-SCE model was used in conjunction with load forecasting and scheduled generation data to create an ANN-SCE identifier. The model successfully simulated SCE dynamics. 13 refs., 10 figs.

Optimization of thermal conductivity lightweight brick type AAC (Autoclaved Aerated Concrete) effect of Si & Ca composition by using Artificial Neural Network (ANN)

Science.gov (United States)

Zulkifli; Wiryawan, G. P.

2018-03-01

Lightweight brick is the most important component of building construction, therefore it is necessary to have lightweight thermal, mechanical and aqustic thermal properties that meet the standard, in this paper which is discussed is the domain of light brick thermal conductivity properties. The advantage of lightweight brick has a low density (500-650 kg/m3), more economical, can reduce the load 30-40% compared to conventional brick (clay brick). In this research, Artificial Neural Network (ANN) is used to predict the thermal conductivity of lightweight brick type Autoclaved Aerated Concrete (AAC). Based on the training and evaluation that have been done on 10 model of ANN with number of hidden node 1 to 10, obtained that ANN with 3 hidden node have the best performance. It is known from the mean value of MSE (Mean Square Error) validation for three training times of 0.003269. This ANN was further used to predict the thermal conductivity of four light brick samples. The predicted results for each of the AAC1, AAC2, AAC3 and AAC4 light brick samples were 0.243 W/m.K, respectively; 0.29 W/m.K; 0.32 W/m.K; and 0.32 W/m.K. Furthermore, ANN is used to determine the effect of silicon composition (Si), Calcium (Ca), to light brick thermal conductivity. ANN simulation results show that the thermal conductivity increases with increasing Si composition. Si content is allowed maximum of 26.57%, while the Ca content in the range 20.32% - 30.35%.

Science of the science, drug discovery and artificial neural networks.

Science.gov (United States)

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

Directory of Open Access Journals (Sweden)

Ch. Sanjay

2014-12-01

Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

An ANN application for water quality forecasting.

Science.gov (United States)

Palani, Sundarambal; Liong, Shie-Yui; Tkalich, Pavel

2008-09-01

Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-alpha. A time lag up to 2Delta(t) appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.

Prediction of Film Cooling Effectiveness on a Gas Turbine Blade Leading Edge Using ANN and CFD

Science.gov (United States)

Dávalos, J. O.; García, J. C.; Urquiza, G.; Huicochea, A.; De Santiago, O.

2018-05-01

In this work, the area-averaged film cooling effectiveness (AAFCE) on a gas turbine blade leading edge was predicted by employing an artificial neural network (ANN) using as input variables: hole diameter, injection angle, blowing ratio, hole and columns pitch. The database used to train the network was built using computational fluid dynamics (CFD) based on a two level full factorial design of experiments. The CFD numerical model was validated with an experimental rig, where a first stage blade of a gas turbine was represented by a cylindrical specimen. The ANN architecture was composed of three layers with four neurons in hidden layer and Levenberg-Marquardt was selected as ANN optimization algorithm. The AAFCE was successfully predicted by the ANN with a regression coefficient R2<0.99 and a root mean square error RMSE=0.0038. The ANN weight coefficients were used to estimate the relative importance of the input parameters. Blowing ratio was the most influential parameter with relative importance of 40.36 % followed by hole diameter. Additionally, by using the ANN model, the relationship between input parameters was analyzed.

Perbandingan Metode ANN-PSO Dan ANN-GA Dalam Pemodelan Komposisi Pakan Kambing Peranakan Etawa (PE Untuk Optimasi Kandungan Gizi

Directory of Open Access Journals (Sweden)

Canny Amerilyse Caesar

2016-09-01

Abstract Milk is one of the animal protein sources which it contains all of the substances needed by human body. The main milk producer cattle in Indonesia is dairy cow, however its milk production has not fulfilled the society needs. The alternative is the goat, the Etawa crossbreed (PE. The high quality of milk nutrients content is greatly influenced by some factors one of them, is the food factor. The PE goat livestock division of the UPT Cattle Breeding and the Cattle Food Greenery in Singosari-Malang still faces the problem, it is the low ability in giving the food composition for PE goat. This flaw affects the quality of the produced milk. It needs the artificial science of the milk nutrients contain in order to determine the food composition to produce premium milk with the optimum nutrients contain. The writer uses the method of the Artificial Neural Network (ANN and the Particle Swarm Optimization (PSO to make the modeling of goat food in optimizing the content of goat milk nutrients. In the analysis of the examination that is done with the case of 36 kg goat weight, also the food type used is the 70 % Odot grass and 30% Raja grass can increase the nutrients contain of the protein milk for 0.707% and decrease the fat nutrients contain for 0.879%. If uses the method of Artificial Neural Network (ANN and Genethic Algorithm (GA can increase the nutriens contain of the protein for 0.0852% and decrease the fat nutients contain for 2.3254%.   Key Words : Goat Milk, Optimization, Artificial Neural Network (ANN, Particle Swarm Optimization (PSO, Genetic Algorithm (GA, the food nutrients contain.

Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN

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Ahmad Ali

2018-03-01

Full Text Available Nowadays, wireless power transfer is ubiquitously used in wireless rechargeable sensor networks (WSNs. Currently, the energy limitation is a grave concern issue for WSNs. However, lifetime enhancement of sensor networks is a challenging task need to be resolved. For addressing this issue, a wireless charging vehicle is an emerging technology to expand the overall network efficiency. The present study focuses on the enhancement of overall network lifetime of the rechargeable wireless sensor network. To resolve the issues mentioned above, we propose swarm intelligence based hard clustering approach using fireworks algorithm with the adaptive transfer function (FWA-ATF. In this work, the virtual clustering method has been applied in the routing process which utilizes the firework optimization algorithm. Still now, an FWA-ATF algorithm yet not applied by any researcher for RWSN. Furthermore, the validation study of the proposed method using the artificial neural network (ANN backpropagation algorithm incorporated in the present study. Different algorithms are applied to evaluate the performance of proposed technique that gives the best results in this mechanism. Numerical results indicate that our method outperforms existing methods and yield performance up to 80% regarding energy consumption and vacation time of wireless charging vehicle.

iAnn

DEFF Research Database (Denmark)

Jimenez, Rafael C; Albar, Juan P; Bhak, Jong

2013-01-01

We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add...... submitted events, and these are subsequently accessed via web services. Thus, once an iAnn widget is incorporated into a website, it permanently shows timely relevant information as if it were native to the remote site. At the same time, announcements submitted to the repository are automatically...

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN).

Science.gov (United States)

Titah, Harmin Sulistiyaning; Halmi, Mohd Izuan Effendi Bin; Abdullah, Siti Rozaimah Sheikh; Hasan, Hassimi Abu; Idris, Mushrifah; Anuar, Nurina

2018-06-07

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg -1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R 2 (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.

Review of Artificial Neural Networks (ANN) applied to corrosion monitoring

International Nuclear Information System (INIS)

Mabbutt, S; Picton, P; Shaw, P; Black, S

2012-01-01

The assessment of corrosion within an engineering system often forms an important aspect of condition monitoring but it is a parameter that is inherently difficult to measure and predict. The electrochemical nature of the corrosion process allows precise measurements to be made. Advances in instruments, techniques and software have resulted in devices that can gather data and perform various analysis routines that provide parameters to identify corrosion type and corrosion rate. Although corrosion rates are important they are only useful where general or uniform corrosion dominates. However, pitting, inter-granular corrosion and environmentally assisted cracking (stress corrosion) are examples of corrosion mechanisms that can be dangerous and virtually invisible to the naked eye. Electrochemical noise (EN) monitoring is a very useful technique for detecting these types of corrosion and it is the only non-invasive electrochemical corrosion monitoring technique commonly available. Modern instrumentation is extremely sensitive to changes in the system and new experimental configurations for gathering EN data have been proven. In this paper the identification of localised corrosion by different data analysis routines has been reviewed. In particular the application of Artificial Neural Network (ANN) analysis to corrosion data is of key interest. In most instances data needs to be used with conventional theory to obtain meaningful information and relies on expert interpretation. Recently work has been carried out using artificial neural networks to investigate various types of corrosion data in attempts to predict corrosion behaviour with some success. This work aims to extend this earlier work to identify reliable electrochemical indicators of localised corrosion onset and propagation stages.

An Experimental Investigation into the Optimal Processing Conditions for the CO2 Laser Cladding of 20 MnCr5 Steel Using Taguchi Method and ANN

Science.gov (United States)

Mondal, Subrata; Bandyopadhyay, Asish.; Pal, Pradip Kumar

2010-10-01

This paper presents the prediction and evaluation of laser clad profile formed by means of CO2 laser applying Taguchi method and the artificial neural network (ANN). Laser cladding is one of the surface modifying technologies in which the desired surface characteristics of any component can be achieved such as good corrosion resistance, wear resistance and hardness etc. Laser is used as a heat source to melt the anti-corrosive powder of Inconel-625 (Super Alloy) to give a coating on 20 MnCr5 substrate. The parametric study of this technique is also attempted here. The data obtained from experiments have been used to develop the linear regression equation and then to develop the neural network model. Moreover, the data obtained from regression equations have also been used as supporting data to train the neural network. The artificial neural network (ANN) is used to establish the relationship between the input/output parameters of the process. The established ANN model is then indirectly integrated with the optimization technique. It has been seen that the developed neural network model shows a good degree of approximation with experimental data. In order to obtain the combination of process parameters such as laser power, scan speed and powder feed rate for which the output parameters become optimum, the experimental data have been used to develop the response surfaces.

Prediction ofWater Quality Parameters (NO3, CL in Karaj Riverby Usinga Combinationof Wavelet Neural Network, ANN and MLRModels

Directory of Open Access Journals (Sweden)

T. Rajaee

2016-10-01

Full Text Available IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN, Wavelet Neural Network combination (WANN and multi linear regression (MLR models, to predict next month the Nitrate (NO3 and Chloride (CL ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated. Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL has been put related to the past amounts of the same time series (NO3 / CL and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data as training set, next 10% of data (12 months and 10% of the end of time series (terminal 12 months were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q and mentioned qualityparameters(NO3 / CL were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E and the Root Mean Square

Assessment of ANN and SVM models for estimating normal direct irradiation (H_b)

International Nuclear Information System (INIS)

Santos, Cícero Manoel dos; Escobedo, João Francisco; Teramoto, Érico Tadao; Modenese Gorla da Silva, Silvia Helena

2016-01-01

Highlights: • The performance of SVM and ANN in estimating Normal Direct Irradiation (H_b) was evaluated. • 12 models using different input variables are developed (hourly and daily partitions). • The most relevant input variables for DNI are kt, H_s_c and insolation ratio (r′ = n/N). • Support Vector Machine (SVM) provides accurate estimates and outperforms the Artificial Neural Network (ANN). - Abstract: This study evaluates the estimation of hourly and daily normal direct irradiation (H_b) using machine learning techniques (ML): Artificial Neural Network (ANN) and Support Vector Machine (SVM). Time series of different meteorological variables measured over thirteen years in Botucatu were used for training and validating ANN and SVM. Seven different sets of input variables were tested and evaluated, which were chosen based on statistical models reported in the literature. Relative Mean Bias Error (rMBE), Relative Root Mean Square Error (rRMSE), determination coefficient (R"2) and “d” Willmott index were used to evaluate ANN and SVM models. When compared to statistical models which use the same set of input variables (R"2 between 0.22 and 0.78), ANN and SVM show higher values of R"2 (hourly models between 0.52 and 0.88; daily models between 0.42 and 0.91). Considering the input variables, atmospheric transmissivity of global radiation (kt), integrated solar constant (H_s_c) and insolation ratio (n/N, n is sunshine duration and N is photoperiod) were the most relevant in ANN and SVM models. The rMBE and rRMSE values in the two time partitions of SVM models are lower than those obtained with ANN. Hourly ANN and SVM models have higher rRMSE values than daily models. Optimal performance with hourly models was obtained with ANN4"h (rMBE = 12.24%, rRMSE = 23.99% and “d” = 0.96) and SVM4"h (rMBE = 1.75%, rRMSE = 20.10% and “d” = 0.96). Optimal performance with daily models was obtained with ANN2"d (rMBE = −3.09%, rRMSE = 18.95% and “d” = 0

LFC based adaptive PID controller using ANN and ANFIS techniques

Directory of Open Access Journals (Sweden)

Mohamed I. Mosaad

2014-12-01

Full Text Available This paper presents an adaptive PID Load Frequency Control (LFC for power systems using Neuro-Fuzzy Inference Systems (ANFIS and Artificial Neural Networks (ANN oriented by Genetic Algorithm (GA. PID controller parameters are tuned off-line by using GA to minimize integral error square over a wide-range of load variations. The values of PID controller parameters obtained from GA are used to train both ANFIS and ANN. Therefore, the two proposed techniques could, online, tune the PID controller parameters for optimal response at any other load point within the operating range. Testing of the developed techniques shows that the adaptive PID-LFC could preserve optimal performance over the whole loading range. Results signify superiority of ANFIS over ANN in terms of performance measures.

A Sensitive ANN Based Differential Relay for Transformer Protection with Security against CT Saturation and Tap Changer Operation

OpenAIRE

KHORASHADI-ZADEH, Hassan; LI, Zuyi

2014-01-01

This paper presents an artificial neural network (ANN) based scheme for fault identification in power transformer protection. The proposed scheme is featured by the application of ANN to identifying system patterns, the unique choice of harmonics of positive sequence differential currents as ANN inputs, the effective handling of current transformer (CT) saturation with an ANN based approach, and the consideration of tap changer position for correcting secondary CT current. Performanc...

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study.

Science.gov (United States)

Grossi, Enzo; Buscema, Massimo P; Snowdon, David; Antuono, Piero

2007-06-21

Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old. The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts. By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same. Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus. The results of this study

Fault diagnosis in nuclear power plants using an artificial neural network technique

International Nuclear Information System (INIS)

Chou, H.P.; Prock, J.; Bonfert, J.P.

1993-01-01

Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

Applications of artificial neural networks in medical science.

Science.gov (United States)

Patel, Jigneshkumar L; Goyal, Ramesh K

2007-09-01

Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

Anne-Ly Reimaa : "Suhtlemisel on oluline avatus" / Anne-Ly Reimaa ; interv. Tiia Linnard

Index Scriptorium Estoniae

Reimaa, Anne-Ly

2005-01-01

Ilmunud ka: Severnoje Poberezhje : Subbota 3. september lk. 5. Intervjueeritav oma tööst Brüsselis, kus esindab Eesti linnade liitu ja Eesti maaomavalitsuste liitu. Arvamust avaldavad Anne Jundas ja Kaia Kaldvee. Lisa: CV

Process Control Strategies for Dual-Phase Steel Manufacturing Using ANN and ANFIS

Science.gov (United States)

Vafaeenezhad, H.; Ghanei, S.; Seyedein, S. H.; Beygi, H.; Mazinani, M.

2014-11-01

In this research, a comprehensive soft computational approach is presented for the analysis of the influencing parameters on manufacturing of dual-phase steels. A set of experimental data have been gathered to obtain the initial database used for the training and testing of both artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The parameters used in the strategy were intercritical annealing temperature, carbon content, and holding time which gives off martensite percentage as an output. A fraction of the data set was chosen to train both ANN and ANFIS, and the rest was put into practice to authenticate the act of the trained networks while seeing unseen data. To compare the obtained results, coefficient of determination and root mean squared error indexes were chosen. Using artificial intelligence methods, it is not necessary to consider and establish a preliminary mathematical model and formulate its affecting parameters on its definition. In conclusion, the martensite percentages corresponding to the manufacturing parameters can be determined prior to a production using these controlling algorithms. Although the results acquired from both ANN and ANFIS are very encouraging, the proposed ANFIS has enhanced performance over the ANN and takes better effect on cost-reduction profit.

Comparison of Conventional and ANN Models for River Flow Forecasting

Science.gov (United States)

Jain, A.; Ganti, R.

2011-12-01

Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

THE FEMINISM AND FEMININITY OF ANN VERONICA IN H. G. WELLS' ANN VERONICA

Directory of Open Access Journals (Sweden)

Liem Satya Limanta

2002-01-01

Full Text Available H.G. Well's Ann Veronica structurally seems to be divided into two parts; the first deals with Ann Veronica's struggle to get equality with men and freedom in most aspects of life, such as in politics, economics, education, and sexuality; the second describes much the other side of her individuality which she cannot deny, namely her femininity, such as her crave for love, marriage, maternity, and beauty. H.G. Wells describes vividly the two elements in Ann Veronica, feminism and femininity. As a feminist, Ann Veronica rebelled against her authoritative Victorian father, who regarded women only as men's property to be protected from the harsh world outside. On the other side, Ann could not deny her being a woman after she fell in love with Capes. Her femininity from the second half of the novel then is explored. Although the novel ends with the depiction of the domestic life of Ann Veronica, it does not mean that the feminism is gone altogether. The key point is that the family life Ann chooses as a `submissive' wife and good mother is her choice. It is very different if it is forced on her to do. Thus, this novel depicts both sides of Ann Veronica, her feminism and her femininity.

Optimization of Nd: YAG Laser Marking of Alumina Ceramic Using RSM And ANN

International Nuclear Information System (INIS)

Peter, Josephine; Doloi, B.; Bhattacharyya, B.

2011-01-01

The present research papers deals with the artificial neural network (ANN) and the response surface methodology (RSM) based mathematical modeling and also an optimization analysis on marking characteristics on alumina ceramic. The experiments have been planned and carried out based on Design of Experiment (DOE). It also analyses the influence of the major laser marking process parameters and the optimal combination of laser marking process parametric setting has been obtained. The output of the RSM optimal data is validated through experimentation and ANN predictive model. A good agreement is observed between the results based on ANN predictive model and actual experimental observations.

Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

Science.gov (United States)

Balabin, Roman M; Lomakina, Ekaterina I

2011-06-28

A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is

Solar radiation modelling using ANNs for different climates in China

International Nuclear Information System (INIS)

Lam, Joseph C.; Wan, Kevin K.W.; Yang, Liu

2008-01-01

Artificial neural networks (ANNs) were used to develop prediction models for daily global solar radiation using measured sunshine duration for 40 cities covering nine major thermal climatic zones and sub-zones in China. Coefficients of determination (R 2 ) for all the 40 cities and nine climatic zones/sub-zones are 0.82 or higher, indicating reasonably strong correlation between daily solar radiation and the corresponding sunshine hours. Mean bias error (MBE) varies from -3.3 MJ/m 2 in Ruoqiang (cold climates) to 2.19 MJ/m 2 in Anyang (cold climates). Root mean square error (RMSE) ranges from 1.4 MJ/m 2 in Altay (severe cold climates) to 4.01 MJ/m 2 in Ruoqiang. The three principal statistics (i.e., R 2 , MBE and RMSE) of the climatic zone/sub-zone ANN models are very close to the corresponding zone/sub-zone averages of the individual city ANN models, suggesting that climatic zone ANN models could be used to estimate global solar radiation for locations within the respective zones/sub-zones where only measured sunshine duration data are available. (author)

Playing tag with ANN: boosted top identification with pattern recognition

International Nuclear Information System (INIS)

Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu; Lee, Seung J.; Perelstein, Maxim

2015-01-01

Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p T in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

Playing tag with ANN: boosted top identification with pattern recognition

Energy Technology Data Exchange (ETDEWEB)

Almeida, Leandro G. [Institut de Biologie de l’École Normale Supérieure (IBENS), Inserm 1024- CNRS 8197,46 rue d’Ulm, 75005 Paris (France); Backović, Mihailo [Center for Cosmology, Particle Physics and Phenomenology - CP3,Universite Catholique de Louvain,Louvain-la-neuve (Belgium); Cliche, Mathieu [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States); Lee, Seung J. [Department of Physics, Korea Advanced Institute of Science and Technology,335 Gwahak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); School of Physics, Korea Institute for Advanced Study,Seoul 130-722 (Korea, Republic of); Perelstein, Maxim [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States)

2015-07-17

Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image' of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p{sub T} in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

Application of ANN and fuzzy logic algorithms for streamflow ...

Indian Academy of Sciences (India)

1Department of Soil and Water Engineering, College of Technology and Engineering, Maharana Pratap. University of ... It was found that, ANN model performance improved with increasing .... algorithm uses supervised learning that provides.

Integration of multi-technology on oil spill emergency preparedness.

Science.gov (United States)

Liao, Zhenliang; Hannam, Phillip M; Xia, Xiaowei; Zhao, Tingting

2012-10-01

This paper focuses on the integration of technologies including Case-Based Reasoning (CBR), Genetic Algorithm (GA) and Artificial Neural Network (ANN) for establishing emergency preparedness for oil spill accidents. In CBR, the Frame method is used to define case representation, and the HEOM (Heterogeneous Euclidean-Overlap Metric) is improved to define the similarity of case properties. In GA, we introduce an Improved Genetic Algorithm (IGA) that achieves case adaptation, in which technologies include the Multi-Parameter Cascade Code method, the Small Section method for generation of an initial population, the Multi-Factor Integrated Fitness Function, and Niche technology for genetic operations including selection, crossover, and mutation. In ANN, a modified back-propagation algorithm is employed to train the algorithm to quickly improve system preparedness. Through the analysis of 32 fabricated oil spill cases, an oil spill emergency preparedness system based on the integration of CBR, GA and ANN is introduced. In particular, the development of ANN is presented and analyzed. The paper also discusses the efficacy of our integration approach. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model

International Nuclear Information System (INIS)

Koutroumanidis, Theodoros; Ioannou, Konstantinos; Arabatzis, Garyfallos

2009-01-01

Throughout history, energy resources have acquired a strategic significance for the economic growth and social welfare of any country. The large-scale oil crisis of 1973 coupled with various environmental protection issues, have led many countries to look for new, alternative energy sources. Biomass and fuelwood in particular, constitutes a major renewable energy source (RES) that can make a significant contribution, as a substitute for oil. This paper initially provides a description of the contribution of renewable energy sources to the production of electricity, and also examines the role of forests in the production of fuelwood in Greece. Following this, autoregressive integrated moving average (ARIMA) models, artificial neural networks (ANN) and a hybrid model are used to predict the future selling prices of the fuelwood (from broadleaved and coniferous species) produced by Greek state forest farms. The use of the ARIMA-ANN hybrid model provided the optimum prediction results, thus enabling decision-makers to proceed with a more rational planning for the production and fuelwood market. (author)

Neuropathological findings processed by artificial neural networks (ANNs can perfectly distinguish Alzheimer's patients from controls in the Nun Study

Directory of Open Access Journals (Sweden)

Snowdon David

2007-06-01

Full Text Available Abstract Background Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old. The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs analysis Methods The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts. Results By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same. Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance

Quick and reliable estimation of power distribution in a PHWR by ANN

International Nuclear Information System (INIS)

Dubey, B.P.; Jagannathan, V.; Kataria, S.K.

1998-01-01

Knowledge of the distribution of power in all the channels of a Pressurised Heavy Water Reactor (PHWR) as a result of a perturbation caused by one or more of the regulating devices is very important from the operation and maintenance point of view of the reactor. Theoretical design codes available for this purpose take several minutes to calculate the channel power distribution on modern PCs. Artificial Neural networks (ANNs) have been employed in predicting channel power distribution of Indian PHWRs for any given configuration of regulating devices of the reactor. ANNs produce the result much faster and with good accuracy. This paper describes the methodology of ANN, its reliability, the validation range, and scope for its possible on-line use in the actual reactor

Proceedings of the Neural Network Workshop for the Hanford Community

Energy Technology Data Exchange (ETDEWEB)

Keller, P.E.

1994-01-01

These proceedings were generated from a series of presentations made at the Neural Network Workshop for the Hanford Community. The abstracts and viewgraphs of each presentation are reproduced in these proceedings. This workshop was sponsored by the Computing and Information Sciences Department in the Molecular Science Research Center (MSRC) at the Pacific Northwest Laboratory (PNL). Artificial neural networks constitute a new information processing technology that is destined within the next few years, to provide the world with a vast array of new products. A major reason for this is that artificial neural networks are able to provide solutions to a wide variety of complex problems in a much simpler fashion than is possible using existing techniques. In recognition of these capabilities, many scientists and engineers are exploring the potential application of this new technology to their fields of study. An artificial neural network (ANN) can be a software simulation, an electronic circuit, optical system, or even an electro-chemical system designed to emulate some of the brain`s rudimentary structure as well as some of the learning processes that are believed to take place in the brain. For a very wide range of applications in science, engineering, and information technology, ANNs offer a complementary and potentially superior approach to that provided by conventional computing and conventional artificial intelligence. This is because, unlike conventional computers, which have to be programmed, ANNs essentially learn from experience and can be trained in a straightforward fashion to carry out tasks ranging from the simple to the highly complex.

Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN)

International Nuclear Information System (INIS)

Hosseinpour, Soleiman; Aghbashlo, Mortaza; Tabatabaei, Meisam; Khalife, Esmail

2016-01-01

Highlights: • Estimating the biodiesel CN from its FAMEs profile using ANN-based PLS approach. • Comparing the capability of ANN-adapted PLS approach with the standard PLS model. • Exact prediction of biodiesel CN from it FAMEs profile using ANN-based PLS method. • Developing an easy-to-use software using ANN-PLS model for computing the biodiesel CN. - Abstract: Cetane number (CN) is among the most important properties of biodiesel because it quantifies combustion speed or in better words, ignition quality. Experimental measurement of biodiesel CN is rather laborious and expensive. However, the high proportionality of biodiesel fatty acid methyl esters (FAMEs) profile with its CN is very appealing to develop straightforward and inexpensive computerized tools for biodiesel CN estimation. Unfortunately, correlating the chemical structure of biodiesel to its CN using conventional statistical and mathematical approaches is very difficult. To solve this issue, partial least square (PLS) adapted by artificial neural network (ANN) was introduced and examined herein as an innovative approach for the exact estimation of biodiesel CN from its FAMEs profile. In the proposed approach, ANN paradigm was used for modeling the inner relation between the input and the output PLS score vectors. In addition, the capability of the developed method in predicting the biodiesel CN was compared with the basal PLS method. The accuracy of the developed approaches for computing the biodiesel CN was assessed using three statistical criteria, i.e., coefficient of determination (R"2), mean-squared error (MSE), and percentage error (PE). The ANN-adapted PLS method predicted the biodiesel CN with an R"2 value higher than 0.99 demonstrating the fidelity of the developed model over the classical PLS method with a markedly lower R"2 value of about 0.85. In order to facilitate the use of the proposed model, an easy-to-use computer program was also developed on the basis of ANN-adapted PLS

Application of artificial neural networks in analysis of CHF experimental data in round tubes

International Nuclear Information System (INIS)

Huang Yanping; Chen Bingde; Lang Xuemei; Wang Xiaojun; Shan Jianqiang; Jia Dounan

2004-01-01

Artificial neural networks (ANNs) are applied successfully to analyze the critical heat flux (CHF) experimental data from some round tubes in this paper. A set of software adopting artificial neural network method for predicting CHF in round tube and a set of CHF database are gotten. Comparing with common CHF correlations and CHF look-up table, ANN method has stronger ability of allow-wrong and nice robustness. The CHF predicting software adopting artificial neural network technology can improve the predicting accuracy in a wider parameter range, and is easier to update and to use. The artificial neural network method used in this paper can be applied to some similar physical problems. (authors)

Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

Directory of Open Access Journals (Sweden)

Christopher E Hart

2006-12-01

Full Text Available A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes are explicitly disfavored in one network module (G2, relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of

Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy

Directory of Open Access Journals (Sweden)

Maytham S. Ahmed

2016-09-01

Full Text Available Demand response (DR program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is proposed using the hybrid lightning search algorithm (LSA-based artificial neural network (ANN to predict the optimal ON/OFF status for home appliances. Consequently, the scheduled operation of several appliances is improved in terms of cost savings. In the proposed approach, a set of the most common residential appliances are modeled, and their activation is controlled by the hybrid LSA-ANN based home energy management scheduling controller. Four appliances, namely, air conditioner, water heater, refrigerator, and washing machine (WM, are developed by Matlab/Simulink according to customer preferences and priority of appliances. The ANN controller has to be tuned properly using suitable learning rate value and number of nodes in the hidden layers to schedule the appliances optimally. Given that finding proper ANN tuning parameters is difficult, the LSA optimization is hybridized with ANN to improve the ANN performances by selecting the optimum values of neurons in each hidden layer and learning rate. Therefore, the ON/OFF estimation accuracy by ANN can be improved. Results of the hybrid LSA-ANN are compared with those of hybrid particle swarm optimization (PSO based ANN to validate the developed algorithm. Results show that the hybrid LSA-ANN outperforms the hybrid PSO based ANN. The proposed scheduling algorithm can significantly reduce the peak-hour energy consumption during the DR event by up to 9.7138% considering four appliances per 7-h period.

Research on NGN network control technology

Science.gov (United States)

Li, WenYao; Zhou, Fang; Wu, JianXue; Li, ZhiGuang

2004-04-01

Nowadays NGN (Next Generation Network) is the hotspot for discussion and research in IT section. The NGN core technology is the network control technology. The key goal of NGN is to realize the network convergence and evolution. Referring to overlay network model core on Softswitch technology, circuit switch network and IP network convergence realized. Referring to the optical transmission network core on ASTN/ASON, service layer (i.e. IP layer) and optical transmission convergence realized. Together with the distributing feature of NGN network control technology, on NGN platform, overview of combining Softswitch and ASTN/ASON control technology, the solution whether IP should be the NGN core carrier platform attracts general attention, and this is also a QoS problem on NGN end to end. This solution produces the significant practical meaning on equipment development, network deployment, network design and optimization, especially on realizing present network smooth evolving to the NGN. This is why this paper puts forward the research topic on the NGN network control technology. This paper introduces basics on NGN network control technology, then proposes NGN network control reference model, at the same time describes a realizable network structure of NGN. Based on above, from the view of function realization, NGN network control technology is discussed and its work mechanism is analyzed.

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

Science.gov (United States)

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

Prediction of Splitting Tensile Strength of Concrete Containing Zeolite and Diatomite by ANN

Directory of Open Access Journals (Sweden)

E. Gülbandılar

2017-01-01

Full Text Available This study was designed to investigate with two different artificial neural network (ANN prediction model for the behavior of concrete containing zeolite and diatomite. For purpose of constructing this model, 7 different mixes with 63 specimens of the 28, 56 and 90 days splitting tensile strength experimental results of concrete containing zeolite, diatomite, both zeolite and diatomite used in training and testing for ANN systems was gathered from the tests. The data used in the ANN models are arranged in a format of seven input parameters that cover the age of samples, Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer and an output parameter which is splitting tensile strength of concrete. In the model, the training and testing results have shown that two different ANN systems have strong potential as a feasible tool for predicting 28, 56 and 90 days the splitting tensile strength of concrete containing zeolite and diatomite.

Optimization Study of Hydrogen Gas Adsorption on Zig-zag Single-walled Carbon Nanotubes: The Artificial Neural Network Analysis

Science.gov (United States)

Nasruddin; Lestari, M.; Supriyadi; Sholahudin

2018-03-01

The use of hydrogen gas in fuel cell technology has a huge opportunity to be applied in upcoming vehicle technology. One of the most important problems in fuel cell technology is the hydrogen storage. The adsorption of hydrogen in carbon-based materials attracts a lot of attention because of its reliability. This study investigated the adsorption of hydrogen gas in Single-walled Carbon Nano Tubes (SWCNT) with chilarity of (0, 12), (0, 15), and (0, 18) to find the optimum chilarity. Artificial Neural Networks (ANN) can be used to predict the hydrogen storage capacity at different pressure and temperature conditions appropriately, using simulated series of data. The Artificial Neural Network is modeled as a predictor of the hydrogen adsorption capacity which provides solutions to some deficiencies in molecular dynamics (MD) simulations. In a previous study, ANN configurations have been developed for 77k, 233k, and 298k temperatures in hydrogen gas storage. To prepare this prediction, ANN is modeled to find out the configurations that exist in the set of training and validation of specified data selection, the distance between data, and the number of neurons that produce the smallest error. This configuration is needed to make an accurate artificial neural network. The configuration of neural network was then applied to this research. The neural network analysis results show that the best configuration of artificial neural network in hydrogen storage is at 233K temperature i.e. on SWCNT with chilarity of (0.12).

An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran

Directory of Open Access Journals (Sweden)

Mahdi Saadat

2014-02-01

Full Text Available Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings. In this paper, an attempt has been made to present an application of artificial neural network (ANN to predict the blast-induced ground vibration of the Gol-E-Gohar (GEG iron mine, Iran. A four-layer feed-forward back propagation multi-layer perceptron (MLP was used and trained with Levenberg–Marquardt algorithm. To construct ANN models, the maximum charge per delay, distance from blasting face to monitoring point, stemming and hole depth were taken as inputs, whereas peak particle velocity (PPV was considered as an output parameter. A database consisting of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R2 and mean square error (MSE were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R2 of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models.

Simulation model of ANN based maximum power point tracking controller for solar PV system

Energy Technology Data Exchange (ETDEWEB)

Rai, Anil K.; Singh, Bhupal [Department of Electrical and Electronics Engineering, Ajay Kumar Garg Engineering College, Ghaziabad 201009 (India); Kaushika, N.D.; Agarwal, Niti [School of Research and Development, Bharati Vidyapeeth College of Engineering, A-4 Paschim Vihar, New Delhi 110063 (India)

2011-02-15

In this paper the simulation model of an artificial neural network (ANN) based maximum power point tracking controller has been developed. The controller consists of an ANN tracker and the optimal control unit. The ANN tracker estimates the voltages and currents corresponding to a maximum power delivered by solar PV (photovoltaic) array for variable cell temperature and solar radiation. The cell temperature is considered as a function of ambient air temperature, wind speed and solar radiation. The tracker is trained employing a set of 124 patterns using the back propagation algorithm. The mean square error of tracker output and target values is set to be of the order of 10{sup -5} and the successful convergent of learning process takes 1281 epochs. The accuracy of the ANN tracker has been validated by employing different test data sets. The control unit uses the estimates of the ANN tracker to adjust the duty cycle of the chopper to optimum value needed for maximum power transfer to the specified load. (author)

WEPP and ANN models for simulating soil loss and runoff in a semi-arid Mediterranean region.

Science.gov (United States)

Albaradeyia, Issa; Hani, Azzedine; Shahrour, Isam

2011-09-01

This paper presents the use of both the Water Erosion Prediction Project (WEPP) and the artificial neural network (ANN) for the prediction of runoff and soil loss in the central highland mountainous of the Palestinian territories. Analyses show that the soil erosion is highly dependent on both the rainfall depth and the rainfall event duration rather than on the rainfall intensity as mostly mentioned in the literature. The results obtained from the WEPP model for the soil loss and runoff disagree with the field data. The WEPP underestimates both the runoff and soil loss. Analyses conducted with the ANN agree well with the observation. In addition, the global network models developed using the data of all the land use type show a relatively unbiased estimation for both runoff and soil loss. The study showed that the ANN model could be used as a management tool for predicting runoff and soil loss.

Intelligent MRTD testing for thermal imaging system using ANN

Science.gov (United States)

Sun, Junyue; Ma, Dongmei

2006-01-01

The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task, for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type, the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP, but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly, we use frame grabber to capture the 4-bar target image data. Then according to image gray scale, we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets, along with known target visibility, are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm, demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.

The Segmentation of Point Clouds with K-Means and ANN (artifical Neural Network)

Science.gov (United States)

Kuçak, R. A.; Özdemir, E.; Erol, S.

2017-05-01

Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

THE SEGMENTATION OF POINT CLOUDS WITH K-MEANS AND ANN (ARTIFICAL NEURAL NETWORK

Directory of Open Access Journals (Sweden)

R. A. Kuçak

2017-05-01

Full Text Available Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM which is a type of ANN (Artificial Neural Network segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Institute of Scientific and Technical Information of China (English)

Xia Hua; Gang Zhang; Jiawei Yang; Zhengyuan Li

2015-01-01

Aiming at the low accuracy problem of power system short⁃term load forecasting by traditional methods, a back⁃propagation artifi⁃cial neural network (BP⁃ANN) based method for short⁃term load forecasting is presented in this paper. The forecast points are re⁃lated to prophase adjacent data as well as the periodical long⁃term historical load data. Then the short⁃term load forecasting model of Shanxi Power Grid (China) based on BP⁃ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP⁃ANN method is simple and with higher precision and practicality.

Artificial neural network modelling

CERN Document Server

Samarasinghe, Sandhya

2016-01-01

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

Digital Repository Service at National Institute of Oceanography (India)

Mandal, S.; SubbaRao; Harish, N.; Lokesha

Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models...

Intelligent neural network diagnostic system

International Nuclear Information System (INIS)

Mohamed, A.H.

2010-01-01

Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

Development and Application of ANN Model for Worker Assignment into Virtual Cells of Large Sized Configurations

International Nuclear Information System (INIS)

Murali, R. V.; Fathi, Khalid; Puri, A. B.

2010-01-01

This paper presents an extended version of study already undertaken on development of an artificial neural networks (ANNs) model for assigning workforce into virtual cells under virtual cellular manufacturing systems (VCMS) environments. Previously, the same authors have introduced this concept and applied it to virtual cells of two-cell configuration and the results demonstrated that ANNs could be a worth applying tool for carrying out workforce assignments. In this attempt, three-cell configurations problems are considered for worker assignment task. Virtual cells are formed under dual resource constraint (DRC) context in which the number of available workers is less than the total number of machines available. Since worker assignment tasks are quite non-linear and highly dynamic in nature under varying inputs and conditions and, in parallel, ANNs have the ability to model complex relationships between inputs and outputs and find similar patterns effectively, an attempt was earlier made to employ ANNs into the above task. In this paper, the multilayered perceptron with feed forward (MLP-FF) neural network model has been reused for worker assignment tasks of three-cell configurations under DRC context and its performance at different time periods has been analyzed. The previously proposed worker assignment model has been reconfigured and cell formation solutions available for three-cell configuration in the literature are used in combination to generate datasets for training ANNs framework. Finally, results of the study have been presented and discussed.

Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells.

Science.gov (United States)

Yetilmezsoy, Kaan; Demirel, Sevgi

2008-05-30

A three-layer artificial neural network (ANN) model was developed to predict the efficiency of Pb(II) ions removal from aqueous solution by Antep pistachio (Pistacia Vera L.) shells based on 66 experimental sets obtained in a laboratory batch study. The effect of operational parameters such as adsorbent dosage, initial concentration of Pb(II) ions, initial pH, operating temperature, and contact time were studied to optimise the conditions for maximum removal of Pb(II) ions. On the basis of batch test results, optimal operating conditions were determined to be an initial pH of 5.5, an adsorbent dosage of 1.0 g, an initial Pb(II) concentration of 30 ppm, and a temperature of 30 degrees C. Experimental results showed that a contact time of 45 min was generally sufficient to achieve equilibrium. After backpropagation (BP) training combined with principal component analysis (PCA), the ANN model was able to predict adsorption efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 11 neurons and a linear transfer function (purelin) at output layer. The Levenberg-Marquardt algorithm (LMA) was found as the best of 11 BP algorithms with a minimum mean squared error (MSE) of 0.000227875. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient of about 0.936 for five model variables used in this study.

Anne Fine

Directory of Open Access Journals (Sweden)

Philip Gaydon

2015-04-01

Full Text Available An interview with Anne Fine with an introduction and aside on the role of children’s literature in our lives and development, and our adult perceptions of the suitability of childhood reading material. Since graduating from Warwick in 1968 with a BA in Politics and History, Anne Fine has written over fifty books for children and eight for adults, won the Carnegie Medal twice (for Goggle-Eyes in 1989 and Flour Babies in 1992, been a highly commended runner-up three times (for Bill’s New Frock in 1989, The Tulip Touch in 1996, and Up on Cloud Nine in 2002, been shortlisted for the Hans Christian Andersen Award (the highest recognition available to a writer or illustrator of children’s books, 1998, undertaken the positon of Children’s Laureate (2001-2003, and been awarded an OBE for her services to literature (2003. Warwick presented Fine with an Honorary Doctorate in 2005. Philip Gaydon’s interview with Anne Fine was recorded as part of the ‘Voices of the University’ oral history project, co-ordinated by Warwick’s Institute of Advanced Study.

ANN-based wavelet analysis for predicting electrical signal from photovoltaic power supply system

Energy Technology Data Exchange (ETDEWEB)

Mellit, A. [Medea Univ., Medea (Algeria). Inst. of Science Engineering, Dept. of Electronics

2007-07-01

This study was conducted to predict different electrical signals from a photovoltaic power supply system (PVPS) using an artificial neural networks (ANN) with wavelet analysis. It involved the creation of a database of electrical signals (PV-generator current, voltage, battery current voltage, regulator current and voltage) obtained from an experimental PVPS system installed in the south of Algeria. The potential applications were for sizing and analyzing the performance of PVPS systems; control of maximum power point tracker (MPPT) in order to deliver the maximum energy from the PV-array; prediction of the optimal configuration (PV-array and battery sizing) of PVPS systems; expert configuration of PV-systems; faults diagnosis; supervision; and, control and monitoring. First, based on the wavelet analysis each electrical signal was mapped in several time frequency domains. The PV-system was then divided into 3-subsystems corresponding to ANN-PV generator model, ANN-battery model, and ANN-regulator model. An example of day-by-day prediction for each electrical signal was presented. The results of the proposed approach were in good agreement with experimental results. In addition, the accuracy of the proposed approach was more satisfactory when only ANN was used. It was concluded that this methodology offers the possibility of developing a new expert configuration of PVPS by implementing the soft computing ANN-wavelet program with a digital signal processing (DSP) circuit. 26 refs., 1 tab., 5 figs.

Ann Tenno salapaigad / Margit Tõnson

Index Scriptorium Estoniae

Tõnson, Margit, 1978-

2011-01-01

Fotograaf Ann Tenno aiandushuvist, pildistamisest maailma erinevates paikades. Uutest suundadest (fototöötlus, fractal art, soojuskaameraga pildistamine) tema loomingus. Katkendeid Ann Tenno 2010. aastal ilmunud proosaraamatust "Üle unepiiri"

Development of ANN-based models to predict the static response and dynamic response of a heat exchanger in a real MVAC system

International Nuclear Information System (INIS)

Hu Qinhua; So, Albert T P; Tse, W L; Ren, Qingchang

2005-01-01

This paper presents a systematic approach to develop artificial neural network (ANN) models to predict the performance of a heat exchanger operating in real mechanical ventilation and air-conditioning (MVAC) system. Two approaches were attempted and presented. Every detailed components of the MVAC system have been considered and we attempt to model each of them by one ANN. This study used the neural network technique to obtain a static and a dynamic model for a heat exchanger mounted in an air handler unit (AHU), which is the key component of the MVAC system. It has been verified that almost all of the predicted values of the ANN model were within 95% - 105% of the measured values, with a consistent mean relative error (MRE) smaller than 2.5%. The paper details our experiences in using ANNs, especially those with back-propagation (BP) structures. Also, the weights and biases of our trained-up ANN models are listed out, which serve as good reference for readers to deal with their own situations

Determining degree of roasting in cocoa beans by artificial neural network (ANN)-based electronic nose system and gas chromatography/mass spectrometry (GC/MS).

Science.gov (United States)

Tan, Juzhong; Kerr, William L

2018-08-01

Roasting is a critical step in chocolate processing, where moisture content is decreased and unique flavors and texture are developed. The determination of the degree of roasting in cocoa beans is important to ensure the quality of chocolate. Determining the degree of roasting relies on human specialists or sophisticated chemical analyses that are inaccessible to small manufacturers and farmers. In this study, an electronic nose system was constructed consisting of an array of gas sensors and used to detect volatiles emanating from cocoa beans roasted for 0, 20, 30 and 40 min. The several signals were used to train a three-layer artificial neural network (ANN). Headspace samples were also analyzed by gas chromatography/mass spectrometry (GC/MS), with 23 select volatiles used to train a separate ANN. Both ANNs were used to predict the degree of roasting of cocoa beans. The electronic nose had a prediction accuracy of 94.4% using signals from sensors TGS 813, 826, 822, 830, 830, 2620, 2602 and 2610. In comparison, the GC/MS predicted the degree of roasting with an accuracy of 95.8%. The electronic nose system is able to predict the extent of roasting, as well as a more sophisticated approach using GC/MS. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Modelling flow dynamics in water distribution networks using ...

African Journals Online (AJOL)

One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics ...

Technologies for Home Networking

DEFF Research Database (Denmark)

A broad overview of the home networking field, ranging from wireless technologies to practical applications. In the future, it is expected that private networks (e.g. home networks) will become part of the global network ecosystem, participating in sharing their own content, running IP...

A call for water-efficient technologies (an interview with dr. Anne Elings)

NARCIS (Netherlands)

Elings, A.

2012-01-01

During the recent International Flower Trade Expo in Nairobi, Wageningen UR Greenhouse Horticulture project leader and greenhouse horticulture specialist Anne Elings pointed out that Kenya is not a water scarce country but management of the same is wanting. HortiNews had a chat with him.

Interpretable neural networks with BP-SOM

NARCIS (Netherlands)

Weijters, A.J.M.M.; Bosch, van den A.P.J.; Pobil, del A.P.; Mira, J.; Ali, M.

1998-01-01

Artificial Neural Networks (ANNS) are used successfully in industry and commerce. This is not surprising since neural networks are especially competitive for complex tasks for which insufficient domain-specific knowledge is available. However, interpretation of models induced by ANNS is often

Application of Artificial Neural Networks to Complex Groundwater Management Problems

International Nuclear Information System (INIS)

Coppola, Emery; Poulton, Mary; Charles, Emmanuel; Dustman, John; Szidarovszky, Ferenc

2003-01-01

As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models

Optimization of microwave-assisted extraction of total extract, stevioside and rebaudioside-A from Stevia rebaudiana (Bertoni) leaves, using response surface methodology (RSM) and artificial neural network (ANN) modelling.

Science.gov (United States)

Ameer, Kashif; Bae, Seong-Woo; Jo, Yunhee; Lee, Hyun-Gyu; Ameer, Asif; Kwon, Joong-Ho

2017-08-15

Stevia rebaudiana (Bertoni) consists of stevioside and rebaudioside-A (Reb-A). We compared response surface methodology (RSM) and artificial neural network (ANN) modelling for their estimation and predictive capabilities in building effective models with maximum responses. A 5-level 3-factor central composite design was used to optimize microwave-assisted extraction (MAE) to obtain maximum yield of target responses as a function of extraction time (X 1 : 1-5min), ethanol concentration, (X 2 : 0-100%) and microwave power (X 3 : 40-200W). Maximum values of the three output parameters: 7.67% total extract yield, 19.58mg/g stevioside yield, and 15.3mg/g Reb-A yield, were obtained under optimum extraction conditions of 4min X 1 , 75% X 2 , and 160W X 3 . The ANN model demonstrated higher efficiency than did the RSM model. Hence, RSM can demonstrate interaction effects of inherent MAE parameters on target responses, whereas ANN can reliably model the MAE process with better predictive and estimation capabilities. Copyright © 2017. Published by Elsevier Ltd.

Annely Peebo kutsus presidendi kontserdile / Maria Ulfsak

Index Scriptorium Estoniae

Ulfsak, Maria, 1981-

2003-01-01

Laulja Anneli Peebo kohtus president Arnold Rüütliga, et anda üle kutse Andrea Bocelli ja Annely Peebo ühiskontserdile. Vt. samas: Andrea Bocelli ja Annely Peebo kontsert Tallinna lauluväljakul 23. augustil; Andrea Bocelli

Implementation of ANN on CCHP system to predict trigeneration performance with consideration of various operative factors

International Nuclear Information System (INIS)

Anvari, Simin; Taghavifar, Hadi; Saray, Rahim Khoshbakhti; Khalilarya, Shahram; Jafarmadar, Samad

2015-01-01

Highlights: • ANN modeling tool was implemented on the CCHP system. • The best ANN topology was detected 10–8–9 with Levenberg–Marquadt algorithm. • The system is more sensitive of CC outlet temperature and turbine isentropic efficiency. • The lowest RMSE = 3.13e−5 and the best R 2 = 0.999 is related to lambda and second law efficiency terms, respectively. - Abstract: A detailed investigation was aimed based on numerical thermodynamic survey and artificial neural network (ANN) modeling of the trigeneration system. The results are presented in two pivotal frameworks namely the sensitivity analysis and ANN prediction capability of proposed modeling. The underlying operative parameters were chosen as input parameters from different cycles and components, while the exergy efficiency, exergy loss, coefficient of performance, heating load exergy, lambda, gas turbine power, exergy destruction, actual outlet air compressor temperature, and heat recovery gas steam generator (HRSG) outlet temperature were taken as objective output parameters for the modeling purpose. Up to now, no significant step was taken to investigate the compound power plant with thermodynamic analyses and network predictability hybrid in such a detailed oriented approach. It follows that multilayer perceptron neural network with back propagation algorithm deployed with 10–8–9 configuration results in the modeling reliability ranged within R 2 = 0.995–0.999. When dataset treated with trainlm learning algorithm and diversified neurons, the mean square error (MSE) is obtained equal to 0.2175. This denotes a powerful modeling achievement in both scientific and industrial scale to save considerable computational cost on combined cooling, heating, and power system in accomplishment of boosting the energy efficiency and system maintenance

Toward automatic time-series forecasting using neural networks.

Science.gov (United States)

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

Effectiveness of ANN for seismic behaviour prediction considering geometric configuration effect in concrete gravity dams

Directory of Open Access Journals (Sweden)

Mohd. Saqib

2016-09-01

Full Text Available In this study, an Artificial Neural Networks (ANN model is built and verified for quick estimation of the structural parameter obtained for a concrete gravity dam section due to seismic excitation. The database of numerous inputs and outputs obtained through Abaqus which are further converted into dimensionless forms in the statistical software (MATLAB to build the ANN model. The developed model can be used for accurate estimation of this parameter. The results showed an excellent capability of the model to predict the outputs with high accuracy and reduced computational time.

Neural Networks to model the innovativeness perception of co-creative firms

DEFF Research Database (Denmark)

Tanev, Stoyan

2012-01-01

contribution is to make a quantitative analysis in order to assess the relationship between value co-creation and innovation in technology-driven firms: we are using Artificial Neural Network (ANN) to investigate the relationship between value co-creation and innovativeness, and Self Organising Map (SOM) models...

Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

OpenAIRE

Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

2014-01-01

Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

Ann Modeling for Grey Particles Produced from Interactions of Different Projectiles with Emulsion Nuclei at 4.5 AGEV/C

International Nuclear Information System (INIS)

El-Bakry, M.N.Y.; Basha, A.M.; Rashed, N.; Mahmoud, M.A.; Radi, A.

2008-01-01

Artificial Neural Network (ANN) is one of the important tools in high energy physics. In this paper, we are using ANN for modeling the multiplicity distributions of grey particles produced from interactions of P, 3 He, 4 He, 6 Li, 12 C, 24 Mg, and 32 S with emulsion nuclei, light nuclei (CNO), and heavy nuclei (Ag Br). The equations of these distributions were obtained

Using neural networks for prediction of nuclear parameters

Energy Technology Data Exchange (ETDEWEB)

Pereira Filho, Leonidas; Souto, Kelling Cabral, E-mail: leonidasmilenium@hotmail.com, E-mail: kcsouto@bol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ (Brazil); Machado, Marcelo Dornellas, E-mail: dornemd@eletronuclear.gov.br [Eletrobras Termonuclear S.A. (GCN.T/ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear

2013-07-01

Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

Using neural networks for prediction of nuclear parameters

International Nuclear Information System (INIS)

Pereira Filho, Leonidas; Souto, Kelling Cabral; Machado, Marcelo Dornellas

2013-01-01

Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

Comparison of ANN and RKS approaches to model SCC strength

Science.gov (United States)

Prakash, Aravind J.; Sathyan, Dhanya; Anand, K. B.; Aravind, N. R.

2018-02-01

Self compacting concrete (SCC) is a high performance concrete that has high flowability and can be used in heavily reinforced concrete members with minimal compaction segregation and bleeding. The mix proportioning of SCC is highly complex and large number of trials are required to get the mix with the desired properties resulting in the wastage of materials and time. The research on SCC has been highly empirical and no theoretical relationships have been developed between the mixture proportioning and engineering properties of SCC. In this work effectiveness of artificial neural network (ANN) and random kitchen sink algorithm(RKS) with regularized least square algorithm(RLS) in predicting the split tensile strength of the SCC is analysed. Random kitchen sink algorithm is used for mapping data to higher dimension and classification of this data is done using Regularized least square algorithm. The training and testing data for the algorithm was obtained experimentally using standard test procedures and materials available. Total of 40 trials were done which were used as the training and testing data. Trials were performed by varying the amount of fine aggregate, coarse aggregate, dosage and type of super plasticizer and water. Prediction accuracy of the ANN and RKS model is checked by comparing the RMSE value of both ANN and RKS. Analysis shows that eventhough the RKS model is good for large data set, its prediction accuracy is as good as conventional prediction method like ANN so the split tensile strength model developed by RKS can be used in industries for the proportioning of SCC with tailor made property.

Improvement of radiation dose estimation due to nuclear accidents using deep neural network and GPU

Energy Technology Data Exchange (ETDEWEB)

Desterro, Filipe S.M.; Almeida, Adino A.H.; Pereira, Claudio M.N.A., E-mail: filipesantana18@gmail.com, E-mail: adino@ien.gov.br, E-mail: cmcoelho@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

2017-07-01

Recently, the use of mobile devices has been proposed for dose assessment during nuclear accidents. The idea is to support field teams, providing an approximated estimation of the dose distribution map in the vicinity of the nuclear power plant (NPP), without needing to be connected to the NPP systems. In order to provide such stand-alone execution, the use of artificial neural networks (ANN) has been proposed in substitution of the complex and time consuming physical models executed by the atmospheric dispersion radionuclide (ADR) system. One limitation observed on such approach is the very time-consuming training of the ANNs. Moreover, if the number of input parameters increases the performance of standard ANNs, like Multilayer-Perceptron (MLP) with backpropagation training, is affected leading to unreasonable training time. To improve learning, allowing better dose estimations, more complex ANN architectures are required. ANNs with many layers (much more than a typical number of layers), referred to as Deep Neural Networks (DNN), for example, have demonstrating to achieve better results. On the other hand, the training of such ANNs is very much slow. In order to allow the use of such DNNs in a reasonable training time, a parallel programming solution, using Graphic Processing Units (GPU) and Computing Unified Device Architecture (CUDA) is proposed. This work focuses on the study of computational technologies for improvement of the ANNs to be used in the mobile application, as well as their training algorithms. (author)

Improvement of radiation dose estimation due to nuclear accidents using deep neural network and GPU

International Nuclear Information System (INIS)

Desterro, Filipe S.M.; Almeida, Adino A.H.; Pereira, Claudio M.N.A.

2017-01-01

Recently, the use of mobile devices has been proposed for dose assessment during nuclear accidents. The idea is to support field teams, providing an approximated estimation of the dose distribution map in the vicinity of the nuclear power plant (NPP), without needing to be connected to the NPP systems. In order to provide such stand-alone execution, the use of artificial neural networks (ANN) has been proposed in substitution of the complex and time consuming physical models executed by the atmospheric dispersion radionuclide (ADR) system. One limitation observed on such approach is the very time-consuming training of the ANNs. Moreover, if the number of input parameters increases the performance of standard ANNs, like Multilayer-Perceptron (MLP) with backpropagation training, is affected leading to unreasonable training time. To improve learning, allowing better dose estimations, more complex ANN architectures are required. ANNs with many layers (much more than a typical number of layers), referred to as Deep Neural Networks (DNN), for example, have demonstrating to achieve better results. On the other hand, the training of such ANNs is very much slow. In order to allow the use of such DNNs in a reasonable training time, a parallel programming solution, using Graphic Processing Units (GPU) and Computing Unified Device Architecture (CUDA) is proposed. This work focuses on the study of computational technologies for improvement of the ANNs to be used in the mobile application, as well as their training algorithms. (author)

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

Science.gov (United States)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Using ANN and EPR models to predict carbon monoxide concentrations in urban area of Tabriz

Directory of Open Access Journals (Sweden)

Mohammad Shakerkhatibi

2015-09-01

Full Text Available Background: Forecasting of air pollutants has become a popular topic of environmental research today. For this purpose, the artificial neural network (AAN technique is widely used as a reliable method for forecasting air pollutants in urban areas. On the other hand, the evolutionary polynomial regression (EPR model has recently been used as a forecasting tool in some environmental issues. In this research, we compared the ability of these models to forecast carbon monoxide (CO concentrations in the urban area of Tabriz city. Methods: The dataset of CO concentrations measured at the fixed stations operated by the East Azerbaijan Environmental Office along with meteorological data obtained from the East Azerbaijan Meteorological Bureau from March 2007 to March 2013, were used as input for the ANN and EPR models. Results: Based on the results, the performance of ANN is more reliable in comparison with EPR. Using the ANN model, the correlation coefficient values at all monitoring stations were calculated above 0.85. Conversely, the R2 values for these stations were obtained <0.41 using the EPR model. Conclusion: The EPR model could not overcome the nonlinearities of input data. However, the ANN model displayed more accurate results compared to the EPR. Hence, the ANN models are robust tools for predicting air pollutant concentrations.

Before the year 2000: Artificial neural networks may set the standard

International Nuclear Information System (INIS)

Michal, R.A.

1994-01-01

The use of artifical neural networks (ANNs) for monitoring of equipment and components in nuclear power plants could be commonplace before the turn of the century. Within five years, the relative inexpensiveness of neural networks could usher in a technology that will be used to detect incipient faults in machinery and increase effectiveness of maintenance scheduling. Working since November 1992 with the Electric Power Research Institute on research and development of the technology, SynEx and another Virginia-based company, A ampersand T, Inc., will later this year demonstrate prototype ANN systems at Consolidated Edison Company and New York State Electric Gas fossil fuel power plants. (Fossil fuel plants were chosen for the project because of easier access, as opposed to the security measures in place at nuclear facilities.) The demonstration will utilize sensors and the neural network systems to detect abnormal equipment behavior, sending signals back to centralized monitoring boards located in each plant's control room. The cost of the project, including research and development, will reach $1 million. However, the cost of installing a neural network at a nuclear plant within the next five years, according to Birdsall, could be as low as $10,000 to $15,000, with hopes of reducing the expenditure to just $5000

Control of GMA Butt Joint Welding Based on Neural Networks

DEFF Research Database (Denmark)

Christensen, Kim Hardam; Sørensen, Torben

2004-01-01

This paper presents results from an experimentally based research on Gas Metal Arc Welding (GMAW), controlled by the artificial neural network (ANN) technology. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a high degree of quality......-linear least square error minimization, has been used with the back-propagation algorithm for training the network, while a Bayesian regularization technique has been successfully applied for minimizing the risk of inexpedient over-training....

Artificial Neural Networks For Hadron Hadron Cross-sections

International Nuclear Information System (INIS)

ELMashad, M.; ELBakry, M.Y.; Tantawy, M.; Habashy, D.M.

2011-01-01

In recent years artificial neural networks (ANN ) have emerged as a mature and viable framework with many applications in various areas. Artificial neural networks theory is sometimes used to refer to a branch of computational science that uses neural networks as models to either simulate or analyze complex phenomena and/or study the principles of operation of neural networks analytically. In this work a model of hadron- hadron collision using the ANN technique is present, the hadron- hadron based ANN model calculates the cross sections of hadron- hadron collision. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness

FLASH-FLOOD MODELLING WITH ARTIFICIAL NEURAL NETWORKS USING RADAR RAINFALL ESTIMATES

Directory of Open Access Journals (Sweden)

Dinu Cristian

2017-09-01

Full Text Available The use of artificial neural networks (ANNs in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.

Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

Science.gov (United States)

Mostafa, Hesham

2017-08-01

Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

Directory of Open Access Journals (Sweden)

Igor Peško

2017-01-01

Full Text Available Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs and support vector machines (SVM are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

Novel Formulation of Adaptive MPC as EKF Using ANN Model: Multiproduct Semibatch Polymerization Reactor Case Study.

Science.gov (United States)

Kamesh, Reddi; Rani, Kalipatnapu Yamuna

2017-12-01

In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control estimation over control horizon based on minimizing the deviation of model output predictions from set points along the prediction horizon. An industrial case study for temperature control of a multiproduct semibatch polymerization reactor posed as a challenge problem has been considered as a test bed to apply the proposed ANN-EKFMPC strategy at supervisory level as a cascade control configuration along with proportional integral controller [ANN-EKFMPC with PI (ANN-EKFMPC-PI)]. The proposed approach is formulated incorporating all aspects of MPC including move suppression factor for control effort minimization and constraint-handling capability including terminal constraints. The nominal stability analysis and offset-free tracking capabilities of the proposed controller are proved. Its performance is evaluated by comparison with a standard MPC-based cascade control approach using the same adaptive ANN model. The ANN-EKFMPC-PI control configuration has shown better controller performance in terms of temperature tracking, smoother input profiles, as well as constraint-handling ability compared with the ANN-MPC with PI approach for two products in summer and winter. The proposed scheme is found to be versatile although it is based on a purely data-driven model with online parameter estimation.

Social networking services: technologies and applications

OpenAIRE

Puzyrnyy, Oleksandr

2011-01-01

Puzyrnyy, Oleksandr. 2011. Social networking services: technologies and applications. Bachelor's Thesis. Kemi-Tornio University of Applied Sciences. Business and Culture. Pages 52. The aim of this thesis is to describe the concept of social networking, its technological base, business opportunities and future perspectives. The study discovers how social networks are made and which different purposes they might have. In addition, social networking is viewed as a part of business strategy o...

Technological Developments in Networking, Education and Automation

CERN Document Server

Elleithy, Khaled; Iskander, Magued; Kapila, Vikram; Karim, Mohammad A; Mahmood, Ausif

2010-01-01

"Technological Developments in Networking, Education and Automation" includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the following areas: Computer Networks: Access Technologies, Medium Access Control, Network architectures and Equipment, Optical Networks and Switching, Telecommunication Technology, and Ultra Wideband Communications. Engineering Education and Online Learning: including development of courses and systems for engineering, technical and liberal studies programs; online laboratories; intelligent

Dynamic artificial neural networks with affective systems.

Directory of Open Access Journals (Sweden)

Catherine D Schuman

Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

Use of neural networks to monitor power plant components

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Tsoukalas, L.H.

1992-01-01

A new methodology is presented for nondestructive evaluation (NDE) of check valve performance and degradation. Artificial neural network (ANN) technology is utilized for processing frequency domain signatures of check valves operating in a nuclear power plant (NPP). Acoustic signatures obtained from different locations on a check valve are transformed from the time domain to the frequency domain and then used as input to a pretrained neural network. The neural network has been trained with data sets corresponding to normal operation, therefore establishing a basis for check valve satisfactory performance. Results obtained from the proposed methodology demonstrate the ability of neural networks to perform accurate and quick evaluations of check valve performance

Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES

International Nuclear Information System (INIS)

Muyeen, S.M.; Hasanien, Hany M.; Al-Durra, Ahmed

2014-01-01

Highlights: • We present an ANN-controlled SMES in this paper. • The objective is to enhance transient stability of WF connected to power system. • The control strategy depends on a PWM VSC and DC–DC converter. • The effectiveness of proposed controller is compared with PI controller. • The validity of the proposed system is verified by simulation results. - Abstract: This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) system to enhance the transient stability of wind farms connected to a multi-machine power system during network disturbances. The control strategy of SMES depends mainly on a sinusoidal pulse width modulation (PWM) voltage source converter (VSC) and an adaptive ANN-controlled DC–DC converter using insulated gate bipolar transistors (IGBTs). The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of proportional-integral (PI)-controlled SMES optimized by response surface methodology and genetic algorithm (RSM–GA) considering both of symmetrical and unsymmetrical faults. For realistic responses, real wind speed data and two-mass drive train model of wind turbine generator system is considered in the analyses. The validity of the proposed system is verified by the simulation results which are performed using the laboratory standard dynamic power system simulator PSCAD/EMTDC. Notably, the proposed adaptive ANN-controlled SMES enhances the transient stability of wind farms connected to a multi-machine power system

Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods

NARCIS (Netherlands)

Ziari, H.; Sobhani, J.; Ayoubinejad, J.; Hartmann, Timo

2015-01-01

Prediction of pavement condition is one of the most important issues in pavement management systems. In this paper, capabilities of artificial neural networks (ANNs) and group method of data handling (GMDH) methods in predicting flexible pavement conditions were analysed in three levels: in 1 year,

Networking Technologies and the Rate of Technological Change

Directory of Open Access Journals (Sweden)

Charles Mitchell

2005-12-01

Full Text Available Network technology is changing rapidly and those adept at ICT analysis need resolve rate of change issues. Developments in networking now are in the direction of heuristic intelligence. Since about 1980, networking techniques have encouraged combining bits of information with imagination cognitively to improve ideas about reality. ICT enterprise projects utilize networking to sustain requisite imagination. Assumptions and misassuptions of project builders are rationally comprehended as networking sustains creative processes. The monopolization of valuable network techniques influences in the direction of esoteric networking. Data presents that substantial knowledge and networking is now occurring globally. As a netaphor, networking

Artificial neural network detects human uncertainty

Science.gov (United States)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

A gentle introduction to artificial neural networks.

Science.gov (United States)

Zhang, Zhongheng

2016-10-01

Artificial neural network (ANN) is a flexible and powerful machine learning technique. However, it is under utilized in clinical medicine because of its technical challenges. The article introduces some basic ideas behind ANN and shows how to build ANN using R in a step-by-step framework. In topology and function, ANN is in analogue to the human brain. There are input and output signals transmitting from input to output nodes. Input signals are weighted before reaching output nodes according to their respective importance. Then the combined signal is processed by activation function. I simulated a simple example to illustrate how to build a simple ANN model using nnet() function. This function allows for one hidden layer with varying number of units in that layer. The basic structure of ANN can be visualized with plug-in plot.nnet() function. The plot function is powerful that it allows for varieties of adjustment to the appearance of the neural networks. Prediction with ANN can be performed with predict() function, similar to that of conventional generalized linear models. Finally, the prediction power of ANN is examined using confusion matrix and average accuracy. It appears that ANN is slightly better than conventional linear model.

A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

Directory of Open Access Journals (Sweden)

Afaz Uddin Ahmed

2014-01-01

Full Text Available An artificial neural network (ANN and affinity propagation (AP algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

Science.gov (United States)

Ahmed, Afaz Uddin; Tariqul Islam, Mohammad; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

Dynamically stable associative learning: a neurobiologically based ANN and its applications

Science.gov (United States)

Vogl, Thomas P.; Blackwell, Kim L.; Barbour, Garth; Alkon, Daniel L.

1992-07-01

Most currently popular artificial neural networks (ANN) are based on conceptions of neuronal properties that date back to the 1940s and 50s, i.e., to the ideas of McCullough, Pitts, and Hebb. Dystal is an ANN based on current knowledge of neurobiology at the cellular and subcellular level. Networks based on these neurobiological insights exhibit the following advantageous properties: (1) A theoretical storage capacity of bN non-orthogonal memories, where N is the number of output neurons sharing common inputs and b is the number of distinguishable (gray shade) levels. (2) The ability to learn, store, and recall associations among noisy, arbitrary patterns. (3) A local synaptic learning rule (learning depends neither on the output of the post-synaptic neuron nor on a global error term), some of whose consequences are: (4) Feed-forward, lateral, and feed-back connections (as well as time-sensitive connections) are possible without alteration of the learning algorithm; (5) Storage allocation (patch creation) proceeds dynamically as associations are learned (self- organizing); (6) The number of training set presentations required for learning is small (different expressions and/or corrupted by noise, and on reading hand-written digits (98% accuracy) and hand-printed Japanese Kanji (90% accuracy) is demonstrated.

Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

Directory of Open Access Journals (Sweden)

Zahid Farid

2016-01-01

Full Text Available In indoor environments, WiFi (RSS based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs. This model exploits machine learning, in particular Artificial Natural Network (ANN techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

Application of ann-based decision making pattern recognition to fishing operations

Energy Technology Data Exchange (ETDEWEB)

Akhlaghinia, M.; Torabi, F.; Wilton, R.R. [University of Regina, Saskatchewan (Canada). Faculty of Engineering. Dept. of Petroleum Engineering], e-mail: Farshid.Torabi@uregina.ca

2010-10-15

Decision making is a crucial part of fishing operations. Proper decisions should be made to prevent wasted time and associated costs on unsuccessful operations. This paper presents a novel model to help drilling managers decide when to commence and when to quit a fishing operation. A decision making model based on Artificial Neural Network (ANN) has been developed that utilizes Pattern Recognition based on 181 fishing incidents from one of the most fish-prone fields of the southwest of Iran. All parameters chosen to train the ANN-Based Pattern Recognition Tool are assumed to play a role in the success of the fishing operation and are therefore used to decide whether a fishing operation should be performed or not. If the tool deems the operation suitable for consideration, a cost analysis of the fishing operation can then be performed to justify its overall cost. (author)

Anne-Mette Langes plan for ADHD kongressen

DEFF Research Database (Denmark)

Lange, Anne-Mette

2017-01-01

http://medicinsktidsskrift.dk/behandlinger/psykiatri/699-anne-mette-langes-plan-for-adhd-kongressen.html......http://medicinsktidsskrift.dk/behandlinger/psykiatri/699-anne-mette-langes-plan-for-adhd-kongressen.html...

Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

Science.gov (United States)

Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

2015-09-15

The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

2011 : Qu'elle année !

CERN Multimedia

Staff Association

2012-01-01

« Quelle année ! Et quelle fin d’année ! La star de l’année a été le LHC, avec ses expériences, qui une fois de plus ont été sous les feux de la rampe. Mais on doit aussi citer toute une troupe d’acteurs importants, dans des domaines aussi différents que l’antimatière et l’expérience CLOUD. » Voilà ce que le Directeur général nous a écrit le 20 décembre dans son message avec ses vœux de fin d’année. Sans oublier, bien sûr, les fameux neutrinos hyperrapides vers Gran Sasso qui ont mis le CERN sur le devant de la scène mondiale. Ces succès qui font la fierté et la force de l’Organisation ont été rendus possibles «&...

Prediction of some physical and drying properties of terebinth fruit (Pistacia atlantica L.) using Artificial Neural Networks.

Science.gov (United States)

Kaveh, Mohammad; Chayjan, Reza Amiri

2014-01-01

Drying of terebinth fruit was conducted to provide microbiological stability, reduce product deterioration due to chemical reactions, facilitate storage and lower transportation costs. Because terebinth fruit is susceptible to heat, the selection of a suitable drying technology is a challenging task. Artificial neural networks (ANNs) are used as a nonlinear mapping structures for modelling and prediction of some physical and drying properties of terebinth fruit. Drying characteristics of terebinth fruit with an initial moisture content of 1.16 (d.b.) was studied in an infrared fluidized bed dryer. Different levels of air temperatures (40, 55 and 70°C), air velocities (0.93, 1.76 and 2.6 m/s) and infrared (IR) radiation powers (500, 1000 and 1500 W) were applied. In the present study, the application of Artificial Neural Network (ANN) for predicting the drying moisture diffusivity, energy consumption, shrinkage, drying rate and moisture ratio (output parameter for ANN modelling) was investigated. Air temperature, air velocity, IR radiation and drying time were considered as input parameters. The results revealed that to predict drying rate and moisture ratio a network with the TANSIG-LOGSIG-TANSIG transfer function and Levenberg-Marquardt (LM) training algorithm made the most accurate predictions for the terebinth fruit drying. The best results for ANN at predications were R2 = 0.9678 for drying rate, R2 = 0.9945 for moisture ratio, R2 = 0.9857 for moisture diffusivity and R2 = 0.9893 for energy consumption. Results indicated that artificial neural network can be used as an alternative approach for modelling and predicting of terebinth fruit drying parameters with high correlation. Also ANN can be used in optimization of the process.

An Examination of Application of Artificial Neural Network in Cognitive Radios

International Nuclear Information System (INIS)

Salau, H Bello; Onwuka, E N; Aibinu, A M

2013-01-01

Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined

An Examination of Application of Artificial Neural Network in Cognitive Radios

Science.gov (United States)

Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

2013-12-01

Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

Modelling and automatic reactive power control of isolated wind-diesel hybrid power systems using ANN

International Nuclear Information System (INIS)

Bansal, R.C.

2008-01-01

This paper presents an artificial neural network (ANN) based approach to tune the parameters of the static var compensator (SVC) reactive power controller over a wide range of typical load model parameters. The gains of PI (proportional integral) based SVC are optimised for typical values of the load voltage characteristics (n q ) by conventional techniques. Using the generated data, the method of multi-layer feed forward ANN with error back propagation training is employed to tune the parameters of the SVC. An ANN tuned SVC controller has been applied to control the reactive power of a variable slip/speed isolated wind-diesel hybrid power system. It is observed that the maximum deviations of all parameters are more for larger values of n q . It has been shown that initially synchronous generator supplies the reactive power required by the induction generator and/or load, and the latter reactive power is purely supplied by the SVC

Modelling and automatic reactive power control of isolated wind-diesel hybrid power systems using ANN

Energy Technology Data Exchange (ETDEWEB)

Bansal, R.C. [Electrical and Electronics Engineering Division, School of Engineering and Physics, The University of the South Pacific, Suva (Fiji)

2008-02-15

This paper presents an artificial neural network (ANN) based approach to tune the parameters of the static var compensator (SVC) reactive power controller over a wide range of typical load model parameters. The gains of PI (proportional integral) based SVC are optimised for typical values of the load voltage characteristics (n{sub q}) by conventional techniques. Using the generated data, the method of multi-layer feed forward ANN with error back propagation training is employed to tune the parameters of the SVC. An ANN tuned SVC controller has been applied to control the reactive power of a variable slip/speed isolated wind-diesel hybrid power system. It is observed that the maximum deviations of all parameters are more for larger values of n{sub q}. It has been shown that initially synchronous generator supplies the reactive power required by the induction generator and/or load, and the latter reactive power is purely supplied by the SVC. (author)

Efficient computation in adaptive artificial spiking neural networks

NARCIS (Netherlands)

D. Zambrano (Davide); R.B.P. Nusselder (Roeland); H.S. Scholte; S.M. Bohte (Sander)

2017-01-01

textabstractArtificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of

A computation ANN model for quantifying the global solar radiation: A case study of Al-Aqabah-Jordan

International Nuclear Information System (INIS)

Abolgasem, I M; Alghoul, M A; Ruslan, M H; Chan, H Y; Khrit, N G; Sopian, K

2015-01-01

In this paper, a computation model is developed to predict the global solar radiation (GSR) in Aqaba city based on the data recorded with association of Artificial Neural Networks (ANN). The data used in this work are global solar radiation (GSR), sunshine duration, maximum and minimum air temperature and relative humidity. These data are available from Jordanian meteorological station over a period of two years. The quality of GSR forecasting is compared by using different Learning Algorithms. The decision of changing the ANN architecture is essentially based on the predicted results to obtain the best ANN model for monthly and seasonal GSR. Different configurations patterns were tested using available observed data. It was found that the model using mainly sunshine duration and air temperature as inputs gives accurate results. The ANN model efficiency and the mean square error values show that the prediction model is accurate. It is found that the effect of the three learning algorithms on the accuracy of the prediction model at the training and testing stages for each time scale is mostly within the same accuracy range. (paper)

Applications of artificial neural networks in Nuclear Medicine

International Nuclear Information System (INIS)

Maddalena, D.J.

1993-01-01

Artificial neural networks (ANNs) are computer-based mathematical models developed to have analogous functions to idealized simple biological nervous systems. They consist of layers of processing elements, which are considered to be analogous to the nerve cells (neurons) and these are interconnected to form a network which is in essence a parallel computer even though they are most likely to be run on non-parallel computers such as personal computers or workstations. The parallel processing nature of the ANNs gives them the characteristics of speed, reliability and generalisation. The speed occurs because many bits of information can be input and analysed simultaneously. Reliability occurs because the networks can produce reasonable results even when some input data are missing or inaccurate. Generalisation is the ability of the network to estimate reasonable results when faced with new data outside its normal range of experience. There are two main classes of ANN - supervised and un-supervised. Supervised ANNs are trained to build internal algorithms relating patterns of inputs to outputs. After learning the relationship between the inputs and outputs they are able to classify patterns and make decisions of predictions based upon new patterns of inputs. The most frequently used ANN for biomedical applications is a supervised type called the back propagation ANN which has an excellent ability to predict and classify data and is becoming commonly used throughout the biomedical field. This article will discuss back propagation ANN structure. Its use for image analysis and diagnostic classification in various imaging modalities including Single Photon Emission Computed Tomography and Positron Emission Tomography 17 refs., 2 figs

Artificial neural networks as a tool in urban storm drainage

DEFF Research Database (Denmark)

Loke, E.; Warnaars, E.A.; Jacobsen, P.

1997-01-01

The introduction of Artificial Neural Networks (ANNs) as a tool in the field of urban storm drainage is discussed. Besides some basic theory on the mechanics of ANNs and a general classification of the different types of ANNs, two ANN application examples are presented: The prediction of runoff...

Development of IT-based data communication network technology

International Nuclear Information System (INIS)

Hong, Seok Boong; Jeong, K. I.; Yoo, Y. R.

2010-10-01

- Developing broadband high-reliability real-time communications technology for NPP - Developing reliability and performance validation technology for communications network - Developing security technology for NPP communications network - Developing field communications network for harsh environment of NPP - International standard registration(Oct. 28, 2009, IEC 61500

Visionary network 2030. Technology vision for future distribution network

International Nuclear Information System (INIS)

Kumpulainen, L.; Laaksonen, H.; Komulainen, R.

2006-11-01

Objective of this research was to create the long term vision of a distribution network technology to be used for the near future rebuild and necessary R and D efforts. Present status of the grid was briefly handled and created scenarios for the operational environment changes and available technology International view was used for getting familiar with the present solutions and future expectations in other countries. Centralised power generation is supposed to form the majority, but also the distributed generation will play more and more important role, which is hard to predict due to the uncertainty of the development of the regulation. Higher reliability and safety in major faults are expected from the future network with the reasonable costs. Impact of the climate change and impregnant using restrictions cause difficulties especially for the overhead lines in the forests. In the rural network also the ageing is the problem. For the urban networks the land usage and environmental issues get more challenging and the network reinforcement is necessary due to the increased use of electricity. As a result several technical solutions are available. Additions to the technology today, several new solutions were introduced. Important solutions in the future network are supposed to be the wide range of underground cable, high degree utilisation of the communication and network automation solutions, considerable shorter protection zones and new layout solution. In a long run the islanding enabled by the distributed energy systems and totally new network structures and solutions based on power electronics are supposed to improve the power quality and profitability. Separate quality classes in network design principally are also supposed to be approved. Getting into the vision needs also the Roadmap project, which coordinates and focuses the development of the industry. So the limited national development resources can be effectively utilised. A coordinated national

Software-Defined Networks as a Stage of the Network Technology Evolution

Directory of Open Access Journals (Sweden)

A. A. Krasotin

2013-01-01

Full Text Available The authors of the article focus on the concept of a software defined network. In the beginning, the brief historical account is given concerning software defined networks as a scientific concept, its formation and technological and scientific meaning. The software defined network concept is treated in the article not as the final state-of-the-art in networking, but rather as a possible step and direction in the development of a networking paradigm. The article touches on pros and cons as well of the software defined networking and gives an account of possible stages of development of this technology in the context of other technologies, considering its hybrid with MPLS, as an example. OpenFlow protocol constitutes the main part of the article. The authors further discuss various kinds of existing libraries realizing programmable management routines for a software defined network using OpenFlow. All of these libraries provide API for building modular applications for software defined network management. Touching on practical side of implementation the results of comparative tests of throughput and latency, achieved with these libraries are shown.

Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.

Science.gov (United States)

Deeb, Omar; Shaik, Basheerulla; Agrawal, Vijay K

2014-10-01

Quantitative Structure-Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models.

Survey of Promising Technologies for 5G Networks

Directory of Open Access Journals (Sweden)

Nam Tuan Le

2016-01-01

Full Text Available As an enhancement of cellular networks, the future-generation 5G network can be considered an ultra-high-speed technology. The proposed 5G network might include all types of advanced dominant technologies to provide remarkable services. Consequently, new architectures and service management schemes for different applications of the emerging technologies need to be recommended to solve issues related to data traffic capacity, high data rate, and reliability for ensuring QoS. Cloud computing, Internet of things (IoT, and software-defined networking (SDN have become some of the core technologies for the 5G network. Cloud-based services provide flexible and efficient solutions for information and communications technology by reducing the cost of investing in and managing information technology infrastructure. In terms of functionality, SDN is a promising architecture that decouples control planes and data planes to support programmability, adaptability, and flexibility in ever-changing network architectures. However, IoT combines cloud computing and SDN to achieve greater productivity for evolving technologies in 5G by facilitating interaction between the physical and human world. The major objective of this study provides a lawless vision on comprehensive works related to enabling technologies for the next generation of mobile systems and networks, mainly focusing on 5G mobile communications.

Artificial earthquake record generation using cascade neural network

Directory of Open Access Journals (Sweden)

Bani-Hani Khaldoon A.

2017-01-01

Full Text Available This paper presents the results of using artificial neural networks (ANN in an inverse mapping problem for earthquake accelerograms generation. This study comprises of two parts: 1-D site response analysis; performed for Dubai Emirate at UAE, where eight earthquakes records are selected and spectral matching are performed to match Dubai response spectrum using SeismoMatch software. Site classification of Dubai soil is being considered for two classes C and D based on shear wave velocity of soil profiles. Amplifications factors are estimated to quantify Dubai soil effect. Dubai’s design response spectra are developed for site classes C & D according to International Buildings Code (IBC -2012. In the second part, ANN is employed to solve inverse mapping problem to generate time history earthquake record. Thirty earthquakes records and their design response spectrum with 5% damping are used to train two cascade forward backward neural networks (ANN1, ANN2. ANN1 is trained to map the design response spectrum to time history and ANN2 is trained to map time history records to the design response spectrum. Generalized time history earthquake records are generated using ANN1 for Dubai’s site classes C and D, and ANN2 is used to evaluate the performance of ANN1.

Artificial neural networks approach on solar parabolic dish cooker

International Nuclear Information System (INIS)

Lokeswaran, S.; Eswaramoorthy, M.

2011-01-01

This paper presents heat transfer analysis of solar parabolic dish cooker using Artificial Neural Network (ANN). The objective of this study to envisage thermal performance parameters such as receiver plate and pot water temperatures of the solar parabolic dish cooker by using the ANN for experimental data. An experiment is conducted under two cases (1) cooker with plain receiver and (2) cooker with porous receiver. The Back Propagation (BP) algorithm is used to train and test networks and ANN predictions are compared with experimental results. Different network configurations are studied by the aid of searching a relatively better network for prediction. The results showed a good regression analysis with the correlation coefficients in the range of 0.9968-0.9992 and mean relative errors (MREs) in the range of 1.2586-4.0346% for the test data set. Thus ANN model can successfully be used for the prediction of the thermal performance parameters of parabolic dish cooker with reasonable degree of accuracy. (authors)

The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing C. I. Basic Yellow 28 by electrocoagulation process

International Nuclear Information System (INIS)

Daneshvar, N.; Khataee, A.R.; Djafarzadeh, N.

2006-01-01

In this paper, electrocoagulation has been used for removal of color from solution containing C. I. Basic Yellow 28. The effect of operational parameters such as current density, initial pH of the solution, time of electrolysis, initial dye concentration, distance between the electrodes, retention time and solution conductivity were studied in an attempt to reach higher removal efficiency. Our results showed that the increase of current density up to 80 A m -2 enhanced the color removal efficiency, the electrolysis time was 7 min and the range of pH was determined 5-8. It was found that for achieving a high color removal percent, the conductivity of the solution and the initial concentration of dye should be 10 mS cm -1 and 50 mg l -1 , respectively. An artificial neural networks (ANN) model was developed to predict the performance of decolorization efficiency by EC process based on experimental data obtained in a laboratory batch reactor. A comparison between the predicted results of the designed ANN model and experimental data was also conducted. The model can describe the color removal percent under different conditions

The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing C. I. Basic Yellow 28 by electrocoagulation process

Energy Technology Data Exchange (ETDEWEB)

Daneshvar, N. [Water and Wastewater Treatment Research Laboratory, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz (Iran, Islamic Republic of)]. E-mail: nezam_daneshvar@yahoo.com; Khataee, A.R. [Water and Wastewater Treatment Research Laboratory, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz (Iran, Islamic Republic of)]. E-mail: ar_khataee@yahoo.com; Djafarzadeh, N. [Water and Wastewater Treatment Research Laboratory, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz (Iran, Islamic Republic of)]. E-mail: n.jafarzadeh@gmail.com

2006-10-11

In this paper, electrocoagulation has been used for removal of color from solution containing C. I. Basic Yellow 28. The effect of operational parameters such as current density, initial pH of the solution, time of electrolysis, initial dye concentration, distance between the electrodes, retention time and solution conductivity were studied in an attempt to reach higher removal efficiency. Our results showed that the increase of current density up to 80 A m{sup -2} enhanced the color removal efficiency, the electrolysis time was 7 min and the range of pH was determined 5-8. It was found that for achieving a high color removal percent, the conductivity of the solution and the initial concentration of dye should be 10 mS cm{sup -1} and 50 mg l{sup -1}, respectively. An artificial neural networks (ANN) model was developed to predict the performance of decolorization efficiency by EC process based on experimental data obtained in a laboratory batch reactor. A comparison between the predicted results of the designed ANN model and experimental data was also conducted. The model can describe the color removal percent under different conditions.

Data-Driven Modeling of Complex Systems by means of a Dynamical ANN

Science.gov (United States)

Seleznev, A.; Mukhin, D.; Gavrilov, A.; Loskutov, E.; Feigin, A.

2017-12-01

The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: (i) determining the phase subspace of the system, or embedding, from available time series and (ii) constructing an evolution operator acting in this reduced subspace. In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network (ANN) with special topology. The proposed ANN-based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold. Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on. The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed. The study was supported by the Russian Science Foundation (grant #16-12-10198).

Aspects of artificial neural networks and experimental noise

NARCIS (Netherlands)

Derks, E.P.P.A.

1997-01-01

About a decade ago, artificial neural networks (ANN) have been introduced to chemometrics for solving problems in analytical chemistry. ANN are based on the functioning of the brain and can be used for modeling complex relationships within chemical data. An ANN-model can be obtained by earning or

Social Networks and Technology Adoption

OpenAIRE

Hogset, Heidi

2005-01-01

This study analyzes social network effects on Kenyan smallholders' decision to adopt improved natural resource management techniques. These effects are decomposed into effects from social influence and learning through networks (strong ties), group effects, weak ties effects, informal finance, and conflicts arising from technological externalities, controlling for non-network effects.

Measurement and ANN prediction of pH-dependent solubility of nitrogen-heterocyclic compounds.

Science.gov (United States)

Sun, Feifei; Yu, Qingni; Zhu, Jingke; Lei, Lecheng; Li, Zhongjian; Zhang, Xingwang

2015-09-01

Based on the solubility of 25 nitrogen-heterocyclic compounds (NHCs) measured by saturation shake-flask method, artificial neural network (ANN) was employed to the study of the quantitative relationship between the structure and pH-dependent solubility of NHCs. With genetic algorithm-multivariate linear regression (GA-MLR) approach, five out of the 1497 molecular descriptors computed by Dragon software were selected to describe the molecular structures of NHCs. Using the five selected molecular descriptors as well as pH and the partial charge on the nitrogen atom of NHCs (QN) as inputs of ANN, a quantitative structure-property relationship (QSPR) model without using Henderson-Hasselbalch (HH) equation was successfully developed to predict the aqueous solubility of NHCs in different pH water solutions. The prediction model performed well on the 25 model NHCs with an absolute average relative deviation (AARD) of 5.9%, while HH approach gave an AARD of 36.9% for the same model NHCs. It was found that QN played a very important role in the description of NHCs and, with QN, ANN became a potential tool for the prediction of pH-dependent solubility of NHCs. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating SPT-N Value Based on Soil Resistivity using Hybrid ANN-PSO Algorithm

Science.gov (United States)

Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

2018-04-01

Standard Penetration Resistance (N value) is used in many empirical geotechnical engineering formulas. Meanwhile, soil resistivity is a measure of soil’s resistance to electrical flow. For a particular site, usually, only a limited N value data are available. In contrast, resistivity data can be obtained extensively. Moreover, previous studies showed evidence of a correlation between N value and resistivity value. Yet, no existing method is able to interpret resistivity data for estimation of N value. Thus, the aim is to develop a method for estimating N-value using resistivity data. This study proposes a hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) method to estimate N value using resistivity data. Five different ANN-PSO models based on five boreholes were developed and analyzed. The performance metrics used were the coefficient of determination, R2 and mean absolute error, MAE. Analysis of result found that this method can estimate N value (R2 best=0.85 and MAEbest=0.54) given that the constraint, Δ {\\bar{l}}ref, is satisfied. The results suggest that ANN-PSO method can be used to estimate N value with good accuracy.

Obituary: Anne Barbara Underhill, 1920-2003

Science.gov (United States)

Roman, Nancy Grace

2003-12-01

Anne was born in Vancouver, British Columbia on 12 June 1920. Her parents were Frederic Clare Underhill, a civil engineer and Irene Anna (née Creery) Underhill. She had a twin brother and three younger brothers. As a young girl she was active in Girl Guides and graduated from high school winning the Lieutenant Governor's medal as one of the top students in the Province. She also excelled in high school sports. Her mother died when Anne was 18 and, while undertaking her university studies, Anne assisted in raising her younger brothers. Her twin brother was killed in Italy during World War II (1944), a loss that Anne felt deeply. Possibly because of fighting to get ahead in astronomy, a field overwhelming male when she started, she frequently appeared combative. At the University of British Columbia, Anne obtained a BA (honors) in Chemistry (1942), followed by a MA in 1944. After working for the NRC in Montreal for a year, she studied at the University of Toronto prior to entering the University of Chicago in 1946 to obtain her PhD. Her thesis was the first model computed for a multi-layered stellar atmosphere (1948). During this time she worked with Otto Struve, developing a lifetime interest in hot stars and the analysis of their high dispersion spectra. She received two fellowships from the University Women of Canada. She received a U.S. National Research Fellowship to work at the Copenhagen Observatory, and upon its completion, she returned to British Columbia to work at the Dominion Astrophysical Observatory as a research scientist from 1949--1962. During this period she spent a year at Harvard University as a visiting professor and at Princeton where she used their advanced computer to write the first code for modeling stellar atmospheres. Anne was invited to the University of Utrecht (Netherlands) as a full professor in 1962. She was an excellent teacher, well liked by the students in her classes, and by the many individuals that she guided throughout her

Nuclear power plant monitoring using real-time learning neural network

International Nuclear Information System (INIS)

Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

1994-01-01

In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

Building secure network by integrated technology

International Nuclear Information System (INIS)

An Dehai; Xu Rongsheng; Liu Baoxu

2000-01-01

The author introduces a method which can realize the most powerful network security prevention by the network security integrated technologies such as firewall, realtime monitor, network scanner, Web detection and security, etc

Resilient Disaster Network Based on Software Defined Cognitive Wireless Network Technology

Directory of Open Access Journals (Sweden)

Goshi Sato

2015-01-01

Full Text Available In order to temporally recover the information network infrastructure in disaster areas from the Great East Japan Earthquake in 2011, various wireless network technologies such as satellite IP network, 3G, and Wi-Fi were effectively used. However, since those wireless networks are individually introduced and installed but not totally integrated, some of networks were congested due to the sudden network traffic generation and unbalanced traffic distribution, and eventually the total network could not effectively function. In this paper, we propose a disaster resilient network which integrates various wireless networks into a cognitive wireless network that users can use as an access network to the Internet at the serious disaster occurrence. We designed and developed the disaster resilient network based on software defined network (SDN technology to automatically select the best network link and route among the possible access networks to the Internet by periodically monitoring their network states and evaluate those using extended AHP method. In order to verify the usefulness of our proposed system, a prototype system is constructed and its performance is evaluated.

Anneli Randla kaitses doktorikraadi Cambridge'is / Anneli Randla ; interv. Reet Varblane

Index Scriptorium Estoniae

Randla, Anneli, 1970-

1999-01-01

5. mail kaitses Cambridge'is esimese eesti kunstiteadlasena doktorikraadi Anneli Randla. Töö teema: kerjusmungaordukloostrite arhitektuur Põhja-Euroopas. Juhendaja dr. Deborah Howard. Doktorikraadile esitatavatest nõudmistest, doktoritöö kaitsmisest, magistrikraadi kaitsnu õppimisvõimalustest Cambridge's.

Survey of network and information security technology

International Nuclear Information System (INIS)

Liu Baoxu; Wang Xiaozhen

2007-01-01

With the rapidly development of the computer network technology and informationize working of our Country, Network and Information Security issues becomes the focal point problem that people shows solicitude for. On the basis analysing security threat and challenge of network information and their developing trend. This paper briefly analyses and discusses the main relatively study direction and content about the theory, technology and practice of Network and Information Security. (authors)

Artificial intelligence. Application of the Statistical Neural Networks computer program in nuclear medicine

International Nuclear Information System (INIS)

Stefaniak, B.; Cholewinski, W.; Tarkowska, A.

2005-01-01

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer application of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. In this paper practical aspects of scientific application of ANN in medicine using the Statistical Neural Networks Computer program, were presented. Several steps of data analysis with the above ANN software package were discussed shortly, from material selection and its dividing into groups to the types of obtained results. The typical problems connected with assessing scintigrams by ANN were also described. (author)

Neural network based control of Doubly Fed Induction Generator in wind power generation

Science.gov (United States)

Barbade, Swati A.; Kasliwal, Prabha

2012-07-01

To complement the other types of pollution-free generation wind energy is a viable option. Previously wind turbines were operated at constant speed. The evolution of technology related to wind systems industry leaded to the development of a generation of variable speed wind turbines that present many advantages compared to the fixed speed wind turbines. In this paper the phasor model of DFIG is used. This paper presents a study of a doubly fed induction generator driven by a wind turbine connected to the grid, and controlled by artificial neural network ANN controller. The behaviour of the system is shown with PI control, and then as controlled by ANN. The effectiveness of the artificial neural network controller is compared to that of a PI controller. The SIMULINK/MATLAB simulation for Doubly Fed Induction Generator and corresponding results and waveforms are displayed.

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242

Directory of Open Access Journals (Sweden)

Ahmed R. J. Almusawi

2016-01-01

Full Text Available This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot’s joint angles.

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Science.gov (United States)

Dülger, L. Canan; Kapucu, Sadettin

2016-01-01

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

Science.gov (United States)

Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

2016-01-01

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

BrainCrafter: An investigation into human-based neural network engineering

DEFF Research Database (Denmark)

Piskur, J.; Greve, P.; Togelius, J.

2015-01-01

This paper presents the online application Brain-Crafter, in which users can manually build artificial neural networks (ANNs) to control a robot in a maze environment. Users can either start to construct networks from scratch or elaborate on networks created by other users. In particular, Brain......Crafter was designed to study how good we as humans are at building ANNs for control problems and if collaborating with other users can facilitate this process. The results in this paper show that (1) some users were in fact able to successfully construct ANNs that solve the navigation tasks, (2) collaboration between...

Modeling of methane emissions using artificial neural network approach

Directory of Open Access Journals (Sweden)

Stamenković Lidija J.

2015-01-01

Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Directory of Open Access Journals (Sweden)

Yi Xiao

2013-02-01

Full Text Available Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN and wavelet neural network (WNN are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO. Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

Networking and Information Technology Workforce Study: Final Report

Data.gov (United States)

Networking and Information Technology Research and Development, Executive Office of the President — This report presents the results of a study of the global Networking and Information Technology NIT workforce undertaken for the Networking and Information...

Ann tuleb Rakverest Võrru

Index Scriptorium Estoniae

2009-01-01

Võru kultuurimajas Kannel etendub 17. aprillil Rakvere teatri noortelavastus "Kuidas elad? ...Ann?!" Aidi Valliku jutustuse põhjal. Lavastaja Sven Heiberg. Mängivad ka Viljandi Kultuuriakadeemia teatritudengid

Artificial neural network (ANN) method for modeling of sunset yellow dye adsorption using zinc oxide nanorods loaded on activated carbon: Kinetic and isotherm study

Science.gov (United States)

Maghsoudi, M.; Ghaedi, M.; Zinali, A.; Ghaedi, A. M.; Habibi, M. H.

2015-01-01

In this research, ZnO nanoparticle loaded on activated carbon (ZnO-NPs-AC) was synthesized simply by a low cost and nontoxic procedure. The characterization and identification have been completed by different techniques such as SEM and XRD analysis. A three layer artificial neural network (ANN) model is applicable for accurate prediction of dye removal percentage from aqueous solution by ZnO-NRs-AC following conduction of 270 experimental data. The network was trained using the obtained experimental data at optimum pH with different ZnO-NRs-AC amount (0.005-0.015 g) and 5-40 mg/L of sunset yellow dye over contact time of 0.5-30 min. The ANN model was applied for prediction of the removal percentage of present systems with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with 6 neurons. The minimum mean squared error (MSE) of 0.0008 and coefficient of determination (R2) of 0.998 were found for prediction and modeling of SY removal. The influence of parameters including adsorbent amount, initial dye concentration, pH and contact time on sunset yellow (SY) removal percentage were investigated and optimal experimental conditions were ascertained. Optimal conditions were set as follows: pH, 2.0; 10 min contact time; an adsorbent dose of 0.015 g. Equilibrium data fitted truly with the Langmuir model with maximum adsorption capacity of 142.85 mg/g for 0.005 g adsorbent. The adsorption of sunset yellow followed the pseudo-second-order rate equation.

Refrigerant flow through electronic expansion valve: Experiment and neural network modeling

International Nuclear Information System (INIS)

Cao, Xiang; Li, Ze-Yu; Shao, Liang-Liang; Zhang, Chun-Lu

2016-01-01

Highlights: • Experimental data from different sources were used in comparison of EEV models. • Artificial neural network in EEV modeling is superior to literature correlations. • Artificial neural network with 4-4-1 structure and S function is recommended. • Artificial neural network is flexible for EEV mass flow rate and opening prediction. - Abstract: Electronic expansion valve (EEV) plays a crucial role in controlling refrigerant mass flow rate of refrigeration or heat pump systems for energy savings. However, complexities in two-phase throttling process and geometry make accurate modeling of EEV flow characteristics more difficult. This paper developed an artificial neural network (ANN) model using refrigerant inlet and outlet pressures, inlet subcooling, EEV opening as ANN inputs, refrigerant mass flow rate as ANN output. Both linear and nonlinear transfer functions in hidden layer were used and compared to each other. Experimental data from multiple sources including in-house experiments of one EEV with R410A were used for ANN training and test. In addition, literature correlations were compared with ANN as well. Results showed that the ANN model with nonlinear transfer function worked well in all cases and it is much accurate than the literature correlations. In all cases, nonlinear ANN predicted refrigerant mass flow rates within ±0.4% average relative deviation (A.D.) and 2.7% standard deviation (S.D.), meanwhile it predicted the EEV opening at 0.1% A.D. and 2.1% S.D.

Mobile infostation network technology

Science.gov (United States)

Rajappan, Gowri; Acharya, Joydeep; Liu, Hongbo; Mandayam, Narayan; Seskar, Ivan; Yates, Roy

2006-05-01

Inefficient use of network resources on the battlefield is a serious liability: if an asset communicates with the network command for data-a terrain map, for instance-it ties up the end-to-end network resources. When many such assets contend for data simultaneously, traffic is limited by the slowest link along the path from the network command to the asset. A better approach is for a local server, known as an infostation, to download data on an anticipated-need basis when the network load is low. The infostation can then dump data when needed to the assets over a high-speed wireless connection. The infostation serves the local assets over an OFDM-based wireless data link that has MIMO enhancements for high data rate and robustness. We aim for data rate in excess of 100 Mbps, spectral efficiency in excess of 5 bits/sec/Hz, and robustness to poor channel conditions and jammers. We propose an adaptive physical layer that determines power levels, modulation schemes, and the MIMO enhancements to use based on the channel state and the level of interference in the system. We also incorporate the idea of superuser: a user who is allowed preferential use of the high data rate link. We propose a MAC that allows for this priority-based bandwidth allocation scheme. The proposed infostation MAC is integrated tightly with the physical layer through a cross-layer design. We call the proposed infostation PHY, MAC, and network technology, collectively, as the Mobile Infostation Network Technology (MINT).

Application of Ann for Prediction of Co2+, Cd2+ and Zn2+ Ions Uptake by R. Squarrosus Biomass in Single and Binary Mixtures

Directory of Open Access Journals (Sweden)

Nemeček Peter

2014-06-01

Full Text Available Discharge of heavy metals into aquatic ecosystems has become a matter of concern over the last few decades. The search for new technologies involving the removal of toxic metals from wastewaters has directed the attention to biosorption, based on metal binding capacities of various biological materials. Degree of sorbent affinity for the sorbate determines its distribution between the solid and liquid phases and this behavior can be described by adsorption isotherm models (Freundlich and Langmuir isotherm models representing the classical approach. In this study, an artificial neural network (ANN was proposed to predict the sorption efficiency in single and binary component solutions of Cd2+, Zn2+ and Co2+ ions by biosorbent prepared from biomass of moss Rhytidiadelphus squarrosus. Calculated non-linear ANN models presented in this paper are advantageous for its capability of successful prediction, which can be problematic in the case of classical isotherm approach. Quality of prediction was proved by strong agreement between calculated and measured data, expressed by the coefficient of determination in both, single and binary metal systems (R2= 0.996 and R2= 0.987, respectively. Another important benefit of these models is necessity of significantly smaller amount of data (about 50% for the model calculation. Also, it is possible to calculate Qeq for all studied metals by one combined ANN model, which totally overcomes a classical isotherm approach

Prototype-Incorporated Emotional Neural Network.

Science.gov (United States)

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

The Prediction of Bandwidth On Need Computer Network Through Artificial Neural Network Method of Backpropagation

Directory of Open Access Journals (Sweden)

Ikhthison Mekongga

2014-02-01

Full Text Available The need for bandwidth has been increasing recently. This is because the development of internet infrastructure is also increasing so that we need an economic and efficient provider system. This can be achieved through good planning and a proper system. The prediction of the bandwidth consumption is one of the factors that support the planning for an efficient internet service provider system. Bandwidth consumption is predicted using ANN. ANN is an information processing system which has similar characteristics as the biologic al neural network.  ANN  is  chosen  to  predict  the  consumption  of  the  bandwidth  because  ANN  has  good  approachability  to  non-linearity.  The variable used in ANN is the historical load data. A bandwidth consumption information system was built using neural networks  with a backpropagation algorithm to make the use of bandwidth more efficient in the future both in the rental rate of the bandwidth and in the usage of the bandwidth.Keywords: Forecasting, Bandwidth, Backpropagation

Neural network versus classical time series forecasting models

Science.gov (United States)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Performance evaluation of an irreversible Miller cycle comparing FTT (finite-time thermodynamics) analysis and ANN (artificial neural network) prediction

International Nuclear Information System (INIS)

Mousapour, Ashkan; Hajipour, Alireza; Rashidi, Mohammad Mehdi; Freidoonimehr, Navid

2016-01-01

In this paper, the first and second-laws efficiencies are applied to performance analysis of an irreversible Miller cycle. In the irreversible cycle, the linear relation between the specific heat of the working fluid and its temperature, the internal irreversibility described using the compression and expansion efficiencies, the friction loss computed according to the mean velocity of the piston and the heat-transfer loss are considered. The effects of various design parameters, such as the minimum and maximum temperatures of the working fluid and the compression ratio on the power output and the first and second-laws efficiencies of the cycle are discussed. In the following, a procedure named ANN is used for predicting the thermal efficiency values versus the compression ratio, and the minimum and maximum temperatures of the Miller cycle. Nowadays, Miller cycle is widely used in the automotive industry and the obtained results of this study will provide some significant theoretical grounds for the design optimization of the Miller cycle. - Highlights: • The performance of an irreversible Miller cycle is investigated using FFT. • The effects of design parameters on the performance of the cycle are investigated. • ANN is applied to predict the thermal efficiency and the power output values. • There is an excellent correlation between FTT and ANN data. • ANN can be applied to predict data where FTT analysis has not been performed.

Combined wavelet transform-artificial neural network use in tablet active content determination by near-infrared spectroscopy.

Science.gov (United States)

Chalus, Pascal; Walter, Serge; Ulmschneider, Michel

2007-05-22

The pharmaceutical industry faces increasing regulatory pressure to optimize quality control. Content uniformity is a basic release test for solid dosage forms. To accelerate test throughput and comply with the Food and Drug Administration's process analytical technology initiative, attention is increasingly turning to nondestructive spectroscopic techniques, notably near-infrared (NIR) spectroscopy (NIRS). However, validation of NIRS using requisite linearity and standard error of prediction (SEP) criteria remains a challenge. This study applied wavelet transformation of the NIR spectra of a commercial tablet to build a model using conventional partial least squares (PLS) regression and an artificial neural network (ANN). Wavelet coefficients in the PLS and ANN models reduced SEP by up to 60% compared to PLS models using mathematical spectra pretreatment. ANN modeling yielded high-linearity calibration and a correlation coefficient exceeding 0.996.

Neural networks in signal processing

International Nuclear Information System (INIS)

Govil, R.

2000-01-01

Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

Anne Veski : "Ju siis ei ole minu rahvusvaheline kuulsus meie presidendi kõrvu jõudnud" / Anne Veski ; interv. Tiia Linnard

Index Scriptorium Estoniae

Veski, Anne, 1956-

2008-01-01

Laulja Anne Veski arutlusi kontserttegevusest Venemaal ja elust Eestis. Muuhulgas on juttu ka sellest, et Anne Veskit pole kunagi kutsutud presidendi iseseisvuspäeva vastuvõtule. Ilmunud ka: Severnoje Poberezhje 20. märts 2008, lk. 6

Handbook of social network technologies and applications

CERN Document Server

Furht, Borko

2010-01-01

Social networking is a concept that has existed for a long time; however, with the explosion of the Internet, social networking has become a tool for people to connect and communicate in ways that were impossible in the past. The recent development of Web 2.0 has provided many new applications, such as Myspace, Facebook, and LinkedIn. The purpose of ""Handbook of Social Networks: Technologies and Applications"" is to provide comprehensive guidelines on the current and future trends in social network technologies and applications in the field of Web-based Social Networks. This handbook includes

Assessment of spatial distribution of soil heavy metals using ANN-GA, MSLR and satellite imagery.

Science.gov (United States)

Naderi, Arman; Delavar, Mohammad Amir; Kaboudin, Babak; Askari, Mohammad Sadegh

2017-05-01

This study aims to assess and compare heavy metal distribution models developed using stepwise multiple linear regression (MSLR) and neural network-genetic algorithm model (ANN-GA) based on satellite imagery. The source identification of heavy metals was also explored using local Moran index. Soil samples (n = 300) were collected based on a grid and pH, organic matter, clay, iron oxide contents cadmium (Cd), lead (Pb) and zinc (Zn) concentrations were determined for each sample. Visible/near-infrared reflectance (VNIR) within the electromagnetic ranges of satellite imagery was applied to estimate heavy metal concentrations in the soil using MSLR and ANN-GA models. The models were evaluated and ANN-GA model demonstrated higher accuracy, and the autocorrelation results showed higher significant clusters of heavy metals around the industrial zone. The higher concentration of Cd, Pb and Zn was noted under industrial lands and irrigation farming in comparison to barren and dryland farming. Accumulation of industrial wastes in roads and streams was identified as main sources of pollution, and the concentration of soil heavy metals was reduced by increasing the distance from these sources. In comparison to MLSR, ANN-GA provided a more accurate indirect assessment of heavy metal concentrations in highly polluted soils. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.

Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model

Energy Technology Data Exchange (ETDEWEB)

Cadenas, Erasmo [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro (Mexico); Rivera, Wilfrido [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)

2010-12-15

In this paper the wind speed forecasting in the Isla de Cedros in Baja California, in the Cerro de la Virgen in Zacatecas and in Holbox in Quintana Roo is presented. The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. In order to do wind speed forecasting Hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANN) models were developed. The ARIMA models were first used to do the wind speed forecasting of the time series and then with the obtained errors ANN were built taking into account the nonlinear tendencies that the ARIMA technique could not identify, reducing with this the final errors. Once the Hybrid models were developed 48 data out of sample for each one of the sites were used to do the wind speed forecasting and the results were compared with the ARIMA and the ANN models working separately. Statistical error measures such as the mean error (ME), the mean square error (MSE) and the mean absolute error (MAE) were calculated to compare the three methods. The results showed that the Hybrid models predict the wind velocities with a higher accuracy than the ARIMA and ANN models in the three examined sites. (author)

FE-ANN based modeling of 3D Simple Reinforced Concrete Girders for Objective Structural Health Evaluation : Tech Transfer Summary

Science.gov (United States)

2017-06-01

The objective of this study was to develop an objective, quantitative method for evaluating damage to bridge girders by using artificial neural networks (ANNs). This evaluation method, which is a supplement to visual inspection, requires only the res...

Prediction of shear and tensile strength of the diffusion bonded AA5083 and AA7075 aluminium alloy using ANN

International Nuclear Information System (INIS)

Sagai Francis Britto, A.; Raj, R. Edwin; Mabel, M. Carolin

2017-01-01

Diffusion bonding is a pressure welding technique to establish bonds by inter diffusion of atoms. Bonding characteristics were generated by varying the significant process conditions such as the bonding temperature, the pressing load and the duration of pressure while bonding the aluminium alloys AA5083 and AA7075. Deriving analytical correlation with the process variables to weld strength is quite involved due to the non-linear dependency of the process variables with the mechanical strength of the joints. An arbitrary function approximation mechanism, the artificial neural network (ANN) is therefore employed to develop the models for predicting the mechanical properties of the bonded joints. Back propagation technique, which alters the network weights to minimize the mean square error was used to develop the ANN models. The models were tested, validated and found to be satisfactory with good prediction accuracy.

Prediction of shear and tensile strength of the diffusion bonded AA5083 and AA7075 aluminium alloy using ANN

Energy Technology Data Exchange (ETDEWEB)

Sagai Francis Britto, A. [Department of Mechanical Engineering, St.Xavier' s Catholic College of Engineering, Nagercoil 629003,Tamil Nadu (India); Raj, R. Edwin, E-mail: redwinraj@gmail.com [Department of Mechanical Engineering, St.Xavier' s Catholic College of Engineering, Nagercoil 629003,Tamil Nadu (India); Mabel, M. Carolin [Department of Electrical and Electronics Engineering, St.Xavier' s Catholic College of Engineering, Nagercoil 629003,Tamil Nadu (India)

2017-04-24

Diffusion bonding is a pressure welding technique to establish bonds by inter diffusion of atoms. Bonding characteristics were generated by varying the significant process conditions such as the bonding temperature, the pressing load and the duration of pressure while bonding the aluminium alloys AA5083 and AA7075. Deriving analytical correlation with the process variables to weld strength is quite involved due to the non-linear dependency of the process variables with the mechanical strength of the joints. An arbitrary function approximation mechanism, the artificial neural network (ANN) is therefore employed to develop the models for predicting the mechanical properties of the bonded joints. Back propagation technique, which alters the network weights to minimize the mean square error was used to develop the ANN models. The models were tested, validated and found to be satisfactory with good prediction accuracy.

Implementing Signature Neural Networks with Spiking Neurons.

Science.gov (United States)

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

Selection in sugarcane families with artificial neural networks

Directory of Open Access Journals (Sweden)

Bruno Portela Brasileiro

2015-04-01

Full Text Available The objective of this study was to evaluate Artificial Neural Networks (ANN applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS, demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.

Statistical learning problem of artificial neural network to control roofing process

Directory of Open Access Journals (Sweden)

Lapidus Azariy

2017-01-01

Full Text Available Now software developed on the basis of artificial neural networks (ANN has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy. Also designing of ANN architecture is related to learning problem known as “curse of dimensionality”. This problem is important for the study of construction process management because of the difficulty to get training data from construction sites. In previous studies the authors have designed a 4-layer feedforward ANN with a unit model of 12-5-4-1 to approximate estimation and prediction of roofing process. This paper presented the statistical learning side of created ANN with simple-error-minimization algorithm. The sample size to efficient training and the confidence interval of network outputs defined. In conclusion the authors predicted successful ANN learning in a large construction business company within a short space of time.

Mary Anne Chambers | IDRC - International Development Research ...

International Development Research Centre (IDRC) Digital Library (Canada)

A former Member of Provincial Parliament, Mary Anne served as Minister of Training, Colleges and Universities, and Minister of Children and Youth Services in the Government of Ontario. She is also a former senior vice-president of Scotiabank. A graduate of the University of Toronto, Mary Anne has received honorary ...

Use of artificial neural networks on optical track width measurements

Science.gov (United States)

Smith, Richard J.; See, Chung W.; Somekh, Mike G.; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

Network Gateway Technology: The Issue of Redundancy towards ...

African Journals Online (AJOL)

Everyone connected to the Internet is concerned about two basic things: the availability of network services and the speed of the network. Network gateway redundancy technology falls within these categories and happens to be one of the newest technologies which only few companies, such as mobile companies and ...

The parallel implementation of a backpropagation neural network and its applicability to SPECT image reconstruction

Energy Technology Data Exchange (ETDEWEB)

Kerr, John Patrick [Iowa State Univ., Ames, IA (United States)

1992-01-01

The objective of this study was to determine the feasibility of using an Artificial Neural Network (ANN), in particular a backpropagation ANN, to improve the speed and quality of the reconstruction of three-dimensional SPECT (single photon emission computed tomography) images. In addition, since the processing elements (PE)s in each layer of an ANN are independent of each other, the speed and efficiency of the neural network architecture could be better optimized by implementing the ANN on a massively parallel computer. The specific goals of this research were: to implement a fully interconnected backpropagation neural network on a serial computer and a SIMD parallel computer, to identify any reduction in the time required to train these networks on the parallel machine versus the serial machine, to determine if these neural networks can learn to recognize SPECT data by training them on a section of an actual SPECT image, and to determine from the knowledge obtained in this research if full SPECT image reconstruction by an ANN implemented on a parallel computer is feasible both in time required to train the network, and in quality of the images reconstructed.

Survey of Promising Technologies for 5G Networks

OpenAIRE

Le, Nam Tuan; Hossain, Mohammad Arif; Islam, Amirul; Kim, Do-yun; Choi, Young-June; Jang, Yeong Min

2016-01-01

As an enhancement of cellular networks, the future-generation 5G network can be considered an ultra-high-speed technology. The proposed 5G network might include all types of advanced dominant technologies to provide remarkable services. Consequently, new architectures and service management schemes for different applications of the emerging technologies need to be recommended to solve issues related to data traffic capacity, high data rate, and reliability for ensuring QoS. Cloud computing, I...

Use of artificial neural networks for transport energy demand modeling

International Nuclear Information System (INIS)

Murat, Yetis Sazi; Ceylan, Halim

2006-01-01

The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem

Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator

Directory of Open Access Journals (Sweden)

Khaoula Ghefiri

2018-04-01

Full Text Available Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG systems aim to provide clean and reliable electrical power. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances.

Prediction of groundwater levels from lake levels and climate data using ANN approach

OpenAIRE

Dogan, Ahmet; Demirpence, Husnu; Cobaner, Murat

2008-01-01

There are many environmental concerns relating to the quality and quantity of surface and groundwater. It is very important to estimate the quantity of water by using readily available climate data for managing water resources of the natural environment. As a case study an artificial neural network (ANN) methodology is developed for estimating the groundwater levels (upper Floridan aquifer levels) as a function of monthly averaged precipitation, evaporation, and measured levels of Magnolia an...

Space-based Networking Technology Developments in the Interplanetary Network Directorate Information Technology Program

Science.gov (United States)

Clare, Loren; Clement, B.; Gao, J.; Hutcherson, J.; Jennings, E.

2006-01-01

Described recent development of communications protocols, services, and associated tools targeted to reduce risk, reduce cost and increase efficiency of IND infrastructure and supported mission operations. Space-based networking technologies developed were: a) Provide differentiated quality of service (QoS) that will give precedence to traffic that users have selected as having the greatest importance and/or time-criticality; b) Improve the total value of information to users through the use of QoS prioritization techniques; c) Increase operational flexibility and improve command-response turnaround; d) Enable new class of networked and collaborative science missions; e) Simplify applications interfaces to communications services; and f) Reduce risk and cost from a common object model and automated scheduling and communications protocols. Technologies are described in three general areas: communications scheduling, middleware, and protocols. Additionally developed simulation environment, which provides comprehensive, quantitative understanding of the technologies performance within overall, evolving architecture, as well as ability to refine & optimize specific components.

A survey on the wireless sensor network technology

International Nuclear Information System (INIS)

Kim, Jae Hee; Jun, Hyeong Seop; Lee, Jae Cheol; Choi, Yoo Rak

2007-12-01

Wireless sensor technology is required in the safety inspection for safety-critical unit of nuclear power plant. This report describes wireless sensor technology related with the project named 'Development of a remote care system of NPP components based on the network and safety database'. This report includes contents of methodology and status of sensor network construction, status of zigbee sensor network, problem of security and sensor battery. Energy harvesting technology will be mentioned on the next report

Nuclear power plant fault-diagnosis using artificial neural networks

International Nuclear Information System (INIS)

Kim, Keehoon; Aljundi, T.L.; Bartlett, E.B.

1992-01-01

Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses

A neural network for noise correlation classification

Science.gov (United States)

Paitz, Patrick; Gokhberg, Alexey; Fichtner, Andreas

2018-02-01

We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20 per cent or less can be achieved when the network training is performed with as little as 3.5 per cent and 16 per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35 per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.

Evaluation Of The Advanced Operating System Of The Ann Arbor Transportation Authority : Driver And Dispatcher Perceptions Of AATA'S Advanced Operating System

Science.gov (United States)

1999-01-01

In 1997, the Ann Arbor (Michigan) Transportation Authority began deploying advanced public transportation systems (APTS) technologies in its fixed route and paratransit operations. The project's concept is the integration of a range of such technolog...

Diversity Networks

Science.gov (United States)

and professional growth of women through networking, mentoring and training. We strive to ensure that will be used. National Processing Center Seniors Leader: Jo Anne Hankins Champion: Eric Milliner NO

Design of Jetty Piles Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Yongjei Lee

2014-01-01

Full Text Available To overcome the complication of jetty pile design process, artificial neural networks (ANN are adopted. To generate the training samples for training ANN, finite element (FE analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.

Autonomous vision networking: miniature wireless sensor networks with imaging technology

Science.gov (United States)

Messinger, Gioia; Goldberg, Giora

2006-09-01

The recent emergence of integrated PicoRadio technology, the rise of low power, low cost, System-On-Chip (SOC) CMOS imagers, coupled with the fast evolution of networking protocols and digital signal processing (DSP), created a unique opportunity to achieve the goal of deploying large-scale, low cost, intelligent, ultra-low power distributed wireless sensor networks for the visualization of the environment. Of all sensors, vision is the most desired, but its applications in distributed sensor networks have been elusive so far. Not any more. The practicality and viability of ultra-low power vision networking has been proven and its applications are countless, from security, and chemical analysis to industrial monitoring, asset tracking and visual recognition, vision networking represents a truly disruptive technology applicable to many industries. The presentation discusses some of the critical components and technologies necessary to make these networks and products affordable and ubiquitous - specifically PicoRadios, CMOS imagers, imaging DSP, networking and overall wireless sensor network (WSN) system concepts. The paradigm shift, from large, centralized and expensive sensor platforms, to small, low cost, distributed, sensor networks, is possible due to the emergence and convergence of a few innovative technologies. Avaak has developed a vision network that is aided by other sensors such as motion, acoustic and magnetic, and plans to deploy it for use in military and commercial applications. In comparison to other sensors, imagers produce large data files that require pre-processing and a certain level of compression before these are transmitted to a network server, in order to minimize the load on the network. Some of the most innovative chemical detectors currently in development are based on sensors that change color or pattern in the presence of the desired analytes. These changes are easily recorded and analyzed by a CMOS imager and an on-board DSP processor

Network speech systems technology program

Science.gov (United States)

Weinstein, C. J.

1981-09-01

This report documents work performed during FY 1981 on the DCA-sponsored Network Speech Systems Technology Program. The two areas of work reported are: (1) communication system studies in support of the evolving Defense Switched Network (DSN) and (2) design and implementation of satellite/terrestrial interfaces for the Experimental Integrated Switched Network (EISN). The system studies focus on the development and evaluation of economical and endurable network routing procedures. Satellite/terrestrial interface development includes circuit-switched and packet-switched connections to the experimental wideband satellite network. Efforts in planning and coordination of EISN experiments are reported in detail in a separate EISN Experiment Plan.

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

Science.gov (United States)

Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

78 FR 17418 - Rural Health Information Technology Network Development Grant

Science.gov (United States)

2013-03-21

... Information Technology Network Development Grant AGENCY: Health Resources and Services Administration (HRSA...-competitive replacement award under the Rural Health Information Technology Network Development Grant (RHITND... relinquishing its fiduciary responsibilities for the Rural Health Information Technology Network Development...

Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA

Directory of Open Access Journals (Sweden)

A.K. Gupta

2017-01-01

Full Text Available An experimental work is conducted on counter flow plate fin compact heat exchanger using offset strip fin under different mass flow rates. The training, testing, and validation set of data has been collected by conducting experiments. Next, artificial neural network merged with Genetic Algorithm (GA utilized to measure the performance of plate-fin compact heat exchanger. The main aim of present research is to measure the performance of plate-fin compact heat exchanger and to provide full explanations. An artificial neural network predicted simulated data, which verified with experimental data under 10–20% error. Then, the authors examined two well-known global search techniques, simulated annealing and the genetic algorithm. The proposed genetic algorithm and Simulated Annealing (SA results have been summarized. The parameters are impartially important for good results. With the emergence of a new data-driven modeling technique, Neuro-fuzzy based systems are established in academic and practical applications. The neuro-fuzzy interference system (ANFIS has also been examined to undertake the problem related to plate-fin heat exchanger performance measurement under various parameters. Moreover, Parallel with ANFIS model and Artificial Neural Network (ANN model has been created with emphasizing the accuracy of the different techniques. A wide range of statistical indicators used to assess the performance of the models. Based on the comparison, it was revealed that technical ANFIS improve the accuracy of estimates in the small pool and tropical ANN.

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

Science.gov (United States)

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

Application of Artificial Neural Networks in Canola Crop Yield Prediction

Directory of Open Access Journals (Sweden)

S. J. Sajadi

2014-02-01

Full Text Available Crop yield prediction has an important role in agricultural policies such as specification of the crop price. Crop yield prediction researches have been based on regression analysis. In this research canola yield was predicted using Artificial Neural Networks (ANN using 11 crop year climate data (1998-2009 in Gonbad-e-Kavoos region of Golestan province. ANN inputs were mean weekly rainfall, mean weekly temperature, mean weekly relative humidity and mean weekly sun shine hours and ANN output was canola yield (kg/ha. Multi-Layer Perceptron networks (MLP with Levenberg-Marquardt backpropagation learning algorithm was used for crop yield prediction and Root Mean Square Error (RMSE and square of the Correlation Coefficient (R2 criterions were used to evaluate the performance of the ANN. The obtained results show that the 13-20-1 network has the lowest RMSE equal to 101.235 and maximum value of R2 equal to 0.997 and is suitable for predicting canola yield with climate factors.

Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review)

International Nuclear Information System (INIS)

Sewsynker-Sukai, Yeshona; Faloye, Funmilayo; Kana, Evariste Bosco Gueguim

2016-01-01

In view of the looming energy crisis as a result of depleting fossil fuel resources and environmental concerns from greenhouse gas emissions, the need for sustainable energy sources has secured global attention. Research is currently focused towards renewable sources of energy due to their availability and environmental friendliness. Biofuel production like other bioprocesses is controlled by several process parameters including pH, temperature and substrate concentration; however, the improvement of biofuel production requires a robust process model that accurately relates the effect of input variables to the process output. Artificial neural networks (ANNs) have emerged as a tool for modelling complex, non-linear processes. ANNs are applied in the prediction of various processes; they are useful for virtual experimentations and can potentially enhance bioprocess research and development. In this study, recent findings on the application of ANN for the modelling and optimization of biohydrogen, biogas, biodiesel, microbial fuel cell technology and bioethanol are reviewed. In addition, comparative studies on the modelling efficiency of ANN and other techniques such as the response surface methodology are briefly discussed. The review highlights the efficiency of ANNs as a modelling and optimization tool in biofuel process development

Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

Energy Technology Data Exchange (ETDEWEB)

Yousefi, M.; Omid, M.; Rafiee, Sh. [Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of); Ghaderi, S. F. [Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

2013-07-01

Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.

Artificial neural networks applied to forecasting time series.

Science.gov (United States)

Montaño Moreno, Juan J; Palmer Pol, Alfonso; Muñoz Gracia, Pilar

2011-04-01

This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fit regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research.

Artificial Neural Networks for Thermochemical Conversion of Biomass

DEFF Research Database (Denmark)

Puig Arnavat, Maria; Bruno, Joan Carles

2015-01-01

Artificial neural networks (ANNs), extensively used in different fields, have been applied for modeling biomass gasification processes in fluidized bed reactors. Two ANN models are presented, one for circulating fluidized bed gasifiers and another for bubbling fluidized bed gasifiers. Both models...

Integrating neural network technology and noise analysis

International Nuclear Information System (INIS)

Uhrig, R.E.; Oak Ridge National Lab., TN

1995-01-01

The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check values and bearings illustrate the usefulness of the methodology developed. (Author)

Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.

Science.gov (United States)

Vuckovic, Aleksandra; Radivojevic, Vlada; Chen, Andrew C N; Popovic, Dejan

2002-06-01

We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37+/-1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.

Tomographic image reconstruction using Artificial Neural Networks

International Nuclear Information System (INIS)

Paschalis, P.; Giokaris, N.D.; Karabarbounis, A.; Loudos, G.K.; Maintas, D.; Papanicolas, C.N.; Spanoudaki, V.; Tsoumpas, Ch.; Stiliaris, E.

2004-01-01

A new image reconstruction technique based on the usage of an Artificial Neural Network (ANN) is presented. The most crucial factor in designing such a reconstruction system is the network architecture and the number of the input projections needed to reconstruct the image. Although the training phase requires a large amount of input samples and a considerable CPU time, the trained network is characterized by simplicity and quick response. The performance of this ANN is tested using several image patterns. It is intended to be used together with a phantom rotating table and the γ-camera of IASA for SPECT image reconstruction

Final Technical Report, Wind Generator Project (Ann Arbor)

Energy Technology Data Exchange (ETDEWEB)

Geisler, Nathan [City of Ann Arbor, MI (United States)

2017-03-20

A Final Technical Report (57 pages) describing educational exhibits and devices focused on wind energy, and related outreach activities and programs. Project partnership includes the City of Ann Arbor, MI and the Ann Arbor Hands-on Museum, along with additional sub-recipients, and U.S. Department of Energy/Office of Energy Efficiency and Renewable Energy (EERE). Report relays key milestones and sub-tasks as well as numerous graphics and images of five (5) transportable wind energy demonstration devices and five (5) wind energy exhibits designed and constructed between 2014 and 2016 for transport and use by the Ann Arbor Hands-on Museum.

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Directory of Open Access Journals (Sweden)

Miloš Madić

2015-01-01

Full Text Available In this paper, an attempt has been made to develop a mathematical model in order to study the relationship between laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position, and kerf taper angle obtained in CO2 laser cutting of AISI 304 stainless steel. To this aim, a single hidden layer artificial neural network (ANN trained with gradient descent with momentum algorithm was used. To obtain an experimental database for the ANN training, laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of the cutting parameters. Statistically assessed as adequate, ANN model was then used to investigate the effect of the laser cutting parameters on the kerf taper angle by generating 2D and 3D plots. It was observed that the kerf taper angle was highly sensitive to the selected laser cutting parameters, as well as their interactions. In addition to modeling, by applying the Monte Carlo method on the developed kerf taper angle ANN model, the near optimal laser cutting parameter settings, which minimize kerf taper angle, were determined.

Improved transformer protection using probabilistic neural network ...

African Journals Online (AJOL)

user

secure and dependable protection for power transformers. Owing to its superior learning and generalization capabilities Artificial. Neural Network (ANN) can considerably enhance the scope of WI method. ANN approach is faster, robust and easier to implement than the conventional waveform approach. The use of neural ...

Condition Monitoring for DC-link Capacitors Based on Artificial Neural Network Algorithm

DEFF Research Database (Denmark)

Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

2015-01-01

hardware will reduce the cost, and therefore could be more promising for industry applications. A condition monitoring method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implementation of the ANN to the DC-link capacitor condition monitoring in a back......In power electronic systems, capacitor is one of the reliability critical components . Recently, the condition monitoring of capacitors to estimate their health status have been attracted by the academic research. Industry applications require more reliable power electronics products...... with preventive maintenance. However, the existing capacitor condition monitoring methods suffer from either increased hardware cost or low estimation accuracy, being the challenges to be adopted in industry applications. New development in condition monitoring technology with software solutions without extra...

Networking Technologies for Future Home Networks Using 60 GHz Radio

NARCIS (Netherlands)

Wang, J.

2010-01-01

Networking technologies have been changing the life of people in their private residential space. With the arrival of high definition (HD) multimedia services and broadband communications into the living space, future home networks are expected to support high speed device-to-device connectivity

Artificial neural networks in the nuclear engineering (Part 2)

International Nuclear Information System (INIS)

Baptista Filho, Benedito Dias

2002-01-01

The field of Artificial Neural Networks (ANN), one of the branches of Artificial Intelligence has been waking up a lot of interest in the Nuclear Engineering (NE). ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in high complexity problems of control. The first part of this work began a discussion with feed-forward neural networks in back-propagation. In this part of the work, the Multi-synaptic neural networks is applied to control problems. Also, the self-organized maps is presented in a typical pattern classification problem: transients classification. The main purpose of the work is to show that ANN can be successfully used in NE if a carefully choice of its type is done: the application sets this choice. (author)

Channel Equalization Using Multilayer Perceptron Networks

Directory of Open Access Journals (Sweden)

Saba Baloch

2012-07-01

Full Text Available In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks. The simulated network is a multilayer feedforward Perceptron ANN, which has been trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network. This paper presents a very effective method for blind channel equalization, being more efficient than the pre-existing algorithms. The obtained results show a visible reduction in the noise content.

CUDA-accelerated genetic feedforward-ANN training for data mining

International Nuclear Information System (INIS)

Patulea, Catalin; Peace, Robert; Green, James

2010-01-01

We present an implementation of genetic algorithm (GA) training of feedforward artificial neural networks (ANNs) targeting commodity graphics cards (GPUs). By carefully mapping the problem onto the unique GPU architecture, we achieve order-of-magnitude speedup over a conventional CPU implementation. Furthermore, we show that the speedup is consistent across a wide range of data set sizes, making this implementation ideal for large data sets. This performance boost enables the genetic algorithm to search a larger subset of the solution space, which results in more accurate pattern classification. Finally, we demonstrate this method in the context of the 2009 UC San Diego Data Mining Contest, achieving a world-class lift on a data set of 94682 e-commerce transactions.

CUDA-accelerated genetic feedforward-ANN training for data mining

Energy Technology Data Exchange (ETDEWEB)

Patulea, Catalin; Peace, Robert; Green, James, E-mail: cpatulea@sce.carleton.ca, E-mail: rpeace@sce.carleton.ca, E-mail: jrgreen@sce.carleton.ca [School of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 (Canada)

2010-11-01

We present an implementation of genetic algorithm (GA) training of feedforward artificial neural networks (ANNs) targeting commodity graphics cards (GPUs). By carefully mapping the problem onto the unique GPU architecture, we achieve order-of-magnitude speedup over a conventional CPU implementation. Furthermore, we show that the speedup is consistent across a wide range of data set sizes, making this implementation ideal for large data sets. This performance boost enables the genetic algorithm to search a larger subset of the solution space, which results in more accurate pattern classification. Finally, we demonstrate this method in the context of the 2009 UC San Diego Data Mining Contest, achieving a world-class lift on a data set of 94682 e-commerce transactions.

Modeling of policies for reduction of GHG emissions in energy sector using ANN: case study-Croatia (EU).

Science.gov (United States)

Bolanča, Tomislav; Strahovnik, Tomislav; Ukić, Šime; Stankov, Mirjana Novak; Rogošić, Marko

2017-07-01

This study describes the development of tool for testing different policies for reduction of greenhouse gas (GHG) emissions in energy sector using artificial neural networks (ANNs). The case study of Croatia was elaborated. Two different energy consumption scenarios were used as a base for calculations and predictions of GHG emissions: the business as usual (BAU) scenario and sustainable scenario. Both of them are based on predicted energy consumption using different growth rates; the growth rates within the second scenario resulted from the implementation of corresponding energy efficiency measures in final energy consumption and increasing share of renewable energy sources. Both ANN architecture and training methodology were optimized to produce network that was able to successfully describe the existing data and to achieve reliable prediction of emissions in a forward time sense. The BAU scenario was found to produce continuously increasing emissions of all GHGs. The sustainable scenario was found to decrease the GHG emission levels of all gases with respect to BAU. The observed decrease was attributed to the group of measures termed the reduction of final energy consumption through energy efficiency measures.

Plans & Policies for Technology in Education: A Compendium. A Technology Leadership Network Special Report.

Science.gov (United States)

National School Boards Association, Alexandria, VA. Inst. for the Transfer of Technology to Education.

This document shows how education leaders nationwide--many of them part of the National School Boards Association's 345-district Technology Leadership Network--have addressed technology-related policy issues such as copyright, purchasing, network/Internet use, and ethics as well as technology planning topics including staff development, classroom…

DANNP: an efficient artificial neural network pruning tool

KAUST Repository

Alshahrani, Mona

2017-11-06

Background Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS) methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge, DANNP (publicly

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs

Directory of Open Access Journals (Sweden)

Adel Taha Abbas

2018-05-01

Full Text Available Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth–Pareto optimization of an artificial neural network (ANN is presented in this paper for surface roughness (Ra prediction of one component in computer numerical control (CNC turning over minimal machining time (Tm and at prime machining costs (C. An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP, to predict Ra, Tm, and C, in relation to cutting speed, vc, depth of cut, ap, and feed per revolution, fr. For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values vc, ap, and fr. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, Tm = 0.358 min/cm3, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed vc = 250 m/min, cutting depth ap = 1.0 mm, and feed per revolution fr = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

Science.gov (United States)

Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

2007-01-01

To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

Development of an ANN optimized mucoadhesive buccal tablet containing flurbiprofen and lidocaine for dental pain.

Science.gov (United States)

Hussain, Amjad; Syed, Muhammad Ali; Abbas, Nasir; Hanif, Sana; Arshad, Muhammad Sohail; Bukhari, Nadeem Irfan; Hussain, Khalid; Akhlaq, Muhammad; Ahmad, Zeeshan

2016-06-01

A novel mucoadhesive buccal tablet containing flurbiprofen (FLB) and lidocaine HCl (LID) was prepared to relieve dental pain. Tablet formulations (F1-F9) were prepared using variable quantities of mucoadhesive agents, hydroxypropyl methyl cellulose (HPMC) and sodium alginate (SA). The formulations were evaluated for their physicochemical properties, mucoadhesive strength and mucoadhesion time, swellability index and in vitro release of active agents. Release of both drugs depended on the relative ratio of HPMC:SA. However, mucoadhesive strength and mucoadhesion time were better in formulations, containing higher proportions of HPMC compared to SA. An artificial neural network (ANN) approach was applied to optimise formulations based on known effective parameters (i.e., mucoadhesive strength, mucoadhesion time and drug release), which proved valuable. This study indicates that an effective buccal tablet formulation of flurbiprofen and lidocaine can be prepared via an optimized ANN approach.

Performing particle image velocimetry using artificial neural networks: a proof-of-concept

Science.gov (United States)

Rabault, Jean; Kolaas, Jostein; Jensen, Atle

2017-12-01

Traditional programs based on feature engineering are underperforming on a steadily increasing number of tasks compared with artificial neural networks (ANNs), in particular for image analysis. Image analysis is widely used in fluid mechanics when performing particle image velocimetry (PIV) and particle tracking velocimetry (PTV), and therefore it is natural to test the ability of ANNs to perform such tasks. We report for the first time the use of convolutional neural networks (CNNs) and fully connected neural networks (FCNNs) for performing end-to-end PIV. Realistic synthetic images are used for training the networks and several synthetic test cases are used to assess the quality of each network’s predictions and compare them with state-of-the-art PIV software. In addition, we present tests on real-world data that prove ANNs can be used not only with synthetic images but also with more noisy, imperfect images obtained in a real experimental setup. While the ANNs we present have slightly higher root mean square error than state-of-the-art cross-correlation methods, they perform better near edges and allow for higher spatial resolution than such methods. In addition, it is likely that one could with further work develop ANNs which perform better that the proof-of-concept we offer.

Anne Frank relaunched in the world of comics and graphic novels

NARCIS (Netherlands)

Ribbens, Kees

2017-01-01

Recently the Basel-based Anne Frank Fonds proudly presented the Graphic Diary of Anne Frank. The impression is created as if this is the first ever comic book version of Anne Frank’s narrative. This article shows that there were various predecessors.

Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data

Energy Technology Data Exchange (ETDEWEB)

Sozen, Adnan; Ozalp, Mehmet [Gazi Univ., Mechanical Education Dept., Ankara (Turkey); Arcaklioglu, Erol [Krkkale Univ., Mechanical Engineering Dept., Krkkale (Turkey)

2004-11-01

Turkey is located at the Mediterranean at 36 deg and 42 deg N latitudes and has a typical Mediterranean climate. The solar energy potential is very high in Turkey. The yearly average solar radiation is 3.6 kW h/m{sup 2} day, and the total yearly radiation period is {approx}2610 h. This study consists of two cases. Firstly, the main focus of this study is to put forward the solar energy potential in Turkey using artificial neural networks (ANNs). Secondly, in this study, the best approach was investigated for each station by using different learning algorithms and a logistic sigmoid transfer function in the neural network with developed software. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (Ankara, Samsun, Edirne, Istanbul-Goztepe, Van, Izmir, Denizli, Sanl urfa, Mersin, Adana, Gaziantep, Ayd n, Bursa, Diyarbak r, Yozgat, Antalya and Mugla) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration and mean temperature) are used in the input layer of the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.735% and R{sup 2} values were found to be about 99.893% for the testing stations. However, these values were found to be 4.398% and 99.965% for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar potential values from the ANN are given in the form of monthly maps. These maps are of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Turkey. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the

Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data

International Nuclear Information System (INIS)

Soezen, Adnan; Arcaklioglu, Erol; Oezalp, Mehmet

2004-01-01

Turkey is located at the Mediterranean at 36 deg. and 42 deg. N latitudes and has a typical Mediterranean climate. The solar energy potential is very high in Turkey. The yearly average solar radiation is 3.6 kW h/m 2 day, and the total yearly radiation period is ∼2610 h. This study consists of two cases. Firstly, the main focus of this study is to put forward the solar energy potential in Turkey using artificial neural networks (ANNs). Secondly, in this study, the best approach was investigated for each station by using different learning algorithms and a logistic sigmoid transfer function in the neural network with developed software. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (Ankara, Samsun, Edirne, Istanbul-Goeztepe, Van, Izmir, Denizli, Sanliurfa, Mersin, Adana, Gaziantep, Aydin, Bursa, Diyarbakir, Yozgat, Antalya and Mugla) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration and mean temperature) are used in the input layer of the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.735% and R 2 values were found to be about 99.893% for the testing stations. However, these values were found to be 4.398% and 99.965% for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar potential values from the ANN are given in the form of monthly maps. These maps are of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Turkey. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar

Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

Energy Technology Data Exchange (ETDEWEB)

Yousefi, M.; Omid, M.; Rafiee, Sh. [Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of); Ghaderi, S.F. [Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

2013-07-01

Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.

Chiral topological phases from artificial neural networks

Science.gov (United States)

Kaubruegger, Raphael; Pastori, Lorenzo; Budich, Jan Carl

2018-05-01

Motivated by recent progress in applying techniques from the field of artificial neural networks (ANNs) to quantum many-body physics, we investigate to what extent the flexibility of ANNs can be used to efficiently study systems that host chiral topological phases such as fractional quantum Hall (FQH) phases. With benchmark examples, we demonstrate that training ANNs of restricted Boltzmann machine type in the framework of variational Monte Carlo can numerically solve FQH problems to good approximation. Furthermore, we show by explicit construction how n -body correlations can be kept at an exact level with ANN wave functions exhibiting polynomial scaling with power n in system size. Using this construction, we analytically represent the paradigmatic Laughlin wave function as an ANN state.

Application of neural networks in coastal engineering - An overview

Digital Repository Service at National Institute of Oceanography (India)

Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.

Artificial Neural Network (ANN) is being applied to solve a wide variety of coastal/ocean engineering problems. In practical terms ANNs are non-linear modeling tools and they can be used to model complex relationship between the input and output...

Gross domestic product estimation based on electricity utilization by artificial neural network

Science.gov (United States)

Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

2018-01-01

The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.

Science.gov (United States)

Singh, Nandita; Chakrapani, G J

2015-08-01

The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, the source of Bhagirathi River in the Himalaya. Discharge, rainfall and temperature have been considered as the main controlling factors of variations in sediment concentration in the dynamic glacial environment of Gangotri. Fourteen feed forward neural networks with error back propagation algorithm have been created, trained and tested for prediction of sediment concentration. Seven models (T1-T7) have been trained and tested in the non-updating mode whereas remaining seven models (T1a-T7a) have been trained in the updating mode. The non-updating mode refers to the scenario where antecedent time (previous time step) values are not used as input to the model. In case of the updating mode, antecedent time values are used as network inputs. The inputs applied in the models are either the variables mentioned above as individual factors (single input networks) or a combination of them (multi-input networks). The suitability of employing antecedent time-step values as network inputs has hence been checked by comparative analysis of model performance in the two modes. The simple feed forward network has been improvised with a series parallel non-linear autoregressive with exogenous input (NARX) architecture wherein true values of sediment concentration have been fed as input during training. In the glacial scenario of Gangotri, maximum sediment movement takes place during the melt period (May-October). Hence, daily data of discharge, rainfall, temperature and sediment concentration for five consecutive melt periods (May-October, 2000-2004) have been used for modelling. High Coefficient of determination values [0.77-0.88] have been obtained between observed and ANN-predicted values of sediment concentration. The study has brought out relationships between variables that are not reflected in normal statistical analysis. A

Educational Technology Network: a computer conferencing system dedicated to applications of computers in radiology practice, research, and education.

Science.gov (United States)

D'Alessandro, M P; Ackerman, M J; Sparks, S M

1993-11-01

Educational Technology Network (ET Net) is a free, easy to use, on-line computer conferencing system organized and funded by the National Library of Medicine that is accessible via the SprintNet (SprintNet, Reston, VA) and Internet (Merit, Ann Arbor, MI) computer networks. It is dedicated to helping bring together, in a single continuously running electronic forum, developers and users of computer applications in the health sciences, including radiology. ET Net uses the Caucus computer conferencing software (Camber-Roth, Troy, NY) running on a microcomputer. This microcomputer is located in the National Library of Medicine's Lister Hill National Center for Biomedical Communications and is directly connected to the SprintNet and the Internet networks. The advanced computer conferencing software of ET Net allows individuals who are separated in space and time to unite electronically to participate, at any time, in interactive discussions on applications of computers in radiology. A computer conferencing system such as ET Net allows radiologists to maintain contact with colleagues on a regular basis when they are not physically together. Topics of discussion on ET Net encompass all applications of computers in radiological practice, research, and education. ET Net has been in successful operation for 3 years and has a promising future aiding radiologists in the exchange of information pertaining to applications of computers in radiology.

Development of LC-MS determination method and back-propagation ANN pharmacokinetic model of corynoxeine in rat.

Science.gov (United States)

Ma, Jianshe; Cai, Jinzhang; Lin, Guanyang; Chen, Huilin; Wang, Xianqin; Wang, Xianchuan; Hu, Lufeng

2014-05-15

Corynoxeine(CX), isolated from the extract of Uncaria rhynchophylla, is a useful and prospective compound in the prevention and treatment for vascular diseases. A simple and selective liquid chromatography mass spectrometry (LC-MS) method was developed to determine the concentration of CX in rat plasma. The chromatographic separation was achieved on a Zorbax SB-C18 (2.1 mm × 150 mm, 5 μm) column with acetonitrile-0.1% formic acid in water as mobile phase. Selective ion monitoring (SIM) mode was used for quantification using target ions m/z 383 for CX and m/z 237 for the carbamazepine (IS). After the LC-MS method was validated, it was applied to a back-propagation artificial neural network (BP-ANN) pharmacokinetic model study of CX in rats. The results showed that after intravenous administration of CX, it was mainly distributed in blood and eliminated quickly, t1/2 was less than 1h. The predicted concentrations generated by BP-ANN model had a high correlation coefficient (R>0.99) with experimental values. The developed BP-ANN pharmacokinetic model can be used to predict the concentration of CX in rats. Copyright © 2014 Elsevier B.V. All rights reserved.

Networks dynamics in the case of emerging technologies

Energy Technology Data Exchange (ETDEWEB)

Rotolo, D

2016-07-01

This research in progress aims at increasing our understanding of how collaborative networks form, evolve and are configured in the case of emerging technologies. The architecture of the relationships among the variety of organisational actors involved in the emergence process exerts a significant influence in shaping technological change in certain directions rather than others, especially in the early stage of emergence. As a result, socially optimal or desirable technological trajectories may be ‘opportunistically’ rejected. Our empirical analysis is based on a case-study of an emerging medical technology, namely ‘microneedles’. On the basis of co-authorship data reported in 1,943 publications on the topic from 1990 to 2014, longitudinal collaboration (co-authorship) networks were built at two levels: affiliation and author. We examined the dynamics of co-authorship networks by building on recent methodological advancements in network analysis, i.e. Exponential Random Graph Models (ERGMs). These models enable us to make statistical inferences about on the extent to which a network configuration occurs more than could be expected by chance and to identify which social mechanisms may be shaping the network in certain configurations. The findings of the statistical analyses (currently in progress) combined with the qualitative understanding of the case will increase our understanding of which mechanisms are more likely to drive the network dynamics in the case of emerging technologies. These include evidence of the extent to which the likelihood of forming, maintaining, or terminating ties among actors (authors or affiliations) is affected by actors’ covariates such as types of organisations, diversity/specialisation of the research undertaken, and status. These findings have potential to provide important inputs for policymaking process in the case of emerging technologies. (Author)

Networks dynamics in the case of emerging technologies

International Nuclear Information System (INIS)

Rotolo, D

2016-01-01

This research in progress aims at increasing our understanding of how collaborative networks form, evolve and are configured in the case of emerging technologies. The architecture of the relationships among the variety of organisational actors involved in the emergence process exerts a significant influence in shaping technological change in certain directions rather than others, especially in the early stage of emergence. As a result, socially optimal or desirable technological trajectories may be ‘opportunistically’ rejected. Our empirical analysis is based on a case-study of an emerging medical technology, namely ‘microneedles’. On the basis of co-authorship data reported in 1,943 publications on the topic from 1990 to 2014, longitudinal collaboration (co-authorship) networks were built at two levels: affiliation and author. We examined the dynamics of co-authorship networks by building on recent methodological advancements in network analysis, i.e. Exponential Random Graph Models (ERGMs). These models enable us to make statistical inferences about on the extent to which a network configuration occurs more than could be expected by chance and to identify which social mechanisms may be shaping the network in certain configurations. The findings of the statistical analyses (currently in progress) combined with the qualitative understanding of the case will increase our understanding of which mechanisms are more likely to drive the network dynamics in the case of emerging technologies. These include evidence of the extent to which the likelihood of forming, maintaining, or terminating ties among actors (authors or affiliations) is affected by actors’ covariates such as types of organisations, diversity/specialisation of the research undertaken, and status. These findings have potential to provide important inputs for policymaking process in the case of emerging technologies. (Author)

Neural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensing

Science.gov (United States)

Lin, Bin; An, Jubai; Brown, Carl E.; Chen, Weiwei

2003-05-01

In this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

Institute of Scientific and Technical Information of China (English)

X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

2003-01-01

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

Science.gov (United States)

Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy

2017-12-01

Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

Fault tolerance of artificial neural networks with applications in critical systems

Science.gov (United States)

Protzel, Peter W.; Palumbo, Daniel L.; Arras, Michael K.

1992-01-01

This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

Prediction of temperature and HAZ in thermal-based processes with Gaussian heat source by a hybrid GA-ANN model

Science.gov (United States)

Fazli Shahri, Hamid Reza; Mahdavinejad, Ramezanali

2018-02-01

Thermal-based processes with Gaussian heat source often produce excessive temperature which can impose thermally-affected layers in specimens. Therefore, the temperature distribution and Heat Affected Zone (HAZ) of materials are two critical factors which are influenced by different process parameters. Measurement of the HAZ thickness and temperature distribution within the processes are not only difficult but also expensive. This research aims at finding a valuable knowledge on these factors by prediction of the process through a novel combinatory model. In this study, an integrated Artificial Neural Network (ANN) and genetic algorithm (GA) was used to predict the HAZ and temperature distribution of the specimens. To end this, a series of full factorial design of experiments were conducted by applying a Gaussian heat flux on Ti-6Al-4 V at first, then the temperature of the specimen was measured by Infrared thermography. The HAZ width of each sample was investigated through measuring the microhardness. Secondly, the experimental data was used to create a GA-ANN model. The efficiency of GA in design and optimization of the architecture of ANN was investigated. The GA was used to determine the optimal number of neurons in hidden layer, learning rate and momentum coefficient of both output and hidden layers of ANN. Finally, the reliability of models was assessed according to the experimental results and statistical indicators. The results demonstrated that the combinatory model predicted the HAZ and temperature more effective than a trial-and-error ANN model.

Latest Trends in Home Networking Technologies

Science.gov (United States)

Tsutsui, Akihiro

Broadband access service, including FTTH, is now in widespread use in Japan. More than half of the households that have broadband Internet access construct local area networks (home networks) in their homes. In addition, information appliances such as personal computers, networked audio, and visual devices and game machines are connected to home networks, and many novel service applications are provided via the Internet. However, it is still difficult to install and incorporate these devices and services because networked devices have been developed in different communities. I briefly explain the current status of information appliances and home networking technologies and services and discuss some of the problems in this and their solutions.

Proceedings of a Conference on Telecommunication Technologies, Networkings and Libraries

Science.gov (United States)

Knight, N. K.

1981-12-01

Current and developing technologies for digital transmission of image data likely to have an impact on the operations of libraries and information centers or provide support for information networking are reviewed. Technologies reviewed include slow scan television, teleconferencing, and videodisc technology and standards development for computer network interconnection through hardware and software, particularly packet switched networks computer network protocols for library and information service applications, the structure of a national bibliographic telecommunications network; and the major policy issues involved in the regulation or deregulation of the common communications carriers industry.

Design and value of service oriented technologies for smart business networking

NARCIS (Netherlands)

Alt, R.; Smits, M.T.; Beverungen, D.; Tuunanen, T.; Wijnhoven, F.

2014-01-01

Business networks that effectively use technologies and outperform competing networks are known as smart business networks. Theory hypothesizes that smart business networking requires a ‘Networked Business Operating System’ (NBOS), a technological architecture consisting of business logic, that

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

Directory of Open Access Journals (Sweden)

Ridha Djemal

2017-01-01

Full Text Available Autism spectrum disorder (ASD is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD of autism ‎based on electroencephalography (EEG signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT, entropy (En, and artificial neural network (ANN. DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

Neural Network Based Load Frequency Control for Restructuring ...

African Journals Online (AJOL)

Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

Neural network model for proton-proton collision at high energy

International Nuclear Information System (INIS)

El-Bakry, M.Y.; El-Metwally, K.A.

2003-01-01

Developments in artificial intelligence (AI) techniques and their applications to physics have made it feasible to develop and implement new modeling techniques for high-energy interactions. In particular, AI techniques of artificial neural networks (ANN) have recently been used to design and implement more effective models. The primary purpose of this paper is to model the proton-proton (p-p) collision using the ANN technique. Following a review of the conventional techniques and an introduction to the neural network, the paper presents simulation test results using an p-p based ANN model trained with experimental data. The p-p based ANN model calculates the multiplicity distribution of charged particles and the inelastic cross section of the p-p collision at high energies. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness

The application of artificial neural networks in astronomy

Science.gov (United States)

Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

2006-12-01

Artificial Neural Networks (ANNs) are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds: supervised and unsupervised. The supervised approach lays the stress on "teaching" a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful when the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness and dynamically rapid response as well as strong capability of dealing with non-linear problems. In the last few years there has been an increasing interest toward the astronomical applications of ANNs. In this paper, the authors firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network toplogies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. They summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. Here, the focus is on some of the most interesting fields of its application, for example: object detection, star/galaxy classification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been

A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain

Directory of Open Access Journals (Sweden)

Patricia Jimeno-Sáez

2018-02-01

Full Text Available Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT and Artificial Neural Network (ANN models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during different periods of flows using regional flow duration curves (FDCs. Specifically, the FDCs were divided into five sectors: very low, low, medium, high and very high flow. This segmentation of flow allows analysis of the model performance for every important discharge event precisely. In this study, the models were applied in two catchments in Peninsular Spain with contrasting climatic conditions: Atlantic and Mediterranean climates. The results indicate that SWAT and ANNs were generally good tools in daily streamflow modelling. However, SWAT was found to be more successful in relation to better simulation of lower flows, while ANNs were superior at estimating higher flows in all cases.

Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

Science.gov (United States)

Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

2016-08-01

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous

Network Gateway Technology: The Issue of Redundancy towards ...

African Journals Online (AJOL)

The Internet has provided advancement in the areas of network and networking facilities. Everyone connected to the Internet is concerned about two basic things: the availability of network services and the speed of the network. Network gateway redundancy technology falls within these categories and happens to be one of ...

Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN

Directory of Open Access Journals (Sweden)

Ali Reza Ghanizadeh

2014-01-01

Full Text Available Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000 four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS and Artificial Neural Network (ANN methods were then employed to predict the effective length (i.e., frequency of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation.

Development of an ANN optimized mucoadhesive buccal tablet containing flurbiprofen and lidocaine for dental pain

Directory of Open Access Journals (Sweden)

Hussain Amjad

2016-06-01

Full Text Available A novel mucoadhesive buccal tablet containing flurbiprofen (FLB and lidocaine HCl (LID was prepared to relieve dental pain. Tablet formulations (F1-F9 were prepared using variable quantities of mucoadhesive agents, hydroxypropyl methyl cellulose (HPMC and sodium alginate (SA. The formulations were evaluated for their physicochemical properties, mucoadhesive strength and mucoadhesion time, swellability index and in vitro release of active agents. Release of both drugs depended on the relative ratio of HPMC:SA. However, mucoadhesive strength and mucoadhesion time were better in formulations, containing higher proportions of HPMC compared to SA. An artificial neural network (ANN approach was applied to optimise formulations based on known effective parameters (i.e., mucoadhesive strength, mucoadhesion time and drug release, which proved valuable. This study indicates that an effective buccal tablet formulation of flurbiprofen and lidocaine can be prepared via an optimized ANN approach.

Optimal factor evaluation for the dissolution of alumina from Azaraegbelu clay in acid solution using RSM and ANN comparative analysis

Directory of Open Access Journals (Sweden)

P.E. Ohale

2017-12-01

Full Text Available Artificial neural network (ANN and Response Surface Methodology based on a 25−1 fractional factorial design were used as tools for simulation and optimisation of the dissolution process for Azaraegbelu clay. A feedforward neural network model with Levenberg–Marquard back propagating training algorithm was adapted to predict the response (alumina yield. The studied input variables were temperature, stirring speed, clay to acid dosage, leaching time and leachant concentration. The raw clay was characterized for structure elucidation via FTIR, SEM and X-ray diffraction spectroscopic techniques and the result indicates that the clay is predominantly kaolinite. Leachant concentration and dosage ratio were found to be the most significant process parameter with p-value of 0.0001. The performance of the ANN and RSM model showed adequate prediction of the response, with AAD of 11.6% and 3.6%, and R2 of 0.9733 and 0.9568, respectively. A non-dominated optimal response of 81.45% yield of alumina at 4.6 M sulphuric acid concentration, 214 min leaching time, 0.085 g/ml dosage and 214 rpm stirring speed was established as a viable route for reduced material and operating cost via RSM. Keywords: Alumina dissolution, ANN modelling, Azaraegbelu, Clay, RSM

Ant colony optimization and neural networks applied to nuclear power plant monitoring

International Nuclear Information System (INIS)

Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez

2015-01-01

A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)

Ant colony optimization and neural networks applied to nuclear power plant monitoring

Energy Technology Data Exchange (ETDEWEB)

Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez, E-mail: gean@usp.br, E-mail: delvonei@ipen.br, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2015-07-01

A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

Science.gov (United States)

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

降雨が流出に影響を及ぼす日数のANN^※を利用した推測

OpenAIRE

山田, 幸寿; 四俵, 正俊

2000-01-01

Groundwater runoff is originated from the rain of the past. The influential period of rain on groundwater runoff is said to be from one month to one year. The authors carried out long term runoff estimation for Shonai River Basin, Chubu, Japan by means of artificial neural networks (ANN). The period of strong influence of rain on the runoff was sought by comparing the accuracy of estimations with various periods of rain used as inputs of ANN. One month was found probable as the influential pe...

Seafloor classification using acoustic backscatter echo-waveform - Artificial neural network applications

Digital Repository Service at National Institute of Oceanography (India)

Chakraborty, B.; Mahale, V.; Navelkar, G.S.; Desai, R.G.P.

In this paper seafloor classifications system based on artificial neural network (ANN) has been designed. The ANN architecture employed here is a combination of Self Organizing Feature Map (SOFM) and Linear Vector Quantization (LVQ1). Currently...

Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

Science.gov (United States)

Kim, Seongjung; Kim, Jongman; Ahn, Soonjae; Kim, Youngho

2018-04-18

Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam

Directory of Open Access Journals (Sweden)

A. El-Shafie

2011-03-01

Full Text Available Artificial neural networks (ANN have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces problems. One of the major shortcomings is the search for the optimal input pattern in order to enhance the forecasting capabilities for the output. The second challenge is the over-fitting problem during the training procedure and this occurs when ANN loses its generalization. In this research, autocorrelation and cross correlation analyses are suggested as a method for searching the optimal input pattern. On the other hand, two generalized methods namely, Regularized Neural Network (RNN and Ensemble Neural Network (ENN models are developed to overcome the drawbacks of classical ANN models. Using Generalized Neural Network (GNN helped avoid over-fitting of training data which was observed as a limitation of classical ANN models. Real inflow data collected over the last 130 years at Lake Nasser was used to train, test and validate the proposed model. Results show that the proposed GNN model outperforms non-generalized neural network and conventional auto-regressive models and it could provide accurate inflow forecasting.

Artificial neural network modelling for organic and total nitrogen removal of aerobic granulation under steady-state condition.

Science.gov (United States)

Gong, H; Pishgar, R; Tay, J H

2018-04-27

Aerobic granulation is a recent technology with high level of complexity and sensitivity to environmental and operational conditions. Artificial neural networks (ANNs), computational tools capable of describing complex non-linear systems, are the best fit to simulate aerobic granular bioreactors. In this study, two feedforward backpropagation ANN models were developed to predict chemical oxygen demand (Model I) and total nitrogen removal efficiencies (Model II) of aerobic granulation technology under steady-state condition. Fundamentals of ANN models and the steps to create them were briefly reviewed. The models were respectively fed with 205 and 136 data points collected from laboratory-, pilot-, and full-scale studies on aerobic granulation technology reported in the literature. Initially, 60%, 20%, and 20%, and 80%, 10%, and 10% of the points in the corresponding datasets were randomly chosen and used for training, testing, and validation of Model I, and Model II, respectively. Overall coefficient of determination (R 2 ) value and mean squared error (MSE) of the two models were initially 0.49 and 15.5, and 0.37 and 408, respectively. To improve the model performance, two data division methods were used. While one method is generic and potentially applicable to other fields, the other can only be applied to modelling the performance of aerobic granular reactors. R 2 value and MSE were improved to 0.90 and 2.54, and 0.81 and 121.56, respectively, after applying the new data division methods. The results demonstrated that ANN-based models were capable simulation approach to predict a complicated process like aerobic granulation.

Reservoir parameter estimation using a hybrid neural network

Energy Technology Data Exchange (ETDEWEB)

Aminzadeh, F. [DGB USA and FACT Inc., Sugarland, TX (United States); Barhen, J.; Glover, C.W. [Oak Ridge National Laboratory (United States). Center for Engineering Systems Advanced Resesarch; Toomarian, N.B. [California Institute of Technology (United States). Jet Propulsion Laboratory

2000-10-01

The accuracy of an artificial neural network (ANN) algorithm is a crucial issue in the estimation of an oil field's reservoir properties from the log and seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN's accuracy statistic from a finite sample set. In addition, we also show that an ANN's classification accuracy is dramatically improved by transforming the ANN's input feature space to a dimensionally smaller new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN's convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These techniques for estimating ANN accuracy bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data. (author)

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs.

Science.gov (United States)

Abbas, Adel Taha; Pimenov, Danil Yurievich; Erdakov, Ivan Nikolaevich; Taha, Mohamed Adel; Soliman, Mahmoud Sayed; El Rayes, Magdy Mostafa

2018-05-16

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth⁻Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness ( Ra ) prediction of one component in computer numerical control (CNC) turning over minimal machining time ( T m ) and at prime machining costs ( C ). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra , T m , and C , in relation to cutting speed, v c , depth of cut, a p , and feed per revolution, f r . For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values v c , a p , and f r . The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, T m = 0.358 min/cm³, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed v c = 250 m/min, cutting depth a p = 1.0 mm, and feed per revolution f r = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

Introducing Artificial Neural Networks through a Spreadsheet Model

Science.gov (United States)

Rienzo, Thomas F.; Athappilly, Kuriakose K.

2012-01-01

Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

[Algorithms of artificial neural networks--practical application in medical science].

Science.gov (United States)

Stefaniak, Bogusław; Cholewiński, Witold; Tarkowska, Anna

2005-12-01

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer applications of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. This paper presents practical aspects of scientific application of ANN in medicine using widely available algorithms. Several main steps of analysis with ANN were discussed starting from material selection and dividing it into groups, to the quality assessment of obtained results at the end. The most frequent, typical reasons for errors as well as the comparison of ANN method to the modeling by regression analysis were also described.

Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

Science.gov (United States)

Yu, Peigen; Low, Mei Yin; Zhou, Weibiao

2018-01-01

In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by dosing solutions of 0.1% green tea extract with differing concentrations of eight flavour keys deemed to be important for green tea aroma and taste, based on a D-optimal experimental design, before undergoing commercial sterilisation. Sensory evaluation of the green tea model system was carried out using an untrained consumer panel to obtain hedonic liking scores of the samples. Regression models were subsequently trained to objectively predict the consumer liking scores of the green tea model systems. A linear partial least squares (PLS) regression model was developed to describe the effects of the eight flavour keys on consumer liking, with a coefficient of determination (R 2 ) of 0.733, and a root-mean-square error (RMSE) of 3.53%. The PLS model was further augmented with an artificial neural network (ANN) to establish a PLS-ANN hybrid model. The established hybrid model was found to give a better prediction of consumer liking scores, based on its R 2 (0.875) and RMSE (2.41%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Mass storage technology in networks

Science.gov (United States)

Ishii, Katsunori; Takeda, Toru; Itao, Kiyoshi; Kaneko, Reizo

1990-08-01

Trends and features of mass storage subsystems in network are surveyed and their key technologies spotlighted. Storage subsystems are becoming increasingly important in new network systems in which communications and data processing are systematically combined. These systems require a new class of high-performance mass-information storage in order to effectively utilize their processing power. The requirements of high transfer rates, high transactional rates and large storage capacities, coupled with high functionality, fault tolerance and flexibility in configuration, are major challenges in storage subsystems. Recent progress in optical disk technology has resulted in improved performance of on-line external memories to optical disk drives, which are competing with mid-range magnetic disks. Optical disks are more effective than magnetic disks in using low-traffic random-access file storing multimedia data that requires large capacity, such as in archive use and in information distribution use by ROM disks. Finally, it demonstrates image coded document file servers for local area network use that employ 130mm rewritable magneto-optical disk subsystems.

US long distance fiber optic networks: Technology, evolution and advanced concepts. Volume 2: Fiber optic technology and long distance networks

Science.gov (United States)

1986-10-01

The study projects until 2000 the evolution of long distance fiber optic networks in the U.S. Volume 1 is the Executive Summary. Volume 2 focuses on fiber optic components and systems that are directly related to the operation of long-haul networks. Optimistic, pessimistic and most likely scenarios of technology development are presented. The activities of national and regional companies implementing fiber long haul networks are also highlighted, along with an analysis of the market and regulatory forces affecting network evolution. Volume 3 presents advanced fiber optic network concept definitions. Inter-LATA traffic is quantified and forms the basis for the construction of 11-, 15-, 17-, and 23-node networks. Using the technology projections from Volume 2, a financial model identifies cost drivers and determines circuit mile costs between any two LATAs. A comparison of fiber optics with alternative transmission concludes the report.

Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose

Directory of Open Access Journals (Sweden)

Hui-Qin Zou

2014-01-01

Full Text Available Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area. Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers’ safety and efficacy. In recent decades, electronic nose (E-nose has been studied as an alternative approach. In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN classification model. Feature selection algorithms, including principal component analysis (PCA and BestFirst + CfsSubsetEval (BC, were applied in the improvement of RBF-ANN models. Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100% remained the same as in the original model with higher-dimension data. It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants. This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model.

Creation of a European network dedicated to technology transfer

CERN Multimedia

2008-01-01

The CERN Council recently approved the creation of a technology transfer network, whose aim will be to improve European industry’s access to the technologies developed by the particle physics community in the Member States. The gas detectors for the TOTEM experiment (GEM) offer potential for fruitful collaboration within the framework of the TT network. Many other technologies are going down the same road.The desire to set up a technology transfer network follows on from the European Strategy for Particle Physics, approved by the CERN Council on 14 July 2006 in Lisbon. In this context, special emphasis was laid on European industry’s participation in the implementation of particle physics programmes and, in particular, its access to the new technologies developed by the scientific community. It was recognised that effort needs to be put into improving the efficiency of technology transfer...

Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology

International Nuclear Information System (INIS)

Torres-Ramírez, M.; Elizondo, D.; García-Domingo, B.; Nofuentes, G.; Talavera, D.L.

2015-01-01

This work is aimed at verifying that in sunny inland locations artificial intelligence techniques may provide an estimation of the spectral irradiance with adequate accuracy for photovoltaic applications. An ANN (artificial neural network) based method was developed, trained and tested to model the spectral distributions between wavelengths ranging from 350 to 1050 nm. Only commonly available input data such as geographical information regarding location, specific date and time together with horizontal global irradiance and ambient temperature are required. Historical information from a 24-month experimental campaign carried out in Jaén (Spain) provided the necessary data to train and test the ANN tool. A Kohonen self-organized map was used as innovative technique to classify the whole input dataset and build a small and representative training dataset. The shape of the spectral irradiance distribution, the in-plane global irradiance (G T ) and irradiation (H T ) and the APE (average photon energy) values obtained through the ANN method were statistically compared to the experimental ones. In terms of shape distribution fitting, the mean relative deformation error stays below 4.81%. The root mean square percentage error is around 6.89% and 0.45% when estimating G T and APE, respectively. Regarding H T , errors lie below 3.18% in all cases. - Highlights: • ANN-based model to estimate the spectral irradiance distribution in sunny inland locations. • MRDE value stay below 4.81% in spectral irradiance distribution shape fitting. • RMSPE is about 6.89% for the in-plane global irradiance and 0.45% for the average photon energy. • Errors stay below 3.18% for all the months of the year in incident irradiation terms. • Improvement of assessment of the impact of the solar spectrum in the performance of a PV module

Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview

Science.gov (United States)

Rao, B. K. N.; Srinivasa Pai, P.; Nagabhushana, T. N.

2012-05-01

Rolling - Element Bearings are extensively used in almost all global industries. Any critical failures in these vitally important components would not only affect the overall systems performance but also its reliability, safety, availability and cost-effectiveness. Proactive strategies do exist to minimise impending failures in real time and at a minimum cost. Continuous innovative developments are taking place in the field of Artificial Neural Networks (ANNs) technology. Significant research and development are taking place in many universities, private and public organizations and a wealth of published literature is available highlighting the potential benefits of employing ANNs in intelligently monitoring, diagnosing, prognosing and managing rolling-element bearing failures. This paper attempts to critically review the recent trends in this topical area of interest.

Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview

International Nuclear Information System (INIS)

Rao, B K N; Pai, P Srinivasa; Nagabhushana, T N

2012-01-01

Rolling - Element Bearings are extensively used in almost all global industries. Any critical failures in these vitally important components would not only affect the overall systems performance but also its reliability, safety, availability and cost-effectiveness. Proactive strategies do exist to minimise impending failures in real time and at a minimum cost. Continuous innovative developments are taking place in the field of Artificial Neural Networks (ANNs) technology. Significant research and development are taking place in many universities, private and public organizations and a wealth of published literature is available highlighting the potential benefits of employing ANNs in intelligently monitoring, diagnosing, prognosing and managing rolling-element bearing failures. This paper attempts to critically review the recent trends in this topical area of interest.

Artificial Neural Network Analysis of Xinhui Pericarpium Citri ...

African Journals Online (AJOL)

Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer ... N-hexane (HPLC grade) was purchased from. Fisher Scientific. ..... Simultaneous Quantification of Seven Flavonoids in.

Copula Entropy coupled with Wavelet Neural Network Model for Hydrological Prediction

Science.gov (United States)

Wang, Yin; Yue, JiGuang; Liu, ShuGuang; Wang, Li

2018-02-01

Artificial Neural network(ANN) has been widely used in hydrological forecasting. in this paper an attempt has been made to find an alternative method for hydrological prediction by combining Copula Entropy(CE) with Wavelet Neural Network(WNN), CE theory permits to calculate mutual information(MI) to select Input variables which avoids the limitations of the traditional linear correlation(LCC) analysis. Wavelet analysis can provide the exact locality of any changes in the dynamical patterns of the sequence Coupled with ANN Strong non-linear fitting ability. WNN model was able to provide a good fit with the hydrological data. finally, the hybrid model(CE+WNN) have been applied to daily water level of Taihu Lake Basin, and compared with CE ANN, LCC WNN and LCC ANN. Results showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models.

[Anne Arold. Kontrastive Analyse...] / Paul Alvre

Index Scriptorium Estoniae

Alvre, Paul, 1921-2008

2001-01-01

Arvustus: Arold, Anne. Kontrastive analyse der Wortbildungsmuster im Deutschen und im Estnischen (am Beispiel der Aussehensadjektive). Tartu, 2000. (Dissertationes philologiae germanicae Universitatis Tartuensis)

Estimation of Optimum Dilution in the GMAW Process Using Integrated ANN-GA

Directory of Open Access Journals (Sweden)

P. Sreeraj

2013-01-01

Full Text Available To improve the corrosion resistant properties of carbon steel, usually cladding process is used. It is a process of depositing a thick layer of corrosion resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. By cladding these properties can be achieved with minimum cost. The main problem faced on cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to optimize various input process parameters (welding current, welding speed, gun angle, and contact tip to work distance and pinch to get optimum dilution in stainless steel cladding of low carbon structural steel plates using gas metal arc welding (GMAW. Experiments were conducted based on central composite rotatable design with full replication technique, and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. In this study, artificial neural network (ANN and genetic algorithm (GA techniques were integrated and labeled as integrated ANN-GA to estimate optimal process parameters in GMAW to get optimum dilution.

Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

International Nuclear Information System (INIS)

Correa, R.; Chesta, M.A.; Morales, J.R.; Dinator, M.I.; Requena, I.; Vila, I.

2006-01-01

An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses

Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

Energy Technology Data Exchange (ETDEWEB)

Correa, R. [Universidad Tecnologica Metropolitana, Departamento de Fisica, Av. Jose Pedro Alessandri 1242, Nunoa, Santiago (Chile)]. E-mail: rcorrea@utem.cl; Chesta, M.A. [Universidad Nacional de Cordoba, Facultad de Matematica, Astronomia y Fisica, Medina Allende s/n Ciudad Universitaria, 5000 Cordoba (Argentina)]. E-mail: chesta@famaf.unc.edu.ar; Morales, J.R. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: rmorales@uchile.cl; Dinator, M.I. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: mdinator@uchile.cl; Requena, I. [Universidad de Granada, Departamento de Ciencias de la Computacion e Inteligencia Artificial, Daniel Saucedo Aranda s/n, 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es; Vila, I. [Universidad de Chile, Facultad de Ciencias, Departamento de Ecologia, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: limnolog@uchile.cl

2006-08-15

An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

Directory of Open Access Journals (Sweden)

T. Pasupathi

2014-05-01

Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

Application of artificial neural networks in the analysis of multi-particle data

International Nuclear Information System (INIS)

Kunze, M.

1995-01-01

During the past years artificial neural networks (ANN) have gained increasing interest not only in the regime of financial forecast and data mining, but also in the field of particle physics. Up to now artificial neural networks have mostly been applied in high energy physics trigger studies. The use of ANNs in medium energy physics data analysis is summarized. (author). 21 refs., 9 figs

ANN Synthesis Model of Single-Feed Corner-Truncated Circularly Polarized Microstrip Antenna with an Air Gap for Wideband Applications

Directory of Open Access Journals (Sweden)

Zhongbao Wang

2014-01-01

Full Text Available A computer-aided design model based on the artificial neural network (ANN is proposed to directly obtain patch physical dimensions of the single-feed corner-truncated circularly polarized microstrip antenna (CPMA with an air gap for wideband applications. To take account of the effect of the air gap, an equivalent relative permittivity is introduced and adopted to calculate the resonant frequency and Q-factor of square microstrip antennas for obtaining the training data sets. ANN architectures using multilayered perceptrons (MLPs and radial basis function networks (RBFNs are compared. Also, six learning algorithms are used to train the MLPs for comparison. It is found that MLPs trained with the Levenberg-Marquardt (LM algorithm are better than RBFNs for the synthesis of the CPMA. An accurate model is achieved by using an MLP with three hidden layers. The model is validated by the electromagnetic simulation and measurements. It is enormously useful to antenna engineers for facilitating the design of the single-feed CPMA with an air gap.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

Directory of Open Access Journals (Sweden)

Yang Liu

2015-01-01

Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Photovoltaic generator. Estimate of the energy produced by neural networks; Generador fotovoltaico. Estimacion de la energia producida mediante redes neuronales

Energy Technology Data Exchange (ETDEWEB)

Almonacid, F.; Rus, C.; Perez-Higueras, P.; Hontoria, L.

2010-07-01

Despite the great technological advances in photovoltaic and in particular in network-connected systems, efforts are still required in research, technological development and innovation (i + d + i) must be aimed primarily at addressing the different system parts. one aspect that can help achieve this goal is majorette estimation methods of energy produced by photovoltaic generators. There are a number of cases resulting in a decrease of the expected energy. In this paper we will compare a standard method widely used in the estimation of the power of the photovoltaic generator with another novel method, developed at the University of Jaen, based on artificial neural networks (ANN). (Author) 9 refs.

[The research of near-infrared blood glucose measurement using particle swarm optimization and artificial neural network].

Science.gov (United States)

Dai, Juan; Ji, Zhong; Du, Yubao

2017-08-01

Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.

Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO Using an Artificial Neural Network-Genetic Algorithm (ANN-GA

Directory of Open Access Journals (Sweden)

Xuedan Shi

2017-06-01

Full Text Available Rhodamine B (Rh B is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM, X-ray diffraction (XRD, Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM and artificial neural network hybridized with genetic algorithm (ANN-GA. The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0% was determined using the ANN-GA model, which was compatible with the experimental value (86.4%. Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model.

Mobile Computing and Ubiquitous Networking: Concepts, Technologies and Challenges.

Science.gov (United States)

Pierre, Samuel

2001-01-01

Analyzes concepts, technologies and challenges related to mobile computing and networking. Defines basic concepts of cellular systems. Describes the evolution of wireless technologies that constitute the foundations of mobile computing and ubiquitous networking. Presents characterization and issues of mobile computing. Analyzes economical and…

Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification

Directory of Open Access Journals (Sweden)

Vijay G. S.

2012-01-01

Full Text Available The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR and reducing the root-mean-square error (RMSE. In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN and the Support Vector Machine (SVM, for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher’s Criterion (FC. Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal.

FPGA implementation of adaptive ANN controller for speed regulation of permanent magnet stepper motor drives

Energy Technology Data Exchange (ETDEWEB)

Hasanien, Hany M., E-mail: Hanyhasanien@ieee.or [Dept. of Elec. Power and Machines, Faculty of Eng., Ain Shams Univ., Cairo (Egypt)

2011-02-15

This paper presents a novel adaptive artificial neural network (ANN) controller, which applies on permanent magnet stepper motor (PMSM) for regulating its speed. The dynamic response of the PMSM with the proposed controller is studied during the starting process under the full load torque and under load disturbance. The effectiveness of the proposed adaptive ANN controller is then compared with that of the conventional PI controller. The proposed methodology solves the problem of nonlinearities and load changes of PMSM drives. The proposed controller ensures fast and accurate dynamic response with an excellent steady state performance. Matlab/Simulink tool is used for this dynamic simulation study. The main contribution of this work is the implementation of the proposed controller on field programmable gate array (FPGA) hardware to drive the stepper motor. The driver is built on FPGA Spartan-3E Starter from Xilinx. Experimental results are presented to demonstrate the validity and effectiveness of the proposed control scheme.

ANN based controller for three phase four leg shunt active filter for power quality improvement

Directory of Open Access Journals (Sweden)

J. Jayachandran

2016-03-01

Full Text Available In this paper, an artificial neural network (ANN based one cycle control (OCC strategy is proposed for the DSTATCOM shunted across the load in three phase four wire distribution system. The proposed control strategy mitigates harmonic/reactive currents, ensures balanced and sinusoidal source current from the supply mains that are nearly in phase with the supply voltage and compensates neutral current under varying source and load conditions. The proposed control strategy is superior over conventional methods as it eliminates, the sensors needed for sensing load current and coupling inductor current, in addition to the multipliers and the calculation of reference currents. ANN controllers are implemented to maintain voltage across the capacitor and as a compensator to compensate neutral current. The DSTATCOM performance is validated for all possible conditions of source and load by simulation using MATLAB software and simulation results prove the efficacy of the proposed control over conventional control strategy.

FPGA implementation of adaptive ANN controller for speed regulation of permanent magnet stepper motor drives

International Nuclear Information System (INIS)

Hasanien, Hany M.

2011-01-01

This paper presents a novel adaptive artificial neural network (ANN) controller, which applies on permanent magnet stepper motor (PMSM) for regulating its speed. The dynamic response of the PMSM with the proposed controller is studied during the starting process under the full load torque and under load disturbance. The effectiveness of the proposed adaptive ANN controller is then compared with that of the conventional PI controller. The proposed methodology solves the problem of nonlinearities and load changes of PMSM drives. The proposed controller ensures fast and accurate dynamic response with an excellent steady state performance. Matlab/Simulink tool is used for this dynamic simulation study. The main contribution of this work is the implementation of the proposed controller on field programmable gate array (FPGA) hardware to drive the stepper motor. The driver is built on FPGA Spartan-3E Starter from Xilinx. Experimental results are presented to demonstrate the validity and effectiveness of the proposed control scheme.

Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

DEFF Research Database (Denmark)

Buus, S.; Lauemoller, S.L.; Worning, Peder

2003-01-01

We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict...

Knowledge network for medical technology management in Mexico.

Science.gov (United States)

Licona, Fabiola Martínez; Leehan, Joaquín Azpiroz; Méndez, Miguel Cadena; Yuriar, Salvador Duarte; Salazar, Raúl Molina; Gilmore, Amador Terán

2009-10-01

The role of biomedical engineers (BMEs) has changed widely over the years, from managing a group of technicians to the planning of large installations and the management of medical technology countrywide. As the technology has advanced, the competence of BMEs has been challenged because it is no longer possible to be an expert in every component of the technology involved in running a hospital. Our approach has been to form a network of professionals that are experts in different fields related to medical technology, where work is coordinated to provide high quality services at the planning and execution stages of projects related to medical technology. A study of the procedures involved in the procurement of medical technology has been carried out over the years. These experiences have been compared with several case studies where the approach to problem solving in this area has been multidisciplinary. Planning and execution phases of projects involving medical technology management have been identified. After several instances of collaboration among experts from different fields, a network for management of healthcare technology has been formed at our institution that incorporates the experience from different departments that were dealing separately with projects involving medical technology. This network has led us to propose this approach to solve medical technology management projects, where the strengths of each subgroup complement each other. This structure will lead to a more integrated approach to healthcare technology management and will ensure higher quality solutions.

Gap Filling of Daily Sea Levels by Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Lyubka Pashova

2013-06-01

Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.

Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

KAUST Repository

AlShahrani, Mona

2015-01-01

Artificial Neural Networks (ANNs) are known to be among the most effective and expressive machine learning models. Their impressive abilities to learn have been reflected in many broad application domains such as image recognition, medical diagnosis, online banking, robotics, dynamic systems, and many others. ANNs with multiple layers of complex non-linear transformations (a.k.a Deep ANNs) have shown recently successful results in the area of computer vision and speech recognition. ANNs are parametric models that approximate unknown functions in which parameter values (weights) are adapted during training. ANN’s weights can be large in number and thus render the trained model more complex with chances for “overfitting” training data. In this study, we explore the effects of network pruning on performance of ANNs and ranking of features that describe the data. Simplified ANN model results in fewer parameters, less computation and faster training. We investigate the use of Hessian-based pruning algorithms as well as simpler ones (i.e. non Hessian-based) on nine datasets with varying number of input features and ANN parameters. The Hessian-based Optimal Brain Surgeon algorithm (OBS) is robust but slow. Therefore a faster parallel Hessian- approximation is provided. An additional speedup is provided using a variant we name ‘Simple n Optimal Brain Surgeon’ (SNOBS), which represents a good compromise between robustness and time efficiency. For some of the datasets, the ANN pruning experiments show on average 91% reduction in the number of ANN parameters and about 60% - 90% in the number of ANN input features, while maintaining comparable or better accuracy to the case when no pruning is applied. Finally, we show through a comprehensive comparison with seven state-of-the art feature filtering methods that the feature selection and ranking obtained as a byproduct of the ANN pruning is comparable in accuracy to these methods.

Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

KAUST Repository

AlShahrani, Mona

2015-05-24

Artificial Neural Networks (ANNs) are known to be among the most effective and expressive machine learning models. Their impressive abilities to learn have been reflected in many broad application domains such as image recognition, medical diagnosis, online banking, robotics, dynamic systems, and many others. ANNs with multiple layers of complex non-linear transformations (a.k.a Deep ANNs) have shown recently successful results in the area of computer vision and speech recognition. ANNs are parametric models that approximate unknown functions in which parameter values (weights) are adapted during training. ANN’s weights can be large in number and thus render the trained model more complex with chances for “overfitting” training data. In this study, we explore the effects of network pruning on performance of ANNs and ranking of features that describe the data. Simplified ANN model results in fewer parameters, less computation and faster training. We investigate the use of Hessian-based pruning algorithms as well as simpler ones (i.e. non Hessian-based) on nine datasets with varying number of input features and ANN parameters. The Hessian-based Optimal Brain Surgeon algorithm (OBS) is robust but slow. Therefore a faster parallel Hessian- approximation is provided. An additional speedup is provided using a variant we name ‘Simple n Optimal Brain Surgeon’ (SNOBS), which represents a good compromise between robustness and time efficiency. For some of the datasets, the ANN pruning experiments show on average 91% reduction in the number of ANN parameters and about 60% - 90% in the number of ANN input features, while maintaining comparable or better accuracy to the case when no pruning is applied. Finally, we show through a comprehensive comparison with seven state-of-the art feature filtering methods that the feature selection and ranking obtained as a byproduct of the ANN pruning is comparable in accuracy to these methods.

Application of neural networks in experimental physics

International Nuclear Information System (INIS)

Kisel', I.V.; Neskromnyj, V.N.; Ososkov, G.A.

1993-01-01

The theoretical foundations of numerous models of artificial neural networks (ANN) and their applications to the actual problems of associative memory, optimization and pattern recognition are given. This review contains also numerous using of ANN in the experimental physics both as the hardware realization of fast triggering systems for even selection and for the following software implementation of the trajectory data recognition

A frequency-domain approach to improve ANNs generalization quality via proper initialization.

Science.gov (United States)

Chaari, Majdi; Fekih, Afef; Seibi, Abdennour C; Hmida, Jalel Ben

2018-08-01

The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e., generalization capabilities. As an alternative to the conventional time-domain methods, the proposed approach eliminates the approximate nature of network validation using an excess of unseen data. The benefits of the proposed approach are demonstrated using two numerical examples, where two trained networks performed similarly on the training and the validation data sets, yet they revealed a significant difference in prediction accuracy when tested using a different data set. This observation is of utmost importance in modeling applications requiring a high degree of accuracy. The efficiency of the proposed approach is further demonstrated on a real-world problem, where unlike other initialization methods, a more conclusive assessment of generalization is achieved. On the practical front, subtle methodological and implementational facets are addressed to ensure reproducibility and pinpoint the limitations of the proposed approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

Raingauge-Based Rainfall Nowcasting with Artificial Neural Network

Science.gov (United States)

Liong, Shie-Yui; He, Shan

2010-05-01

Rainfall forecasting and nowcasting are of great importance, for instance, in real-time flood early warning systems. Long term rainfall forecasting demands global climate, land, and sea data, thus, large computing power and storage capacity are required. Rainfall nowcasting's computing requirement, on the other hand, is much less. Rainfall nowcasting may use data captured by radar and/or weather stations. This paper presents the application of Artificial Neural Network (ANN) on rainfall nowcasting using data observed at weather and/or rainfall stations. The study focuses on the North-East monsoon period (December, January and February) in Singapore. Rainfall and weather data from ten stations, between 2000 and 2006, were selected and divided into three groups for training, over-fitting test and validation of the ANN. Several neural network architectures were tried in the study. Two architectures, Backpropagation ANN and Group Method of Data Handling ANN, yielded better rainfall nowcasting, up to two hours, than the other architectures. The obtained rainfall nowcasts were then used by a catchment model to forecast catchment runoff. The results of runoff forecast are encouraging and promising.With ANN's high computational speed, the proposed approach may be deliverable for creating the real-time flood early warning system.

Nuclear power plant status diagnostics using artificial neural networks

International Nuclear Information System (INIS)

Bartlett, E.B.; Uhrig, R.E.

1991-01-01

In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results

Secure, Mobile, Wireless Network Technology Designed, Developed, and Demonstrated

Science.gov (United States)

Ivancic, William D.; Paulsen, Phillip E.

2004-01-01

The inability to seamlessly disseminate data securely over a high-integrity, wireless broadband network has been identified as a primary technical barrier to providing an order-of-magnitude increase in aviation capacity and safety. Secure, autonomous communications to and from aircraft will enable advanced, automated, data-intensive air traffic management concepts, increase National Air Space (NAS) capacity, and potentially reduce the overall cost of air travel operations. For the first time ever, secure, mobile, network technology was designed, developed, and demonstrated with state-ofthe- art protocols and applications by a diverse, cooperative Government-industry team led by the NASA Glenn Research Center. This revolutionary technology solution will make fundamentally new airplane system capabilities possible by enabling secure, seamless network connections from platforms in motion (e.g., cars, ships, aircraft, and satellites) to existing terrestrial systems without the need for manual reconfiguration. Called Mobile Router, the new technology autonomously connects and configures networks as they traverse from one operating theater to another. The Mobile Router demonstration aboard the Neah Bay, a U.S. Coast Guard vessel stationed in Cleveland, Ohio, accomplished secure, seamless interoperability of mobile network systems across multiple domains without manual system reconfiguration. The Neah Bay was chosen because of its low cost and communications mission similarity to low-Earth-orbiting satellite platforms. This technology was successfully advanced from technology readiness level (TRL) 2 (concept and/or application formation) to TRL 6 (system model or prototype demonstration in a relevant environment). The secure, seamless interoperability offered by the Mobile Router and encryption device will enable several new, vehicle-specific and systemwide technologies to perform such things as remote, autonomous aircraft performance monitoring and early detection and

[The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

Science.gov (United States)

Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

2016-03-01

The Fourier Transform Infrared Spectroscopy (FTIR) is established to find the geographic origins of Chinese wolfberry quickly. In the paper, the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR. The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform. Compared with common windows shifting smoothing preprocessing, standard normal variation correction and multiplicative scatter correction, wavelet transform is an effective spectrum data preprocessing method. Before establishing model through the artificial neural networks, the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks, and at the same time the related parameters of the artificial neural networks model are also discussed in detail. The survey shows even if the infrared spectroscopy data is compressed to 1/8 of its original data, the spectral information and analytical accuracy are not deteriorated. The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network (BP-ANN) model and the geographic origins of Chinese wolfberry are used for parameters of export. Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network. The number of hidden layer neurons is 5, and the number of output layer neuron is 1. The transfer function of hidden layer is tansig, while the transfer function of output layer is purelin. Network training function is trainl and the learning function of weights and thresholds is learngdm. net. trainParam. epochs=1 000, while net. trainParam. goal = 0.001. The recognition rate of 100% is to be achieved. It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry. The infrared spectral analysis technology

Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

Energy Technology Data Exchange (ETDEWEB)

Eswari J, Satya; Chandrakar, Neha [National Institute of Technology Raipur, Raipur (India)

2016-04-15

Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

Towards the systematic development of medical networking technology.

Science.gov (United States)

Faust, Oliver; Shetty, Ravindra; Sree, S Vinitha; Acharya, Sripathi; Acharya U, Rajendra; Ng, E Y K; Poo, Chua Kok; Suri, Jasjit

2011-12-01

Currently, there is a disparity in the availability of doctors between urban and rural areas of developing countries. Most experienced doctors and specialists, as well as advanced diagnostic technologies, are available in urban areas. People living in rural areas have less or sometimes even no access to affordable healthcare facilities. Increasing the number of doctors and charitable medical hospitals or deploying advanced medical technologies in these areas might not be economically feasible, especially in developing countries. We need to mobilize science and technology to master this complex, large scale problem in an objective, logical, and professional way. This can only be achieved with a collaborative effort where a team of experts works on both technical and non-technical aspects of this health care divide. In this paper we use a systems engineering framework to discuss hospital networks which might be solution for the problem. We argue that with the advancement in communication and networking technologies, economically middle class people and even some rural poor have access to internet and mobile communication systems. Thus, Hospital Digital Networking Technologies (HDNT), such as telemedicine, can be developed to utilize internet, mobile and satellite communication systems to connect primitive rural healthcare centers to well advanced modern urban setups and thereby provide better consultation and diagnostic care to the needy people. This paper describes requirements and limitations of the HDNTs. It also presents the features of telemedicine, the implementation issues and the application of wireless technologies in the field of medical networking.

Arabic Handwriting Recognition Using Neural Network Classifier

African Journals Online (AJOL)

pc

2018-03-05

Mar 5, 2018 ... an OCR using Neural Network classifier preceded by a set of preprocessing .... Artificial Neural Networks (ANNs), which we adopt in this research, consist of ... advantage and disadvantages of each technique. In [9],. Khemiri ...

Router Agent Technology for Policy-Based Network Management

Science.gov (United States)

Chow, Edward T.; Sudhir, Gurusham; Chang, Hsin-Ping; James, Mark; Liu, Yih-Chiao J.; Chiang, Winston

2011-01-01

This innovation can be run as a standalone network application on any computer in a networked environment. This design can be configured to control one or more routers (one instance per router), and can also be configured to listen to a policy server over the network to receive new policies based on the policy- based network management technology. The Router Agent Technology transforms the received policies into suitable Access Control List syntax for the routers it is configured to control. It commits the newly generated access control lists to the routers and provides feedback regarding any errors that were faced. The innovation also automatically generates a time-stamped log file regarding all updates to the router it is configured to control. This technology, once installed on a local network computer and started, is autonomous because it has the capability to keep listening to new policies from the policy server, transforming those policies to router-compliant access lists, and committing those access lists to a specified interface on the specified router on the network with any error feedback regarding commitment process. The stand-alone application is named RouterAgent and is currently realized as a fully functional (version 1) implementation for the Windows operating system and for CISCO routers.

[Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

Science.gov (United States)

Liang, Xue; Ji, Hai-yan; Wang, Peng-xin; Rao, Zhen-hong; Shen, Bing-hui

2010-01-01

Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.

ANN-GA based optimization of a high ash coal-fired supercritical power plant

International Nuclear Information System (INIS)

Suresh, M.V.J.J.; Reddy, K.S.; Kolar, Ajit Kumar

2011-01-01

Highlights: → Neuro-genetic power plant optimization is found to be an efficient methodology. → Advantage of neuro-genetic algorithm is the possibility of on-line optimization. → Exergy loss in combustor indicates the effect of coal composition on efficiency. -- Abstract: The efficiency of coal-fired power plant depends on various operating parameters such as main steam/reheat steam pressures and temperatures, turbine extraction pressures, and excess air ratio for a given fuel. However, simultaneous optimization of all these operating parameters to achieve the maximum plant efficiency is a challenging task. This study deals with the coupled ANN and GA based (neuro-genetic) optimization of a high ash coal-fired supercritical power plant in Indian climatic condition to determine the maximum possible plant efficiency. The power plant simulation data obtained from a flow-sheet program, 'Cycle-Tempo' is used to train the artificial neural network (ANN) to predict the energy input through fuel (coal). The optimum set of various operating parameters that result in the minimum energy input to the power plant is then determined by coupling the trained ANN model as a fitness function with the genetic algorithm (GA). A unit size of 800 MWe currently under development in India is considered to carry out the thermodynamic analysis based on energy and exergy. Apart from optimizing the design parameters, the developed model can also be used for on-line optimization when quick response is required. Furthermore, the effect of various coals on the thermodynamic performance of the optimized power plant is also determined.

Artificial neural network models for biomass gasification in fluidized bed gasifiers

DEFF Research Database (Denmark)

Puig Arnavat, Maria; Hernández, J. Alfredo; Bruno, Joan Carles

2013-01-01

Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine...

Applying Trusted Network Technology To Process Control Systems

Science.gov (United States)

Okhravi, Hamed; Nicol, David

Interconnections between process control networks and enterprise networks expose instrumentation and control systems and the critical infrastructure components they operate to a variety of cyber attacks. Several architectural standards and security best practices have been proposed for industrial control systems. However, they are based on older architectures and do not leverage the latest hardware and software technologies. This paper describes new technologies that can be applied to the design of next generation security architectures for industrial control systems. The technologies are discussed along with their security benefits and design trade-offs.

Comparative study on the predictability of statistical models (RSM and ANN) on the behavior of optimized buccoadhesive wafers containing Loratadine and their in vivo assessment.

Science.gov (United States)

Chakraborty, Prithviraj; Parcha, Versha; Chakraborty, Debarupa D; Ghosh, Amitava

2016-01-01

Buccoadhesive wafer dosage form containing Loratadine is formulated utilizing Formulation by Design (FbD) approach incorporating sodium alginate and lactose monohydrate as independent variable employing solvent casting method. The wafers were statistically optimized using Response Surface Methodology (RSM) and Artificial Neural Network algorithm (ANN) for predicting physicochemical and physico-mechanical properties of the wafers as responses. Morphologically wafers were tested using SEM. Quick disintegration of the samples was examined employing Optical Contact Angle (OCA). The comparison of the predictability of RSM and ANN showed a high prognostic capacity of RSM model over ANN model in forecasting mechanical and physicochemical properties of the wafers. The in vivo assessment of the optimized buccoadhesive wafer exhibits marked increase in bioavailability justifying the administration of Loratadine through buccal route, bypassing hepatic first pass metabolism.

Predicting pressure drop in venturi scrubbers with artificial neural networks.

Science.gov (United States)

Nasseh, S; Mohebbi, A; Jeirani, Z; Sarrafi, A

2007-05-08

In this study a new approach based on artificial neural networks (ANNs) has been used to predict pressure drop in venturi scrubbers. The main parameters affecting the pressure drop are mainly the gas velocity in the throat of venturi scrubber (V(g)(th)), liquid to gas flow rate ratio (L/G), and axial distance of the venturi scrubber (z). Three sets of experimental data from five different venturi scrubbers have been applied to design three independent ANNs. Comparing the results of these ANNs and the calculated results from available models shows that the results of ANNs have a better agreement with experimental data.

Research on Network Defense Strategy Based on Honey Pot Technology

Science.gov (United States)

Hong, Jianchao; Hua, Ying

2018-03-01

As a new network security technology of active defense, The honeypot technology has become a very effective and practical method of decoy attackers. The thesis discusses the theory, structure, characteristic, design and implementation of Honeypot in detail. Aiming at the development of means of attack, put forward a kind of network defense technology based on honeypot technology, constructing a virtual Honeypot demonstrate the honeypot’s functions.

Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce

Directory of Open Access Journals (Sweden)

Alcinei Mistico Azevedo

2015-12-01

Full Text Available The efficiency of artificial neural networks (ANN to model complex problems may enable the prediction of characteristics that are hard to measure, providing better results than the traditional indirect selection. Thus, this study aimed to investigate the potential of using artificial neural networks (ANN for indirect selection against early flowering in lettuce, identify the influence of genotype by environment interaction in this strategy and compare your results with the traditional indirect selection. The number of days to anthesis were used as the desired output and the information of six characteristics (fresh weight of shoots, mass of marketable fresh matter of shoots, commercial dry matter of shoots, average diameter of the head, head circumference and leaf number as input file for the training of the ANN-MLP (Perceptron Multi-Layer. The use of ANN has great potential adjustment for indirect selection for genetic improvement of lettuce against early flowering. The selection based on the predicted values by network provided estimates of gain selection largest that traditional indirect selection. The ANN trained with data from an experiment have low power extrapolation to another experiment, due to effect of interaction genotype by environment. The ANNs trained simultaneously with data from different experiments presented greater predictive power and extrapolation.

Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks

Science.gov (United States)

Nikelshpur, Dmitry O.

2014-01-01

Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…

Inverse problems using ANN in long range atmospheric dispersion with signature analysis picked scattered numerical sensors from CFD

International Nuclear Information System (INIS)

Sharma, Pavan K.; Gera, B.; Ghosh, A.K.; Kushwaha, H.S.

2010-01-01

Scalar dispersion in the atmosphere is an important area wherein different approaches are followed in development of good analytical model. The analyses based on Computational Fluid Dynamics (CFD) codes offer an opportunity of model development based on first principles of physics and hence such models have an edge over the existing models. Both forward and backward calculation methods are being developed for atmospheric dispersion around NPPs at BARC Forward modeling methods, which describe the atmospheric transport from sources to receptors, use forward-running transport and dispersion models or computational fluid dynamics models which are run many times, and the resulting dispersion field is compared to observations from multiple sensors. Backward or inverse modeling methods use only one model run in the reverse direction from the receptors to estimate the upwind sources. Inverse modeling methods include adjoint and tangent linear models, Kalman filters, and variational data assimilation, and neural network. The present paper is aimed at developing a new approach where the identified specific signatures at receptor points form the basis for source estimation or inversions. This approach is expected to reduce the large transient data sets to reduced and meaningful data sets. In fact this reduces the inherently transient data set into a time independent mean data set. Forward computation were carried out with CFD code for various case to generate a large set of data to train the ANN. Specific signature analysis was carried out to find the parameters of interest for ANN training like peak concentration, time to reach peak concentration and time to fall, the ANN was trained with data and source strength and location were predicted from ANN. Inverse problem was performed using ANN approach in long range atmospheric dispersion. An illustration of application of CFD code for atmospheric dispersion studies for a hypothetical case is also included in the paper. (author)

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

Science.gov (United States)

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

A study of using smartphone to detect and identify construction workers' near-miss falls based on ANN

Science.gov (United States)

Zhang, Mingyuan; Cao, Tianzhuo; Zhao, Xuefeng

2018-03-01

As an effective fall accident preventive method, insight into near-miss falls provides an efficient solution to find out the causes of fall accidents, classify the type of near-miss falls and control the potential hazards. In this context, the paper proposes a method to detect and identify near-miss falls that occur when a worker walks in a workplace based on artificial neural network (ANN). The energy variation generated by workers who meet with near-miss falls is measured by sensors embedded in smart phone. Two experiments were designed to train the algorithm to identify various types of near-miss falls and test the recognition accuracy, respectively. At last, a test was conducted by workers wearing smart phones as they walked around a simulated construction workplace. The motion data was collected, processed and inputted to the trained ANN to detect and identify near-miss falls. Thresholds were obtained to measure the relationship between near-miss falls and fall accidents in a quantitate way. This approach, which integrates smart phone and ANN, will help detect near-miss fall events, identify hazardous elements and vulnerable workers, providing opportunities to eliminate dangerous conditions in a construction site or to alert possible victims that need to change their behavior before the occurrence of a fall accident.

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Directory of Open Access Journals (Sweden)

Lei Chen

2017-06-01

Full Text Available Event-based runoff–pollutant relationships have been the key for water quality management, but the scarcity of measured data results in poor model performance, especially for multiple rainfall events. In this study, a new framework was proposed for event-based non-point source (NPS prediction and evaluation. The artificial neural network (ANN was used to extend the runoff–pollutant relationship from complete data events to other data-scarce events. The interpolation method was then used to solve the problem of tail deviation in the simulated pollutographs. In addition, the entropy method was utilized to train the ANN for comprehensive evaluations. A case study was performed in the Three Gorges Reservoir Region, China. Results showed that the ANN performed well in the NPS simulation, especially for light rainfall events, and the phosphorus predictions were always more accurate than the nitrogen predictions under scarce data conditions. In addition, peak pollutant data scarcity had a significant impact on the model performance. Furthermore, these traditional indicators would lead to certain information loss during the model evaluation, but the entropy weighting method could provide a more accurate model evaluation. These results would be valuable for monitoring schemes and the quantitation of event-based NPS pollution, especially in data-poor catchments.

The Role of Electronic Learning Technology in Networks Systems

International Nuclear Information System (INIS)

Abd ELhamid, A.; Ayad, N.M.A.; Fouad, Y.; Abdelkader, T.

2016-01-01

Recently, Electronic Learning Technology (ELT) has been widely spread as one of the new technologies in the world through using Information and Communication Technology (ICT). One of the strategies of ELT is Simulation, for instance Military and Medical simulations that are used to avoid risks and reduce Costs. A wireless communication network refers to any network not physically connected by cables, which enables the desired convenience and mobility for the user. Wireless communication networks have been useful in areas such as commerce, education and defense. According to the nature of a particular application, they can be used in home-based and industrial systems or in commercial and military environments. Historically, Mobile Ad-hoc Networks (MANET) have primarily been used for tactical military network related applications to improve battlefield communications/ survivability. MANET is a collection of wireless nodes that can dynamically be set up anywhere and anytime without using any pre-existing network infrastructure. Mobility in wireless networks basically refers to nodes changing its point of attachment to the network. Also, how the end terminals can move, there are many mobility models described the movement of nodes, many researchers use the Random Way point Mobility Model (RWPM). In this paper, a Graphical User Interface (GUI) for RWPM simulation is introduced as a proposal to be used through ELT Project. In the research area of computer and communications networks, simulation is a very useful technique for the behavior of networks

Pre-optimization of radiotherapy treatment planning: an artificial neural network classification aided technique

International Nuclear Information System (INIS)

Hosseini-Ashrafi, M.E.; Bagherebadian, H.; Yahaqi, E.

1999-01-01

A method has been developed which, by using the geometric information from treatment sample cases, selects from a given data set an initial treatment plan as a step for treatment plan optimization. The method uses an artificial neural network (ANN) classification technique to select a best matching plan from the 'optimized' ANN database. Separate back-propagation ANN classifiers were trained using 50, 60 and 77 examples for three groups of treatment case classes (up to 21 examples from each class were used). The performance of the classifiers in selecting the correct treatment class was tested using the leave-one-out method; the networks were optimized with respect their architecture. For the three groups used in this study, successful classification fractions of 0.83, 0.98 and 0.93 were achieved by the optimized ANN classifiers. The automated response of the ANN may be used to arrive at a pre-plan where many treatment parameters may be identified and therefore a significant reduction in the steps required to arrive at the optimum plan may be achieved. Treatment planning 'experience' and also results from lengthy calculations may be used for training the ANN. (author)

On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

Directory of Open Access Journals (Sweden)

Agam Gupta

2015-07-01

Full Text Available With the advancement in the field of Artificial Intelligence, there have been considerable efforts to develop technologies for pattern recognition related to medical diagnosis. Artificial Neural Networks (ANNs, a significant piece of Artificial Intelligence forms the base for most of the marvels in the former field. However, ANNs face the problem of premature convergence at a local minimum and inability to set hyper-parameters (like the number of neurons, learning rate, etc. while using Back Propagation Algorithm (BPA. In this paper, we have used the Genetic Algorithm (GA for the evolution of the ANN, which overcomes the limitations of the BPA. Since GA alone cannot fit for a high-dimensional, complex and multi-modal optimization landscape of the ANN, BPA is used as a local search algorithm to aid the evolution. The contributions of GA and BPA in the resultant approach are adjudged to determine the magnitude of local search necessary for optimization, striking a clear balance between exploration and exploitation in the evolution. The algorithm was applied to deal with the problem of Breast Cancer diagnosis. Results showed that under optimal settings, hybrid algorithm performs better than BPA or GA alone.

Evaluation of Seasonal, ANN, and Hybrid Models in Modeling Urban Water Consumption A Case Study of Rash City

Directory of Open Access Journals (Sweden)

Seyed Nematollah Mousavi

2016-09-01

Full Text Available Forecasting future water consumption in cities to plan for the required capacities in urban water supply systems (including water transmission networks and water treatment facilities depends on the application of behavioral models of uban water consumption. Being located in the North-South corridor, Rasht City is assuming a new role to play in the national economy as a foreign trade center. It will, thus, be necessary to review its present urban infrastructure in order to draft the required infrastructural development plans for meeting the city’s future water demands. The three Seasonal Autoregressive Integrated Moving Average (SARIMA, Artificial Neural Network (ANN, and SARIMABP approaches were employed in present study to model and forecast Rasht urban water consumption using monthly time series for the period 2001‒2008 of urban water consumption in Rasht. The seasonal unit root test was applied to develop the relevant SARIMA model. Results showed that all the seasonal and non-seasonal unit roots are present in all the frequencies in the monthly time series for Rasht urban water consumption. Using a proper filter, the SAIMA patterns were estimated. In a second stage the SARIMA output was used to determine the ANN output and the hybrid SARIMABP structure was accordingly constructed. The values for Rasht urban water consumption predicted by the three models indicated the superiority of the SARIMABP hybrid model as evidenced by the forecast error index of 0.41% obtained for this model. The other two models of SARIMA and ANN were, however, found to yield acceptable results for urban water managers since the forecasting error recorded for them was below 1%.

Networking Technologies for Future Home Networks Using 60 GHz Radio

OpenAIRE

Wang, J.

2010-01-01

Networking technologies have been changing the life of people in their private residential space. With the arrival of high definition (HD) multimedia services and broadband communications into the living space, future home networks are expected to support high speed device-to-device connectivity with Quality-of-Service (QoS) provisioning. There is no prize for guessing that it has to be wireless communication which creates maximal freedom. Nevertheless, it is doubtful that today's home networ...

Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks

Directory of Open Access Journals (Sweden)

Najla S Dar-Odeh

2010-05-01

Full Text Available Najla S Dar-Odeh1, Othman M Alsmadi2, Faris Bakri3, Zaer Abu-Hammour2, Asem A Shehabi3, Mahmoud K Al-Omiri1, Shatha M K Abu-Hammad4, Hamzeh Al-Mashni4, Mohammad B Saeed4, Wael Muqbil4, Osama A Abu-Hammad1 1Faculty of Dentistry, 2Faculty of Engineering and Technology, 3Faculty of Medicine, University of Jordan, Amman, Jordan; 4Dental Department, University of Jordan Hospital, Amman, JordanObjective: To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU based on a set of appropriate input data.Participants and methods: Artificial neural networks (ANN software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants.Results: The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits.Conclusions: Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily.Keywords: artifical neural networks, recurrent, aphthous ulceration, ulcer

Design of alluvial Egyptian irrigation canals using artificial neural networks method

Directory of Open Access Journals (Sweden)

Hassan Ibrahim Mohamed

2013-06-01

Full Text Available In the present study, artificial neural networks method (ANNs is used to estimate the main parameters which used in design of stable alluvial channels. The capability of ANN models to predict the stable alluvial channels dimensions is investigated, where the flow rate and sediment mean grain size were considered as input variables and wetted perimeter, hydraulic radius, and water surface slope were considered as output variables. The used ANN models are based on a back propagation algorithm to train a multi-layer feed-forward network (Levenberg Marquardt algorithm. The proposed models were verified using 311 data sets of field data collected from 61 manmade canals and drains. Several statistical measures and graphical representation are used to check the accuracy of the models in comparison with previous empirical equations. The results of the developed ANN model proved that this technique is reliable in such field compared with previously developed methods.

Ado Vabbe preemia Anne Parmastole

Index Scriptorium Estoniae

2003-01-01

Tartu Kunstimajas Tartu kunsti aastalõpunäitus. Kujundaja Mari Nõmmela. Anne Parmastole A. Vabbe, Silja Salmistule E-Kunstisalongi, Lii Jürgensonile EDA, Jüri Marranile Wilde kohviku, Sami Makkonenile AS Vunder ja Tartu Õlletehase A. Le Coq ning Eda Lõhmusele AS Merko Tartu preemia

A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator

Directory of Open Access Journals (Sweden)

Panchanand Jha

2014-07-01

Full Text Available Inverse kinematic is one of the most interesting problems of industrial robot. The inverse kinematics problem in robotics is about the determination of joint angles for a desired Cartesian position of the end effector. It comprises of the computation need to find the joint angles for a given Cartesian position and orientation of the end effectors to control a robot arm. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network is one such technique which can be gainfully used to yield the acceptable results. This paper proposes a structured artificial neural network (ANN model to find the inverse kinematics solution of a 4-dof SCARA manipulator. The ANN model used is a multi-layered perceptron neural network (MLPNN, wherein gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that multi-layered perceptron neural network gives minimum mean square error.

Estimating wheat and maize daily evapotranspiration using artificial neural network

Science.gov (United States)

Abrishami, Nazanin; Sepaskhah, Ali Reza; Shahrokhnia, Mohammad Hossein

2018-02-01

In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RHmax), minimum relative humidity (RHmin), average relative humidity (RHave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ETC) values calculated by ETC = ET0 × K C equation (ET0 from Penman-Monteith equation and K C from FAO-56, ANNC) were used as outputs, and for the other five structures, the ETC values measured by weighing lysimeter (ANNM) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN1 C (8-4-2-1), with T max, T min, RHmax, RHmin, U 2, n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN5 C (5-2-3-1), with T max, T min, Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ETC used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.

Network technology for depot modernization

Energy Technology Data Exchange (ETDEWEB)

Hostick, C.J.

1990-12-01

This report was prepared by the Pacific Northwest Laboratory to summarize existing and emerging information system technology and standards applicable to Depot System Command (DESCOM) modernization efforts. The intent of this summarization is to provide the Revitalization of Army Depots for the Year 2000 (READY 2000) team a clear understanding of the enabling information system technologies required to support effective modernization activities. Much of the information contained in this report was acquired during the last year in support of the US Army Armament, Munitions, and Chemical Command (AMCCOM) Facility Integrated Manufacturing Management System (FIMMS) project at PNL, which is targeting the modernization of plant-wide information systems at Army Ammunition Plants. The objective of information system modernization is to improve the effectiveness of an organization in performing its mission. Information system modernization strives to meet this objective by creating an environment where data is electronically captured near the source and readily available to all areas of the organization. Advanced networks, together with related information system technology, are the enabling mechanisms that make modern information system infrastructures possible. The intent of this paper is to present an overview of advanced information system network technology to support depot modernization planners in making technology management decisions. Existing and emerging Open System Interconnection (OSI) and Government Open System Interconnection Profile (GOSIP) standards are explained, as well as a brief assessment of existing products compliant with these standards. Finally, recommendations for achieving plant-wide integration using existing products are presented, and migration strategies for full OSI compliance are introduced. 5 refs., 16 figs. (JF)

Prediction of thermophysical properties of mixed refrigerants using artificial neural network

International Nuclear Information System (INIS)

Sencan, Arzu; Koese, Ismail Ilke; Selbas, Resat

2011-01-01

The determination of thermophysical properties of the refrigerants is very important for thermodynamic analysis of vapor compression refrigeration systems. In this paper, an artificial neural network (ANN) is proposed to determine properties as heat conduction coefficient, dynamic viscosity, kinematic viscosity, thermal diffusivity, density, specific heat capacity of refrigerants. Five alternative refrigerants are considered: R413A, R417A, R422A, R422D and R423A. The training and validation were performed with good accuracy. The thermophysical properties of the refrigerants are formulated using artificial neural network (ANN) methodology. Liquid and vapor thermophysical properties of refrigerants with new formulation obtained from ANN can be easily estimated. The method proposed offers more flexibility and therefore thermodynamic analysis of vapor compression refrigeration systems is fairly simplified.

BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.

Directory of Open Access Journals (Sweden)

Erxu Pi

Full Text Available Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, 'Savannah' and 'Princess VII'. Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments. Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R(2 values of germination prediction function could be significantly improved from about 0.6940-0.8177 (DQEM approach to 0.9439-0.9813 (BP-ANN-QE. These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale

A new evolutionary system for evolving artificial neural networks.

Science.gov (United States)

Yao, X; Liu, Y

1997-01-01

This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Directory of Open Access Journals (Sweden)

Wenqian Ruan

2017-12-01

Full Text Available The commercially available nanoscale zerovalent zinc (nZVZ was used as an adsorbent for the removal of malachite green (MG from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO and artificial neural network coupled with genetic algorithm (ANN-GA were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12% was compatible with the experimental value (90.72%. Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0 were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

Estimation of reservoir parameter using a hybrid neural network

Energy Technology Data Exchange (ETDEWEB)

Aminzadeh, F. [FACT, Suite 201-225, 1401 S.W. FWY Sugarland, TX (United States); Barhen, J.; Glover, C.W. [Center for Engineering Systems Advanced Research, Oak Ridge National Laboratory, Oak Ridge, TN (United States); Toomarian, N.B. [Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA (United States)

1999-11-01

Estimation of an oil field's reservoir properties using seismic data is a crucial issue. The accuracy of those estimates and the associated uncertainty are also important information. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bound on an Artificial Neural Network's (ANN) accuracy statistic from a finite sample set. In addition, we also show that an ANN's classification accuracy is dramatically improved by transforming the ANN's input feature space to a dimensionally smaller, new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN's convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These technique for estimating ANN accuracy bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data.

Artificial neural networks to forecast biomass of Pacific sardine and its environment

DEFF Research Database (Denmark)

Cisneros Mata, M.A.; Brey, T.; Jarre, Astrid

1996-01-01

We tested the forecasting performance of artificial neural networks (ANNs) using several time series of environmental and biotic data pertaining to the California Current (CC) neritic ecosystem. ANNs performed well predicting CC monthly 10-m depth temperature up to nine years in advance, using te...

Evaluation of neural networks to identify types of activity using accelerometers

NARCIS (Netherlands)

Vries, S.I. de; Garre, F.G.; Engbers, L.H.; Hildebrandt, V.H.; Buuren, S. van

2011-01-01

Purpose: To develop and evaluate two artificial neural network (ANN) models based on single-sensor accelerometer data and an ANN model based on the data of two accelerometers for the identification of types of physical activity in adults. Methods: Forty-nine subjects (21 men and 28 women; age range

Evaluation Of The Advanced Operating System Of The Ann Arbor Transportation Authority : Transfer And On-Time Performance Study : Before And After AOS Implementation, October 1996 - May 1999

Science.gov (United States)

1999-01-01

In 1997, the Ann Arbor (Michigan) Transportation Authority began deploying advanced public transportation systems (APTS) technologies in its fixed route and paratransit operations. The project's concept is the integration of a range of such technolog...

Fault Tolerant Neural Network for ECG Signal Classification Systems

Directory of Open Access Journals (Sweden)

MERAH, M.

2011-08-01

Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

Differentiating Agar wood Oil Quality Using Artificial Neural Network

International Nuclear Information System (INIS)

Nurlaila Ismail; Nor Azah Mohd Ali; Mailina Jamil; Saiful Nizam Tajuddin; Mohd Nasir Taib

2013-01-01

Agar wood oil is well known as expensive oil extracted from the resinous of fragrant heartwood. The oil is getting high demand in the market especially from the Middle East countries, China and Japan because of its unique odor. As part of an on-going research in grading the agar wood oil quality, the application of Artificial Neural Network (ANN) is proposed in this study to analyze agar wood oil quality using its chemical profiles. The work involves of selected agar wood oil from low and high quality, the extraction of chemical compounds using GC-MS and Z-score to identify of the significant compounds as input to the network. The ANN programming algorithm was developed and computed automatically via Matlab software version R2010a. Back-propagation training algorithm and sigmoid transfer function were used to optimize the parameters in the training network. The result obtained showed the capability of ANN in analyzing the agar wood oil quality hence beneficial for the further application such as grading and classification for agar wood oil. (author)

Determination of fluence-to-dose conversion coefficients by means of artificial neural networks

International Nuclear Information System (INIS)

Soto B, T. G.; Rivera P, E.; De Leon M, H. A.; Hernandez D, V. M.; Vega C, H. R.; Gallego, E.; Lorente, A.

2012-10-01

In this paper is presented an Artificial Neural Network (Ann) that has been designed, trained and validated to determinate the effective dose e, ambient dose equivalent h(10) and personal dose equivalent hp(10,θ) fluence-to-dose conversion coefficients at different positions, having as only input data 7 count rates obtained with a Bonner Sphere Spectrometer (Bss) system. A set of 211 neutron spectra and the fluence-to-dose conversion coefficients published by the International Atomic Energy Agency were used to train and validate the Ann. This set was divided into 2 subsets, one of 181 elements to train the Ann and the remaining 30 to validate it. The Ann was trained using Bss count rates as input data and the fluence-to-dose conversion coefficients as output data. The network was validated and tested with the set of 30 elements that were not used during the training process. Good results were obtained proving that Ann are a good choice for calculating the fluence-to-dose conversion coefficients having as only data the count rates obtained with a Bss. (Author)

Determination of fluence-to-dose conversion coefficients by means of artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Soto B, T. G.; Rivera P, E.; De Leon M, H. A.; Hernandez D, V. M.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego, E.; Lorente, A., E-mail: tzinnia.soto@gmail.com [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, Jose Gutierrez Abascal No. 2, 28006 Madrid (Spain)

2012-10-15

In this paper is presented an Artificial Neural Network (Ann) that has been designed, trained and validated to determinate the effective dose e, ambient dose equivalent h(10) and personal dose equivalent hp(10,{theta}) fluence-to-dose conversion coefficients at different positions, having as only input data 7 count rates obtained with a Bonner Sphere Spectrometer (Bss) system. A set of 211 neutron spectra and the fluence-to-dose conversion coefficients published by the International Atomic Energy Agency were used to train and validate the Ann. This set was divided into 2 subsets, one of 181 elements to train the Ann and the remaining 30 to validate it. The Ann was trained using Bss count rates as input data and the fluence-to-dose conversion coefficients as output data. The network was validated and tested with the set of 30 elements that were not used during the training process. Good results were obtained proving that Ann are a good choice for calculating the fluence-to-dose conversion coefficients having as only data the count rates obtained with a Bss. (Author)

Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study

Energy Technology Data Exchange (ETDEWEB)

Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu [Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 and Radiation Oncology Department, Stanford University School of Medicine, Stanford, California 94305 (United States); Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 (United States); Radiation Oncology Department, Stanford University School of Medicine, Stanford, California 94305 (United States); Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 (United States)

2011-10-15

Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE{sup 3} v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the {gamma}-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average {gamma}-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average {gamma}-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations

Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study

International Nuclear Information System (INIS)

Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu

2011-01-01

Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE 3 v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the γ-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average γ-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average γ-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations have been

An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts

Directory of Open Access Journals (Sweden)

Mahmoud Barghash

2015-01-01

Full Text Available Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN’s performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

Applications of Artificial Neural Network for the Prediction of Pool Boiling Curves

International Nuclear Information System (INIS)

Su, Guanghui; Fukuda, K.; Morita, K.

2002-01-01

Artificial neural network (ANN) has the advantage that the best-fit correlations of experimental data will no longer be necessary for predicting unknowns from the known parameters. The ANN was applied to predict the pool boiling curves in this paper. The database of experimental data presented by Berenson, Dhuga et al., and Bui and Dhir etc. were used in the analysis. The database is subdivided in two subsets. The first subset is used to train the network and the second one is used to test the network after the training process. The input parameters of the ANN are: wall superheat ΔT w , surface roughness, steady/transient heating/transient cooling, subcooling, Surface inclination and pressure. The output parameter is heat flux q. The proposed methodology allows us to achieve the accuracy that satisfies the user's convergence criterion and it is suitable for pool boiling curve data processing. (authors)

DeAnnIso: a tool for online detection and annotation of isomiRs from small RNA sequencing data.

Science.gov (United States)

Zhang, Yuanwei; Zang, Qiguang; Zhang, Huan; Ban, Rongjun; Yang, Yifan; Iqbal, Furhan; Li, Ao; Shi, Qinghua

2016-07-08

Small RNA (sRNA) Sequencing technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent variations from their canonical sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). However, integrated tool to precisely detect and systematically annotate isomiRs from sRNA sequencing data is still in great demand. Here, we present an online tool, DeAnnIso (Detection and Annotation of IsomiRs from sRNA sequencing data). DeAnnIso can detect all the isomiRs in an uploaded sample, and can extract the differentially expressing isomiRs from paired or multiple samples. Once the isomiRs detection is accomplished, detailed annotation information, including isomiRs expression, isomiRs classification, SNPs in miRNAs and tissue specific isomiR expression are provided to users. Furthermore, DeAnnIso provides a comprehensive module of target analysis and enrichment analysis for the selected isomiRs. Taken together, DeAnnIso is convenient for users to screen for isomiRs of their interest and useful for further functional studies. The server is implemented in PHP + Perl + R and available to all users for free at: http://mcg.ustc.edu.cn/bsc/deanniso/ and http://mcg2.ustc.edu.cn/bsc/deanniso/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Wireless Cognitive Networks Technologies and Protocols

OpenAIRE

Loscri , Valeria; Maskooki , Arash; Mitton , Nathalie; Vegni , Anna Maria

2015-01-01

International audience; Software Defined Radio and Cognitive Radio applied to Wireless Sensor Networks and Body Area Networks represent an intriguing and really recent paradigm, which represents an objective of study of several researchers. In order to make this technology effective, it is necessary to consider an analytical model of communication capacity, energy consumption and congestion, to effectively exploit the Software Defined Radio and Cognitive Radio in this type of systems. This ch...

Discussion on the Technology and Method of Computer Network Security Management

Science.gov (United States)

Zhou, Jianlei

2017-09-01

With the rapid development of information technology, the application of computer network technology has penetrated all aspects of society, changed people's way of life work to a certain extent, brought great convenience to people. But computer network technology is not a panacea, it can promote the function of social development, but also can cause damage to the community and the country. Due to computer network’ openness, easiness of sharing and other characteristics, it had a very negative impact on the computer network security, especially the loopholes in the technical aspects can cause damage on the network information. Based on this, this paper will do a brief analysis on the computer network security management problems and security measures.

A novel application of artificial neural network for wind speed estimation

Science.gov (United States)

Fang, Da; Wang, Jianzhou

2017-05-01

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.

Software Defined Networking Demands on Software Technologies

DEFF Research Database (Denmark)

Galinac Grbac, T.; Caba, Cosmin Marius; Soler, José

2015-01-01

Software Defined Networking (SDN) is a networking approach based on a centralized control plane architecture with standardised interfaces between control and data planes. SDN enables fast configuration and reconfiguration of the network to enhance resource utilization and service performances....... This new approach enables a more dynamic and flexible network, which may adapt to user needs and application requirements. To this end, systemized solutions must be implemented in network software, aiming to provide secure network services that meet the required service performance levels. In this paper......, we review this new approach to networking from an architectural point of view, and identify and discuss some critical quality issues that require new developments in software technologies. These issues we discuss along with use case scenarios. Here in this paper we aim to identify challenges...

Ethernet access network based on free-space optic deployment technology

Science.gov (United States)

Gebhart, Michael; Leitgeb, Erich; Birnbacher, Ulla; Schrotter, Peter

2004-06-01

The satisfaction of all communication needs from single households and business companies over a single access infrastructure is probably the most challenging topic in communications technology today. But even though the so-called "Last Mile Access Bottleneck" is well known since more than ten years and many distribution technologies have been tried out, the optimal solution has not yet been found and paying commercial access networks offering all service classes are still rare today. Conventional services like telephone, radio and TV, as well as new and emerging services like email, web browsing, online-gaming, video conferences, business data transfer or external data storage can all be transmitted over the well known and cost effective Ethernet networking protocol standard. Key requirements for the deployment technology driven by the different services are high data rates to the single customer, security, moderate deployment costs and good scalability to number and density of users, quick and flexible deployment without legal impediments and high availability, referring to the properties of optical and wireless communication. We demonstrate all elements of an Ethernet Access Network based on Free Space Optic distribution technology. Main physical parts are Central Office, Distribution Network and Customer Equipment. Transmission of different services, as well as configuration, service upgrades and remote control of the network are handled by networking features over one FSO connection. All parts of the network are proven, the latest commercially available technology. The set up is flexible and can be adapted to any more specific need if required.

Bridging the Gap from Networking Technologies to Applications: Workshop Report

Science.gov (United States)

Johnson, Marjory J.; desJardins, Richard

2000-01-01

The objective of the Next Generation Internet (NGI) Federal program is threefold, encompassing development of networking technologies, high-performance network testbeds, and revolutionary applications. There have been notable advances in emerging network technologies and several nationwide testbeds have been established, but the integration of emerging technologies into applications is lagging. To help bridge this gap between developers of NGI networking technologies and developers of NGI applications, the NASA Research and Education Network (NREN) project hosted a two-day workshop at NASA Ames Research Center in August 1999. This paper presents a summary of the results of this workshop and also describes some of the challenges NREN is facing while incorporating new technologies into HPCC and other NASA applications. The workshop focused on three technologies - Quality of Service (QoS), advanced multicast, and security-and five major NGI application areas - telemedicine, digital earth, digital video, distributed data-intensive applications, and computational infrastructure applications. Network technology experts, application developers, and NGI testbed representatives came together at the workshop to promote cross-fertilization between the groups. Presentations on the first day, including an overview of the three technologies, application case studies and testbed status reports, laid the foundation for discussions on the second day. The objective of these latter discussions, held within smaller breakout groups, was to establish a coherent picture of the current status of the various pieces of each of the three technologies, to create a roadmap outlining future technology development, and to offer technological guidance to application developers. In this paper we first present a brief overview of the NGI applications that were represented at the workshop, focusing on the identification of technological advances that have successfully been incorporated in each

Beam-orientation customization using an artificial neural network

International Nuclear Information System (INIS)

Rowbottom, C.G.; Webb, S.; Oldham, M.

1999-01-01

A methodology for the constrained customization of coplanar beam orientations in radiotherapy treatment planning using an artificial neural network (ANN) has been developed. The geometry of the patients, with cancer of the prostate, was modelled by reducing the external contour, planning target volume (PTV) and organs at risk (OARs) to a set of cuboids. The coordinates and size of the cuboids were given to the ANN as inputs. A previously developed beam-orientation constrained-customization (BOCC) scheme employing a conventional computer algorithm was used to determine the customized beam orientations in a training set containing 45 patient datasets. Twelve patient datasets not involved in the training of the artificial neural network were used to test whether the ANN was able to map the inputs to customized beam orientations. Improvements from the customized beam orientations were compared with standard treatment plans with fixed gantry angles and plans produced from the BOCC scheme. The ANN produced customized beam orientations within 5 deg. of the BOCC scheme in 62.5% of cases. The average difference in the beam orientations produced by the ANN and the BOCC scheme was 7.7 deg. (±1.7, 1 SD). Compared with the standard treatment plans, the BOCC scheme produced plans with an increase in the average tumour control probability (TCP) of 5.7% (±1.4, 1 SD) whilst the ANN generated plans increased the average TCP by 3.9% (±1.3, 1 SD). Both figures refer to the TCP at a fixed rectal normal tissue complication probability (NTCP) of 1%. In conclusion, even using a very simple model for the geometry of the patient, an ANN was able to produce beam orientations that were similar to those produced by a conventional computer algorithm. (author)

Identification of nonlinear dynamics in power plant components using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Fernandez, B.; Tsai, W.K.

1990-01-01

Advances in digital computer technology have enabled widespread implementation of closed-loop digital control systems in a variety of industries. In some instances, however, the complexity of the plant and the uncertainty associated with the parameters involved in the mathematical modeling narrow the range of applicability of most systematic control system design methodologies. A multiyear project has been initiated to assess the feasibility of the artificial neural networks (ANNs) technology for computerized enhanced diagnostics and control of nuclear power plant components. At this stage of the project, a new methodology, based on backpropagation learning, has been developed for identifying the nonlinear dynamic systems from a set of input-output data known as the training set

Ethanol production from steam exploded rapeseed straw and the process simulation using artificial neural networks

DEFF Research Database (Denmark)

Talebnia, Farid; Mighani, Moein; Rahimnejad, Mostafa

2015-01-01

and 67% of maximum theoretical value. Next, data of the experimental runs were exploited for modeling the processes by artificial neural networks (ANNs) and performance of the developed models was evaluated. The ANN-based models showed a great potential for time-course prediction of the studied processes....... Efficiency of the joint network for simulating the whole process was also determined and promising results were obtained....

Artificial neural networks in neutron dosimetry

International Nuclear Information System (INIS)

Vega-Carrillo, H. R.; Hernandez-Davila, V. M.; Manzanares-Acuna, E.; Mercado, G. A.; Gallego, E.; Lorente, A.; Perales-Munoz, W. A.; Robles-Rodriguez, J. A.

2006-01-01

An artificial neural network (ANN) has been designed to obtain neutron doses using only the count rates of a Bonner spheres spectrometer (BSS). Ambient, personal and effective neutron doses were included. One hundred and eighty-one neutron spectra were utilised to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in the BSS and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing were carried out in the MATLAB R environment. The impact of uncertainties in BSS count rates upon the dose quantities calculated with the ANN was investigated by modifying by ±5% the BSS count rates used in the training set. The use of ANNs in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem. (authors)

Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods

International Nuclear Information System (INIS)

Almonacid, F.; Rus, C.; Hontoria, L.; Munoz, F.J.

2010-01-01

The presence of PV modules made with new technologies and materials is increasing in PV market, in special Thin Film Solar Modules (TFSM). They are ready to make a substantial contribution to the world's electricity generation. Although Si wafer-based cells account for the most of increase, technologies of thin film have been those of the major growth in last three years. During 2007 they grew 133%. On the other hand, manufacturers provide ratings for PV modules for conditions referred to as Standard Test Conditions (STC). However, these conditions rarely occur outdoors, so the usefulness and applicability of the indoors characterisation in standard test conditions of PV modules is a controversial issue. Therefore, to carry out a correct photovoltaic engineering, a suitable characterisation of PV module electrical behaviour is necessary. The IDEA Research Group from Jaen University has developed a method based on artificial neural networks (ANNs) to electrical characterisation of PV modules. An ANN was able to generate V-I curves of si-crystalline PV modules for any irradiance and module cell temperature. The results show that the proposed ANN introduces a good accurate prediction for si-crystalline PV modules performance when compared with the measured values. Now, this method is going to be applied for electrical characterisation of PV CIS modules. Finally, a comparative study with other methods, of electrical characterisation, is done. (author)

Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

Science.gov (United States)

Cui, Ying; Gierl, Mark; Guo, Qi

2016-01-01

The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

Application of neural networks for the prediction of multidirectional magnetostriction

CERN Document Server

Baumgartinger, N; Pfützner, H; Krismanic, G

2000-01-01

This paper describes attempts to use artificial neural networks (ANNs) for the prediction of magnetostriction (MS) characteristics of transformer core materials. In this first approach, the ANNs were trained with data from a rotational single-sheet tester to predict MS in rolling direction (r.d.) as a function of material grade, amplitude and shape of multidirectional magnetisation as well as the level of additional mechanical stress. It is shown that ANNs are able to forecast the corresponding relative MS changes in an approximate way.

Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

International Nuclear Information System (INIS)

Schulze, P; Schmidl, E; Grund, T; Lampke, T

2016-01-01

For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels. (paper)

Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

Science.gov (United States)

Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

2016-03-01

For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

An artificial neural network model for rainfall forecasting in Bangkok, Thailand

Directory of Open Access Journals (Sweden)

N. Q. Hung

2009-08-01

Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

Anne Veesaar astus Valgas üles uudses rollis / Jaan Rapp

Index Scriptorium Estoniae

Rapp, Jaan

2010-01-01

2010. aasta iga kuu viimasel reedel esitab näitlejanna Anne Veesaar Raadio Ruudus katkendeid Valgamaa kirjanike loomingust. 14. jaanuaril kohtumisel lugejatega rääkis Valgas sündinud näitlejanna oma elulooraamatust "Anne Veesaar : elus, see on kõige tähtsam", mille on kirja pannud Helen Eelrand, ja oma praegustest tegemistest

Kõnelused Tartus / Anne Untera

Index Scriptorium Estoniae

Untera, Anne, 1951-

2007-01-01

8.-10. V Tartus toimunud eesti, läti ja saksa kunstiteadlaste ühisseminarist. Alexander Knorre rääkis Karl August Senffi, Ilona Audere Friedrich Ludwig von Maydelli, Mai Levin Karl Alexander von Winkleri, Kristiana Abele Johann Walter-Kurau (1869-1932), Anne Untera Konstantin ja Sally von Kügelgeni, Epp Preem Julie Hagen-Schwartzi, Friedrich Gross Eduard von Gebhardti ja Katharina Hadding Ida Kerkoviuse (1879-1970) loomingust

CCTV networking and digital technology

CERN Document Server

Damjanovski, Vlado

2005-01-01

Closed circuit television (CCTV) is experiencing a leap in technology using digital techniques, networking and the Internet. The new edition of this high-level professional reference retains the particulars that made the first edition a success, including the details of CCD cameras, lenses, coaxial cables, fiber-optics, and system design, but it is expanded to cover all video compression techniques used in the ever increasing assortment of digital video recorders (DVRs) available on the market today. This new edition of the book CCTV demystifies DVR technology. It also serves to clarify the te

A Network Analysis Model for Selecting Sustainable Technology

Directory of Open Access Journals (Sweden)

Sangsung Park

2015-09-01

Full Text Available Most companies develop technologies to improve their competitiveness in the marketplace. Typically, they then patent these technologies around the world in order to protect their intellectual property. Other companies may use patented technologies to develop new products, but must pay royalties to the patent holders or owners. Should they fail to do so, this can result in legal disputes in the form of patent infringement actions between companies. To avoid such situations, companies attempt to research and develop necessary technologies before their competitors do so. An important part of this process is analyzing existing patent documents in order to identify emerging technologies. In such analyses, extracting sustainable technology from patent data is important, because sustainable technology drives technological competition among companies and, thus, the development of new technologies. In addition, selecting sustainable technologies makes it possible to plan their R&D (research and development efficiently. In this study, we propose a network model that can be used to select the sustainable technology from patent documents, based on the centrality and degree of a social network analysis. To verify the performance of the proposed model, we carry out a case study using actual patent data from patent databases.

Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting

Directory of Open Access Journals (Sweden)

Jianzhong Zhou

2018-05-01

Full Text Available This paper introduces three artificial neural network (ANN architectures for monthly streamflow forecasting: a radial basis function network, an extreme learning machine, and the Elman network. Three ensemble techniques, a simple average ensemble, a weighted average ensemble, and an ANN-based ensemble, were used to combine the outputs of the individual ANN models. The objective was to highlight the performance of the general regression neural network-based ensemble technique (GNE through an improvement of monthly streamflow forecasting accuracy. Before the construction of an ANN model, data preanalysis techniques, such as empirical wavelet transform (EWT, were exploited to eliminate the oscillations of the streamflow series. Additionally, a theory of chaos phase space reconstruction was used to select the most relevant and important input variables for forecasting. The proposed GNE ensemble model has been applied for the mean monthly streamflow observation data from the Wudongde hydrological station in the Jinsha River Basin, China. Comparisons and analysis of this study have demonstrated that the denoised streamflow time series was less disordered and unsystematic than was suggested by the original time series according to chaos theory. Thus, EWT can be adopted as an effective data preanalysis technique for the prediction of monthly streamflow. Concurrently, the GNE performed better when compared with other ensemble techniques.

Group colocation behavior in technological social networks.

Directory of Open Access Journals (Sweden)

Chloë Brown

Full Text Available We analyze two large datasets from technological networks with location and social data: user location records from an online location-based social networking service, and anonymized telecommunications data from a European cellphone operator, in order to investigate the differences between individual and group behavior with respect to physical location. We discover agreements between the two datasets: firstly, that individuals are more likely to meet with one friend at a place they have not visited before, but tend to meet at familiar locations when with a larger group. We also find that groups of individuals are more likely to meet at places that their other friends have visited, and that the type of a place strongly affects the propensity for groups to meet there. These differences between group and solo mobility has potential technological applications, for example, in venue recommendation in location-based social networks.

Implementation of multi-layer feed forward neural network on PIC16F877 microcontroller

International Nuclear Information System (INIS)

Nur Aira Abd Rahman

2005-01-01

Artificial Neural Network (ANN) is an electronic model based on the neural structure of the brain. Similar to human brain, ANN consists of interconnected simple processing units or neurons that process input to generate output signals. ANN operation is divided into 2 categories; training mode and service mode. This project aims to implement ANN on PIC micro-controller that enable on-chip or stand alone training and service mode. The input can varies from sensors or switches, while the output can be used to control valves, motors, light source and a lot more. As partial development of the project, this paper reports the current status and results of the implemented ANN. The hardware fraction of this project incorporates Microchip PIC16F877A microcontrollers along with uM-FPU math co-processor. uM-FPU is a 32-bit floating point co-processor utilized to execute complex calculation requires by the sigmoid activation function for neuron. ANN algorithm is converted to software program written in assembly language. The implemented ANN structure is three layer with one hidden layer, and five neurons with two hidden neurons. To prove the operability and functionality, the network is trained to solve three common logic gate operations; AND, OR, and XOR. This paper concludes that the ANN had been successfully implemented on PIC16F877a and uM-FPU math co-processor hardware that works accordingly on both training and service mode. (Author)

Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network

Energy Technology Data Exchange (ETDEWEB)

Kurt, Hueseyin; Atik, Kemal; Oezkaymak, Mehmet; Recebli, Ziyaddin [Zonguldak Karaelmas University, Karabuk Technical Education Faculty, 78200 Karabuk (Turkey)

2008-02-15

Work to date has shown that Artificial Neural Network (ANN) has not been used for predicting thermal performance parameters of a solar cooker. The objective of this study is to predict thermal performance parameters such as absorber plate, enclosure air and pot water temperatures of the experimentally investigated box type solar cooker by using the ANN. Data set is obtained from the box type solar cooker which was tested under various experimental conditions. A feed-forward neural network based on back propagation algorithm was developed to predict the thermal performance of solar cooker with and without reflector. Mathematical formulations derived from the ANN model are presented for each predicting temperatures. The experimental data set consists of 126 values. These were divided into two groups, of which the 96 values were used for training/learning of the network and the rest of the data (30 values) for testing/validation of the network performance. The performance of the ANN predictions was evaluated by comparing the prediction results with the experimental results. The results showed a good regression analysis with the correlation coefficients in the range of 0.9950-0.9987 and mean relative errors (MREs) in the range of 3.925-7.040% for the test data set. The regression coefficients indicated that the ANN model can successfully be used for the prediction of the thermal performance parameters of a box type solar cooker with a high degree of accuracy. (author)

Implementation of a feed-forward artificial neural network in VHDL on FPGA

NARCIS (Netherlands)

Dondon, P.; Carvalho, J.; Gardere, R.; Lahalle, P.; Tsenov, G.; Mladenov, V.M.; Reljin, B.; Stankovic, S.

2014-01-01

Describing an Artificial Neural Network (ANN) using VHDL allows a further implementation of such a system on FPGA. Indeed, the principal point of using FPGA for ANNs is flexibility that gives it an advantage toward other systems like ASICS which are entirely dedicated to one unique architecture and

Battery Performance Modelling ad Simulation: a Neural Network Based Approach

Science.gov (United States)

Ottavianelli, Giuseppe; Donati, Alessandro

2002-01-01

This project has developed on the background of ongoing researches within the Control Technology Unit (TOS-OSC) of the Special Projects Division at the European Space Operations Centre (ESOC) of the European Space Agency. The purpose of this research is to develop and validate an Artificial Neural Network tool (ANN) able to model, simulate and predict the Cluster II battery system's performance degradation. (Cluster II mission is made of four spacecraft flying in tetrahedral formation and aimed to observe and study the interaction between sun and earth by passing in and out of our planet's magnetic field). This prototype tool, named BAPER and developed with a commercial neural network toolbox, could be used to support short and medium term mission planning in order to improve and maximise the batteries lifetime, determining which are the future best charge/discharge cycles for the batteries given their present states, in view of a Cluster II mission extension. This study focuses on the five Silver-Cadmium batteries onboard of Tango, the fourth Cluster II satellite, but time restrains have allowed so far to perform an assessment only on the first battery. In their most basic form, ANNs are hyper-dimensional curve fits for non-linear data. With their remarkable ability to derive meaning from complicated or imprecise history data, ANN can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. ANNs learn by example, and this is why they can be described as an inductive, or data-based models for the simulation of input/target mappings. A trained ANN can be thought of as an "expert" in the category of information it has been given to analyse, and this expert can then be used, as in this project, to provide projections given new situations of interest and answer "what if" questions. The most appropriate algorithm, in terms of training speed and memory storage requirements, is clearly the Levenberg

An analysis of communications and networking technologies for the smart grid

Energy Technology Data Exchange (ETDEWEB)

Garcia Hernandez, Joaquin [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico)

2013-03-01

The Smart Grid concept has been foreseen as the integration of the electrical generation, transmission and distribution network and the data communications network. Although, traditional communications interfaces, protocols and standards has been used in the electrical grid in an isolated manner, modern communications network is considered as the fundamental enabling technology within the future Smart Grid. Modern communications technologies, protocol architectures and standards can help to build a common communications network infrastructure for data transport between customer premises, power substations, and power distribution systems, utility control centers and utility data centers. The Smart Grid will support traditional applications such as SCADA, distribution automation (DA), energy management systems (EMS), demand site management (DSM) and automatic meter reading (AMR), etc., as well as new applications like advanced metering infrastructure (AMI), substation automation (SA), microgrids, distributed generation, grid monitoring and control, data storage and analysis, among others. To make this possible, the Smart Grid requires a two-way wide area communications network between different dispersed areas, from generation, to distribution to consumer premises. In fact, it will consist of many different types of communications networks such as wide area networks, local area network, home area networks, etc. This requires a new architectural approach to implement a common communications infrastructure that provides the reliability, scalability, security and interoperability to support multiple applications. In addition, open standards addressing interoperability, are key for the development and deployment of the Smart Grid as a true integrated network. A communications backbone is necessary to provide interoperability. To achieve the level of networking, interoperability and security that meets the technical requirements of the Smart Grid, its data communications

Prediction of littoral drift with artificial neural networks

Digital Repository Service at National Institute of Oceanography (India)

Singh, A.K.; Deo, M.C.; SanilKumar, V.

of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables...

Optimization of multilayer neural network parameters for speaker recognition

Science.gov (United States)

Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

2016-05-01

This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

Science.gov (United States)

Aytaç Korkmaz, Sevcan; Binol, Hamidullah

2018-03-01

Patients who die from stomach cancer are still present. Early diagnosis is crucial in reducing the mortality rate of cancer patients. Therefore, computer aided methods have been developed for early detection in this article. Stomach cancer images were obtained from Fırat University Medical Faculty Pathology Department. The Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features of these images are calculated. At the same time, Sammon mapping, Stochastic Neighbor Embedding (SNE), Isomap, Classical multidimensional scaling (MDS), Local Linear Embedding (LLE), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Laplacian Eigenmaps methods are used for dimensional the reduction of the features. The high dimension of these features has been reduced to lower dimensions using dimensional reduction methods. Artificial neural networks (ANN) and Random Forest (RF) classifiers were used to classify stomach cancer images with these new lower feature sizes. New medical systems have developed to measure the effects of these dimensions by obtaining features in different dimensional with dimensional reduction methods. When all the methods developed are compared, it has been found that the best accuracy results are obtained with LBP_MDS_ANN and LBP_LLE_ANN methods.

An Overview of Computer Network security and Research Technology

OpenAIRE

Rathore, Vandana

2016-01-01

The rapid development in the field of computer networks and systems brings both convenience and security threats for users. Security threats include network security and data security. Network security refers to the reliability, confidentiality, integrity and availability of the information in the system. The main objective of network security is to maintain the authenticity, integrity, confidentiality, availability of the network. This paper introduces the details of the technologies used in...

ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks.

Science.gov (United States)

González-Díaz, Humberto; Herrera-Ibatá, Diana María; Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Orbegozo-Medina, Ricardo Alfredo; Pazos, Alejandro

2014-03-24

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.

ARTIFICIAL NEURAL NETWORK AND WAVELET DECOMPOSITION IN THE FORECAST OF GLOBAL HORIZONTAL SOLAR RADIATION

Directory of Open Access Journals (Sweden)

Luiz Albino Teixeira Júnior

2015-04-01

Full Text Available This paper proposes a method (denoted by WD-ANN that combines the Artificial Neural Networks (ANN and the Wavelet Decomposition (WD to generate short-term global horizontal solar radiation forecasting, which is an essential information for evaluating the electrical power generated from the conversion of solar energy into electrical energy. The WD-ANN method consists of two basic steps: firstly, it is performed the decomposition of level p of the time series of interest, generating p + 1 wavelet orthonormal components; secondly, the p + 1 wavelet orthonormal components (generated in the step 1 are inserted simultaneously into an ANN in order to generate short-term forecasting. The results showed that the proposed method (WD-ANN improved substantially the performance over the (traditional ANN method.

Wireless local area network. A new technology of network

International Nuclear Information System (INIS)

Wu Yunjun; Zhao Zongtao

2003-01-01

This paper introduces Wireless Local Area Network (WLAN), including the concept, history, characters and the foreground of its development, it also narrates in detail the several key techniques used to implement IEEE802.11 WLAN, and ideas on key technology of future progress in wireless LAN field have also been presented. (authors)

Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

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Chun-tian Cheng

2015-07-01

Full Text Available Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO, ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.

Neural network construction of flow of a viscoelastic fluid of a second order between two eccentric spheres

International Nuclear Information System (INIS)

Elbakry, M.Y.; El-Helly, M.; Elbakry, M.Y.

2010-01-01

Neural networks are widely for solving many scientific linear and non-linear problems. In this work ,we used the artificial neural network (ANN) to simulate and predict the torque and force acting on the outer stationary sphere due to steady state motion of the second order fluid between two eccentric spheres by a rotating inner sphere with an angular velocity Ω. the (ANN) model has been trained based on the experimental data to produce the torque and force at different eccentricities. The experimental and trained torque and force are compared. The designed ANN shows a good match to the experimental data.

APLIKASI MODEL ARTIFICIAL NEURAL NETWORKS UNTUK STOCK FORECASTING DI PASAR MODAL INDONESIA

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Christian Herdinata

2017-03-01

Full Text Available This research showed the applicat ion of model Art if icial Neural Networks (ANN orJaringan Syaraf Tiruan (JST at the f ield of monetary science, especially for the applicat ion off inancial forecast ing. ANN or JST was a new alternat ive for the applicat ion of f inancialforecast ing.The purpose of this research was to know whether the stock index instantaneouslyand fully ref lect historical informat ion, in Indonesia Stock Exchange (IDX. The research usedcomparison between return of technical t rading rule based Art if icial Neural Networks (ANNmodel and return of buy & hold st rategy. The result showed that the weakness form ofef f icient market hypothesis was rejected in the Indonesian capital market . Expectat ion ofthis research was giving informat ion and securing the market perpet rators that st ill enabledto get abnormal of return by doing commerce in chnical through forecast ing of model Art ificial Neural Networks (ANN or Jaringan Syaraf Tiruan ( JST.

A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain

OpenAIRE

Patricia Jimeno-Sáez; Javier Senent-Aparicio; Julio Pérez-Sánchez; David Pulido-Velazquez

2018-01-01

Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during differe...

Application of wireless sensor network technology in logistics information system

Science.gov (United States)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2017-04-01

This paper introduces the basic concepts of active RFID (WSN-ARFID) based on wireless sensor networks and analyzes the shortcomings of the existing RFID-based logistics monitoring system. Integrated wireless sensor network technology and the scrambling point of RFID technology. A new real-time logistics detection system based on WSN and RFID, a model of logistics system based on WSN-ARFID is proposed, and the feasibility of this technology applied to logistics field is analyzed.

Heterogeneous Wireless Mesh Network Technology Evaluation for Space Proximity and Surface Applications

Science.gov (United States)

DeCristofaro, Michael A.; Lansdowne, Chatwin A.; Schlesinger, Adam M.

2014-01-01

NASA has identified standardized wireless mesh networking as a key technology for future human and robotic space exploration. Wireless mesh networks enable rapid deployment, provide coverage in undeveloped regions. Mesh networks are also self-healing, resilient, and extensible, qualities not found in traditional infrastructure-based networks. Mesh networks can offer lower size, weight, and power (SWaP) than overlapped infrastructure-perapplication. To better understand the maturity, characteristics and capability of the technology, we developed an 802.11 mesh network consisting of a combination of heterogeneous commercial off-the-shelf devices and opensource firmware and software packages. Various streaming applications were operated over the mesh network, including voice and video, and performance measurements were made under different operating scenarios. During the testing several issues with the currently implemented mesh network technology were identified and outlined for future work.

Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

DEFF Research Database (Denmark)

Buus, S.; Lauemoller, S.L.; Worning, Peder

2003-01-01

We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict bind...... of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa....

Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

Science.gov (United States)

Everson, Howard T.; And Others

This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

Neural networks and its application in biomedical engineering

International Nuclear Information System (INIS)

Husnain, S.K.; Bhatti, M.I.

2002-01-01

Artificial network (ANNs) is an information processing system that has certain performance characteristics in common with biological neural networks. A neural network is characterized by connections between the neurons, method of determining the weights on the connections and its activation functions while a biological neuron has three types of components that are of particular interest in understanding an artificial neuron: its dendrites, soma, and axon. The actin of the chemical transmitter modifies the incoming signal. The study of neural networks is an extremely interdisciplinary field. Computer-based diagnosis is an increasingly used method that tries to improve the quality of health care. Systems on Neural Networks have been developed extensively in the last ten years with the hope that medical diagnosis and therefore medical care would improve dramatically. The addition of a symbolic processing layer enhances the ANNs in a number of ways. It is, for instance, possible to supplement a network that is purely diagnostic with a level that recommends or nodes in order to more closely simulate the nervous system. (author)

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

Science.gov (United States)

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-08-06

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs).

Science.gov (United States)

Hernández Suárez, Marcos; Astray Dopazo, Gonzalo; Larios López, Dina; Espinosa, Francisco

2015-01-01

There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA) have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID) algorithm and Artificial Neural Network (ANN) models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain). Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID) tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit's goodness between 44 and 100

Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID and Artificial Neural Network Models (ANNs.

Directory of Open Access Journals (Sweden)

Marcos Hernández Suárez

Full Text Available There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA or Factor Analysis (FA have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID algorithm and Artificial Neural Network (ANN models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain. Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit's goodness

PREDICTING THE EFFECTIVENESS OF WEB INFORMATION SYSTEMS USING NEURAL NETWORKS MODELING: FRAMEWORK & EMPIRICAL TESTING

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Dr. Kamal Mohammed Alhendawi

2018-02-01

Full Text Available The information systems (IS assessment studies have still used the commonly traditional tools such as questionnaires in evaluating the dependent variables and specially effectiveness of systems. Artificial neural networks have been recently accepted as an effective alternative tool for modeling the complicated systems and widely used for forecasting. A very few is known about the employment of Artificial Neural Network (ANN in the prediction IS effectiveness. For this reason, this study is considered as one of the fewest studies to investigate the efficiency and capability of using ANN for forecasting the user perceptions towards IS effectiveness where MATLAB is utilized for building and training the neural network model. A dataset of 175 subjects collected from international organization are utilized for ANN learning where each subject consists of 6 features (5 quality factors as inputs and one Boolean output. A percentage of 75% o subjects are used in the training phase. The results indicate an evidence on the ANN models has a reasonable accuracy in forecasting the IS effectiveness. For prediction, ANN with PURELIN (ANNP and ANN with TANSIG (ANNTS transfer functions are used. It is found that both two models have a reasonable prediction, however, the accuracy of ANNTS model is better than ANNP model (88.6% and 70.4% respectively. As the study proposes a new model for predicting IS dependent variables, it could save the considerably high cost that might be spent in sample data collection in the quantitative studies in the fields science, management, education, arts and others.

Advanced Communication and Networking Technologies for Mars Exploration

Science.gov (United States)

Bhasin, Kul; Hayden, Jeff; Agre, Jonathan R.; Clare, Loren P.; Yan, Tsun-Yee

2001-01-01

Next-generation Mars communications networks will provide communications and navigation services to a wide variety of Mars science vehicles including: spacecraft that are arriving at Mars, spacecraft that are entering and descending in the Mars atmosphere, scientific orbiter spacecraft, spacecraft that return Mars samples to Earth, landers, rovers, aerobots, airplanes, and sensing pods. In the current architecture plans, the communication services will be provided using capabilities deployed on the science vehicles as well as dedicated communication satellites that will together make up the Mars network. This network will evolve as additional vehicles arrive, depart or end their useful missions. Cost savings and increased reliability will result from the ability to share communication services between missions. This paper discusses the basic architecture that is needed to support the Mars Communications Network part of NASA's Space Science Enterprise (SSE) communications architecture. The network may use various networking technologies such as those employed in the terrestrial Internet, as well as special purpose deep-space protocols to move data and commands autonomously between vehicles, at disparate Mars vicinity sites (on the surface or in near-Mars space) and between Mars vehicles and earthbound users. The architecture of the spacecraft on-board local communications is being reconsidered in light of these new networking requirements. The trend towards increasingly autonomous operation of the spacecraft is aimed at reducing the dependence on resource scheduling provided by Earth-based operators and increasing system fault tolerance. However, these benefits will result in increased communication and software development requirements. As a result, the envisioned Mars communications infrastructure requires both hardware and protocol technology advancements. This paper will describe a number of the critical technology needs and some of the ongoing research

Identification of Lactic Acid Bacteria and Propionic Acid Bacteria using FTIR Spectroscopy and Artificial Neural Networks

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Beata Nalepa

2012-01-01

Full Text Available In the present study, lactic acid bacteria and propionic acid bacteria have been identified at the genus level with the use of artificial neural networks (ANNs and Fourier transform infrared spectroscopy (FTIR. Bacterial strains of the genera Lactobacillus, Lactococcus, Leuconostoc, Streptococcus and Propionibacterium were analyzed since they deliver health benefits and are routinely used in the food processing industry. The correctness of bacterial identification by ANNs and FTIR was evaluated at two stages. At first stage, ANNs were tested based on the spectra of 66 reference bacterial strains. At second stage, the evaluation involved 286 spectra of bacterial strains isolated from food products, deposited in our laboratory collection, and identified by genus-specific PCR. ANNs were developed based on the spectra and their first derivatives. The most satisfactory results were reported for the probabilistic neural network, which was built using a combination of W5W4W3 spectral ranges. This network correctly identified the genus of 95 % of the lactic acid bacteria and propionic acid bacteria strains analyzed.

Artificial neural network controller for automatic ship berthing using head-up coordinate system

Directory of Open Access Journals (Sweden)

Nam-Kyun Im

2018-05-01

Full Text Available The Artificial Neural Network (ANN model has been known as one of the most effective theories for automatic ship berthing, as it has learning ability and mimics the actions of the human brain when performing the stages of ship berthing. However, existing ANN controllers can only bring a ship into a berth in a certain port, where the inputs of the ANN are the same as those of the teaching data. This means that those ANN controllers must be retrained when the ship arrives to a new port, which is time-consuming and costly. In this research, by using the head-up coordinate system, which includes the relative bearing and distance from the ship to the berth, a novel ANN controller is proposed to automatically control the ship into the berth in different ports without retraining the ANN structure. Numerical simulations were performed to verify the effectiveness of the proposed controller. First, teaching data were created in the original port to train the neural network; then, the controller was tested for automatic berthing in other ports, where the initial conditions of the inputs in the head-up coordinate system were similar to those of the teaching data in the original port. The results showed that the proposed controller has good performance for ship berthing in ports. Keywords: Automatic ship berthing, ANN controller, Head-up coordinate system, Low speed, Relative bearing

ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China

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