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Sample records for network ann technique

  1. 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.

  2. Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

    Science.gov (United States)

    Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

    2014-12-01

    This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

  3. On The Comparison of Artificial Neural Network (ANN) and ...

    African Journals Online (AJOL)

    West African Journal of Industrial and Academic Research ... This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for ... Keywords: Multinomial Logistic Regression, Artificial Neural Network, Correct classification rate.

  4. Application of artificial neural networks (ANNs) in wine technology.

    Science.gov (United States)

    Baykal, Halil; Yildirim, Hatice Kalkan

    2013-01-01

    In recent years, neural networks have turned out as a powerful method for numerous practical applications in a wide variety of disciplines. In more practical terms neural networks are one of nonlinear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. In food technology artificial neural networks (ANNs) are useful for food safety and quality analyses, predicting chemical, functional and sensory properties of various food products during processing and distribution. In wine technology, ANNs have been used for classification and for predicting wine process conditions. This review discusses the basic ANNs technology and its possible applications in wine technology.

  5. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Beresford, R

    2000-06-01

    Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine

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

    National Research Council Canada - National Science Library

    Prasada Rao, K; Victor Babu, T; Anuradha, G; Appa Rao, B.V

    ...) 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...

  7. Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox

    Science.gov (United States)

    Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima

    2011-01-01

    Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…

  8. On The Comparison of Artificial Neural Network (ANN) and ...

    African Journals Online (AJOL)

    PROF. OLIVER OSUAGWA

    real life problems ranging from management sciences, business schools, and others [10], [12],. [14], [17]. Moreover, this study aims at comparisons of the model performance of neural network and statistical technique (Multinomial Logistic. Regression) in view of other objectives, using secondary data from the department of.

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

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa [Weill Cornell Medical College, NY, NY (United States)

    2014-06-01

    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.

  10. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M. [Escuela Politecnica Superior, Departamento de Electrotecnia y Electronica, Avda. Menendez Pidal s/n, Cordoba (Spain); Martinez B, M. R.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Calle Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego D, E.; Lorente F, A. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E., E-mail: morvymm@yahoo.com.m [CIEMAT, Laboratorio de Metrologia de Radiaciones Ionizantes, Avda. Complutense 22, 28040 Madrid (Spain)

    2011-02-15

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  11. Sodium Adsorption Ratio (SAR) Prediction of the Chalghazi River Using Artificial Neural Network (ANN) Iran

    OpenAIRE

    Gholamreza Asadollahfardi; Azadeh Hemati; Saber Moradinejad; Rashin Asadollahfardi

    2013-01-01

    Considering the significance of the Sodium Adsorption Ratio (SAR) for growing plants, its prediction is essential for water quality management for irrigation. The SAR prediction in Chelghazy River in Kurdistan, northwest of Iran, using an Artificial Neural Network (ANN) was studied. The study applied the Multilayer Perceptron (MLP) of the ANN to average monthly data, which was collected by the water authority of the Kurdistan province for the period of 1998-2009. The input parameters of the M...

  12. Artificial Neural Networks (ANNs for flood forecasting at Dongola Station in the River Nile, Sudan

    Directory of Open Access Journals (Sweden)

    Sulafa Hag Elsafi

    2014-09-01

    Full Text Available Heavy seasonal rains cause the River Nile in Sudan to overflow and flood the surroundings areas. The floods destroy houses, crops, roads, and basic infrastructure, resulting in the displacement of people. This study aimed to forecast the River Nile flow at Dongola Station in Sudan using an Artificial Neural Network (ANN as a modeling tool and validated the accuracy of the model against actual flow. The ANN model was formulated to simulate flows at a certain location in the river reach, based on flow at upstream locations. Different procedures were applied to predict flooding by the ANN. Readings from stations along the Blue Nile, White Nile, Main Nile, and River Atbara between 1965 and 2003 were used to predict the likelihood of flooding at Dongola Station. The analysis indicated that the ANN provides a reliable means of detecting the flood hazard in the River Nile.

  13. SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

    Directory of Open Access Journals (Sweden)

    Jagadeesh D.Pujari

    2016-06-01

    Full Text Available Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM and artificial neural network (ANN classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.

  14. artificial neural network (ann) approach to electrical load

    African Journals Online (AJOL)

    2004-08-18

    Aug 18, 2004 ... UNIVERSITY POWER HOUSE. A.A.AKINTOLA", G.A. ADEROUNMU and O.E. ... The model was tested using two of the seven feeders of the Obafemi. Awolowo University electric network. The results of .... The architecture of a neural network is the specific arrangement and connections of the neurons that.

  15. Numeric treatment of nonlinear second order multi-point boundary value problems using ANN, GAs and sequential quadratic programming technique

    Directory of Open Access Journals (Sweden)

    Zulqurnain Sabir

    2014-06-01

    Full Text Available In this paper, computational intelligence technique are presented for solving multi-point nonlinear boundary value problems based on artificial neural networks, evolutionary computing approach, and active-set technique. The neural network is to provide convenient methods for obtaining useful model based on unsupervised error for the differential equations. The motivation for presenting this work comes actually from the aim of introducing a reliable framework that combines the powerful features of ANN optimized with soft computing frameworks to cope with such challenging system. The applicability and reliability of such methods have been monitored thoroughly for various boundary value problems arises in science, engineering and biotechnology as well. Comprehensive numerical experimentations have been performed to validate the accuracy, convergence, and robustness of the designed scheme. Comparative studies have also been made with available standard solution to analyze the correctness of the proposed scheme.

  16. ESTIMASI HUBUNGAN KUANTITATIF STRUKTUR-AKTIVITAS (HKSA MENGGUNAKAN ARTIFICIAL NEURAL NETWORKS (ANN

    Directory of Open Access Journals (Sweden)

    Supriyanto

    2009-05-01

    Full Text Available The Quantitative structure-Activity Relationship (QSAR study has been performed on pattern of structure-molecule relationship. Artificial Neural Network (ANN model used to estimate pattern of enzyme activity structure-molecule and atomic location in three-dimension for compound of flavonoid as the predictors. Value of determination coefficient used to compare between actual value and value of estimating by ANN models based on enzyme’s wavelength, so resulting cross validating is obtained. We use Quasy-Newton algorithm with Broyden-Fletcher-Goldfarb-Shanno (BFGS procedure.

  17. Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

    OpenAIRE

    Carvalho,Naiara Barbosa; Minim,Valéria Paula Rodrigues; Silva,Rita de Cássia dos Santos Navarro; Della Lucia,Suzana Maria; Minim,Luis Aantonio

    2013-01-01

    The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combination...

  18. Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process

    Energy Technology Data Exchange (ETDEWEB)

    Yildiz, Sayiter [Engineering Faculty, Cumhuriyet University, Sivas (Turkmenistan)

    2017-09-15

    Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R{sup 2} value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R{sup 2} values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.

  19. Determination of oil well production performance using artificial neural network (ANN linked to the particle swarm optimization (PSO tool

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Ahmadi

    2015-06-01

    In this work, novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network (ANN linked to the particle swarm optimization (PSO tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions. It was found that there is very good match between the modeling output and the real data taken from the literature, so that a very low average absolute error percentage is attained (e.g., <0.82%. The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.

  20. Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

    Science.gov (United States)

    Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

    2016-11-01

    This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

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

    OpenAIRE

    K. Prasada Rao; T. Victor Babu; Anuradha, G.; B.V. Appa Rao

    2016-01-01

    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 evaluat...

  2. Artificial neural networks (ANN: prediction of sensory measurements from instrumental data

    Directory of Open Access Journals (Sweden)

    Naiara Barbosa Carvalho

    2013-12-01

    Full Text Available The objective of this study was to predict by means of Artificial Neural Network (ANN, multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters. Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

  3. Dispersion compensation of fiber optic communication system with direct detection using artificial neural networks (ANNs)

    Science.gov (United States)

    Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.

    2018-02-01

    This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.

  4. Predicting the Deflections of Micromachined Electrostatic Actuators Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Hing Wah LEE

    2009-03-01

    Full Text Available In this study, a general purpose Artificial Neural Network (ANN model based on the feed-forward back-propagation (FFBP algorithm has been used to predict the deflections of a micromachined structures actuated electrostatically under different loadings and geometrical parameters. A limited range of simulation results obtained via CoventorWare™ numerical software will be used initially to train the neural network via back-propagation algorithm. The micromachined structures considered in the analyses are diaphragm, fixed-fixed beams and cantilevers. ANN simulation results are compared with results obtained via CoventorWare™ simulations and existing analytical work for validation purpose. The proposed ANN model accurately predicts the deflections of the micromachined structures with great reduction of simulation efforts, establishing the method superiority. This method can be extended for applications in other sensors particularly for modeling sensors applying electrostatic actuation which are difficult in nature due to the inherent non-linearity of the electro-mechanical coupling response.

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

    Directory of Open Access Journals (Sweden)

    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.

  6. Multifraktalitas dan Studi Komparatif Prediksi Indeks dengan Metode Arima dan Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Harjum Muharam

    2008-09-01

    Full Text Available This paper discusses technical analysis widely used by investors. There are many methods that exist and used by investor to predict the future value of a stock. In this paper we start from finding the value of Hurst (H exponent of LQ 45 Index to know the form of the Index. From H value, we could determinate that the time series data is purely random, or ergodic and ant persistent, or persistent to a certain trend. Two prediction tools were chosen, ARIMA (Auto Regressive Integrated Moving Average which is the de facto standard for univariate prediction model in econometrics and Artificial Neural Network (ANN Back Propagation. Data left from ARIMA is used as an input for both methods. We compared prediction error from each method to determine which method is better. The result shows that LQ45 Index is persistent to a certain trend therefore predictable and for outputted sample data ARIMA outperforms ANN.

  7. 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.

  8. Network acceleration techniques

    Science.gov (United States)

    Crowley, Patricia (Inventor); Awrach, James Michael (Inventor); Maccabe, Arthur Barney (Inventor)

    2012-01-01

    Splintered offloading techniques with receive batch processing are described for network acceleration. Such techniques offload specific functionality to a NIC while maintaining the bulk of the protocol processing in the host operating system ("OS"). The resulting protocol implementation allows the application to bypass the protocol processing of the received data. Such can be accomplished this by moving data from the NIC directly to the application through direct memory access ("DMA") and batch processing the receive headers in the host OS when the host OS is interrupted to perform other work. Batch processing receive headers allows the data path to be separated from the control path. Unlike operating system bypass, however, the operating system still fully manages the network resource and has relevant feedback about traffic and flows. Embodiments of the present disclosure can therefore address the challenges of networks with extreme bandwidth delay products (BWDP).

  9. Prediction of Tourism Demand in Iran by Using Artificial Neural Network (ANN and Supporting Vector Machine (SVR

    Directory of Open Access Journals (Sweden)

    Seyedehelham Sadatiseyedmahalleh

    2016-02-01

    Full Text Available This research examines and proves this effectiveness connected with artificial neural networks (ANNs as an alternative approach to the use of Support Vector Machine (SVR in the tourism research. This method can be used for the tourism industry to define the turism’s demands in Iran. The outcome reveals the use of ANNs in tourism research might result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand evaluation is needed to establish and validate the effects.

  10. Detection of Static Air-Gap Eccentricity in Three Phase induction Motor by Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Hayder O. Alwan

    2017-05-01

    Full Text Available This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 Kw have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between “healthy” and “faulty” machine.

  11. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  13. Application of Artificial Neural Networks (ANNs for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896 Using Predictor Variables

    Directory of Open Access Journals (Sweden)

    C. TURELI BILEN

    2011-10-01

    Full Text Available An evaluation of the performance of artificial networks (ANNs to estimate the weights of blue crab (Callinectes sapidus catches in Yumurtalık Cove (Iskenderun Bay that uses measured predictor variables is presented, including carapace width (CW, sex (male, female and female with eggs, and sampling month. Blue crabs (n=410 were collected each month between 15 September 1996 and 15 May 1998. Sex, CW, and sampling month were used and specified in the input layer of the network. The weights of the blue crabs were utilized in the output layer of the network. A multi-layer perception architecture model was used and was calibrated with the Levenberg Marguardt (LM algorithm. Finally, the values were determined by the ANN model using the actual data. The mean square error (MSE was measured as 3.3, and the best results had a correlation coefficient (R of 0.93. We compared the predictive capacity of the general linear model (GLM versus the Artificial Neural Network model (ANN for the estimation of the weights of blue crabs from independent field data. The results indicated the higher performance capacity of the ANN to predict weights compared to the GLM (R=0.97 vs. R=0.95, raw variable when evaluated against independent field data.

  14. 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

  15. Quantification of phenylpropanoids in commercial Echinacea products using TLC with video densitometry as detection technique and ANN for data modelling.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Loescher, Christine M; Singh, Ragini

    2013-01-01

    Echinacea preparations are among the most popular herbal remedies worldwide. Although it is generally assigned immune enhancement activities, the effectiveness of Echinacea is highly dependent on the Echinacea species, part of the plant used, the age of the plant, its location and the method of extraction. The aim of this study was to investigate the capacity of an artificial neural network (ANN) to analyse thin-layer chromatography (TLC) chromatograms as fingerprint patterns for quantitative estimation of three phenylpropanoid markers (chicoric acid, chlorogenic acid and echinacoside) in commercial Echinacea products. By applying samples with different weight ratios of marker compounds to the system, a database of chromatograms was constructed. One hundred and one signal intensities in each of the TLC chromatograms were correlated to the amounts of applied echinacoside, chlorogenic acid and chicoric acid using an ANN. The developed ANN correlation was used to quantify the amounts of three marker compounds in Echinacea commercial formulations. The minimum quantifiable level of 63, 154 and 98 ng and the limit of detection of 19, 46 and 29 ng were established for echinacoside, chlorogenic acid and chicoric acid respectively. A novel method for quality control of herbal products, based on TLC separation, high-resolution digital plate imaging and ANN data analysis has been developed. The method proposed can be adopted for routine evaluation of the phytochemical variability in Echinacea formulations available in the market. Copyright © 2012 John Wiley & Sons, Ltd.

  16. 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.

  17. Wavelet transform-based artificial neural networks (WT-ANN) in PM10 pollution level estimation, based on circular variables.

    Science.gov (United States)

    Shekarrizfard, Maryam; Karimi-Jashni, A; Hadad, K

    2012-01-01

    In this paper, a novel method in the estimation and prediction of PM(10) is introduced using wavelet transform-based artificial neural networks (WT-ANN). First, the application of wavelet transform, selected for its temporal shift properties and multiresolution analysis characteristics enabling it to reduce disturbing perturbations in input training set data, is presented. Afterward, the circular statistical indices which are used in this method are formally introduced in order to investigate the relation between PM(10) levels and circular meteorological variables. Then, the results of the simulation of PM(10) based on WT-ANN by use of MATLAB software are discussed. The results of the above-mentioned simulation show an enhanced accuracy and speed in PM(10) estimation/prediction and a high degree of robustness compared with traditional ANN models.

  18. 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.

  19. Artificial neural network (ANN velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity

    Directory of Open Access Journals (Sweden)

    Lein Michael

    2008-09-01

    Full Text Available Abstract Background To validate an artificial neural network (ANN based on the combination of PSA velocity (PSAV with a %free PSA-based ANN to enhance the discrimination between prostate cancer (PCa and benign prostate hyperplasia (BPH. Methods The study comprised 199 patients with PCa (n = 49 or BPH (n = 150 with at least three PSA estimations and a minimum of three months intervals between the measurements. Patients were classified into three categories according to PSAV and ANN velocity (ANNV calculated with the %free based ANN "ProstataClass". Group 1 includes the increasing PSA and ANN values, Group 2 the stable values, and Group 3 the decreasing values. Results 71% of PCa patients typically have an increasing PSAV. In comparison, the ANNV only shows this in 45% of all PCa patients. However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65% and increasing values are less frequently (11% vs. 21% if the ANNV is used instead of the PSAV. Conclusion PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV. The ANNV cannot improve the PCa detection rate but may save 11–17% of unnecessary prostate biopsies in known BPH patients.

  20. Techniques for Modelling Network Security

    OpenAIRE

    Lech Gulbinovič

    2012-01-01

    The article compares modelling techniques for network security, including the theory of probability, Markov processes, Petri networks and application of stochastic activity networks. The paper introduces the advantages and disadvantages of the above proposed methods and accepts the method of modelling the network of stochastic activity as one of the most relevant. The stochastic activity network allows modelling the behaviour of the dynamic system where the theory of probability is inappropri...

  1. Underwater Acoustic Networking Techniques

    CERN Document Server

    Otnes, Roald; Casari, Paolo; Goetz, Michael; Husøy, Thor; Nissen, Ivor; Rimstad, Knut; van Walree, Paul; Zorzi, Michele

    2012-01-01

    This literature study presents an overview of underwater acoustic networking. It provides a background and describes the state of the art of all networking facets that are relevant for underwater applications. This report serves both as an introduction to the subject and as a summary of existing protocols, providing support and inspiration for the development of network architectures.

  2. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

    Science.gov (United States)

    Quej, Victor H.; Almorox, Javier; Arnaldo, Javier A.; Saito, Laurel

    2017-03-01

    Daily solar radiation is an important variable in many models. In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatán Peninsula, México. Model performance was assessed with statistical indicators such as root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The performance assessment indicates that the SVM technique with requirements of daily maximum and minimum air temperature, extraterrestrial solar radiation and rainfall has better performance than the other techniques and may be a promising alternative to the usual approaches for predicting solar radiation.

  3. 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.

  4. 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.

  5. Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

    OpenAIRE

    Nabil Ali Alrajeh; Lloret, J.

    2013-01-01

    Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor netw...

  6. Space partitioning strategies for indoor WLAN positioning with cascade-connected ANN structures.

    Science.gov (United States)

    Borenović, Miloš; Nešković, Aleksandar; Budimir, Djuradj

    2011-02-01

    Position information in indoor environments can be procured using diverse approaches. Due to the ubiquitous presence of WLAN networks, positioning techniques in these environments are the scope of intense research. This paper explores two strategies for space partitioning when utilizing cascade-connected Artificial Neural Networks (ANNs) structures for indoor WLAN positioning. A set of cascade-connected ANN structures with different space partitioning strategies are compared mutually and to the single ANN structure. The benefits of using cascade-connected ANNs structures are shown and discussed in terms of the size of the environment, number of subspaces and partitioning strategy. The optimal cascade-connected ANN structures with space partitioning show up to 50% decrease in median error and up to 12% decrease in the average error with respect to the single ANN model. Finally, the single ANN and the optimal cascade-connected ANN model are compared against other well-known positioning techniques.

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

    Directory of Open Access Journals (Sweden)

    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.

  8. Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

    Science.gov (United States)

    Nair, Archana; Singh, Gurjeet; Mohanty, U. C.

    2018-01-01

    The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain.

  9. Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

    Science.gov (United States)

    Nair, Archana; Singh, Gurjeet; Mohanty, U. C.

    2017-08-01

    The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain.

  10. Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA).

    Science.gov (United States)

    Arab, Mohammad M; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

    2017-01-01

    The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin-auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin-auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.

  11. Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA

    Directory of Open Access Journals (Sweden)

    Mohammad M. Arab

    2017-11-01

    Full Text Available The efficiency of a hybrid systems method which combined artificial neural networks (ANNs as a modeling tool and genetic algorithms (GAs as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15 rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs on four growth parameters (outputs, i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW and the quality index (QI of plantlets. Calculation of statistical values such as R2 (coefficient of determination related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3, KIN (9.64, and IBA (2.63 showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53 for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate, the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro

  12. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    OpenAIRE

    Sadik Kamel Gharghan; Rosdiadee Nordin; Mahamod Ismail

    2016-01-01

    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...

  13. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    DR OKE

    Keywords: Artificial neural network; Leakage detection technique; Water distribution; Leakages ... techniques, artificial neural networks (ANNs), genetic algorithms (GA), and probabilistic and evidential reasoning. ANNs are mimicry of ..... Implementation of an online artificial intelligence district meter area flow meter data.

  14. 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.

  15. Application of Artificial Neural Network (ANN for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT

    Directory of Open Access Journals (Sweden)

    Mahmoud S. Nasr

    2012-03-01

    Full Text Available A reliable model for any Wastewater Treatment Plant WWTP is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This paper focuses on applying an Artificial Neural Network (ANN approach with a Feed-Forward Back-Propagation to predict the performance of EL-AGAMY WWTP-Alexandria in terms of Chemical Oxygen Demand (COD, Biochemical Oxygen Demand (BOD and Total Suspended Solids (TSSs data gathered during a research over a 1-year period. The study signifies that the ANN can predict the plant performance with correlation coefficient (R between the observed and predicted output variables reached up to 0.90. Moreover, ANN provides an effective analyzing and diagnosing tool to understand and simulate the non-linear behavior of the plant, and is used as a valuable performance assessment tool for plant operators and decision makers.

  16. PREDIKSI MASA KEDALUWARSA WAFER DENGAN ARTIFICIAL NEURAL NETWORK (ANN BERDASARKAN PARAMETER NILAI KAPASITANSI (Prediction of Wafer Shelf Life Using Artificial Neural Network Based on Capacitance Parameter

    Directory of Open Access Journals (Sweden)

    Erna Rusliana Muhamad Saleh

    2014-02-01

    Full Text Available Wafer is type of biscuit frequently found on expired condition in market, therefore prediction method should be implemented to avoid this condition. apart from the prediction of shelf-life of wafer done by laboratory test, which were time-consuming, expensive, required trained panelists, complex equipment and suitable ambience, artificial neural network (ANN based dielectric parameters was proposed in nthis study. The aim of study was to develop model to predict shelf-life employing aNN based capacitance parameter. Back propagation algorithm with trial and error was applied in variations of nodes per hidden layer, number of hidden layers, activation functions, the function of learnings and epochs. The result of study was the model was able to predict wafer shelf-life. The accuracy level was shown by low MSE value (0.01 and high coefficient correlation value (89.25%. Keywords: artificial Neural Network, shelf-life, waffer, dielectric, capacitance   ABSTRAK Wafer adalah jenis makanan kering yang sering ditemukan kedaluwarsa. Penentuan masa kedaluwarsa dengan observasi laboratorium memiliki beberapa kelemahan, diantaranya memakan waktu, panelis terlatih, suasana yang tepat, biaya dan alat uji yang kompleks. alternatif solusinya adalah penggunaan artificial Neural Network (ANN berbasiskan parameter kapasitansi. Tujuan kerja ilmiah ini adalah untuk memprediksi masa kedaluwarsa wafer menggunakan aNN berbasiskan parameter kapasitansi. algoritma pembelajaran yang digunakan adalah Backpropagation dengan trial and error variasi jumlah node per hidden layer, jumlah hidden layer, fungsi aktivasi, fungsi pembelajaran dan epoch. Hasil prediksi menunjukkan bahwa aNN hasil pelatihan yang dikombinasikan dengan parameter kapasitansi mampu memprediksi masa kedaluwarsa wafer dengan MSE terendah 0,01 dan R tertinggi 89,25%. Kata kunci: Jaringan Syaraf Tiruan, masa kedaluwarsa, wafer, dielektrik, kapasitansi

  17. Application of back-propagation artificial neural network (ANN) to predict crystallite size and band gap energy of ZnO quantum dots

    Science.gov (United States)

    Pelicano, Christian Mark; Rapadas, Nick; Cagatan, Gerard; Magdaluyo, Eduardo

    2017-12-01

    Herein, the crystallite size and band gap energy of zinc oxide (ZnO) quantum dots were predicted using artificial neural network (ANN). Three input factors including reagent ratio, growth time, and growth temperature were examined with respect to crystallite size and band gap energy as response factors. The generated results from neural network model were then compared with the experimental results. Experimental crystallite size and band gap energy of ZnO quantum dots were measured from TEM images and absorbance spectra, respectively. The Levenberg-Marquardt (LM) algorithm was used as the learning algorithm for the ANN model. The performance of the ANN model was then assessed through mean square error (MSE) and regression values. Based on the results, the ANN modelling results are in good agreement with the experimental data.

  18. Prediction of autistic disorder using neuro fuzzy system by applying ANN technique.

    Science.gov (United States)

    Arthi, K; Tamilarasi, A

    2008-11-01

    The major challenge in medical field is to diagnose disorder rather than a disease. In this paper, a neuro fuzzy based model is designed for identification or diagnosis of autism. The problematic areas are gathered from every individual and the related linguistic inputs are converted into fuzzy input values which are in turn given as input to feed forward multilayer neural network. The network is trained using back propagation training algorithm and tested for its performance with the expertise.

  19. Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process.

    Science.gov (United States)

    Rahmanian, Bashir; Pakizeh, Majid; Mansoori, Seyed Ali Akbar; Abedini, Reza

    2011-03-15

    In this study, micellar-enhanced ultrafiltration (MEUF) was applied to remove zinc ions from wastewater efficiently. Frequently, experimental design and artificial neural networks (ANNs) have been successfully used in membrane filtration process in recent years. In the present work, prediction of the permeate flux and rejection of metal ions by MEUF was tested, using design of experiment (DOE) and ANN models. In order to reach the goal of determining all the influential factors and their mutual effect on the overall performance the fractional factorial design has been used. The results show that due to the complexity in generalization of the MEUF process by any mathematical model, the neural network proves to be a very promising method in compared with fractional factorial design for the purpose of process simulation. These mathematical models are found to be reliable and predictive tools with an excellent accuracy, because their AARE was ±0.229%, ±0.017%, in comparison with experimental values for permeate flux and rejection, respectively. Copyright © 2010 Elsevier B.V. All rights reserved.

  20. Feature Selection and ANN Solar Power Prediction

    Directory of Open Access Journals (Sweden)

    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%.

  1. Growth Factor Inhibiting PKC Sensor in E-coli Environment Using Classification Technique and ANN Method

    Directory of Open Access Journals (Sweden)

    T. K. BASAK

    2011-03-01

    Full Text Available Protein kinease C plays an important role in angiogenesis and apoptosis in cancer. During the phase of angiogenesis the growth factor is up regulated where as during apoptosis the growth factor is down regulated. For down regulation of growth factor the pH environment of intra-cellular fluid has a specific range in the alkaline medium. Protein kinease C along with E-coli through interaction of Selenometabolite is able to maintain that alkaline environment for the apoptosis of the cancer cell with inhibition of the growth factor related to antioxidant/oxidant ratio. The present paper through implementation of Artificial Neural Network and Decision Tree has focused on metastasis linked with Capacitance Relaxation phenomena and down regulation of growth factor (VGEF. In this paper a distributed neural network has been applied to a data mining problem for classification of cancer stages inorder to have proper diagnosis of patient with PKC sensor. The Network was trained off line using 270 patterns each of 6 inputs. Using the weight obtained during training, fresh patterns were tested for accuracy in diagnosis linked with the stages of cancer.

  2. Hybrid artificial neural network genetic algorithm technique for modeling chemical oxygen demand removal in anoxic/oxic process.

    Science.gov (United States)

    Ma, Yongwen; Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Wang, Yan; Zhang, Huiping

    2011-01-01

    In this paper, a hybrid artificial neural network (ANN) - genetic algorithm (GA) numerical technique was successfully developed to deal with complicated problems that cannot be solved by conventional solutions. ANNs and Gas were used to model and simulate the process of removing chemical oxygen demand (COD) in an anoxic/oxic system. The minimization of the error function with respect to the network parameters (weights and biases) has been considered as training of the network. Real-coded genetic algorithm was used to train the network in an unsupervised manner. Meanwhile the important process parameters, such as the influent COD (COD(in)), reflux ratio (R(r)), carbon-nitrogen ratio (C/N) and the effluent COD (COD(out)) were considered. The result shows that compared with the performance of ANN model, the performance of the GA-ANN (genetic algorithm - artificial neural network) network was found to be more impressive. Using ANN, the mean absolute percentage error (MAPE), mean squared error (MSE) and correlation coefficient (R) were 9.33×10(-4), 2.82 and 0.98596, respectively; while for the GA-ANN, they were converged to be 4.18×10(-4), 1.12 and 0.99476, respectively.

  3. The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP.

    Science.gov (United States)

    Mustafa, Yasmen A; Jaid, Ghydaa M; Alwared, Abeer I; Ebrahim, Mothana

    2014-06-01

    The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H2O2/Fe(+2)) process. The reaction is influenced by the input concentration of hydrogen peroxide H2O2, amount of the iron catalyst Fe(+2), pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H2O2 = 400 mg/L, Fe(+2) = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72%. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R (2) = 0.9949). The sensitivity analysis showed that all studied variables (H2O2, Fe(+2), pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6%.

  4. AN ANALYSIS OF THE FISH POPULATIONS BY USING ANN AND WAVELET TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Guven OZDEMİR

    2013-01-01

    Full Text Available Air – sea climate, environmental and biological conditions show various differences on several spatio-temporal scales. Climate change associated with anthropogenic activity and natural global multi-decadal climate variations effects on air-sea interactions and water surface–atmosphere–biosphere climate system. In the first part of this paper is related with Artificial Neuro Network analyses for prediction of fish stocks in Marmara and Black Sea. The second part of this study is based on wavelet analyses and, the results were compared with former wavelet and harmonic analyses to explain seasonal effects of NAO and ENSO on fish population. The influence of climatic oscillations (based on NAO and ENSO on monthly catch rates of fish population such as sea bass, Atlantic bonito,blue fish sea (pomatomus population between 1991-2012 in Black Sea and Marmara have been analyzed by discrete wavelet transform (DWT with Meyer and Daubechie's. Wavelet analysis is an efficient method of time series analysis to study non-stationary data. Wavelet analyses allowed us to quantify both the pattern of variability in the time series and non-stationary associations between fish population and climatic signals. Phase analyses were carried out to investigate dependency between the two signals. We reported strong relations between fish stock and climate series for the 4- and 5-yr periodic modes, i.e. the periodic band of the El Niño Southern Oscillation signal propagation in the Black Sea and Marmara Sea. These associations were non-stationary, evidenced from 1995 to 2012. It is recognized that other factors in small, meso and large scales may modulate fish stocks beginning from 1995 and more clearly from 2005

  5. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

    Science.gov (United States)

    Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

    2017-08-01

    The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.

  6. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Science.gov (United States)

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  7. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN and particle swarm optimisation (PSO techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  8. (ann) based dynamic voltage restorer

    African Journals Online (AJOL)

    HOD

    artificial intelligence to provide smart triggering pulses for the DVR to mitigate and to provide compensation against ... the starting of large induction motor [6]. ... ANN-based DVR under voltage sags and swells phenomena. In this case, the ANN is trained off-line, and the trained network is employed for on-line control.

  9. 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...

  10. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    Directory of Open Access Journals (Sweden)

    Sadik Kamel Gharghan

    2016-08-01

    Full Text Available 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.

  11. 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.

  12. Emerging wireless networks concepts, techniques and applications

    CERN Document Server

    Makaya, Christian

    2011-01-01

    An authoritative collection of research papers and surveys, Emerging Wireless Networks: Concepts, Techniques, and Applications explores recent developments in next-generation wireless networks (NGWNs) and mobile broadband networks technologies, including 4G (LTE, WiMAX), 3G (UMTS, HSPA), WiFi, mobile ad hoc networks, mesh networks, and wireless sensor networks. Focusing on improving the performance of wireless networks and provisioning better quality of service and quality of experience for users, it reports on the standards of different emerging wireless networks, applications, and service fr

  13. Classifying Sources Influencing Indoor Air Quality (IAQ Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Shaharil Mad Saad

    2015-05-01

    Full Text Available Monitoring indoor air quality (IAQ is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC, base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

  14. Projecting impacts of climate change on water availability using artificial neural network techniques

    Science.gov (United States)

    Swain, Eric D.; Gomez-Fragoso, Julieta; Torres-Gonzalez, Sigfredo

    2017-01-01

    Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.

  15. Comparison of estimation capabilities of response surface methodology (RSM with artificial neural network (ANN in lipase-catalyzed synthesis of palm-based wax ester

    Directory of Open Access Journals (Sweden)

    Salleh Abu

    2007-08-01

    Full Text Available Abstract Background Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes. Results The coefficient of determination (R2 and absolute average deviation (AAD values between the actual and estimated responses were determined as 1 and 0.002844 for ANN training set, 0.994122 and 1.289405 for ANN test set, and 0.999619 and 0.0256 for RSM training set respectively. The predicted optimum condition was: reaction time 7.38 h, temperature 53.9°C, amount of enzyme 0.149 g, and substrate molar ratio 1:3.41. The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9% and RSM (85.4%. The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively. Conclusion Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities.

  16. Cognitive optical networks: architectures and techniques

    Science.gov (United States)

    Grebeshkov, Alexander Y.

    2017-04-01

    This article analyzes architectures and techniques of the optical networks with taking into account the cognitive methodology based on continuous cycle "Observe-Orient-Plan-Decide-Act-Learn" and the ability of the cognitive systems adjust itself through an adaptive process by responding to new changes in the environment. Cognitive optical network architecture includes cognitive control layer with knowledge base for control of software-configurable devices as reconfigurable optical add-drop multiplexers, flexible optical transceivers, software-defined receivers. Some techniques for cognitive optical networks as flexible-grid technology, broker-oriented technique, machine learning are examined. Software defined optical network and integration of wireless and optical networks with radio over fiber technique and fiber-wireless technique in the context of cognitive technologies are discussed.

  17. Blockmodeling techniques for complex networks

    Science.gov (United States)

    Ball, Brian Joseph

    The class of network models known as stochastic blockmodels has recently been gaining popularity. In this dissertation, we present new work that uses blockmodels to answer questions about networks. We create a blockmodel based on the idea of link communities, which naturally gives rise to overlapping vertex communities. We derive a fast and accurate algorithm to fit the model to networks. This model can be related to another blockmodel, which allows the method to efficiently find nonoverlapping communities as well. We then create a heuristic based on the link community model whose use is to find the correct number of communities in a network. The heuristic is based on intuitive corrections to likelihood ratio tests. It does a good job finding the correct number of communities in both real networks and synthetic networks generated from the link communities model. Two commonly studied types of networks are citation networks, where research papers cite other papers, and coauthorship networks, where authors are connected if they've written a paper together. We study a multi-modal network from a large dataset of Physics publications that is the combination of the two, allowing for directed links between papers as citations, and an undirected edge between a scientist and a paper if they helped to write it. This allows for new insights on the relation between social interaction and scientific production. We also have the publication dates of papers, which lets us track our measures over time. Finally, we create a stochastic model for ranking vertices in a semi-directed network. The probability of connection between two vertices depends on the difference of their ranks. When this model is fit to high school friendship networks, the ranks appear to correspond with a measure of social status. Students have reciprocated and some unreciprocated edges with other students of closely similar rank that correspond to true friendship, and claim an aspirational friendship with a much

  18. Spectrophotometric determination of synthetic colorants using PSO-GA-ANN.

    Science.gov (United States)

    Benvidi, Ali; Abbasi, Saleheh; Gharaghani, Sajjad; Dehghan Tezerjani, Marzieh; Masoum, Saeed

    2017-04-01

    Four common food colorants, containing tartrazine, sunset yellow, ponceau 4R and methyl orange, are simultaneously quantified without prior chemical separation. In this study, an effective artificial neural network (ANN) method is designed for modeling multicomponent absorbance data with the presence of shifts or changes of peak shapes in spectroscopic analysis. Gradient descent methods such as Levenberg-Marquardt function are usually used to determine the parameters of ANN. However, these methods may provide inappropriate parameters. In this paper, we propose combination of genetic algorithms (GA) and partial swarm optimization (PSO) to optimize parameters of ANN, and then the algorithm is used to process the relationship between the absorbance data and the concentration of analytes. The hybrid algorithm has the benefits of both PSO and GA techniques. The performance of this algorithm is compared to the performance of PSO-ANN, PC-ANN and ANN based Levenberg-Marquardt function. The obtained results revealed that the designed model can accurately determine colorant concentrations in real and synthetic samples. According to the observations, it is clear that the proposed hybrid method is a powerful tool to estimate the concentration of food colorants with a high degree of overlap using nonlinear artificial neural network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. An Indoor Positioning Technique Based on a Feed-Forward Artificial Neural Network Using Levenberg-Marquardt Learning Method

    Science.gov (United States)

    Pahlavani, P.; Gholami, A.; Azimi, S.

    2017-09-01

    This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg-Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

  20. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    Computational approaches can be used to detect leakages in water distribution networks. 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 ...

  1. Simulation of Snowmelt Runoff Using SRM Model and Comparison With Neural Networks ANN and ANFIS (Case Study: Kardeh dam basin

    Directory of Open Access Journals (Sweden)

    morteza akbari

    2017-03-01

    of the basin with 2962 meters above sea level. Kardeh dam was primarily constructed on the Kardehriver for providing drinking and agriculture water demand with an annual volume rate of 21.23 million cubic meters. Satellite image: To estimate the level of snow cover, the satellite Landsat ETM+ data at path 35-159, rows 34-159 over the period 2001-2002 were used. Surfaces covered with snow were separated bysnow distinction normalized index (NDSI, But due to the lack of training data for image classification (areas with snow and no snow, the k-means unsupervised classification algorithm was used. Extracting the data from the meteorological and hydrological Since only a gauging station exists at the Kardeh dam site, the daily discharge data recorded at these stations was used. To extract meteorological parameters such as precipitation and temperature data, the records of the three stations Golmakan, Mashhad and Ghouchan, as the stations closest to the dam basin Kardeh were used. The purpose of this study was to simulate snowmelt runoff using SRM hydrological models and to compare the results with the outputs of the neural network models such as the ANN and the ANFIS model. Flow simulation was carried out using SRM, ANN model with the Multilayer Perceptron with back-propagation algorithm, and Sugeno type ANFIS. To evaluate the performance of the models in addition to the standard statistics such as mean square error or mean absolute percentage error, the regression coefficient measures and the difference in volume were used. The results showed that all three models are almost similar in terms of statistical parameters MSE and R and the differences were negligible. SRM model: SRM model is a daily hydrological model. This equation is composed of different components including 14 parameters. The input values were calculated based on the equations of degree-day factor. The evaluation of the model was performed with flow subside factor, coefficient and subtracting volume

  2. 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

  3. 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

  4. Use of artificial neural network (ANN) for the development of bioprocess using Pinus roxburghii fallen foliages for the release of polyphenols and reducing sugars.

    Science.gov (United States)

    Vats, Siddharth; Negi, Sangeeta

    2013-07-01

    In present study, different parameters, i.e., percentage of NaOH, loading volume, microwave power (watt) and volume of water during pretreatment were optimized by ANN for release of polyphenols and sugars from pine fallen foliage. ANN used was feed forward back propagation type with 72 input, 72 output and 10 hidden layers coupled with Lvenberg-Marquardt (LM) training algorithms. The predicted optimal values by generated neural network for alkali pretreatment were 6 ml (0.5% NaOH)/g of substrate, soaking time of 10 min followed by 1 min of 100 W microwave. Pretreated sample on enzymatic hydrolysis at 50°C for 20 h with cocktail of cellulase, xylanase and laccase produced by locally isolated consortia released 668.9 mg/g of total sugar and 265.06 mg/g of total polyphenols. Optimization by ANN showed good yield, therefore, indicating its suitability for bioprocess modeling and control for release of reducing sugars and polyphenols from pine foliage. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

    Science.gov (United States)

    Ghosh, Arpita; Das, Papita; Sinha, Keka

    2015-06-01

    In the present work, spent tea leaves were modified with Ca(OH)2 and used as a new, non-conventional and low-cost biosorbent for the removal of Cu(II) from aqueous solution. Response surface methodology (RSM) and artificial neural network (ANN) were used to develop predictive models for simulation and optimization of the biosorption process. The influence of process parameters (pH, biosorbent dose and reaction time) on the biosorption efficiency was investigated through a two-level three-factor (23) full factorial central composite design with the help of Design Expert. The same design was also used to obtain a training set for ANN. Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set. Results suggest that RSM has better prediction performance as compared to ANN. The biosorption followed Langmuir adsorption isotherm and it followed pseudo-second-order kinetic. The optimum removal efficiency of the adsorbent was found as 96.12 %.

  6. Performance Monitoring Techniques Supporting Cognitive Optical Networking

    DEFF Research Database (Denmark)

    Caballero Jambrina, Antonio; Borkowski, Robert; Zibar, Darko

    2013-01-01

    to solve this issue by realizing a network that can observe, act, learn and optimize its performance, taking into account end-to-end goals. In this letter we present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive...... Heterogeneous Reconfigurable Optical Network. We focus on the approaches developed in the project for optical performance monitoring, which enable the feedback from the physical layer to the cognitive decision system by providing accurate description of the performance of the established lightpaths.......High degree of heterogeneity of future optical networks, such as services with different quality-of-transmission requirements, modulation formats and switching techniques, will pose a challenge for the control and optimization of different parameters. Incorporation of cognitive techniques can help...

  7. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

    Science.gov (United States)

    Soltani, Mahmoud; Omid, Mahmoud; Alimardani, Reza

    2015-05-01

    Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

  8. (ann) based dynamic voltage restorer

    African Journals Online (AJOL)

    HOD

    artificial intelligence to provide smart triggering pulses for the DVR to mitigate and to provide compensation against voltage sags and swells. The Artificial Neural Network (ANN) was trained ... 90% of the nominal rms value and lasting for 0.5cycles. (10msec for 50Hz power system) up to 1 minute. It is considered as the most ...

  9. Comparison between Possibilistic c-Means (PCM and Artificial Neural Network (ANN Classification Algorithms in Land use/ Land cover Classification

    Directory of Open Access Journals (Sweden)

    Ganchimeg Ganbold

    2017-03-01

    Full Text Available There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC, the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

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

    Energy Technology Data Exchange (ETDEWEB)

    Koutroumanidis, Theodoros [Department of Agricultural Development, Democritus University of Thrace, Pantazidou 193, 68200 Orestiada (Greece); Ioannou, Konstantinos [Laboratory of Forest Informatics, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Box 247, 54 124 Thessaloniki (Greece); Arabatzis, Garyfallos [Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200 Orestiada (Greece)

    2009-09-15

    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)

  11. Evolutionary programming technique for reducing complexity of artifical neural networks for breast cancer diagnosis

    Science.gov (United States)

    Lo, Joseph Y.; Land, Walker H., Jr.; Morrison, Clayton T.

    2000-06-01

    An evolutionary programming (EP) technique was investigated to reduce the complexity of artificial neural network (ANN) models that predict the outcome of mammography-induced breast biopsy. By combining input variables consisting of mammography lesion descriptors and patient history data, the ANN predicted whether the lesion was benign or malignant, which may aide in reducing the number of unnecessary benign biopsies and thus the cost of mammography screening of breast cancer. The EP has the ability to optimize the ANN both structurally and parametrically. An EP was partially optimized using a data set of 882 biopsy-proven cases from Duke University Medical Center. Although many different architectures were evolved, the best were often perceptrons with no hidden nodes. A rank ordering of the inputs was performed using twenty independent EP runs. This confirmed the predictive value of the mass margin and patient age variables, and revealed the unexpected usefulness of the history of previous breast cancer. Further work is required to improve the performance of the EP over all cases in general and calcification cases in particular.

  12. Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    Rakan Kh. Antar

    2017-12-01

    Full Text Available In this paper, Artificial Neural Network (ANN technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm and mechanical torque (±7 N.m in steady state and dynamic conditions. The simulation results illustratethe effectiveness of the proposed controller without speed or torque sensors.

  13. Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: Performance and artificial neural network (ANN) modelling studies.

    Science.gov (United States)

    Sahinkaya, Erkan

    2009-05-15

    Sulfidogenic treatment of sulfate (2-10g/L) and zinc (65-677mg/L) containing simulated wastewater was studied in a mesophilic (35 degrees C) CSTR. Ethanol was supplemented (COD/sulfate=0.67) as carbon and energy source for sulfate-reducing bacteria (SRB). The robustness of the system was studied by increasing Zn, COD and sulfate loadings. Sulfate removal efficiency, which was 70% at 2g/L feed sulfate concentration, steadily decreased with increasing feed sulfate concentration and reached 40% at 10g/L. Over 99% Zn removal was attained due to the formation of zinc-sulfide precipitate. COD removal efficiency at 2g/L feed sulfate concentration was over 94%, whereas, it steadily decreased due to the accumulation of acetate at higher loadings. Alkalinity produced from acetate oxidation increased wastewater pH remarkably when feed sulfate concentration was 5g/L or lower. Electron flow from carbon oxidation to sulfate reduction averaged 83+/-13%. The rest of the electrons were most likely coupled with fermentative reactions as the amount of methane production was insignificant. The developed ANN model was very successful as an excellent to reasonable match was obtained between the measured and the predicted concentrations of sulfate (R=0.998), COD (R=0.993), acetate (R=0.976) and zinc (R=0.827) in the CSTR effluent.

  14. Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques.

    Science.gov (United States)

    Roy, Kunal; Pratim Roy, Partha

    2009-07-01

    Twenty-eight structurally diverse cytochrome 3A4 (CYP3A4) inhibitors have been subjected to quantitative structure-activity relationship (QSAR) studies. The analyses were performed with electronic, spatial, topological, and thermodynamic descriptors calculated using Cerius 2 version 10 software. The statistical tools used were linear [multiple linear regression with factor analysis as preprocessing step (FA-MLR), stepwise MLR, partial least squares (PLS), genetic function algorithm (GFA), genetic PLS (G/PLS)] and non-linear methods [artificial neural network (ANN)]. All the five linear modeling methods indicate the importance of n-octanol/water partition coefficient (logP) along with different topological and electronic parameters. The best model obtained from the training set (stepwise regression) based on highest external predictive R(2) value and lowest RMSEP value also showed good internal predictive power. Other models like FA-MLR, PLS, GFA and G/PLS are also of statistically significant internal and external validation characteristics. The best model [according to r(m)(2) for the test set, as defined by P.P. Roy, K. Roy, QSAR Comb. Sci. 27 (2008) 302-313] obtained from ANN showed a good r(2) value (determination coefficient between observed and predicted values) for the test set compounds, which was superior to those of other statistical models except the stepwise regression derived model. However, based upon the r(m)(2) value (test set), which penalizes a model for large differences between observed and predicted values, the stepwise MLR model was found to be inferior to other methods except PLS. Considering r(m)(2) value for the whole set, the G/PLS derived model appears to be the best predictive model for this data set. For choosing the best predictive model from among comparable models, r(m)(2) for the whole set calculated based on leave-one-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested

  15. Wireless Sensor Networks Formation: Approaches and Techniques

    Directory of Open Access Journals (Sweden)

    Miriam Carlos-Mancilla

    2016-01-01

    Full Text Available Nowadays, wireless sensor networks (WSNs emerge as an active research area in which challenging topics involve energy consumption, routing algorithms, selection of sensors location according to a given premise, robustness, efficiency, and so forth. Despite the open problems in WSNs, there are already a high number of applications available. In all cases for the design of any application, one of the main objectives is to keep the WSN alive and functional as long as possible. A key factor in this is the way the network is formed. This survey presents most recent formation techniques and mechanisms for the WSNs. In this paper, the reviewed works are classified into distributed and centralized techniques. The analysis is focused on whether a single or multiple sinks are employed, nodes are static or mobile, the formation is event detection based or not, and network backbone is formed or not. We focus on recent works and present a discussion of their advantages and drawbacks. Finally, the paper overviews a series of open issues which drive further research in the area.

  16. Modeling and adaptive control of a camless engine using neural networks and estimation techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-08-09

    A system to control the cylinder air charge (CAC) in a camless internal combustion (IC) engine was recently developed. The performance of an IC engine connected to an adaptive artificial neural network (ANN) based feedback controller was then investigated. A control oriented model for the engine intake process was created based on thermodynamics laws and was validated against engine experimental data. Input-output data at a speed of 1500 RPM was generated and used to train an ANN model for the engine. The inputs were the intake valve lift (IVL) and closing timing (IVC). The output was the CAC. The controller consisted of a feedforward controller, CAC estimator, and on-line ANN parameter estimator. The feedforward controller provided IVL and IVC that satisfied the driver's torque demand and was the inverse of the engine ANN model. The on-line ANN used the error between the CAC measurement from the CAC estimator and its predicted value from the ANN to update the network's parameters. The feedforward controller was therefore adapted since its operation depended on the ANN model. The adaptation scheme improved the ANN prediction accuracy when the engine parts degraded, the speed changed or when modeling errors occurred. The engine controller exhibited good CAC tracking performance. Computer simulation demonstrated the capability of the camless engine controller. 17 refs., 5 figs.

  17. 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 subm...... disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available....

  18. 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

  19. Performance Parameters Analysis of an XD3P Peugeot Engine Using Artificial Neural Networks (ANN) Concept in MATLAB

    Science.gov (United States)

    Rangaswamy, T.; Vidhyashankar, S.; Madhusudan, M.; Bharath Shekar, H. R.

    2015-04-01

    The current trends of engineering follow the basic rule of innovation in mechanical engineering aspects. For the engineers to be efficient, problem solving aspects need to be viewed in a multidimensional perspective. One such methodology implemented is the fusion of technologies from other disciplines in order to solve the problems. This paper mainly deals with the application of Neural Networks in order to analyze the performance parameters of an XD3P Peugeot engine (used in Ministry of Defence). The basic propaganda of the work is divided into two main working stages. In the former stage, experimentation of an IC engine is carried out in order to obtain the primary data. In the latter stage the primary database formed is used to design and implement a predictive neural network in order to analyze the output parameters variation with respect to each other. A mathematical governing equation for the neural network is obtained. The obtained polynomial equation describes the characteristic behavior of the built neural network system. Finally, a comparative study of the results is carried out.

  20. Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees.

    Science.gov (United States)

    Hearty, Aine P; Gibney, Michael J

    2008-12-01

    At present, the analysis of dietary patterns is based on the intake of individual foods. This article demonstrates how a coding system at the meal level might be analyzed by using data mining techniques. The objective was to evaluate the usability of supervised data mining methods to predict an aspect of dietary quality based on dietary intake with a food-based coding system and a novel meal-based coding system. Food consumption databases from the North-South Ireland Food Consumption Survey 1997-1999 were used. This was a randomized cross-sectional study of 7-d recorded food and nutrient intakes of a representative sample of 1379 Irish adults. Meal definitions were recorded by the respondent. A healthy eating index (HEI) score was developed. Artificial neural networks (ANNs) and decision trees were used to predict quintiles of the HEI based on combinations of foods consumed at breakfast and main meals. This study applied both data mining techniques to the food and meal-based coding systems. The ANN had a slightly higher accuracy than did the decision tree in relation to its ability to predict HEI quintiles 1 and 5 based on the food coding system (78.7% compared with 76.9% and 71.9% compared with 70.1%, respectively). However, the decision tree had higher accuracies than did the ANN on the basis of the meal coding system (67.5% compared with 54.6% and 75.1% compared with 72.4%, respectively). ANNs and decision trees were successfully used to predict an aspect of dietary quality. However, further exploration of the use of ANNs and decision trees in dietary pattern analysis is warranted.

  1. Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

    Directory of Open Access Journals (Sweden)

    Saud Altaf

    2017-01-01

    Full Text Available In this paper, broken rotor bar (BRB fault is investigated by utilizing the Motor Current Signature Analysis (MCSA method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

  2. Techniques Used in String Matching for Network Security

    OpenAIRE

    Jamuna Bhandari

    2014-01-01

    String matching also known as pattern matching is one of primary concept for network security. In this area the effectiveness and efficiency of string matching algorithms is important for applications in network security such as network intrusion detection, virus detection, signature matching and web content filtering system. This paper presents brief review on some of string matching techniques used for network security.

  3. Preliminary Analysis of the efficacy of Artificial neural Network (ANN) and Cellular Automaton (CA) based Land Use Models in Urban Land-Use Planning

    Science.gov (United States)

    Harun, R.

    2013-05-01

    This research provides an opportunity of collaboration between urban planners and modellers by providing a clear theoretical foundations on the two most widely used urban land use models, and assessing the effectiveness of applying the models in urban planning context. Understanding urban land cover change is an essential element for sustainable urban development as it affects ecological functioning in urban ecosystem. Rapid urbanization due to growing inclination of people to settle in urban areas has increased the complexities in predicting that at what shape and size cities will grow. The dynamic changes in the spatial pattern of urban landscapes has exposed the policy makers and environmental scientists to great challenge. But geographic science has grown in symmetry to the advancements in computer science. Models and tools are developed to support urban planning by analyzing the causes and consequences of land use changes and project the future. Of all the different types of land use models available in recent days, it has been found by researchers that the most frequently used models are Cellular Automaton (CA) and Artificial Neural Networks (ANN) models. But studies have demonstrated that the existing land use models have not been able to meet the needs of planners and policy makers. There are two primary causes identified behind this prologue. First, there is inadequate understanding of the fundamental theories and application of the models in urban planning context i.e., there is a gap in communication between modellers and urban planners. Second, the existing models exclude many key drivers in the process of simplification of the complex urban system that guide urban spatial pattern. Thus the models end up being effective in assessing the impacts of certain land use policies, but cannot contribute in new policy formulation. This paper is an attempt to increase the knowledge base of planners on the most frequently used land use model and also assess the

  4. Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

    OpenAIRE

    Malathy, E. M.; Vijayalakshmi Muthuswamy

    2015-01-01

    In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the...

  5. Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing.

    Science.gov (United States)

    Taghadomi-Saberi, Saeedeh; Omid, Mahmoud; Emam-Djomeh, Zahra; Ahmadi, Hojjat

    2014-01-15

    This paper presents a versatile way for estimating antioxidant activity and anthocyanin content at different ripening stages of sweet cherry by combining image processing and two artificial intelligence (AI) techniques. In comparison with common time-consuming laboratory methods for determining these important attributes, this new way is economical and much faster. The accuracy of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models was studied to estimate the outputs. Sensitivity analysis and principal component analysis were used with ANN and ANFIS respectively to specify the most effective attributes on outputs. Among the designed ANNs, two hidden layer networks with 11-14-9-1 and 11-6-20-1 architectures had the highest correlation coefficients and lowest error values for modeling antioxidant activity (R = 0.93) and anthocyanin content (R = 0.98) respectively. ANFIS models with triangular and two-term Gaussian membership functions gave the best results for antioxidant activity (R = 0.87) and anthocyanin content (R = 0.90) respectively. Comparison of the models showed that ANN outperformed ANFIS for this case. By considering the advantages of the applied system and the accuracy obtained in somewhat similar studies, it can be concluded that both techniques presented here have good potential to be used as estimators of proposed attributes. © 2013 Society of Chemical Industry.

  6. 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

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

    Directory of Open Access Journals (Sweden)

    Sukomal Mandal

    2012-06-01

    Full Text Available The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from 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 are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correlation coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

  8. DESIGN OF A VISUAL INTERFACE FOR ANN BASED SYSTEMS

    Directory of Open Access Journals (Sweden)

    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.

  9. Traffic volume estimation using network interpolation techniques.

    Science.gov (United States)

    2013-12-01

    Kriging method is a frequently used interpolation methodology in geography, which enables estimations of unknown values at : certain places with the considerations of distances among locations. When it is used in transportation field, network distanc...

  10. An ANN That Applies Pragmatic Decision on Texts.

    Science.gov (United States)

    Aretoulaki, Maria; Tsujii, Jun-ichi

    A computer-based artificial neural network (ANN) that learns to classify sentences in a text as important or unimportant is described. The program is designed to select the sentences that are important enough to be included in composition of an abstract of the text. The ANN is embedded in a conventional symbolic environment consisting of…

  11. Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

    Science.gov (United States)

    2015-01-01

    In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay. PMID:26237221

  12. Anomaly Detection Techniques for Ad Hoc Networks

    Science.gov (United States)

    Cai, Chaoli

    2009-01-01

    Anomaly detection is an important and indispensable aspect of any computer security mechanism. Ad hoc and mobile networks consist of a number of peer mobile nodes that are capable of communicating with each other absent a fixed infrastructure. Arbitrary node movements and lack of centralized control make them vulnerable to a wide variety of…

  13. 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.

  14. Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis.

    Science.gov (United States)

    Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, Wen-Ming; Li, R K; Wang, Tzu-Hao

    2012-04-01

    Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.

  15. Localization in wireless sensor networks: Classification and evaluation of techniques

    National Research Council Canada - National Science Library

    Ewa Niewiadomska-Szynkiewicz

    2012-01-01

      Localization in wireless sensor networks: Classification and evaluation of techniques Recent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices...

  16. Power Minimization techniques for Networked Data Centers.

    Energy Technology Data Exchange (ETDEWEB)

    Low, Steven; Tang, Kevin

    2011-09-28

    Our objective is to develop a mathematical model to optimize energy consumption at multiple levels in networked data centers, and develop abstract algorithms to optimize not only individual servers, but also coordinate the energy consumption of clusters of servers within a data center and across geographically distributed data centers to minimize the overall energy cost and consumption of brown energy of an enterprise. In this project, we have formulated a variety of optimization models, some stochastic others deterministic, and have obtained a variety of qualitative results on the structural properties, robustness, and scalability of the optimal policies. We have also systematically derived from these models decentralized algorithms to optimize energy efficiency, analyzed their optimality and stability properties. Finally, we have conducted preliminary numerical simulations to illustrate the behavior of these algorithms. We draw the following conclusion. First, there is a substantial opportunity to minimize both the amount and the cost of electricity consumption in a network of datacenters, by exploiting the fact that traffic load, electricity cost, and availability of renewable generation fluctuate over time and across geographical locations. Judiciously matching these stochastic processes can optimize the tradeoff between brown energy consumption, electricity cost, and response time. Second, given the stochastic nature of these three processes, real-time dynamic feedback should form the core of any optimization strategy. The key is to develop decentralized algorithms that can be implemented at different parts of the network as simple, local algorithms that coordinate through asynchronous message passing.

  17. Outlier Detection Techniques For Wireless Sensor Networks: A Survey

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are

  18. Cognitive Heterogeneous Reconfigurable Optical Networks (CHRON): Enabling Technologies and Techniques

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Zibar, Darko; Guerrero Gonzalez, Neil

    2011-01-01

    We present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We introduce and discuss in particular the technologies and techniques that will enable a cognitive optical...

  19. Survey of Green Radio Communications Networks: Techniques and Recent Advances

    Directory of Open Access Journals (Sweden)

    Mohammed H. Alsharif

    2013-01-01

    Full Text Available Energy efficiency in cellular networks has received significant attention from both academia and industry because of the importance of reducing the operational expenditures and maintaining the profitability of cellular networks, in addition to making these networks “greener.” Because the base station is the primary energy consumer in the network, efforts have been made to study base station energy consumption and to find ways to improve energy efficiency. In this paper, we present a brief review of the techniques that have been used recently to improve energy efficiency, such as energy-efficient power amplifier techniques, time-domain techniques, cell switching, management of the physical layer through multiple-input multiple-output (MIMO management, heterogeneous network architectures based on Micro-Pico-Femtocells, cell zooming, and relay techniques. In addition, this paper discusses the advantages and disadvantages of each technique to contribute to a better understanding of each of the techniques and thereby offer clear insights to researchers about how to choose the best ways to reduce energy consumption in future green radio networks.

  20. 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"

  1. Criminal Network Investigation: Processes, Tools, and Techniques

    DEFF Research Database (Denmark)

    Petersen, Rasmus Rosenqvist

    intelligence products that can be disseminated to their customers. Investigators deal with an increasing amount of information from a variety of sources, especially the Internet, all of which are important to their analysis and decision making process. But information abundance is far from the only or most...... a target-centric process model (acquisition, synthesis, sense-making, dissemination, cooperation) encouraging and supporting an iterative and incremental evolution of the criminal network across all five investigation processes. The first priority of the process model is to address the problems of linear...

  2. 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.

  3. Real-time network traffic classification technique for wireless local area networks based on compressed sensing

    Science.gov (United States)

    Balouchestani, Mohammadreza

    2017-05-01

    Network traffic or data traffic in a Wireless Local Area Network (WLAN) is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's Network traffic is the main component for network traffic measurement, network traffic control and simulation. Traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to Identify and organize network traffic which is an important key for improving the QoS feature. To achieve effective network traffic classification, Real-time Network Traffic Classification (RNTC) algorithm for WLANs based on Compressed Sensing (CS) is presented in this paper. The fundamental goal of this algorithm is to solve difficult wireless network management problems. The proposed architecture allows reducing False Detection Rate (FDR) to 25% and Packet Delay (PD) to 15 %. The proposed architecture is also increased 10 % accuracy of wireless transmission, which provides a good background for establishing high quality wireless local area networks.

  4. Techniques for labeling of optical signals in bust switched networks

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Koonen, A. M. J.; Zhang, Jianfeng

    2003-01-01

    We present a review of significant issues related to labeled optical burst switched (LOBS) networks and technologies enabling future optical internet networks. Labeled optical burst switching provides a quick and efficient forwarding mechanism of IP packets/bursts over wavelength division...... multiplexed (WDM) networks due to its single forwarding algorithm, thus yielding low latency, and it enables scaling to terabit rates. Moreover, LOBS is compatible with the general multiprotocol label switching (GMPLS) framework for a unified control plane. We present a review on techniques for labeling...... of optical signals for LOBS networks, including experimental results, we discuss as well issues for further research....

  5. 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

  6. 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.

  7. 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...

  8. Development and Testing of an ANN Model for Estimation of Runoff from a Snow Covered Catchment

    Science.gov (United States)

    Bhadra, A.; Bandyopadhyay, A.; Chakraborty, S.; Roy, S.; Kumar, T.

    2017-06-01

    In this study, an attempt has been made to develop an ANN model to estimate runoff from a snow covered catchment of eastern Himalaya using feed-forward back-propagation algorithm with Levenberg-Marquardt optimization technique. The ANN model was programmed in C++ whereas a user-friendly GUI was developed in VB. The effects of past days rainfall and present day temperature data was observed on the performance of the selected ANN architecture in modelling snowmelt and monsoon season runoff. For this purpose, 8 years' (2003-2010) daily data (rainfall, temperature, and discharge) were collected from CWC which were again divided into two parts (2003-2008 and 2009-2010) for training and testing of the ANN model, respectively. Initially it was found that the network can produce acceptable results with only rainfall data as input, but it needs at least past 3 days rainfall data to account for the antecedent moisture condition of the catchment. Networks 4-16-16-1 (with past 3 days rainfall) and 6-18-18-18-1 (with past 5 days rainfall) resulted modelling efficiency of 79.38 and 82.06% in training and 55.13 and 61.06% in validation, respectively. However, addition of present day temperature data as another input improved the performance in both training (ME 83.10 and 82.22%) and testing (ME 62.64 and 61.89%) marginally.

  9. Cooperative Technique Based on Sensor Selection in Wireless Sensor Network

    OpenAIRE

    ISLAM, M. R.; KIM, J.

    2009-01-01

    An energy efficient cooperative technique is proposed for the IEEE 1451 based Wireless Sensor Networks. Selected numbers of Wireless Transducer Interface Modules (WTIMs) are used to form a Multiple Input Single Output (MISO) structure wirelessly connected with a Network Capable Application Processor (NCAP). Energy efficiency and delay of the proposed architecture are derived for different combination of cluster size and selected number of WTIMs. Optimized constellation parameters are used for...

  10. High Dimensional Modulation and MIMO Techniques for Access Networks

    DEFF Research Database (Denmark)

    Binti Othman, Maisara

    Exploration of advanced modulation formats and multiplexing techniques for next generation optical access networks are of interest as promising solutions for delivering multiple services to end-users. This thesis addresses this from two different angles: high dimensionality carrierless amplitudep...... wired-wireless access networks....... the capacity per wavelength of the femto-cell network. Bit rate up to 1.59 Gbps with fiber-wireless transmission over 1 m air distance is demonstrated. The results presented in this thesis demonstrate the feasibility of high dimensionality CAP in increasing the number of dimensions and their potentially...... to be utilized for multiple service allocation to different users. MIMO multiplexing techniques with OFDM provides the scalability in increasing spectral efficiency and bit rates for RoF systems. High dimensional CAP and MIMO multiplexing techniques are two promising solutions for supporting wired and hybrid...

  11. Geochemical characterization of oceanic basalts using artificial neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Das, P.; Iyer, S.D.

    method is specifically needed to identify the OFB as normal (N-MORB), enriched (E-MORB) and ocean island basalts (OIB). Artificial Neural Network (ANN) technique as a supervised Learning Vector Quantisation (LVQ) is applied to identify the inherent...

  12. 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.

  13. 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)

    Science.gov (United States)

    Shi, Xuedan; Ruan, Wenqian; Hu, Jiwei; Fan, Mingyi; Cao, Rensheng; Wei, Xionghui

    2017-01-01

    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. PMID:28587196

  14. 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).

    Science.gov (United States)

    Shi, Xuedan; Ruan, Wenqian; Hu, Jiwei; Fan, Mingyi; Cao, Rensheng; Wei, Xionghui

    2017-06-03

    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.

  15. Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: Comparative assessment of prediction abilities between response surface methodology (RSM) and artificial neural network (ANN).

    Science.gov (United States)

    Sarve, Antaram; Sonawane, Shriram S; Varma, Mahesh N

    2015-09-01

    The present study estimates the prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for biodiesel synthesis from sesame (Sesamum indicum L.) oil under ultrasonication (20 kHz and 1.2 kW) using barium hydroxide as a basic heterogeneous catalyst. RSM based on a five level, four factor central composite design, was employed to obtain the best possible combination of catalyst concentration, methanol to oil molar ratio, temperature and reaction time for maximum FAME content. Experimental data were evaluated by applying RSM integrating with desirability function approach. The importance of each independent variable on the response was investigated by using sensitivity analysis. The optimum conditions were found to be catalyst concentration (1.79 wt%), methanol to oil molar ratio (6.69:1), temperature (31.92°C), and reaction time (40.30 min). For these conditions, experimental FAME content of 98.6% was obtained, which was in reasonable agreement with predicted one. The sensitivity analysis confirmed that catalyst concentration was the main factors affecting the FAME content with the relative importance of 36.93%. The lower values of correlation coefficient (R(2)=0.781), root mean square error (RMSE=4.81), standard error of prediction (SEP=6.03) and relative percent deviation (RPD=4.92) for ANN compared to those R(2) (0.596), RMSE (6.79), SEP (8.54) and RPD (6.48) for RSM proved better prediction capability of ANN in predicting the FAME content. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. Advanced network programming principles and techniques : network application programming with Java

    CERN Document Server

    Ciubotaru, Bogdan

    2013-01-01

    Answering the need for an accessible overview of the field, this text/reference presents a manageable introduction to both the theoretical and practical aspects of computer networks and network programming. Clearly structured and easy to follow, the book describes cutting-edge developments in network architectures, communication protocols, and programming techniques and models, supported by code examples for hands-on practice with creating network-based applications. Features: presents detailed coverage of network architectures; gently introduces the reader to the basic ideas underpinning comp

  17. A technique for choosing an option for SDH network upgrade

    Directory of Open Access Journals (Sweden)

    V. A. Bulanov

    2014-01-01

    Full Text Available Rapidly developing data transmission technologies result in making the network equipment modernization inevitable. There are various options to upgrade the SDH networks, for example, by increasing the capacity of network overloaded sites, the entire network capacity by replacement of the equipment or by creation of a parallel network, by changing the network structure with the organization of multilevel hierarchy of a network, etc. All options vary in a diversity of parameters starting with the solution cost and ending with the labor intensiveness of their realization. Thus, there are no certain standard approaches to the rules to choose an option for the network development. The article offers the technique for choosing the SHD network upgrade based on method of expert evaluations using as a tool the software complex that allows us to have quickly the quantitative characteristics of proposed network option. The technique is as follows:1. Forming a perspective matrix of services inclination to the SDH networks.2. Developing the several possible options for a network modernization.3. Formation of the list of criteria and a definition of indicators to characterize them by two groups, namely costs of the option implementation and arising losses; positive effect from the option introduction.4. Criteria weight coefficients purpose.5. Indicators value assessment within each criterion for each option by each expert. Rationing of the obtained values of indicators in relation to the maximum value of an indicator among all options.6. Calculating the integrated indicators of for each option by criteria groups.7. Creating a set of Pareto by drawing two criteria groups of points, which correspond to all options in the system of coordinates on the plane. Option choice.In implementation of point 2 the indicators derivation owing to software complex plays a key role. This complex should produce a structure of the network equipment, types of multiplexer sections

  18. Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.

    Science.gov (United States)

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. FUMET: A fuzzy network module extraction technique for gene ...

    Indian Academy of Sciences (India)

    FUMET: A fuzzy network module extraction technique for gene expression data. Priyakshi Mahanta Hasin Afzal Ahmed ... Bhattacharyya1 Ashish Ghosh2. Department of Computer Science and Engineering, Tezpur University, Napaam 784 028, India; Machine Intelligent Unit, Indian Statistical Institute, Kolkata 700 108, India ...

  20. Whitelists Based Multiple Filtering Techniques in SCADA Sensor Networks

    Directory of Open Access Journals (Sweden)

    DongHo Kang

    2014-01-01

    Full Text Available Internet of Things (IoT consists of several tiny devices connected together to form a collaborative computing environment. Recently IoT technologies begin to merge with supervisory control and data acquisition (SCADA sensor networks to more efficiently gather and analyze real-time data from sensors in industrial environments. But SCADA sensor networks are becoming more and more vulnerable to cyber-attacks due to increased connectivity. To safely adopt IoT technologies in the SCADA environments, it is important to improve the security of SCADA sensor networks. In this paper we propose a multiple filtering technique based on whitelists to detect illegitimate packets. Our proposed system detects the traffic of network and application protocol attacks with a set of whitelists collected from normal traffic.

  1. 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)

  2. Development of a new software tool, based on ANN technology, in neutron spectrometry and dosimetry research

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R. [Universidad Autonoma de Zacatecas, Av. Ramon Lopez Velarde 801, A.P. 336, 98000 Zacatecas (Mexico)

    2007-07-01

    Artificial Intelligence is a branch of study which enhances the capability of computers by giving them human-like intelligence. The brain architecture has been extensively studied and attempts have been made to emulate it as in the Artificial Neural Network technology. A large variety of neural network architectures have been developed and they have gained wide-spread popularity over the last few decades. Their application is considered as a substitute for many classical techniques that have been used for many years, as in the case of neutron spectrometry and dosimetry research areas. In previous works, a new approach called Robust Design of Artificial Neural network was applied to build an ANN topology capable to solve the neutron spectrometry and dosimetry problems within the Mat lab programming environment. In this work, the knowledge stored at Mat lab ANN's synaptic weights was extracted in order to develop for first time a customized software application based on ANN technology, which is proposed to be used in the neutron spectrometry and simultaneous dosimetry fields. (Author)

  3. Interaction techniques for selecting and manipulating subgraphs in network visualizations.

    Science.gov (United States)

    McGuffin, Michael J; Jurisica, Igor

    2009-01-01

    We present a novel and extensible set of interaction techniques for manipulating visualizations of networks by selecting subgraphs and then applying various commands to modify their layout or graphical properties. Our techniques integrate traditional rectangle and lasso selection, and also support selecting a node's neighbourhood by dragging out its radius (in edges) using a novel kind of radial menu. Commands for translation, rotation, scaling, or modifying graphical properties (such as opacity) and layout patterns can be performed by using a hotbox (a transiently popped-up, semi-transparent set of widgets) that has been extended in novel ways to integrate specification of commands with 1D or 2D arguments. Our techniques require only one mouse button and one keyboard key, and are designed for fast, gestural, in-place interaction. We present the design and integration of these interaction techniques, and illustrate their use in interactive graph visualization. Our techniques are implemented in NAViGaTOR, a software package for visualizing and analyzing biological networks. An initial usability study is also reported.

  4. Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

    Science.gov (United States)

    Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

    2017-05-01

    Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

  5. 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.

  6. Mathematical analysis techniques for modeling the space network activities

    Science.gov (United States)

    Foster, Lisa M.

    1992-01-01

    The objective of the present work was to explore and identify mathematical analysis techniques, and in particular, the use of linear programming. This topic was then applied to the Tracking and Data Relay Satellite System (TDRSS) in order to understand the space network better. Finally, a small scale version of the system was modeled, variables were identified, data was gathered, and comparisons were made between actual and theoretical data.

  7. Power Optimization Techniques for Next Generation Wireless Networks

    OpenAIRE

    Ratheesh R; Vetrivelan P

    2016-01-01

    The massive data traffic and the need for high speed wireless communication is increasing day by day corresponds to an exponential increase in the consumption of power by Information and Communication Technology (ICT) sector. Reducing consumption of power in wireless network is a challenging topic and has attracted the attention of researches around the globe. Many techniques like multiple-input multiple-output (MIMO), cognitive radio, cooperative heterogeneous communications and new netwo...

  8. Water demand prediction using artificial neural networks and support vector regression

    CSIR Research Space (South Africa)

    Msiza, IS

    2008-11-01

    Full Text Available comparison are Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). In this study it was observed that ANNs perform significantly better than SVMs. This performance is measured against the generalization ability of the two techniques in water...

  9. 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…

  10. Node Augmentation Technique in Bayesian Network Evidence Analysis and Marshaling

    Energy Technology Data Exchange (ETDEWEB)

    Keselman, Dmitry [Los Alamos National Laboratory; Tompkins, George H [Los Alamos National Laboratory; Leishman, Deborah A [Los Alamos National Laboratory

    2010-01-01

    Given a Bayesian network, sensitivity analysis is an important activity. This paper begins by describing a network augmentation technique which can simplifY the analysis. Next, we present two techniques which allow the user to determination the probability distribution of a hypothesis node under conditions of uncertain evidence; i.e. the state of an evidence node or nodes is described by a user specified probability distribution. Finally, we conclude with a discussion of three criteria for ranking evidence nodes based on their influence on a hypothesis node. All of these techniques have been used in conjunction with a commercial software package. A Bayesian network based on a directed acyclic graph (DAG) G is a graphical representation of a system of random variables that satisfies the following Markov property: any node (random variable) is independent of its non-descendants given the state of all its parents (Neapolitan, 2004). For simplicities sake, we consider only discrete variables with a finite number of states, though most of the conclusions may be generalized.

  11. Prediction of 305 d milk yield in Jersey Cattle Using ANN Modelling

    African Journals Online (AJOL)

    ozcan_eren

    Abstract. Artificial neural networks (ANNs) have been shown to be a powerful tool for system modelling in a wide range of .... neural networks have been applied to predict milk yield in dairy sheep (Salehi et al., 1988). Kominakis et al. ... It consists of the choice of ANN algorithm, the structure (number of layers and number of ...

  12. Review of feed forward neural network classification preprocessing techniques

    Science.gov (United States)

    Asadi, Roya; Kareem, Sameem Abdul

    2014-06-01

    The best feature of artificial intelligent Feed Forward Neural Network (FFNN) classification models is learning of input data through their weights. Data preprocessing and pre-training are the contributing factors in developing efficient techniques for low training time and high accuracy of classification. In this study, we investigate and review the powerful preprocessing functions of the FFNN models. Currently initialization of the weights is at random which is the main source of problems. Multilayer auto-encoder networks as the latest technique like other related techniques is unable to solve the problems. Weight Linear Analysis (WLA) is a combination of data pre-processing and pre-training to generate real weights through the use of normalized input values. The FFNN model by using the WLA increases classification accuracy and improve training time in a single epoch without any training cycle, the gradient of the mean square error function, updating the weights. The results of comparison and evaluation show that the WLA is a powerful technique in the FFNN classification area yet.

  13. Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir

    Science.gov (United States)

    Santos, C. A. G.; Freire, P. K. M. M.; Silva, G. B. L.; Silva, R. M.

    2014-09-01

    This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrological data, as there have been few reports in the literature. Finally, the proposed technique is tested using the inflow records to the Três Marias reservoir in São Francisco River basin, Brazil. This transformed signal is used as input for an ANN model to forecast inflows seven days ahead, and the error RMSE decreased by more than 50% (i.e. from 454.2828 to 200.0483).

  14. Impact of sensor installation techniques on seismic network performance

    Science.gov (United States)

    Bainbridge, Geoffrey; Laporte, Michael; Baturan, Dario; Greig, Wesley

    2015-04-01

    The magnitude of completeness (Mc) of a seismic network is determined by a number of factors including station density, self-noise and passband of the sensor used, ambient noise environment and sensor installation method and depth. Sensor installation techniques related to depth are of particular importance due to their impact on overall monitoring network deployment costs. We present a case study which evaluates performance of Trillium Compact Posthole seismometers installed using different methods as well as depths, and evaluate its impact on seismic network operation in terms of the target area of interest average magnitude of completeness in various monitoring applications. We evaluate three sensor installation methods: direct burial in soil at 0.5 m depth, 5 m screwpile and 15 m cemented casing borehole at sites chosen to represent high, medium and low ambient noise environments. In all cases, noise performance improves with depth with noise suppression generally more prominent at higher frequencies but with significant variations from site to site. When extended to overall network performance, the observed noise suppression results in improved (decreased) target area average Mc. However, the extent of the improvement with depth varies significantly, and can be negligible. The increased cost associated with installation at depth uses funds that could be applied to the deployment of additional stations. Using network modelling tools, we compare the improvement in magnitude of completeness and location accuracy associated with increasing installation depth to those associated with increased number of stations. The appropriate strategy is applied on a case-by-case and driven by network-specific performance requirements, deployment constraints and site noise conditions.

  15. An RSS based location estimation technique for cognitive relay networks

    KAUST Repository

    Qaraqe, Khalid A.

    2010-11-01

    In this paper, a received signal strength (RSS) based location estimation method is proposed for a cooperative wireless relay network where the relay is a cognitive radio. We propose a method for the considered cognitive relay network to determine the location of the source using the direct and the relayed signal at the destination. We derive the Cramer-Rao lower bound (CRLB) expressions separately for x and y coordinates of the location estimate. We analyze the effects of cognitive behaviour of the relay on the performance of the proposed method. We also discuss and quantify the reliability of the location estimate using the proposed technique if the source is not stationary. The overall performance of the proposed method is presented through simulations. ©2010 IEEE.

  16. A data-parallelism approach for PSO-ANN based medical image reconstruction on a multi-core system

    Directory of Open Access Journals (Sweden)

    Subramanian Kartheeswaran

    Full Text Available This paper presents the sequential and parallel data decomposition strategies implemented on a Particle Swarm Optimization (PSO algorithm based Artificial Neural Network (PSO-ANN weights optimization for image reconstruction. The application system is developed for the reconstruction of two-dimensional spatial standard Computed Tomography (CT phantom images. It is running on a multi-core computer by varying the number of cores. The feed forward ANN initializes the weight between the ‘ideal’ images that are reconstructed using filtered back projection (FBP technique and the corresponding projection data of CT phantom. In an earlier work, ANN training time is too long. Hence, we propose that the ANN exemplar datasets are decomposed into subsets. Using these subsets, artificial sub neural nets (subnets are initialized and each subnet initial weights are optimized using PSO. Consequently, it was observed that the sequential approach of the proposed method consumes more training time. Hence the parallel strategy is attempted to reduce the computational training time. The parallel approach is further explored for image reconstruction from ‘noisy’ and ‘limited-angle’ datasets also. Keywords: Image reconstruction, Filtered back projection, Artificial neural networks, Particle swarm optimization, Multi-core processors

  17. 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.

  18. Display techniques for dynamic network data in transportation GIS

    Energy Technology Data Exchange (ETDEWEB)

    Ganter, J.H.; Cashwell, J.W.

    1994-05-01

    Interest in the characteristics of urban street networks is increasing at the same time new monitoring technologies are delivering detailed traffic data. These emerging streams of data may lead to the dilemma that airborne remote sensing has faced: how to select and access the data, and what meaning is hidden in them? computer-assisted visualization techniques are needed to portray these dynamic data. Of equal importance are controls that let the user filter, symbolize, and replay the data to reveal patterns and trends over varying time spans. We discuss a prototype software system that addresses these requirements.

  19. Comparison of Artificial Neural Network (ANN Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium

    Directory of Open Access Journals (Sweden)

    Andy P. Dedecker

    2002-01-01

    Full Text Available Modelling has become an interesting tool to support decision making in water management. River ecosystem modelling methods have improved substantially during recent years. New concepts, such as artificial neural networks, fuzzy logic, evolutionary algorithms, chaos and fractals, cellular automata, etc., are being more commonly used to analyse ecosystem databases and to make predictions for river management purposes. In this context, artificial neural networks were applied to predict macroinvertebrate communities in the Zwalm River basin (Flanders, Belgium. Structural characteristics (meandering, substrate type, flow velocity and physical and chemical variables (dissolved oxygen, pH were used as predictive variables to predict the presence or absence of macroinvertebrate taxa in the headwaters and brooks of the Zwalm River basin. Special interest was paid to the frequency of occurrence of the taxa as well as the selection of the predictors and variables to be predicted on the prediction reliability of the developed models. Sensitivity analyses allowed us to study the impact of the predictive variables on the prediction of presence or absence of macroinvertebrate taxa and to define which variables are the most influential in determining the neural network outputs.

  20. Particle swarm optimization of a neural network model in a ...

    Indian Academy of Sciences (India)

    This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high ...

  1. Regularization Techniques to Overcome Overparameterization of Complex Biochemical Reaction Networks.

    Science.gov (United States)

    Howsmon, Daniel P; Hahn, Juergen

    2016-09-01

    Models of biochemical reaction networks commonly contain a large number of parameters while at the same time there is only a limited amount of (noisy) data available for their estimation. As such, the values of many parameters are not well known as nominal parameter values have to be determined from the open scientific literature and a significant number of the values may have been derived in different cell types or organisms than that which is modeled. There clearly is a need to estimate at least some of the parameter values from experimental data, however, the small amount of available data and the large number of parameters commonly found in these types of models, require the use of regularization techniques to avoid over fitting. A tutorial of regularization techniques, including parameter set selection, precedes a case study of estimating parameters in a signal transduction network. Cross validation rather than fitting results are presented to further emphasize the need for models that generalize well to new data instead of simply fitting the current data.

  2. High-sensitivity and specificity of laser-induced autofluorescence spectra for detection of colorectal cancer with an artificial neural network

    Science.gov (United States)

    Kwek, L. C.; Fu, Sheng; Chia, T. C.; Diong, C. H.; Tang, C. L.; Krishnan, S. M.

    2005-07-01

    An artificial neural network (ANN) has been used in various clinical research for the prediction and classification of data in cancer disease. Previous research in this direction focused on the correlation between various input parameters such as age, antigen, and size of tumor growth. Recently, laser-induced autofluorescence (LIAF) techniques have been shown to be a useful noninvasive early diagnostic tool for various cancer diseases. We report on a successful application of ANN to in vitro LIAF spectra. We show that classification of tumor samples with ANN can be done with high sensitivity, specificity, and accuracy. Thus a combination of LIAF techniques and ANN can provide a robust method for clinical diagnosis.

  3. Use of quantitative-structure property relationship (QSPR) and artificial neural network (ANN) based approaches for estimating the octanol-water partition coefficients of the 209 chlorinated trans-azobenzene congeners.

    Science.gov (United States)

    Wilczyńska-Piliszek, Agata J; Piliszek, Sławomir; Falandysz, Jerzy

    2012-01-01

    Polychlorinated azobenzenes (PCABs) can be found as contaminant by products in 3,4-dichloroaniline and its derivatives and in the herbicides Diuron, Linuron, Methazole, Neburon, Propanil and SWEP. Trans congeners of PCABs are physically and chemically more stable and so are environmentally relevant, when compared to unstable cis congeners. In this study, to fulfill gaps on environmentally relevant partitioning properties of PCABs, the values of n-octanol/water partition coefficients (log K(OW)) have been determined for 209 congeners of chloro-trans-azobenzene (Ct-AB) by means of quantitative structure-property relationship (QSPR) approach and artificial neural networks (ANN) predictive ability. The QSPR methods used based on geometry optimalization and quantum-chemical structural descriptors, which were computed on the level of density functional theory (DFT) using B3LYP functional and 6-311++G basis set in Gaussian 03 and of the semi-empirical quantum chemistry method (PM6) of the molecular orbital package (MOPAC). Polychlorinated dibenzo-p-dioxins (PCDDs), -furans (PCDFs) and -biphenyls (PCBs), to which PCABs are related, were reference compounds in this study. An experimentally obtained data on physical and chemical properties of PCDD/Fs and PCBs were reference data for ANN predictions of log K(OW) values of Ct-ABs in this study. Both calculation methods gave similar results in term of absolute log K(OW) values, while the models generated by PM6 are considered highly efficient in time spent, when compared to these by DFT. The estimated log K(OW) values of 209 Ct-ABs varied between 5.22-5.57 and 5.45-5.60 for Mono-, 5.56-6.00 and 5.59-6.07 for Di-, 5.89-6.56 and 5.91-6.46 for Tri-, 6.10-7.05 and 6.13-6.80 for Tetra-, 6.43-7.39 and 6.48-7.14 for Penta-, 6.61-7.78 and 6.98-7.42 for Hexa-, 7.41-7.94 and 7.34-7.86 for Hepta-, 7.99-8.17 and 7.72-8.20 for Octa-, 8.35-8.42 and 8.10-8.62 for NonaCt-ABs, and 8.52-8.60 and 8.81-8.83 for DecaCt-AB. These log K(OW) values

  4. Nox: Anne Carson's Scrapbook Elegy

    OpenAIRE

    Palleau-Papin, Françoise

    2014-01-01

    In the narrative Nox, Anne Carson composes an elegy for her deceased brother, as much as an elegy to the reproduction of the work of art, from the wax tablet to the digital age, by way of the stencil reproduction, to sustain a reflection on our times. She thus invites her readers to a creative reading, that encompasses loss and death.; Dans son récit Nox, Anne Carson compose une élégie à son frère disparu autant qu'une élégie à l'histoire de la reproduction de l'œuvre d'art, depuis la tablett...

  5. Application of chaotic noise reduction techniques to chaotic data ...

    Indian Academy of Sciences (India)

    We propose a novel method of combining artificial neural networks (ANNs) with chaotic noise reduction techniques that captures the metric and dynamic invariants of a chaotic time series, e.g. a time series obtained by iterating the logistic map in chaotic regimes. Our results indicate that while the feedforward neural network ...

  6. Cooperative cognitive radio networking system model, enabling techniques, and performance

    CERN Document Server

    Cao, Bin; Mark, Jon W

    2016-01-01

    This SpringerBrief examines the active cooperation between users of Cooperative Cognitive Radio Networking (CCRN), exploring the system model, enabling techniques, and performance. The brief provides a systematic study on active cooperation between primary users and secondary users, i.e., (CCRN), followed by the discussions on research issues and challenges in designing spectrum-energy efficient CCRN. As an effort to shed light on the design of spectrum-energy efficient CCRN, they model the CCRN based on orthogonal modulation and orthogonally dual-polarized antenna (ODPA). The resource allocation issues are detailed with respect to both models, in terms of problem formulation, solution approach, and numerical results. Finally, the optimal communication strategies for both primary and secondary users to achieve spectrum-energy efficient CCRN are analyzed.

  7. Appraisal of ANN and ANFIS for Predicting Vertical Total Electron ...

    African Journals Online (AJOL)

    The propagation of the GPS signals are interfered by free electrons which are the massive particles in the ionosphere region and results in delays in the ... Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms have been developed for the prediction of VTEC in the ionosphere.

  8. Calibration Technique of the Irradiated Thermocouple using Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Jin Tae; Joung, Chang Young; Ahn, Sung Ho; Yang, Tae Ho; Heo, Sung Ho; Jang, Seo Yoon [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    To correct the signals, the degradation rate of sensors needs to be analyzed, and re-calibration of sensors should be followed periodically. In particular, because thermocouples instrumented in the nuclear fuel rod are degraded owing to the high neutron fluence generated from the nuclear fuel, the periodic re-calibration process is necessary. However, despite the re-calibration of the thermocouple, the measurement error will be increased until next re-calibration. In this study, based on the periodically calibrated temperature - voltage data, an interpolation technique using the artificial neural network will be introduced to minimize the calibration error of the C-type thermocouple under the irradiation test. The test result shows that the calculated voltages derived from the interpolation function have good agreement with the experimental sampling data, and they also accurately interpolate the voltages at arbitrary temperature and neutron fluence. That is, once the reference data is obtained by experiments, it is possible to accurately calibrate the voltage signal at a certain neutron fluence and temperature using an artificial neural network.

  9. 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

  10. 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.

  11. Artificial neural networks predict survival from pancreatic cancer after radical surgery.

    Science.gov (United States)

    Ansari, Daniel; Nilsson, Johan; Andersson, Roland; Regnér, Sara; Tingstedt, Bobby; Andersson, Bodil

    2013-01-01

    Artificial neural networks (ANNs) are nonlinear pattern recognition techniques that can be used as a tool in medical decision making. The objective of this study was to develop an ANN model for predicting survival in patients with pancreatic ductal adenocarcinoma (PDAC). A flexible nonlinear survival model based on ANNs was designed by using clinical and histopathological data from 84 patients who underwent resection for PDAC. Seven of 33 potential risk variables were selected to construct the ANN, including lymph node metastasis, differentiation, body mass index, age, resection margin status, peritumoral inflammation, and American Society of Anesthesiologists grade. Three variables (ie, lymph node metastasis, leukocyte count, and tumor location) were significant according to Cox regression analysis. Harrell's concordance index for the ANN model was .79, and for Cox regression it was .67. For the first time, ANNs have been used to successfully predict individual long-term survival for patients after radical surgery for PDAC. Copyright © 2013 Elsevier Inc. All rights reserved.

  12. Estimation of Paddy Equilibrium Moisture Sorption Using ANNs

    Science.gov (United States)

    Amiri Chayjan, R.; Moazez, Y.

    In this research, Artificial Neural Networks (ANNs) used for prediction of Equilibrium Moisture Content (EMC) of three varieties of paddy (Sadri, Tarom and Khazar) as a new method. Feed forward back propagation and cascade forward back propagation networks with Levenberg-Marquardt and Bayesian regularization training algorithms used for training of input patterns. Optimized trained network has the ability of EMC prediction to test patterns at thermal boundary of 20-40°C and relative humidity boundary of 13.5-87% with R2 = 0.9929 and mean absolute error 0.0229. Comparison between optimized ANN result and empirical model of Henderson showed that artificial neural network not only can simultaneously predict the EMC of samples of all varieties but also has better coefficient of determination and less mean absolute error.

  13. Neural networks for nuclear spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.; Kangas, L.J.; Hashem, S.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States)] [and others

    1995-12-31

    In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN. In this system the ANN is used to automatically identify, radioactive isotopes in real-time from their gamma-ray spectra. Two neural network paradigms are examined: the linear perception and the optimal linear associative memory (OLAM). A comparison of the two paradigms shows that OLAM is superior to linear perception for this application. Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the whole spectrum in the identification process instead of only the individual photo-peaks. For this reason, it is potentially more useful for processing data from lower resolution gamma-ray spectrometers. This approach has been tested with data generated by Monte Carlo simulations and with field data from sodium iodide and Germanium detectors. With the ANN approach, the intense computation takes place during the training process. Once the network is trained, normal operation consists of propagating the data through the network, which results in rapid identification of samples. This approach is useful in situations that require fast response where precise quantification is less important.

  14. 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

  15. 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.

  16. 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.

  17. Toward a Practical Technique to Halt Multiple Virus Outbreaks on Computer Networks

    OpenAIRE

    Hole, Kjell Jørgen

    2012-01-01

    The author analyzes a technique to prevent multiple simultaneous virus epidemics on any vulnerable computer network with inhomogeneous topology. The technique immunizes a small fraction of the computers and utilizes diverse software platforms to halt the virus outbreaks. The halting technique is of practical interest since a network's detailed topology need not be known.

  18. 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

  19. 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.

  20. 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

  1. Ede Kurreli preemia Anneli Tammikule

    Index Scriptorium Estoniae

    2004-01-01

    23. septembrist Eesti Tarbekunsti- ja Disainimuuseumis Maria Puki ja Ivar Lubjaku kujundatud eesti kaasaegse ehtekunsti näitus "Laegas". Avamisel esitleti EKA ehte- ja sepakunsti eriala tutvustavat raamatut "Metall 2" ja anti 2004. a. Ede Kurreli preemia Anneli Tammikule roostevabast terasest fotosöövitatud sarja "2D-3D credit" eest. Parima üliõpilastöö autor Kertu Tuberg. 24. IX toimuva ettekannete päeva kava

  2. FUMET: A fuzzy network module extraction technique for gene ...

    Indian Academy of Sciences (India)

    Supplementary figure 1. (A): Visualization of one of the network modules by GeneMania for dataset 4 (B): Visualization of one of the network modules by GeneMania for dataset 1 (C): Visualization of one of the network modules by GeneMania for dataset 3.

  3. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

    Directory of Open Access Journals (Sweden)

    Declan T. Delaney

    2016-12-01

    Full Text Available No single network solution for Internet of Things (IoT networks can provide the required level of Quality of Service (QoS for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

  4. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection †

    Science.gov (United States)

    Delaney, Declan T.; O’Hare, Gregory M. P.

    2016-01-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks. PMID:27916929

  5. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection.

    Science.gov (United States)

    Delaney, Declan T; O'Hare, Gregory M P

    2016-12-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

  6. Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey

    Directory of Open Access Journals (Sweden)

    Mustafa Akpinar

    2017-06-01

    Full Text Available The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC. For this purpose, households and low-consuming commercial users’ four-year consumption data between the years of 2011–2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE for training utilizing MAPE with a univariate sliding window technique.

  7. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    Science.gov (United States)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

  8. Advanced techniques for multicast service provision in core transport networks

    OpenAIRE

    Fernández del Carpio, Gonzalo

    2012-01-01

    Although the network-based multicast service is the optimal way to support of a large variety of popular applications such as high-definition television (HDTV), videoon- demand (VoD), virtual private LAN service (VPLS), grid computing, optical storage area networks (O-SAN), video conferencing, e-learning, massive multiplayer online role-playing games (MMORPG), networked virtual reality, etc., there are a number of technological and operational reasons that prevents a wider deployment. This Ph...

  9. Methodologies and techniques for analysis of network flow data

    Energy Technology Data Exchange (ETDEWEB)

    Bobyshev, A.; Grigoriev, M.; /Fermilab

    2004-12-01

    Network flow data gathered at the border routers and core switches is used at Fermilab for statistical analysis of traffic patterns, passive network monitoring, and estimation of network performance characteristics. Flow data is also a critical tool in the investigation of computer security incidents. Development and enhancement of flow based tools is an on-going effort. This paper describes the most recent developments in flow analysis at Fermilab.

  10. Measuring the influence of networks on transaction costs using a non-parametric regression technique

    DEFF Research Database (Denmark)

    Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.

    . We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...

  11. A Survey of Neural Network Techniques for Feature Extraction from Text

    OpenAIRE

    John, Vineet

    2017-01-01

    This paper aims to catalyze the discussions about text feature extraction techniques using neural network architectures. The research questions discussed in the paper focus on the state-of-the-art neural network techniques that have proven to be useful tools for language processing, language generation, text classification and other computational linguistics tasks.

  12. Wireless multimedia sensor networks on reconfigurable hardware information reduction techniques

    CERN Document Server

    Ang, Li-minn; Chew, Li Wern; Yeong, Lee Seng; Chia, Wai Chong

    2013-01-01

    Traditional wireless sensor networks (WSNs) capture scalar data such as temperature, vibration, pressure, or humidity. Motivated by the success of WSNs and also with the emergence of new technology in the form of low-cost image sensors, researchers have proposed combining image and audio sensors with WSNs to form wireless multimedia sensor networks (WMSNs).

  13. Outlier detection techniques for wireless sensor networks: A survey

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2010-01-01

    In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection

  14. Application of Artificial Neural Networks and Principal Component Analysis to Predict Results of Infertility Treatment Using the IVF Method

    Directory of Open Access Journals (Sweden)

    Milewski Robert

    2016-12-01

    Full Text Available There are high hopes for using the artificial neural networks (ANN technique to predict results of infertility treatment using the in vitro fertilization (IVF method. Some reports show superiority of the ANN approach over conventional methods. However, fully satisfactory results have not yet been achieved. Hence, there is a need to continue searching for new data describing the treatment process, as well as for new methods of extracting information from these data. There are also some reports that the use of principal component analysis (PCA before the process of training the neural network can further improve the efficiency of generated models. The aim of the study herein presented was to verify the thesis that the use of PCA increases the effectiveness of the prediction by ANN for the analysis of results of IVF treatment. Results for the PCA-ANN approach proved to be slightly better than the ANN approach, however the obtained differences were not statistically significant.

  15. A Comparison of Techniques for Reducing Unicast Traffic in HSR Networks

    Directory of Open Access Journals (Sweden)

    Nguyen Xuan Tien

    2015-10-01

    Full Text Available This paper investigates several existing techniques for reducing high-availability seamless redundancy (HSR unicast traffic in HSR networks for substation automation systems (SAS. HSR is a redundancy protocol for Ethernet networks that provides duplicate frames for separate physical paths with zero recovery time. This feature of HSR makes it very suited for real-time and mission-critical applications such as SAS systems. HSR is one of the redundancy protocols selected for SAS systems. However, the standard HSR protocol generates too much unnecessary redundant unicast traffic in connected-ring networks. This drawback degrades network performance and may cause congestion and delay. Several techniques have been proposed to reduce the redundant unicast traffic, resulting in the improvement of network performance in HSR networks. These HSR traffic reduction techniques are broadly classified into two categories based on their traffic reduction manner, including traffic filtering-based techniques and predefined path-based techniques. In this paper, we provide an overview and comparison of these HSR traffic reduction techniques found in the literature. The concepts, operational principles, network performance, advantages, and disadvantages of these techniques are investigated, summarized. We also provide a comparison of the traffic performance of these HSR traffic reduction techniques.

  16. Memory Compression Techniques for Network Address Management in MPI

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yanfei; Archer, Charles J.; Blocksome, Michael; Parker, Scott; Bland, Wesley; Raffenetti, Ken; Balaji, Pavan

    2017-05-29

    MPI allows applications to treat processes as a logical collection of integer ranks for each MPI communicator, while internally translating these logical ranks into actual network addresses. In current MPI implementations the management and lookup of such network addresses use memory sizes that are proportional to the number of processes in each communicator. In this paper, we propose a new mechanism, called AV-Rankmap, for managing such translation. AV-Rankmap takes advantage of logical patterns in rank-address mapping that most applications naturally tend to have, and it exploits the fact that some parts of network address structures are naturally more performance critical than others. It uses this information to compress the memory used for network address management. We demonstrate that AV-Rankmap can achieve performance similar to or better than that of other MPI implementations while using significantly less memory.

  17. Sybil Defense Techniques in Online Social Networks: A Survey

    National Research Council Canada - National Science Library

    Al-Qurishi, Muhammad; Al-Rakhami, Mabrook; Alamri, Atif; Alrubaian, Majed; Rahman, Sk Md Mizanur; Hossain, M. Shamim

    2017-01-01

    The problem of malicious activities in online social networks, such as Sybil attacks and malevolent use of fake identities, can severely affect the social activities in which users engage while online...

  18. Adverse Outcome Pathway Network Analyses: Techniques and benchmarking the AOPwiki

    Science.gov (United States)

    Abstract: As the community of toxicological researchers, risk assessors, and risk managers adopt the adverse outcome pathway (AOP) paradigm for organizing toxicological knowledge, the number and diversity of adverse outcome pathways and AOP networks are continuing to grow. This ...

  19. Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues

    OpenAIRE

    Mehboob, Usama; Qadir, Junaid; Ali, Salman; Vasilakos, Athanasios

    2014-01-01

    In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorit...

  20. 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.

  1. [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)

  2. Harmonic Mitigation Techniques Applied to Power Distribution Networks

    Directory of Open Access Journals (Sweden)

    Hussein A. Kazem

    2013-01-01

    Full Text Available A growing number of harmonic mitigation techniques are now available including active and passive methods, and the selection of the best-suited technique for a particular case can be a complicated decision-making process. The performance of some of these techniques is largely dependent on system conditions, while others require extensive system analysis to prevent resonance problems and capacitor failure. A classification of the various available harmonic mitigation techniques is presented in this paper aimed at presenting a review of harmonic mitigation methods to researchers, designers, and engineers dealing with power distribution systems.

  3. Application of Soft Computing Techniques and Multiple Regression Models for CBR prediction of Soils

    Directory of Open Access Journals (Sweden)

    Fatimah Khaleel Ibrahim

    2017-08-01

    Full Text Available The techniques of soft computing technique such as Artificial Neutral Network (ANN have improved the predicting capability and have actually discovered application in Geotechnical engineering. The aim of this research is to utilize the soft computing technique and Multiple Regression Models (MLR for forecasting the California bearing ratio CBR( of soil from its index properties. The indicator of CBR for soil could be predicted from various soils characterizing parameters with the assist of MLR and ANN methods. The data base that collected from the laboratory by conducting tests on 86 soil samples that gathered from different projects in Basrah districts. Data gained from the experimental result were used in the regression models and soft computing techniques by using artificial neural network. The liquid limit, plastic index , modified compaction test and the CBR test have been determined. In this work, different ANN and MLR models were formulated with the different collection of inputs to be able to recognize their significance in the prediction of CBR. The strengths of the models that were developed been examined in terms of regression coefficient (R2, relative error (RE% and mean square error (MSE values. From the results of this paper, it absolutely was noticed that all the proposed ANN models perform better than that of MLR model. In a specific ANN model with all input parameters reveals better outcomes than other ANN models.

  4. Developing Visualization Techniques for Semantics-based Information Networks

    Science.gov (United States)

    Keller, Richard M.; Hall, David R.

    2003-01-01

    Information systems incorporating complex network structured information spaces with a semantic underpinning - such as hypermedia networks, semantic networks, topic maps, and concept maps - are being deployed to solve some of NASA s critical information management problems. This paper describes some of the human interaction and navigation problems associated with complex semantic information spaces and describes a set of new visual interface approaches to address these problems. A key strategy is to leverage semantic knowledge represented within these information spaces to construct abstractions and views that will be meaningful to the human user. Human-computer interaction methodologies will guide the development and evaluation of these approaches, which will benefit deployed NASA systems and also apply to information systems based on the emerging Semantic Web.

  5. 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.

  6. Data mining techniques in sensor networks summarization, interpolation and surveillance

    CERN Document Server

    Appice, Annalisa; Fumarola, Fabio; Malerba, Donato

    2013-01-01

    Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data.

  7. Knapsack--TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network.

    Directory of Open Access Journals (Sweden)

    E M Malathy

    Full Text Available In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution yields remarkably better results in terms of the network performance measures such as throughput and delay.

  8. Knapsack--TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network.

    Science.gov (United States)

    Malathy, E M; Muthuswamy, Vijayalakshmi

    2015-01-01

    In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay.

  9. Assessment of Software Modeling Techniques for Wireless Sensor Networks: A Survey

    Directory of Open Access Journals (Sweden)

    John Khalil Jacoub

    2012-03-01

    Full Text Available Wireless Sensor Networks (WSNs monitor environment phenomena and in some cases react in response to the observed phenomena. The distributed nature of WSNs and the interaction between software and hardware components makes it difficult to correctly design and develop WSN systems. One solution to the WSN design challenges is system modeling. In this paper we present a survey of 9 WSN modeling techniques and show how each technique models different parts of the system such as sensor behavior, sensor data and hardware. Furthermore, we consider how each modeling technique represents the network behavior and network topology. We also consider the available supporting tools for each of the modeling techniques. Based on the survey, we classify the modeling techniques and derive examples of the surveyed modeling techniques by using SensIV system.

  10. QoS Provisioning Techniques for Future Fiber-Wireless (FiWi Access Networks

    Directory of Open Access Journals (Sweden)

    Martin Maier

    2010-04-01

    Full Text Available A plethora of enabling optical and wireless access-metro network technologies have been emerging that can be used to build future-proof bimodal fiber-wireless (FiWi networks. Hybrid FiWi networks aim at providing wired and wireless quad-play services over the same infrastructure simultaneously and hold great promise to mitigate the digital divide and change the way we live and work by replacing commuting with teleworking. After overviewing enabling optical and wireless network technologies and their QoS provisioning techniques, we elaborate on enabling radio-over-fiber (RoF and radio-and-fiber (R&F technologies. We describe and investigate new QoS provisioning techniques for future FiWi networks, ranging from traffic class mapping, scheduling, and resource management to advanced aggregation techniques, congestion control, and layer-2 path selection algorithms.

  11. 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 ...

  12. ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm

    Directory of Open Access Journals (Sweden)

    David Cruz

    2016-05-01

    Full Text Available This paper presents a dynamic model for a self-balancing vehicle using the Euler-Lagrange approach. The design and deployment of an artificial neuronal network (ANN in a closed-loop control is described. The ANN is characterized by integration of the extended delta-bar-delta algorithm (DBD, which accelerates the adjustment of synaptic weights. The results of the control strategy in the dynamic model of the robot are also presented.

  13. Evaluation of Techniques to Detect Significant Network Performance Problems using End-to-End Active Network Measurements

    Energy Technology Data Exchange (ETDEWEB)

    Cottrell, R.Les; Logg, Connie; Chhaparia, Mahesh; /SLAC; Grigoriev, Maxim; /Fermilab; Haro, Felipe; /Chile U., Catolica; Nazir, Fawad; /NUST, Rawalpindi; Sandford, Mark

    2006-01-25

    End-to-End fault and performance problems detection in wide area production networks is becoming increasingly hard as the complexity of the paths, the diversity of the performance, and dependency on the network increase. Several monitoring infrastructures are built to monitor different network metrics and collect monitoring information from thousands of hosts around the globe. Typically there are hundreds to thousands of time-series plots of network metrics which need to be looked at to identify network performance problems or anomalous variations in the traffic. Furthermore, most commercial products rely on a comparison with user configured static thresholds and often require access to SNMP-MIB information, to which a typical end-user does not usually have access. In our paper we propose new techniques to detect network performance problems proactively in close to realtime and we do not rely on static thresholds and SNMP-MIB information. We describe and compare the use of several different algorithms that we have implemented to detect persistent network problems using anomalous variations analysis in real end-to-end Internet performance measurements. We also provide methods and/or guidance for how to set the user settable parameters. The measurements are based on active probes running on 40 production network paths with bottlenecks varying from 0.5Mbits/s to 1000Mbit/s. For well behaved data (no missed measurements and no very large outliers) with small seasonal changes most algorithms identify similar events. We compare the algorithms' robustness with respect to false positives and missed events especially when there are large seasonal effects in the data. Our proposed techniques cover a wide variety of network paths and traffic patterns. We also discuss the applicability of the algorithms in terms of their intuitiveness, their speed of execution as implemented, and areas of applicability. Our encouraging results compare and evaluate the accuracy of our

  14. Review Of Prevention Techniques For Denial Of Service DOS Attacks In Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Poonam Rolla

    2015-08-01

    Full Text Available Wireless Sensor Networks comprised of several tiny sensor nodes which are densely deployed over the region to monitor the environmental conditions. These sensor nodes have certain design issues out of which security is the main predominant factor as it effects the whole lifetime of network. DDoS Distributed denial of service attack floods unnecessary packets in the sensor network. A review on DDoS attacks and their prevention techniques have been done in this paper.

  15. Review Of Prevention Techniques For Denial Of Service DOS Attacks In Wireless Sensor Network

    OpenAIRE

    Poonam Rolla; Manpreet Kaur

    2015-01-01

    Wireless Sensor Networks comprised of several tiny sensor nodes which are densely deployed over the region to monitor the environmental conditions. These sensor nodes have certain design issues out of which security is the main predominant factor as it effects the whole lifetime of network. DDoS Distributed denial of service attack floods unnecessary packets in the sensor network. A review on DDoS attacks and their prevention techniques have been done in this paper.

  16. A new application of neural network technique to sensorless speed identification of induction motor

    OpenAIRE

    Mostefai, Mohamed; Miloud, Yahia; Abdullah MILOUDI

    2016-01-01

    A new application of neural network technique to sensorless speed identification of scalar-controlled induction motor is implemented in this paper. The neural network estimates the rotor speed through stator measurements and nominal settings of the motor. By changing the motor parameters, the neural network can estimate the speed of another motor. We evaluated our approach based on the speed response and load disturbance effects on two different motors. The test results demonstrate the feasib...

  17. A new application of neural network technique to sensorless speed identification of induction motor

    Directory of Open Access Journals (Sweden)

    Mohamed MOSTEFAI

    2016-12-01

    Full Text Available A new application of neural network technique to sensorless speed identification of scalar-controlled induction motor is implemented in this paper. The neural network estimates the rotor speed through stator measurements and nominal settings of the motor. By changing the motor parameters, the neural network can estimate the speed of another motor. We evaluated our approach based on the speed response and load disturbance effects on two different motors. The test results demonstrate the feasibility of the method.

  18. Auditing information structures in organizations: A review of data collection techniques for network analysis

    NARCIS (Netherlands)

    Koning, K.H.; de Jong, Menno D.T.

    2005-01-01

    Network analysis is one of the current techniques for investigating organizational communication. Despite the amount of how-to literature about using network analysis to assess information flows and relationships in organizations, little is known about the methodological strengths and weaknesses of

  19. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  20. Novel anti-jamming technique for OCDMA network through FWM in SOA based wavelength converter

    Science.gov (United States)

    Jyoti, Vishav; Kaler, R. S.

    2013-06-01

    In this paper, we propose a novel anti-jamming technique for optical code division multiple access (OCDMA) network through four wave mixing (FWM) in semiconductor optical amplifier (SOA) based wavelength converter. OCDMA signal can be easily jammed with high power jamming signal. It is shown that wavelength conversion through four wave mixing in SOA has improved capability of jamming resistance. It is observed that jammer has no effect on OCDMA network even at high jamming powers by using the proposed technique.

  1. A Soft Technique for Measuring Friction Force Using Neural Network

    Directory of Open Access Journals (Sweden)

    Sunan HUANG

    2011-10-01

    Full Text Available There are two approaches to measure a friction force: force sensor, software estimation algorithm. This paper will focus on software approach to measure friction. The proposed approach uses a neural network (NN to approximate the friction force in a mechanical system. Since the friction force considered is a speed-dependent function, a learning algorithm is adopted to update the NN weights so as to follow unknown friction behaviors. The advantage of the proposed friction estimation method is that it is based on the built NN model, and it does not require the force sensor measurement. Simulation test is given to verify the effectiveness of the proposed approach.

  2. Tribological behaviour predictions of r-GO reinforced Mg composite using ANN coupled Taguchi approach

    Science.gov (United States)

    Kavimani, V.; Prakash, K. Soorya

    2017-11-01

    This paper deals with the fabrication of reduced graphene oxide (r-GO) reinforced Magnesium Metal Matrix Composite (MMC) through a novel solvent based powder metallurgy route. Investigations over basic and functional properties of developed MMC reveals that addition of r-GO improvises the microhardness upto 64 HV but however decrement in specific wear rate is also notified. Visualization of worn out surfaces through SEM images clearly explains for the occurrence of plastic deformation and the presence of wear debris because of ploughing out action. Taguchi coupled Artificial Neural Network (ANN) technique is adopted to arrive at optimal values of the input parameters such as load, reinforcement weight percentage, sliding distance and sliding velocity and thereby achieve minimal target output value viz. specific wear rate. Influence of any of the input parameter over specific wear rate studied through ANOVA reveals that load acting on pin has a major influence with 38.85% followed by r-GO wt. % of 25.82%. ANN model developed to predict specific wear rate value based on the variation of input parameter facilitates better predictability with R-value of 98.4% when compared with the outcomes of regression model.

  3. A Bloom Filter-Powered Technique Supporting Scalable Semantic Discovery in Data Service Networks

    Science.gov (United States)

    Zhang, J.; Shi, R.; Bao, Q.; Lee, T. J.; Ramachandran, R.

    2016-12-01

    More and more Earth data analytics software products are published onto the Internet as a service, in the format of either heavyweight WSDL service or lightweight RESTful API. Such reusable data analytics services form a data service network, which allows Earth scientists to compose (mashup) services into value-added ones. Therefore, it is important to have a technique that is capable of helping Earth scientists quickly identify appropriate candidate datasets and services in the global data service network. Most existing services discovery techniques, however, mainly rely on syntax or semantics-based service matchmaking between service requests and available services. Since the scale of the data service network is increasing rapidly, the run-time computational cost will soon become a bottleneck. To address this issue, this project presents a way of applying network routing mechanism to facilitate data service discovery in a service network, featuring scalability and performance. Earth data services are automatically annotated in Web Ontology Language for Services (OWL-S) based on their metadata, semantic information, and usage history. Deterministic Annealing (DA) technique is applied to dynamically organize annotated data services into a hierarchical network, where virtual routers are created to represent semantic local network featuring leading terms. Afterwards Bloom Filters are generated over virtual routers. A data service search request is transformed into a network routing problem in order to quickly locate candidate services through network hierarchy. A neural network-powered technique is applied to assure network address encoding and routing performance. A series of empirical study has been conducted to evaluate the applicability and effectiveness of the proposed approach.

  4. Cognition-Enabling Techniques in Heterogeneous and Flexgrid Optical Communication Networks

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Caballero Jambrina, Antonio; Saldaña Cercos, Silvia

    2012-01-01

    High degree of heterogeneity of future optical networks, such as services with different quality-of-transmission requirements, modulation formats and switching techniques, will pose a challenge for the control and optimization of different parameters. Incorporation of cognitive techniques can hel...

  5. Computing distance-based topological descriptors of complex chemical networks: New theoretical techniques

    Science.gov (United States)

    Hayat, Sakander

    2017-11-01

    Structure-based topological descriptors/indices of complex chemical networks enable prediction of physico-chemical properties and the bioactivities of these compounds through QSAR/QSPR methods. In this paper, we have developed a rigorous computational and theoretical technique to compute various distance-based topological indices of complex chemical networks. A fullerene is called the IPR (Isolated-Pentagon-Rule) fullerene, if every pentagon in it is surrounded by hexagons only. To ensure the applicability of our technique, we compute certain distance-based indices of an infinite family of IPR fullerenes. Our results show that the proposed technique is more diverse and bears less algorithmic and combinatorial complexity.

  6. Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

    KAUST Repository

    AlQuerm, Ismail A.

    2018-02-21

    There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for

  7. Reliability assessment of restructured power systems using reliability network equivalent and pseudo-sequential simulation techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ding, Yi; Wang, Peng; Goel, Lalit [Nanyang Technological University, School of Electrical and Electronics Engineering, Block S1, Nanyang Avenue, Singapore 639798 (Singapore); Billinton, Roy; Karki, Rajesh [Department of Electrical Engineering, University of Saskatchewan, Saskatoon (Canada)

    2007-10-15

    This paper presents a technique to evaluate reliability of a restructured power system with a bilateral market. The proposed technique is based on the combination of the reliability network equivalent and pseudo-sequential simulation approaches. The reliability network equivalent techniques have been implemented in the Monte Carlo simulation procedure to reduce the computational burden of the analysis. Pseudo-sequential simulation has been used to increase the computational efficiency of the non-sequential simulation method and to model the chronological aspects of market trading and system operation. Multi-state Markov models for generation and transmission systems are proposed and implemented in the simulation. A new load shedding scheme is proposed during generation inadequacy and network congestion to minimize the load curtailment. The IEEE reliability test system (RTS) is used to illustrate the technique. (author)

  8. Broadcast Expenses Controlling Techniques in Mobile Ad-hoc Networks: A Survey

    Directory of Open Access Journals (Sweden)

    Naeem Ahmad

    2016-07-01

    Full Text Available The blind flooding of query packets in route discovery more often characterizes the broadcast storm problem, exponentially increases energy consumption of intermediate nodes and congests the entire network. In such a congested network, the task of establishing the path between resources may become very complex and unwieldy. An extensive research work has been done in this area to improve the route discovery phase of routing protocols by reducing broadcast expenses. The purpose of this study is to provide a comparative analysis of existing broadcasting techniques for the route discovery phase, in order to bring about an efficient broadcasting technique for determining the route with minimum conveying nodes in ad-hoc networks. The study is designed to highlight the collective merits and demerits of such broadcasting techniques along with certain conclusions that would contribute to the choice of broadcasting techniques.

  9. Modeling and Optimization Technique of a Chilled Water AHU Using Artificial Neural Network Methods

    Science.gov (United States)

    Talib, Rand Issa

    Heating, ventilation, and air conditioning (HVAC) systems are widely used in buildings to provide occupants with conditioned air and acceptable indoor air quality. The chilled water system is one Heating, ventilation, and air conditioning systems are widely used in buildings to provide occupants with conditioned air and acceptable indoor air quality. The design of these systems constitutes a large impact on the energy usage and operating cost of buildings they serve. The ability to accurately predict the performance of these systems is integral to designing more energy efficient and sustainable building systems. In this thesis the modeling of a chilled water air handling units using Artificial Neural Networks model is proposed. The Artificial neural network model was built using four inputs (1) Chilled water temperature (CHWT), (2) Chilled water valve position (CWVLV), (3) Mixed air temperature (MAT), and (4) Supply air flow (SAF). The output of the model is to predict supply air temperature. Moreover, another model was constructed to predict the fan power as a function of the fan air flow and fan speed. The data that were collected from a real building in a span of three months were processed. The ANN model was trained using the measured data and different model structure were then tested with various time delay, feedback time, and number of neurons to determine the best structure. In addition, an optimization method is developed to automate the process of finding the best model structure that can produce the best accurate prediction against the actual data. The Coefficient of variances which was used to determine the error value was recorded to be as low as 1.22 for the best model structure. The obtained results validate the Artificial neural network model created as an accurate tool for predicting the performance of a chilled water air handling unit.

  10. Total alkalinity estimation using MLR and neural network techniques

    Science.gov (United States)

    Velo, A.; Pérez, F. F.; Tanhua, T.; Gilcoto, M.; Ríos, A. F.; Key, R. M.

    2013-02-01

    During the last decade, two important collections of carbon relevant hydrochemical data have become available: GLODAP and CARINA. These collections comprise a synthesis of bottle data for all ocean depths from many cruises collected over several decades. For a majority of the cruises at least two carbon parameters were measured. However, for a large number of stations, samples or even cruises, the carbonate system is under-determined (i.e., only one or no carbonate parameter was measured) resulting in data gaps for the carbonate system in these collections. A method for filling these gaps would be very useful, as it would help with estimations of the anthropogenic carbon (Cant) content or quantification of oceanic acidification. The aim of this work is to apply and describe, a 3D moving window multilinear regression algorithm (MLR) to fill gaps in total alkalinity (AT) of the CARINA and GLODAP data collections for the Atlantic. In addition to filling data gaps, the estimated AT values derived from the MLR are useful in quality control of the measurements of the carbonate system, as they can aid in the identification of outliers. For comparison, a neural network algorithm able to perform non-linear predictions was also designed. The goal here was to design an alternative approach to accomplish the same task of filling AT gaps. Both methods return internally consistent results, thereby giving confidence in our approach.

  11. 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.

  12. 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.

  13. Computerized classification of liver disease in MRI using an artificial neural network

    Science.gov (United States)

    Zhang, Xuejun; Kanematsu, Masayuki; Fujita, Hiroshi; Hara, Takeshi; Hoshi, Hiroaki

    2001-07-01

    We developed a software named LiverANN based on artificial neural network (ANN) technique for distinguishing the pathologies of focal liver lesions in magnetic resonance (MR) imaging, which helps radiologists integrate the imaging findings with different pulse sequences and raise the diagnostic accuracy even with radiologists inexperienced in liver MR imaging. In each patient, regions of focal liver lesion on T1-weighted, T2-weighted, and gadolinium-enhanced dynamic MR images obtained in the hepatic arterial and equilibrium phases were placed by a radiologist (M.K.), then the program automatically calculated the brightness and homogeneity into numerical data within the selected areas as the input signals to the ANN. The outputs from the ANN were the 5 categories of focal hepatic diseases: liver cyst, cavernous hemangioma, dysplasia, hepatocellular carcinoma, and metastasis. Fifty cases were used for training the ANN, while 30 cases for testing the performance. The result showed that the LiverANN classified 5 types of focal liver lesions with sensitivity of 93%, which demonstrated the ability of ANN to fuse the complex relationships among the image findings with different sequences, and the ANN-based software may provide radiologists with referential opinion during the radiologic diagnostic procedure.

  14. Computer vision-based method for classification of wheat grains using artificial neural network.

    Science.gov (United States)

    Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

    2017-06-01

    A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

  15. Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

    Directory of Open Access Journals (Sweden)

    Abdullahi Abubakar Mas’ud

    2016-07-01

    Full Text Available In order to investigate how artificial neural networks (ANNs have been applied for partial discharge (PD pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1 determining the optimum weights in training the ANN; (2 using PD data captured over long stressing period in training the ANN; (3 ANN recognizing different PD degradation levels; (4 using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5 understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6 developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

  16. Ann Back Propagation For Forecasting And Simulation Hydroclimatology Data

    Directory of Open Access Journals (Sweden)

    Syaefudin Suhaedi

    2017-10-01

    Full Text Available Government policies in distributing fertilizers and seeds of food crops such as rice and crops depend on the growing season of the farmers. Therefore before conducting the distribution it is necessary to spread early planting season in each region farmers so that the result of distribution is optimal. One of the alternatives that must be done first is to predict the pattern of hydroclimatological data cycle of the coming year to see the pattern of data of previous years. In this case required a method that can be used to predict the hydroclimatological data. The exact method used to make predictions is Artificial Neural Network ANN Back Propagation. As a follow-up step will be predicted by this ANN will be used to build system planning optimal cropping pattern for agricultural crops to avoid harvest failure puso in order to obtain maximum production results so as to support national food security. Based on the results of the simulation is known that ANN Back Propagation with two hidden layer are able to predict hydroclimatological data with an average accuracy of 95.72 - 96.61. While the prediction validation obtained an average percentage error of 1.12 with the accuracy of 99.76. The data used for training testing validation and prediction are data in Central Lombok NTB Indonesia.

  17. Face Recognition using Artificial Neural Network | Endeshaw | Zede ...

    African Journals Online (AJOL)

    Face recognition (FR) is one of the biometric methods to identify the individuals by the features of face. Two Face Recognition Systems (FRS) based on Artificial Neural Network (ANN) have been proposed in this paper based on feature extraction techniques. In the first system, Principal Component Analysis (PCA) has been ...

  18. Energy saving techniques applied over a nation-wide mobile network

    DEFF Research Database (Denmark)

    Perez, Eva; Frank, Philipp; Micallef, Gilbert

    2014-01-01

    Traffic carried over wireless networks has grown significantly in recent years and actual forecasts show that this trend is expected to continue. However, the rapid mobile data explosion and the need for higher data rates comes at a cost of increased complexity and energy consumption of the mobile...... on the energy consumption based on a nation-wide network of a leading European operator. By means of an extensive analysis, we show that with the proposed techniques significant energy savings can be realized....

  19. NEW BURST ASSEMBLY AND SCHEDULING TECHNIQUE FOR OPTICAL BURST SWITCHING NETWORKS

    OpenAIRE

    Kavitha, V.; V.Palanisamy

    2013-01-01

    The Optical Burst Switching is a new switching technology that efficiently utilizes the bandwidth in the optical layer. The key areas to be concentrated in Optical Burst Switching (OBS) networks are the burst assembly and burst scheduling i.e., assignment of wavelengths to the incoming bursts. This study presents a New Burst Assembly and Scheduling (NBAS) technique in a simultaneous multipath transmission for burst loss recovery in OBS networks. A Redundant Burst Segmentation (RBS) is used fo...

  20. A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis.

    Science.gov (United States)

    Huda, A S N; Taib, S; Ghazali, K H; Jadin, M S

    2014-05-01

    Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively

  1. IMPLEMENTATION OF IMPROVED NETWORK LIFETIME TECHNIQUE FOR WSN USING CLUSTER HEAD ROTATION AND SIMULTANEOUS RECEPTION

    Directory of Open Access Journals (Sweden)

    Arun Vasanaperumal

    2015-11-01

    Full Text Available There are number of potential applications of Wireless Sensor Networks (WSNs like wild habitat monitoring, forest fire detection, military surveillance etc. All these applications are constrained for power from a stand along battery power source. So it becomes of paramount importance to conserve the energy utilized from this power source. A lot of efforts have gone into this area recently and it remains as one of the hot research areas. In order to improve network lifetime and reduce average power consumption, this study proposes a novel cluster head selection algorithm. Clustering is the preferred architecture when the numbers of nodes are larger because it results in considerable power savings for large networks as compared to other ones like tree or star. Since majority of the applications generally involve more than 30 nodes, clustering has gained widespread importance and is most used network architecture. The optimum number of clusters is first selected based on the number of nodes in the network. When the network is in operation the cluster heads in a cluster are rotated periodically based on the proposed cluster head selection algorithm to increase the network lifetime. Throughout the network single-hop communication methodology is assumed. This work will serve as an encouragement for further advances in the low power techniques for implementing Wireless Sensor Networks (WSNs.

  2. Predictive control based on neural networks: an application to a fluid catalytic cracking industrial unit

    Directory of Open Access Journals (Sweden)

    V.M.L. Santos

    2000-12-01

    Full Text Available Artificial Neural Networks (ANNs constitute a technology that has recently become the focus of great attention. The reason for this is due mainly to its capacity to treat complex and nonlinear problems. This work consists of the identification and control of a fluid cracking catalytic unit (FCCU using techniques based on multilayered ANNs. The FCC unit is a typical example of a complex and nonlinear process, possessing great interaction among the operation variables and many operational constraints to be attended. Model Predictive Control is indicated in these occasions. The FCC model adopted was validated with plant data by Moro (1992; and was used in this work to replace the real process in the generation of data for the identification of the ANNs and to test the predictive control strategy. The results of the identification and control of the process through ANNs indicate the viability of the technique.

  3. ENERGY EFFICIENCY ANALYSIS OF ERROR CORRECTION TECHNIQUES IN UNDERWATER WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    M. NORDIN B. ZAKARIA

    2011-02-01

    Full Text Available Research in underwater acoustic networks has been developed rapidly to support large variety of applications such as mining equipment and environmental monitoring. As in terrestrial sensor networks; reliable data transport is demanded in underwater sensor networks. The energy efficiency of error correction technique should be considered because of the severe energy constraints of underwater wireless sensor networks. Forward error correction (FEC andautomatic repeat request (ARQ are the two main error correction techniques in underwater networks. In this paper, a mathematical energy efficiency analysis for FEC and ARQ techniques in underwater environment has been done based on communication distance and packet size. The effects of wind speed, and shipping factor are studied. A comparison between FEC and ARQ in terms of energy efficiency is performed; it is found that energy efficiency of both techniquesincreases with increasing packet size in short distances, but decreases in longer distances. There is also a cut-off distance below which ARQ is more energy efficient than FEC, and after which FEC is more energy efficient than ARQ. This cut-off distance decreases by increasing wind speed. Wind speed has great effecton energy efficiency where as shipping factor has unnoticeable effect on energy efficiency for both techniques.

  4. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition

    Science.gov (United States)

    Paul, R R; Mukherjee, A; Dutta, P K; Banerjee, S; Pal, M; Chatterjee, J; Chaudhuri, K; Mukkerjee, K

    2005-01-01

    Aim: To describe a novel neural network based oral precancer (oral submucous fibrosis; OSF) stage detection method. Method: The wavelet coefficients of transmission electron microscopy images of collagen fibres from normal oral submucosa and OSF tissues were used to choose the feature vector which, in turn, was used to train the artificial neural network. Results: The trained network was able to classify normal and oral precancer stages (less advanced and advanced) after obtaining the image as an input. Conclusions: The results obtained from this proposed technique were promising and suggest that with further optimisation this method could be used to detect and stage OSF, and could be adapted for other conditions. PMID:16126873

  5. Application of artificial neural networks with backpropagation technique in the financial data

    Science.gov (United States)

    Jaiswal, Jitendra Kumar; Das, Raja

    2017-11-01

    The propensity of applying neural networks has been proliferated in multiple disciplines for research activities since the past recent decades because of its powerful control with regulatory parameters for pattern recognition and classification. It is also being widely applied for forecasting in the numerous divisions. Since financial data have been readily available due to the involvement of computers and computing systems in the stock market premises throughout the world, researchers have also developed numerous techniques and algorithms to analyze the data from this sector. In this paper we have applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well.

  6. Multipath Routing and Wavelength Assignment Technique in Optical WDM Mesh Networks

    Science.gov (United States)

    Kavitha, T.; Shiyamala, S.; Rajamani, V.

    2017-12-01

    A routing and wavelength assignment (RWA) technique for supporting multipath traffic in optical wavelength-division multiplexing (WDM) mesh network is proposed in this paper. The network can be preceded by accomplishing two processes: one is establishing connection node and the second one is identifying the multipath and assigning wavelength. The connection node is selected based on the load and current traffic-carrying capacity of that node. During wavelength allocation mechanism, cost function is considered as the major criterion. Based on the cost involved in every path, the wavelengths are selected such that wavelength with the minimum cost is allocated to that particular path. This technique efficiently allocates the wavelength to the selected multiple paths and the traffic is routed to the destination using multiple paths with wavelength allocation. For simulation, NS2 simulator is used by applying the optical WDM network simulator patch. The proposed multipath RWA technique is compared with the existing RWA technique. We achieved a throughput of 12,625 packets for ten numbers of wavelengths. But the existing approach achieved a throughput of 10,189 packets only for the same numbers of wavelengths. Channel utilization is more, and delay is less compared with the existing technique. Hence, the proposed method is very efficient, since the router effectively routes the traffic within the network.

  7. Artificial neural networks in pancreatic disease.

    Science.gov (United States)

    Bartosch-Härlid, A; Andersson, B; Aho, U; Nilsson, J; Andersson, R

    2008-07-01

    An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute pancreatitis and pancreatic cancer. PubMed was searched for articles on the use of ANNs in pancreatic diseases using the MeSH terms 'neural networks (computer)', 'pancreatic neoplasms', 'pancreatitis' and 'pancreatic diseases'. A systematic review of the articles was performed. Eleven articles were identified, published between 1993 and 2007. The situations that lend themselves best to analysis by ANNs are complex multifactorial relationships, medical decisions when a second opinion is needed and when automated interpretation is required, for example in a situation of an inadequate number of experts. Conventional linear models have limitations in terms of diagnosis and prediction of outcome in acute pancreatitis and pancreatic cancer. Management of these disorders can be improved by applying ANNs to existing clinical parameters and newly established gene expression profiles. (c) 2008 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd.

  8. Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

    Science.gov (United States)

    Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

    2014-03-01

    Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

  9. Artificial neural networks: Principle and application to model based control of drying systems -- A review

    Energy Technology Data Exchange (ETDEWEB)

    Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

    1998-07-01

    This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

  10. 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. .

  11. 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.

  12. 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.

  13. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks.

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-08-18

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

  14. A STATISTICAL CORRELATION TECHNIQUE AND A NEURAL-NETWORK FOR THE MOTION CORRESPONDENCE PROBLEM

    NARCIS (Netherlands)

    VANDEEMTER, JH; MASTEBROEK, HAK

    A statistical correlation technique (SCT) and two variants of a neural network are presented to solve the motion correspondence problem. Solutions of the motion correspondence problem aim to maintain the identities of individuated elements as they move. In a preprocessing stage, two snapshots of a

  15. A control technique for integration of DG units to the electrical networks

    DEFF Research Database (Denmark)

    Pouresmaeil, Edris; Miguel-Espinar, Carlos; Massot-Campos, Miquel

    2013-01-01

    This paper deals with a multiobjective control technique for integration of distributed generation (DG) resources to the electrical power network. The proposed strategy provides compensation for active, reactive, and harmonic load current components during connection of DG link to the grid. The d...

  16. Overview of the neural network based technique for monitoring of road condition via reconstructed road profiles

    CSIR Research Space (South Africa)

    Ngwangwa, HM

    2008-07-01

    Full Text Available on the road and driver to assess the integrity of road and vehicle infrastructure. In this paper, vehicle vibration data are applied to an artificial neural network to reconstruct the corresponding road surface profiles. The results show that the technique...

  17. Performance Evaluation and Parameter Optimization of Wavelength Division Multiplexing Networks with Importance Sampling Techniques

    NARCIS (Netherlands)

    Remondo Bueno, D.; Srinivasan, R.; Nicola, V.F.; van Etten, Wim; Tattje, H.E.P.

    1998-01-01

    In this paper new adaptive importance sampling techniques are applied to the performance evaluation and parameter optimization of wavelength division multiplexing (WDM) network impaired by crosstalk in an optical cross-connect. Worst-case analysis is carried out including all the beat noise terms

  18. A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

    In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

  19. A hybrid deep neural network and physically based distributed model for river stage prediction

    Science.gov (United States)

    hitokoto, Masayuki; sakuraba, Masaaki

    2016-04-01

    We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network

  20. Under-Actuated Robot Manipulator Positioning Control Using Artificial Neural Network Inversion Technique

    Directory of Open Access Journals (Sweden)

    Ali T. Hasan

    2012-01-01

    Full Text Available This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy.

  1. Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials.

    Science.gov (United States)

    Asteris, Panagiotis G; Roussis, Panayiotis C; Douvika, Maria G

    2017-06-09

    This work presents a soft-sensor approach for estimating critical mechanical properties of sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed for building soft-sensors able to predict the 28-day compressive strength and the modulus of elasticity of sandcrete materials. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of FF ANNs to predict with pinpoint accuracy the mechanical properties of sandcrete materials. Furthermore, the proposed normalization technique has been proven effective and robust compared to other normalization techniques available in the literature.

  2. Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

    Science.gov (United States)

    Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

    2017-12-01

    In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

  3. High capacity fiber optic sensor networks using hybrid multiplexing techniques and their applications

    Science.gov (United States)

    Sun, Qizhen; Li, Xiaolei; Zhang, Manliang; Liu, Qi; Liu, Hai; Liu, Deming

    2013-12-01

    Fiber optic sensor network is the development trend of fiber senor technologies and industries. In this paper, I will discuss recent research progress on high capacity fiber sensor networks with hybrid multiplexing techniques and their applications in the fields of security monitoring, environment monitoring, Smart eHome, etc. Firstly, I will present the architecture of hybrid multiplexing sensor passive optical network (HSPON), and the key technologies for integrated access and intelligent management of massive fiber sensor units. Two typical hybrid WDM/TDM fiber sensor networks for perimeter intrusion monitor and cultural relics security are introduced. Secondly, we propose the concept of "Microstructure-Optical X Domin Refecltor (M-OXDR)" for fiber sensor network expansion. By fabricating smart micro-structures with the ability of multidimensional encoded and low insertion loss along the fiber, the fiber sensor network of simple structure and huge capacity more than one thousand could be achieved. Assisted by the WDM/TDM and WDM/FDM decoding methods respectively, we built the verification systems for long-haul and real-time temperature sensing. Finally, I will show the high capacity and flexible fiber sensor network with IPv6 protocol based hybrid fiber/wireless access. By developing the fiber optic sensor with embedded IPv6 protocol conversion module and IPv6 router, huge amounts of fiber optic sensor nodes can be uniquely addressed. Meanwhile, various sensing information could be integrated and accessed to the Next Generation Internet.

  4. Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China

    Directory of Open Access Journals (Sweden)

    C. W. Dawson

    2002-01-01

    Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

  5. Smart techniques in the dynamic spectrum alocation for cognitive wireless networks

    Directory of Open Access Journals (Sweden)

    Camila Salgado

    2016-09-01

    Full Text Available Objective: The objective of this work is to study the applications of different techniques of artificial intelligence and autonomous learning in the dynamic allocation of spectrum for cognitive wireless networks, especially the distributed ones. Method: The development of this work was done through the study and analysis of some of the most relevant publications in current literature through the search in indexed international journals in ISI and Scopus. Results: the most relevant techniques of artificial intelligence and autonomous learning were determined. Also, the ones with more applicability in the allocation of spectrum for cognitive wireless networks were determined, too. . Conclusions: The implementation of a technique, or set of them, depends on the needs in signal processing, compensation in response times, sample availability, storage capacity, learning ability and robustness, among others.

  6. Evaluation Technique of Chloride Penetration Using Apparent Diffusion Coefficient and Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Yun-Yong Kim

    2014-01-01

    Full Text Available Diffusion coefficient from chloride migration test is currently used; however this cannot provide a conventional solution like total chloride contents since it depicts only ion migration velocity in electrical field. This paper proposes a simple analysis technique for chloride behavior using apparent diffusion coefficient from neural network algorithm with time-dependent diffusion phenomena. For this work, thirty mix proportions of high performance concrete are prepared and their diffusion coefficients are obtained after long term-NaCl submerged test. Considering time-dependent diffusion coefficient based on Fick’s 2nd Law and NNA (neural network algorithm, analysis technique for chloride penetration is proposed. The applicability of the proposed technique is verified through the results from accelerated test, long term submerged test, and field investigation results.

  7. Determination of Complex-Valued Parametric Model Coefficients Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    A. M. Aibinu

    2010-01-01

    Full Text Available A new approach for determining the coefficients of a complex-valued autoregressive (CAR and complex-valued autoregressive moving average (CARMA model coefficients using complex-valued neural network (CVNN technique is discussed in this paper. The CAR and complex-valued moving average (CMA coefficients which constitute a CARMA model are computed simultaneously from the adaptive weights and coefficients of the linear activation functions in a two-layered CVNN. The performance of the proposed technique has been evaluated using simulated complex-valued data (CVD with three different types of activation functions. The results show that the proposed method can accurately determine the model coefficients provided that the network is properly trained. Furthermore, application of the developed CVNN-based technique for MRI K-space reconstruction results in images with improve resolution.

  8. 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.

  9. 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

  10. Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

    KAUST Repository

    Ghazzai, Hakim

    2016-09-16

    Over the last decade, mobile communications have been witnessing a noteworthy increase of data traffic demand that is causing an enormous energy consumption in cellular networks. The reduction of their fossil fuel consumption in addition to the huge energy bills paid by mobile operators is considered as the most important challenges for the next-generation cellular networks. Although most of the proposed studies were focusing on individual physical layer power optimizations, there is a growing necessity to meet the green objective of fifth-generation cellular networks while respecting the user\\'s quality of service. This paper investigates four important techniques that could be exploited separately or together in order to enable wireless operators achieve significant economic benefits and environmental savings: 1) the base station sleeping strategy; 2) the optimized energy procurement from the smart grid; 3) the base station energy sharing; and 4) the green networking collaboration between competitive mobile operators. The presented simulation results measure the gain that could be obtained using these techniques compared with that of traditional scenarios. Finally, this paper discusses the issues and challenges related to the implementations of these techniques in real environments. © 2016 IEEE.

  11. 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,

  12. PREDICTION OF DAILY ACTIVE AND REACTIVE ENERGY CONSUMPTION FOR CITY BYY ANN

    OpenAIRE

    ETEM KÖKLÜKAYA

    1997-01-01

    Artıfıcal neural network (ANN), is used in predctıon of energy and load as it used ın many dıfferent areas of electrıc power system. Energy consuptıon usage center has non-linear veriatıon characteristic.

  13. Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

    Science.gov (United States)

    Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

    2012-04-01

    The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the

  14. An Interference-Aware Distributed Transmission Technique for Dense Small Cell Networks

    DEFF Research Database (Denmark)

    Mahmood, Nurul Huda; Berardinelli, Gilberto; Pedersen, Klaus I.

    2015-01-01

    transmission technique that can efficiently manage the interference in an uncoordinated dense small cell network is investigated in this work. The proposed interference aware scheme only requires instantaneous channel state information at the transmitter end towards the desired receiver. Motivated by penalty...... methods in optimization studies, an interference dependent weighting factor is introduced to control the number of parallel transmission streams. The proposed scheme can outperform a more complex benchmark transmission scheme in terms of the sum network throughput in certain scenarios and with realistic...

  15. Artificial intelligence techniques in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Laughton, M.A.

    1997-12-31

    Since the early to mid 1980s much of the effort in power systems analysis has turned away from the methodology of formal mathematical modelling which came from the fields of operations research, control theory and numerical analysis to the less rigorous techniques of artificial intelligence (AI). Today the main AI techniques found in power systems applications are those utilising the logic and knowledge representations of expert systems, fuzzy systems, artificial neural networks (ANN) and, more recently, evolutionary computing. These techniques will be outlined in this chapter and the power system applications indicated. (Author)

  16. Neural network design for J function approximation in dynamic programming

    CERN Document Server

    Pang, X

    1998-01-01

    This paper shows that a new type of artificial neural network (ANN) -- the Simultaneous Recurrent Network (SRN) -- can, if properly trained, solve a difficult function approximation problem which conventional ANNs -- either feedforward or Hebbian -- cannot. This problem, the problem of generalized maze navigation, is typical of problems which arise in building true intelligent control systems using neural networks. (Such systems are discussed in the chapter by Werbos in K.Pribram, Brain and Values, Erlbaum 1998.) The paper provides a general review of other types of recurrent networks and alternative training techniques, including a flowchart of the Error Critic training design, arguable the only plausible approach to explain how the brain adapts time-lagged recurrent systems in real-time. The C code of the test is appended. As in the first tests of backprop, the training here was slow, but there are ways to do better after more experience using this type of network.

  17. Phosphorus component in AnnAGNPS

    Science.gov (United States)

    Yuan, Y.; Bingner, R.L.; Theurer, F.D.; Rebich, R.A.; Moore, P.A.

    2005-01-01

    The USDA Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) has been developed to aid in evaluation of watershed response to agricultural management practices. Previous studies have demonstrated the capability of the model to simulate runoff and sediment, but not phosphorus (P). The main purpose of this article is to evaluate the performance of AnnAGNPS on P simulation using comparisons with measurements from the Deep Hollow watershed of the Mississippi Delta Management Systems Evaluation Area (MDMSEA) project. A sensitivity analysis was performed to identify input parameters whose impact is the greatest on P yields. Sensitivity analysis results indicate that the most sensitive variables of those selected are initial soil P contents, P application rate, and plant P uptake. AnnAGNPS simulations of dissolved P yield do not agree well with observed dissolved P yield (Nash-Sutcliffe coefficient of efficiency of 0.34, R2 of 0.51, and slope of 0.24); however, AnnAGNPS simulations of total P yield agree well with observed total P yield (Nash-Sutcliffe coefficient of efficiency of 0.85, R2 of 0.88, and slope of 0.83). The difference in dissolved P yield may be attributed to limitations in model simulation of P processes. Uncertainties in input parameter selections also affect the model's performance.

  18. Ann Arbor, Michigan: Solar in Action (Brochure)

    Energy Technology Data Exchange (ETDEWEB)

    2011-10-01

    This brochure provides an overview of the challenges and successes of Ann Arbor, Michigan, a 2007 Solar America City awardee, on the path toward becoming a solar-powered community. Accomplishments, case studies, key lessons learned, and local resource information are given.

  19. 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

  20. Katherine Anne Porter on Her Contemporaries.

    Science.gov (United States)

    Bridges, Phyllis

    Personal experiences with and critical judgments of leading artists and intellectuals of the twentieth century are recorded in Katherine Anne Porter's essays, letters and conversations which provide snapshots of her attitudes and encounters. Porter's commentaries about such contemporaries as Ernest Hemingway, William Faulkner, Saul Bellow,…

  1. A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran

    Science.gov (United States)

    Hamidi, Omid; Poorolajal, Jalal; Sadeghifar, Majid; Abbasi, Hamed; Maryanaji, Zohreh; Faridi, Hamid Reza; Tapak, Lily

    2015-02-01

    This study compared two machine learning techniques, support vector machines (SVM), and artificial neural network (ANN) in modeling monthly precipitation fluctuations. The SVM and ANN approaches were applied to the monthly precipitation data of two synoptic stations in Hamadan (Airport and Nojeh), the west of Iran. To avoid overfitting, the data were divided into two parts of training (70 %) and test sets (30 %). Then, monthly data from July 1976 to June 2001 and data from April 1961 to November 1996 were considered as training set for the Hamadan and Nojeh stations, respectively, and the remaining were used as test set. The results of the SVM model were compared with those of the ANN based on the root mean square errors, mean absolute errors, determination coefficient, and efficiency coefficient criteria. Based on the comparison, it was found that the SVM model outperformed the ANN, and the estimated precipitation values were in good agreement with the corresponding observed values.

  2. Time-of-flight discrimination between gamma-rays and neutrons by neural networks

    OpenAIRE

    Serkan AKKOYUN

    2012-01-01

    In gamma-ray spectroscopy, a number of neutrons are emitted from the nuclei together with the gamma-rays and these neutrons influence gamma-ray spectra. An obvious method of separating between neutrons and gamma-rays is based on the time-of-flight (tof) technique. This work aims obtaining tof distributions of gamma-rays and neutrons by using feed-forward artificial neural network (ANN). It was shown that, ANN can correctly classify gamma-ray and neutron events. Testing of trained networks on ...

  3. Rain gauge network design for flood forecasting using multi-criteria decision analysis and clustering techniques in lower Mahanadi river basin, India

    Directory of Open Access Journals (Sweden)

    Anil Kumar Kar

    2015-09-01

    New hydrological insights for the region: This study establishes different possible key RG networks using Hall’s method, analytical hierarchical process (AHP, self organization map (SOM and hierarchical clustering (HC using the characteristics of each rain gauge occupied Thiessen polygon area. Efficiency of the key networks is tested by artificial neural network (ANN, Fuzzy and NAM rainfall-runoff models. Furthermore, flood forecasting has been carried out using the three most effective RG networks which uses only 7 RGs instead of 14 gauges established in the Kantamal sub-catchment, Mahanadi basin. The Fuzzy logic applied on the key RG network derived using AHP has shown the best result for flood forecasting with efficiency of 82.74% for 1-day lead period. This study demonstrates the design procedure of key RG network for effective flood forecasting particularly when there is difficulty in gathering the information from all RGs.

  4. SIMULATION AND IMPLEMENTATION OF PID-ANN CONTROLLER FOR CHOPPER FED EMBEDDED PMDC MOTOR

    Directory of Open Access Journals (Sweden)

    M. Madheswaran

    2012-04-01

    Full Text Available The closed loop control of PMDC drive with an inner current controller and an outer PID-ANN (Proportional Integral Derivative – Artificial Neural Network based speed controller is designed and presented in this paper. Motor is fed by DC / DC buck converter (DC Chopper. The controller is used to change the duty cycle of the converter and thereby, the voltage fed to the PMDC motor to regulate the speed. The PID-ANN controller designed was evaluated by computer simulation and it was implemented using an 8051 based embedded system. This system will operate in forward motoring with variable speed.

  5. 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.

  6. Deriving margins in prostate cancer radiotherapy treatment: comparison of neural network and fuzzy logic models.

    Science.gov (United States)

    Mzenda, Bongile; Gegov, Alexander; Brown, David J; Petrov, Nedyalko

    2012-01-01

    This study investigates the feasibility of using Artificial Neural Network (ANN) and fuzzy logic based techniques to select treatment margins for dynamically moving targets in the radiotherapy treatment of prostate cancer. The use of data from 15 patients relating error effects to the Tumour Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) radiobiological indices was contrasted against the use of data based on the prostate volume receiving 99% of the prescribed dose (V99%) and the rectum volume receiving more than 60Gy (V60). For the same input data, the results of the ANN were compared to results obtained using a fuzzy system, a fuzzy network and current clinically used statistical techniques. Compared to fuzzy and statistical methods, the ANN derived margins were found to be up to 2 mm larger at small and high input errors and up to 3.5 mm larger at medium input error magnitudes.

  7. Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

    Directory of Open Access Journals (Sweden)

    Sadik Kamel Gharghan

    2016-01-01

    Full Text Available In most wireless sensor network (WSN applications, the sensor nodes (SNs are battery powered and the amount of energy consumed by the nodes in the network determines the network lifespan. For future Internet of Things (IoT applications, reducing energy consumption of SNs has become mandatory. In this paper, an ultra-low-power nRF24L01 wireless protocol is considered for a bicycle WSN. The power consumption of the mobile node on the cycle track was modified by combining adjustable data rate, sleep/wake, and transmission power control (TPC based on two algorithms. The first algorithm was a TPC-based distance estimation, which adopted a novel hybrid particle swarm optimization-artificial neural network (PSO-ANN using the received signal strength indicator (RSSI, while the second algorithm was a novel TPC-based accelerometer using inclination angle of the bicycle on the cycle track. Based on the second algorithm, the power consumption of the mobile and master nodes can be improved compared with the first algorithm and constant transmitted power level. In addition, an analytical model is derived to correlate the power consumption and data rate of the mobile node. The results indicate that the power savings based on the two algorithms outperformed the conventional operation (i.e., without power reduction algorithm by 78%.

  8. Comparison of Available Bandwidth Estimation Techniques in Packet-Switched Mobile Networks

    DEFF Research Database (Denmark)

    López Villa, Dimas; Ubeda Castellanos, Carlos; Teyeb, Oumer Mohammed

    2006-01-01

    of information regarding the available bandwidth in the transport network, as it could end up being the bottleneck rather than the air interface. This paper provides a comparative study of three well known available bandwidth estimation techniques, i.e. TOPP, SLoPS and pathChirp, taking into account......The relative contribution of the transport network towards the per-user capacity in mobile telecommunication systems is becoming very important due to the ever increasing air-interface data rates. Thus, resource management procedures such as admission, load and handover control can make use...... the statistical conditions of the available bandwidth and assessing the variability of their estimations. Simulation-based studies on a mobile transport network show that pathChirp outperforms TOPP and SLoPS, both in terms of accuracy and efficiency....

  9. Efficiency of Software Testing Techniques: A Controlled Experiment Replication and Network Meta-analysis

    Directory of Open Access Journals (Sweden)

    Omar S. Gómez

    2017-07-01

    Full Text Available Background: Common approaches to software verification include static testing techniques, such as code reading, and dynamic testing techniques, such as black-box and white-box testing. Objective: With the aim of gaining a~better understanding of software testing techniques, a~controlled experiment replication and the synthesis of previous experiments which examine the efficiency of code reading, black-box and white-box testing techniques were conducted. Method: The replication reported here is composed of four experiments in which instrumented programs were used. Participants randomly applied one of the techniques to one of the instrumented programs. The outcomes were synthesized with seven experiments using the method of network meta-analysis (NMA. Results: No significant differences in the efficiency of the techniques were observed. However, it was discovered the instrumented programs had a~significant effect on the efficiency. The NMA results suggest that the black-box and white-box techniques behave alike; and the efficiency of code reading seems to be sensitive to other factors. Conclusion: Taking into account these findings, the Authors suggest that prior to carrying out software verification activities, software engineers should have a~clear understanding of the software product to be verified; they can apply either black-box or white-box testing techniques as they yield similar defect detection rates.

  10. SSVEP and ANN based optimal speller design for Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Irshad Ahmad Ansari

    2015-07-01

    Full Text Available This work put forwards an optimal BCI (Brain Computer Interface speller design based on Steady State Visual Evoked Potentials (SSVEP and Artificial Neural Network (ANN in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of  BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM.DOI: 10.15181/csat.v2i2.1059

  11. ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant

    Directory of Open Access Journals (Sweden)

    Francesco Grimaccia

    2017-06-01

    Full Text Available Since the beginning of this century, the share of renewables in Europe’s total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called day-ahead electricity market represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an open energy market can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance.

  12. Energy neutral protocol based on hierarchical routing techniques for energy harvesting wireless sensor network

    Science.gov (United States)

    Muhammad, Umar B.; Ezugwu, Absalom E.; Ofem, Paulinus O.; Rajamäki, Jyri; Aderemi, Adewumi O.

    2017-06-01

    Recently, researchers in the field of wireless sensor networks have resorted to energy harvesting techniques that allows energy to be harvested from the ambient environment to power sensor nodes. Using such Energy harvesting techniques together with proper routing protocols, an Energy Neutral state can be achieved so that sensor nodes can run perpetually. In this paper, we propose an Energy Neutral LEACH routing protocol which is an extension to the traditional LEACH protocol. The goal of the proposed protocol is to use Gateway node in each cluster so as to reduce the data transmission ranges of cluster head nodes. Simulation results show that the proposed routing protocol achieves a higher throughput and ensure the energy neutral status of the entire network.

  13. A Visual Analytics Technique for Identifying Heat Spots in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Marian Sorin Nistor

    2016-12-01

    Full Text Available The decision takers of the public transportation system, as part of urban critical infrastructures, need to increase the system resilience. For doing so, we identified analysis tools for biological networks as an adequate basis for visual analytics in that domain. In the paper at hand we therefore translate such methods for transportation systems and show the benefits by applying them on the Munich subway network. Here, visual analytics is used to identify vulnerable stations from different perspectives. The applied technique is presented step by step. Furthermore, the key challenges in applying this technique on transportation systems are identified. Finally, we propose the implementation of the presented features in a management cockpit to integrate the visual analytics mantra for an adequate decision support on transportation systems.

  14. A Novel Architecture for Adaptive Traffic Control in Network on Chip using Code Division Multiple Access Technique

    OpenAIRE

    Fatemeh. Dehghani; Shahram. Darooei

    2016-01-01

    Network on chip has emerged as a long-term and effective method in Multiprocessor System-on-Chip communications in order to overcome the bottleneck in bus based communication architectures. Efficiency and performance of network on chip is so dependent on the architecture and structure of the network. In this paper a new structure and architecture for adaptive traffic control in network on chip using Code Division Multiple Access technique is presented. To solve the problem of synchronous acce...

  15. Network module detection: Affinity search technique with the multi-node topological overlap measure

    Directory of Open Access Journals (Sweden)

    Horvath Steve

    2009-07-01

    Full Text Available Abstract Background Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. Findings We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST, which is a generalized version of the Cluster Affinity Search Technique (CAST. MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes. We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Conclusion Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

  16. Artificial Neural Networks for Detection of Malaria in RBCs

    CERN Document Server

    Pandit, Purnima

    2016-01-01

    Malaria is one of the most common diseases caused by mosquitoes and is a great public health problem worldwide. Currently, for malaria diagnosis the standard technique is microscopic examination of a stained blood film. We propose use of Artificial Neural Networks (ANN) for the diagnosis of the disease in the red blood cell. For this purpose features / parameters are computed from the data obtained by the digital holographic images of the blood cells and is given as input to ANN which classifies the cell as the infected one or otherwise.

  17. Reconstructing missing daily precipitation data using regression trees and artificial neural networks

    Science.gov (United States)

    Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

  18. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  19. Parameters estimation of squirrel-cage induction motors using ANN and ANFIS

    Directory of Open Access Journals (Sweden)

    Mehdi Ahmadi Jirdehi

    2016-03-01

    Full Text Available In the transient behavior analysis of a squirrel-cage induction motor, the parameters of the single-cage and double-cage models are studied. These parameters are usually hard to obtain. This paper presents two new methods to predict the induction motor parameters in the single-cage and double-cage models based on artificial neural network (ANN and adaptive neuro-fuzzy inference system (ANFIS. For this purpose, the experimental data (manufacturer data of 20 induction motors with the different power are used. The experimental data are including of the starting torque and current, maximum torque, full load sleep, efficiency, rated active power and reactive power. The obtained results from the proposed ANN and ANFIS models are compared with each other and with the experimental data, which show a good agreement between the predicted values and the experimental data. But the proposed ANFIS model is more accurate than the proposed ANN model.

  20. RSM and ANN Modeling of Micro Wire Electrical Discharge Machining of AL 2024 T351

    Directory of Open Access Journals (Sweden)

    Sivaprakasam Palani

    2015-01-01

    Full Text Available This paper presents modeling and analysis of machining characteristics of Micro Wire Electro Discharge Machining (Micro-WEDM process on Aluminium alloy (AL 2024 T351 using the Response Surface Methodology (RSM and Artificial Neural Network (ANN. The input variables of Micro-WEDM process were voltage, capacitance and feed rate. The surface roughness and material removal rate are considered as a response variables. Experiments were carried out on Aluminium alloy using Central Composite Design (CCD. The RSM and ANN models have been developed based on experimental designs. Analysis of variance (ANOVA has been employed to test the significance of RSM model. It has been found out that all the three process parameters are significant and their interaction effects are also significant on the surface roughness and material removal rate. Finally predicted values were compared with ANN.

  1. Analysis of RF MEMS Capacitive Switches by Using Switch EM ANN Models

    Directory of Open Access Journals (Sweden)

    Z. Marinković

    2015-11-01

    Full Text Available Artificial neural networks (ANNs have appeared to be an alternative to the conventional models of RF MEMS switches. In this paper, neural models of an RF MEMS capacitive switch are developed and used for the electrical design of the switch. Namely, an ANN model relating the switch resonant frequency and the bridge dimensions is used to analyze efficiently the switch behavior with changes of bridge dimensions. Furthermore, it is illustrated how the developed model can be used for the determination of bridge dimensions in order to achieve the desired switch resonant frequency. In addition, application of a switch inverse ANN model for the determination of bridge dimensions is analyzed as well.

  2. Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression

    Directory of Open Access Journals (Sweden)

    Neela Deshpande

    2014-12-01

    Full Text Available In the recent past Artificial Neural Networks (ANN have emerged out as a promising technique for predicting compressive strength of concrete. In the present study back propagation was used to predict the 28 day compressive strength of recycled aggregate concrete (RAC along with two other data driven techniques namely Model Tree (MT and Non-linear Regression (NLR. Recycled aggregate is the current need of the hour owing to its environmental friendly aspect of re-use of the construction waste. The study observed that, prediction of 28 day compressive strength of RAC was done better by ANN than NLR and MT. The input parameters were cubic meter proportions of Cement, Natural fine aggregate, Natural coarse Aggregates, recycled aggregates, Admixture and Water (also called as raw data. The study also concluded that ANN performs better when non-dimensional parameters like Sand–Aggregate ratio, Water–total materials ratio, Aggregate–Cement ratio, Water–Cement ratio and Replacement ratio of natural aggregates by recycled aggregates, were used as additional input parameters. Study of each network developed using raw data and each non dimensional parameter facilitated in studying the impact of each parameter on the performance of the models developed using ANN, MT and NLR as well as performance of the ANN models developed with limited number of inputs. The results indicate that ANN learn from the examples and grasp the fundamental domain rules governing strength of concrete.

  3. Performance evaluation of an importance sampling technique in a Jackson network

    Science.gov (United States)

    brahim Mahdipour, E.; Masoud Rahmani, Amir; Setayeshi, Saeed

    2014-03-01

    Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.

  4. Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms

    Directory of Open Access Journals (Sweden)

    Neelamegam Premalatha

    2016-06-01

    Full Text Available Global solar radiation (GSR is an essential parameter for the design and operation of solar energy systems. Long-standing records of global solar radiation data are not available in many places because of the cost and maintenance of the measuring instruments. The major objective of this work is to develop an ANN model for accurately predicting solar radiation. Two ANN models with four different algorithms are considered in the present study. Meteorological data collected for the last 10 years from five different locations across India have been used to train the models. The best ANN algorithm and model are identified based on minimum mean absolute error (MAE and root mean square error (RMSE and maximum linear correlation coefficient (R. Further, the present study confirms that prediction accuracy of the ANN model depends on the complete set of data being used for training the network for the intended application. The developed ANN model has a low mean absolute percentage error (MAPE which ascertains the accuracy and suitability of the model to predict the monthly average global radiation so as to design or evaluate solar energy installations, where the meteorological data measuring facilities are not in place in India.

  5. 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.

  6. Comparing ANNs, EAs, and Trees: a basic machine-learning approach to predictive environmental models.

    Science.gov (United States)

    Williams, J.; Poff, N.

    2005-05-01

    Machine learning techniques for ecological applications or "eco-informatics" are becoming increasingly useful and accessible for ecologists. We evaluated the predictive ability of three commercially available (i.e. user-friendly) software packages for artificial neural networks (ANNs), evolutionary algorithms (EAs), and classification/regression trees (Trees). We analyzed fish and habitat data for streams in the mid-Atlantic region of the U.S., which was collected by the U.S. Environmental Protection Agency (EPA). The data includes over 200 environmental descriptors summarizing watershed, stream, and water chemistry characteristics in addition to derived fish community metrics (i.e. richness, IBI scores, % exotics). In our analysis we predicted individual species presence/absence and fish community metrics as a function of these local and regional scale habitat variables. Predictive ability is evaluated with independent validation data. These approaches could prove especially useful for conservation or management applications where ecologists seek to utilize the most comprehensive data to make predictions at various scales. By employing "user-friendly" software we hope to show that ecologists, without extensive knowledge of computational science, can benefit from these techniques by extracting more information about complex ecosystems. Relative strengths and weaknesses of these three approaches are compared and recommendations for their use in conservation applications are presented.

  7. A method to estimate emission rates from industrial stacks based on neural networks.

    Science.gov (United States)

    Olcese, Luis E; Toselli, Beatriz M

    2004-11-01

    This paper presents a technique based on artificial neural networks (ANN) to estimate pollutant rates of emission from industrial stacks, on the basis of pollutant concentrations measured on the ground. The ANN is trained on data generated by the ISCST3 model, widely accepted for evaluation of dispersion of primary pollutants as a part of an environmental impact study. Simulations using theoretical values and comparison with field data are done, obtaining good results in both cases at predicting emission rates. The application of this technique would allow the local environment authority to control emissions from industrial plants without need of performing direct measurements inside the plant. copyright 2004 Elsevier Ltd.

  8. 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.

  9. Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

    Science.gov (United States)

    Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

    2017-09-01

    Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

  10. WRHT: A Hybrid Technique for Detection of Wormhole Attack in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2016-01-01

    Full Text Available Wormhole attack is a challenging security threat to wireless sensor networks which results in disrupting most of the routing protocols as this attack can be triggered in different modes. In this paper, WRHT, a wormhole resistant hybrid technique, is proposed, which can detect the presence of wormhole attack in a more optimistic manner than earlier techniques. WRHT is based on the concept of watchdog and Delphi schemes and ensures that the wormhole will not be left untreated in the sensor network. WRHT makes use of the dual wormhole detection mechanism of calculating probability factor time delay probability and packet loss probability of the established path in order to find the value of wormhole presence probability. The nodes in the path are given different ranking and subsequently colors according to their behavior. The most striking feature of WRHT consists of its capacity to defend against almost all categories of wormhole attacks without depending on any required additional hardware such as global positioning system, timing information or synchronized clocks, and traditional cryptographic schemes demanding high computational needs. The experimental results clearly indicate that the proposed technique has significant improvement over the existing wormhole attack detection techniques.

  11. Comparison of single and modular ANN based fault detector and ...

    African Journals Online (AJOL)

    user

    development of the architecture of modular ANN based fault detector and classifier is same as that of single ANN based fault detector and classifier. The final architecture of modular ..... phases B1, C2 are high and other phases outputs are well below the threshold limit 0.3. However, neutral “N” output of single. ANN based ...

  12. Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

    Directory of Open Access Journals (Sweden)

    Syed Ali Abbas Kazmi

    2017-02-01

    Full Text Available Distribution networks (DNWs are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN needs to consider choices, primarily distributed generation (DG, network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs based on mult-objective optimizations (MOOs in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at

  13. Comprehensive heat transfer correlation for water/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

    Science.gov (United States)

    Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin

    2017-10-01

    Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene.

  14. Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm.

    Science.gov (United States)

    Kim, Mi Jeong; Maeng, Sung Joon; Cho, Yong Soo

    2015-07-28

    In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.

  15. Reconstruction of chalk pore networks from 2D backscatter electron micrographs using a simulated annealing technique

    Energy Technology Data Exchange (ETDEWEB)

    Talukdar, M.S.; Torsaeter, O. [Department of Petroleum Engineering and Applied Geophysics, Norwegian University of Science and Technology, Trondheim (Norway)

    2002-05-01

    We report the stochastic reconstruction of chalk pore networks from limited morphological information that may be readily extracted from 2D backscatter electron (BSE) images of the pore space. The reconstruction technique employs a simulated annealing (SA) algorithm, which can be constrained by an arbitrary number of morphological descriptors. Backscatter electron images of a high-porosity North Sea chalk sample are analyzed and the morphological descriptors of the pore space are determined. The morphological descriptors considered are the void-phase two-point probability function and lineal path function computed with or without the application of periodic boundary conditions (PBC). 2D and 3D samples have been reconstructed with different combinations of the descriptors and the reconstructed pore networks have been analyzed quantitatively to evaluate the quality of reconstructions. The results demonstrate that simulated annealing technique may be used to reconstruct chalk pore networks with reasonable accuracy using the void-phase two-point probability function and/or void-phase lineal path function. Void-phase two-point probability function produces slightly better reconstruction than the void-phase lineal path function. Imposing void-phase lineal path function results in slight improvement over what is achieved by using the void-phase two-point probability function as the only constraint. Application of periodic boundary conditions appears to be not critically important when reasonably large samples are reconstructed.

  16. Interference-Aware Spectrum Sharing Techniques for Next Generation Wireless Networks

    KAUST Repository

    Qaraqe, Marwa Khalid

    2011-11-20

    Background: Reliable high-speed data communication that supports multimedia application for both indoor and outdoor mobile users is a fundamental requirement for next generation wireless networks and requires a dense deployment of physically coexisting network architectures. Due to the limited spectrum availability, a novel interference-aware spectrum-sharing concept is introduced where networks that suffer from congested spectrums (secondary-networks) are allowed to share the spectrum with other networks with available spectrum (primary-networks) under the condition that limited interference occurs to primary networks. Objective: Multiple-antenna and adaptive rate can be utilized as a power-efficient technique for improving the data rate of the secondary link while satisfying the interference constraint of the primary link by allowing the secondary user to adapt its transmitting antenna, power, and rate according to the channel state information. Methods: Two adaptive schemes are proposed using multiple-antenna transmit diversity and adaptive modulation in order to increase the spectral-efficiency of the secondary link while maintaining minimum interference with the primary. Both the switching efficient scheme (SES) and bandwidth efficient scheme (BES) use the scan-and-wait combining antenna technique (SWC) where there is a secondary transmission only when a branch with an acceptable performance is found; else the data is buffered. Results: In both these schemes the constellation size and selected transmit branch are determined to minimized the average number of switches and achieve the highest spectral efficiency given a minimum bit-error-rate (BER), fading conditions, and peak interference constraint. For delayed sensitive applications, two schemes using power control are used: SES-PC and BES-PC. In these schemes the secondary transmitter sends data using a nominal power level, which is optimized to minimize the average delay. Several numerical examples show

  17. ANN Modeling of a Chemical Humidity Sensing Mechanism

    Directory of Open Access Journals (Sweden)

    Souhil KOUDA

    2010-10-01

    Full Text Available This work aims to achieve a modeling of a resistive-type humidity sensing mechanism (RHSM. This model takes into account the parameters of non-linearity, hysteresis, temperature, frequency, substrate type. Furthermore, we investigated the TiO2 and PMAPTAC concentrations effects on the humidity sensing properties in our model. Using neuronal networks and Matlab environment, we have done the training to realize an analytical model ANN and create a component, accurately express the above parameters variations, for our sensing mechanism model in the PSPICE simulator library. Simulation has been used to evaluate the effect of variations of non-linearity, hysteresis, temperature, frequency, substrate type and TiO2 and PMAPTAC concentrations effects, where the output of this model is identical to the output of the chemical humidity sensing mechanism used.

  18. Image reconstruction of the location of macro-inhomogeneity in random turbid medium by using artificial neural networks

    Science.gov (United States)

    Veksler, Boris A.; Maksimova, Irina L.; Meglinski, Igor V.

    2007-07-01

    Nowadays the artificial neural network (ANN), an effective powerful technique that is able denoting complex input and output relationships, is widely used in different biomedical applications. In present study the applying of ANN for the determination of characteristics of random highly scattering medium (like bio-tissue) is considered. Spatial distribution of the backscattered light calculated by Monte Carlo method is used to train ANN for multiply scattering regimes. The potential opportunities of use of ANN for image reconstruction of an absorbing macro inhomogeneity located in topical layers of random scattering medium are presented. This is especially of high priority because of new diagnostics/treatment developing that is based on the applying gold nano-particles for labeling cancer cells.

  19. Intelligent techniques for system identification and controller tuning in pH process

    Directory of Open Access Journals (Sweden)

    K. Valarmathi

    2009-03-01

    Full Text Available This paper presents an application of Artificial Neural Network (ANN and Genetic Algorithm (GA for system identification for controller tuning in a pH process. In this paper, the ANN based approach is applied to estimate the system parameters. Once the variations in parameters are identified frequently, GA optimally tunes the controller. The simulation results show that the proposed intelligent technique is effective in identifying the parameters and has resulted in a minimum value of the Integral Square Error, peak overshoot and minimum settling time as compared to conventional methods. The experimental results show that their performance is superior and it matches favorably with the simulation results.

  20. Imaging spatially varying biomechanical properties with neural networks

    Science.gov (United States)

    Hoerig, Cameron; Reyes, Wendy; Fabre, Léo.; Ghaboussi, Jamshid; Insana, Michael F.

    2017-03-01

    Elastography comprises a set of modalities that image the biomechanical properties of soft tissues for disease detection and diagnosis. Quasi-static ultrasound elastography, in particular, tracks sub-surface displacements resulting from an applied surface force. The local displacement information and measured surface loads may be used to compute a parametric summary of biomechanical properties; however, the inverse problem is under- determined, limiting most techniques to estimating a single linear-elastic parameter. We previously described a new method to develop mechanical models using a combination of computational mechanics and machine learning that circumvents the limitations associated with the inverse problem. The Autoprogressive method weaves together finite element analysis and artificial neural networks (ANNs) to develop empirical models of mechanical behavior using only measured force-displacement data. We are extending that work by incorporating spatial information with the material properties. Previously, the ANNs accepted only a strain vector input and computed the corresponding stress, meaning any spatial information was encoded in the finite element mesh. Now, using a pair of ANNs working in tandem with spatial coordinates included as part of the input, these new Cartesian ANNs are able to learn the spatially varying mechanical behavior of complex media. We show that a single Cartesian ANN is able to describe the same mechanical behavior of an object that previously required at least two ANNs. Furthermore, we show the new ANNs can learn complex material property distributions and reconstruct images of the Young's modulus distribution, not merely classify, filter, or otherwise process an existing image. For the first time, we present results using Cartesian neural networks within the Autoprogressive Method to form elastic modulus images.

  1. On-line dynamic monitoring automotive exhausts: using BP-ANN for distinguishing multi-components

    Science.gov (United States)

    Zhao, Yudi; Wei, Ruyi; Liu, Xuebin

    2017-10-01

    Remote sensing-Fourier Transform infrared spectroscopy (RS-FTIR) is one of the most important technologies in atmospheric pollutant monitoring. It is very appropriate for on-line dynamic remote sensing monitoring of air pollutants, especially for the automotive exhausts. However, their absorption spectra are often seriously overlapped in the atmospheric infrared window bands, i.e. MWIR (3 5μm). Artificial Neural Network (ANN) is an algorithm based on the theory of the biological neural network, which simplifies the partial differential equation with complex construction. For its preferable performance in nonlinear mapping and fitting, in this paper we utilize Back Propagation-Artificial Neural Network (BP-ANN) to quantitatively analyze the concentrations of four typical industrial automotive exhausts, including CO, NO, NO2 and SO2. We extracted the original data of these automotive exhausts from the HITRAN database, most of which virtually overlapped, and established a mixed multi-component simulation environment. Based on Beer-Lambert Law, concentrations can be retrieved from the absorbance of spectra. Parameters including learning rate, momentum factor, the number of hidden nodes and iterations were obtained when the BP network was trained with 80 groups of input data. By improving these parameters, the network can be optimized to produce necessarily higher precision for the retrieved concentrations. This BP-ANN method proves to be an effective and promising algorithm on dealing with multi-components analysis of automotive exhausts.

  2. Structure-activity correlations for illicit amphetamines using ANN and constitutional descriptors.

    Science.gov (United States)

    Gosav, S; Praisler, M; Dorohoi, D O; Popa, G

    2006-12-15

    The goal of this study was to develop an expert system capable to identify the potential biological activity of new substances having a molecular structure similar to illicit amphetamines. For this purpose we have designed two types of artificial neural network (ANN) systems, which have been trained to classify amphetamines according to their toxicological activity (stimulant amphetamines or hallucinogenic amphetamines) and distinguish them from nonamphetamines. Such a system is essential for testing new molecular structures for epidemiological, clinical, and forensic purposes. The first type of artificial neural network is a "spectral" neural network, which has as input variables the most important 100 absorption intensities from a total of 260 measured for each normalized infrared spectrum 10cm(-1) apart. The spectral data consists of a database built with the GC-FT-IR spectra of the most popular drugs of abuse (mainly central stimulants, hallucinogens, sympathomimetic amines, narcotics and other potent analgesics), precursors and derivatized counterparts. All samples were also characterized by their constitutional descriptors (CDs). For each sample, a number of 45 CDs were computed and introduced as input variables for a second type of ANN, which uses a structural database. The efficiency of this "structural" artificial neural network (CD-ANN) has been improved by optimizing the training set and increasing the number of input variables (CDs). A comparative analysis of the spectral and the structural networks is presented.

  3. Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN-Based Model

    Directory of Open Access Journals (Sweden)

    Emilio Jiménez-Macías

    2014-01-01

    Full Text Available This paper analyses the correlation between the acoustic emission signals and the main parameters of friction stir welding process based on artificial neural networks (ANNs. The acoustic emission signals in Z and Y directions have been acquired by the AE instrument NI USB-9234. Statistical and temporal parameters of discomposed acoustic emission signals using Wavelet Transform have been used as input of the ANN. The outputs of the ANN model include the parameters of tool rotation speed and travel speed, and tool profile, as well as the tensile strength. A multilayer feed-forward neural network has been selected and trained, using Levenberg-Marquardt algorithm for different network architectures. Finally, an analysis of the comparison between the measured and the calculated data is presented. The model obtained can be used to model and develop an automatic control of the parameters of the process and mechanical properties of joint, based on the acoustic emission signals.

  4. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Victor Garcia-Font

    2016-06-01

    Full Text Available In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens’ quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

  5. Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Świłło S.J.

    2013-12-01

    Full Text Available The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

  6. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks.

    Science.gov (United States)

    Garcia-Font, Victor; Garrigues, Carles; Rifà-Pous, Helena

    2016-06-13

    In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens' quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

  7. Achieving energy efficiency in LTE with joint D2D communications and green networking techniques

    KAUST Repository

    Yaacoub, Elias E.

    2013-07-01

    In this paper, the joint operation of cooperative device-to-device (D2D) communications and green cellular communications is investigated. An efficient approach for grouping mobile terminals (MTs) into cooperative clusters is described. In each cluster, MTs cooperate via D2D communications to share content of common interest. Furthermore, an energy-efficient technique for putting BSs in sleep mode in an LTE cellular network is presented. Finally, both methods are combined in order to ensure green communications for both the users\\' MTs and the operator\\'s BSs. The studied methods are investigated in the framework of OFDMA-based state-of-the-art LTE cellular networks, while taking into account intercell interference and resource allocation. © 2013 IEEE.

  8. Prediction of event-based stormwater runoff quantity and quality by ANNs developed using PMI-based input selection

    Science.gov (United States)

    He, Jianxun; Valeo, Caterina; Chu, Angus; Neumann, Norman F.

    2011-03-01

    SummaryEvent-based stormwater runoff quantity and quality modeling remains a challenge since the processes of rainfall induced pollutant discharge are not completely understood. The complexity of physically-based models often limits the practical use of quality models in practice. Artificial neural networks (ANNs) are a data driven modeling approach that can avoid the necessity of fully understanding complex physical processes. In this paper, feed-forward multi-layer perceptron (MLP) network, a popular type of ANN, was applied to predict stormwater runoff quantity and quality including turbidity, specific conductance, water temperature, pH, and dissolved oxygen (DO) in storm events. A recently proposed input selection algorithm based on partial mutual information (PMI), which identifies input variables in a stepwise manner, was employed to select input variable sets for the development of ANNs. The ANNs developed via this approach could produce satisfactory prediction of event-based stormwater runoff quantity and quality. In particular, this approach demonstrated a superior performance over the approach involving ANNs fed by inputs selected using partial correlation and all potential inputs in flow modeling. This result suggests the applicability of PMI in developing ANN models. In addition, the ANN for flow outperformed conventional multiple linear regression (MLR) and multiple nonlinear regression (MNLR) models. For an ANN development of turbidity (multiplied by flow rate) and specific conductance, significant improvement was achieved by including a previous 3-week total rainfall amount into their input variable sets. This antecedent rainfall variable is considered a factor in the availability of land surface pollutants for wash-off. A sensitivity analysis demonstrated the potential role of this rainfall variable on modeling particulate solids and dissolved matters in stormwater runoff.

  9. ZnO nanowall network grown by chemical vapor deposition technique

    Science.gov (United States)

    Mukherjee, Amrita; Dhar, Subhabrata

    2015-06-01

    Network of wedge shaped ZnO nanowalls are grown on c-sapphire by Chemical Vapor Deposition (CVD) technique. Structural studies using x-ray diffraction show much better crystallinity in the nanowall sample as compared to the continuous film. Moreover, the defect related broad green luminescence is found to be suppressed in the nanowall sample. The low temperature photoluminescence study also suggests the quantum confinement of carriers in nanowall sample. Electrical studies performed on the nanowalls show higher conductivity, which has been explained in terms of the reduction of scattering cross-section as a result of 1D quantum confinement of carriers on the tip of the nanowalls.

  10. ZnO nanowall network grown by chemical vapor deposition technique

    Energy Technology Data Exchange (ETDEWEB)

    Mukherjee, Amrita, E-mail: but.then.perhaps@gmail.com; Dhar, Subhabrata [Department of Physics, Indian Institute of Technology Bombay, Powai, Mumbai-400076 (India)

    2015-06-24

    Network of wedge shaped ZnO nanowalls are grown on c-sapphire by Chemical Vapor Deposition (CVD) technique. Structural studies using x-ray diffraction show much better crystallinity in the nanowall sample as compared to the continuous film. Moreover, the defect related broad green luminescence is found to be suppressed in the nanowall sample. The low temperature photoluminescence study also suggests the quantum confinement of carriers in nanowall sample. Electrical studies performed on the nanowalls show higher conductivity, which has been explained in terms of the reduction of scattering cross-section as a result of 1D quantum confinement of carriers on the tip of the nanowalls.

  11. A Comparison of Techniques for Camera Selection and Hand-Off in a Video Network

    Science.gov (United States)

    Li, Yiming; Bhanu, Bir

    Video networks are becoming increasingly important for solving many real-world problems. Multiple video sensors require collaboration when performing various tasks. One of the most basic tasks is the tracking of objects, which requires mechanisms to select a camera for a certain object and hand-off this object from one camera to another so as to accomplish seamless tracking. In this chapter, we provide a comprehensive comparison of current and emerging camera selection and hand-off techniques. We consider geometry-, statistics-, and game theory-based approaches and provide both theoretical and experimental comparison using centralized and distributed computational models. We provide simulation and experimental results using real data for various scenarios of a large number of cameras and objects for in-depth understanding of strengths and weaknesses of these techniques.

  12. Location Estimation in Wireless Sensor Networks Using Spring-Relaxation Technique

    Directory of Open Access Journals (Sweden)

    Qing Zhang

    2010-05-01

    Full Text Available Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN. Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization.

  13. Location estimation in wireless sensor networks using spring-relaxation technique.

    Science.gov (United States)

    Zhang, Qing; Foh, Chuan Heng; Seet, Boon-Chong; Fong, A C M

    2010-01-01

    Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN). Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS) is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization.

  14. Data classification using metaheuristic Cuckoo Search technique for Levenberg Marquardt back propagation (CSLM) algorithm

    Science.gov (United States)

    Nawi, Nazri Mohd.; Khan, Abdullah; Rehman, M. Z.

    2015-05-01

    A nature inspired behavior metaheuristic techniques which provide derivative-free solutions to solve complex problems. One of the latest additions to the group of nature inspired optimization procedure is Cuckoo Search (CS) algorithm. Artificial Neural Network (ANN) training is an optimization task since it is desired to find optimal weight set of a neural network in training process. Traditional training algorithms have some limitation such as getting trapped in local minima and slow convergence rate. This study proposed a new technique CSLM by combining the best features of two known algorithms back-propagation (BP) and Levenberg Marquardt algorithm (LM) for improving the convergence speed of ANN training and avoiding local minima problem by training this network. Some selected benchmark classification datasets are used for simulation. The experiment result show that the proposed cuckoo search with Levenberg Marquardt algorithm has better performance than other algorithm used in this study.

  15. A HIGH PERFORMANCE OPTIMIZATION TECHNIQUE FOR POLE BALANCING PROBLEM

    Directory of Open Access Journals (Sweden)

    Bahadır KARASULU

    2008-02-01

    Full Text Available High performance computing techniques can be used effectively for solution of the complex scientific problems. Pole balancing problem is a basic benchmark tool of robotic field, which is an important field of Artificial Intelligence research areas. In this study, a solution is developed for pole balancing problem using Artificial Neural Network (ANN and high performance computation technique. Algorithm, that basis of the Reinforcement Learning method which is used to find the force of pole's balance, is transfered to parallel environment. In Implementation, C is preferred as programming language and Message Passing Interface (MPI is used for parallel computation technique. Self–Organizing Map (SOM ANN model's neurons (artificial neural nodes and their weights are distributed to six processors of a server computer which equipped with each quad core processor (total 24 processors. In this way, performance values are obtained for different number of artificial neural nodes. Success of method based on results is discussed.

  16. Training a multilayer neural network for the Euro-dollar (EUR/ USD exchange rate

    Directory of Open Access Journals (Sweden)

    Jaime Alberto Villamil Torres

    2010-04-01

    Full Text Available A mathematical tool or model for predicting how an economic variable like the exchange rate (relative price between two currencies will respond is a very important need for investors and policy-makers. Most current techniques are based on statistics, particularly linear time series theory. Artificial neural networks (ANNs are mathematical models which try to emulate biological neural networks’ parallelism and nonlinearity; these models have been successfully applied in Economics and Engineering since the 1980s. ANNs appear to be an alternative for modelling the behaviour of financial variables which resemble (as first approximation a random walk. This paper reports the results of using ANNs for Euro/USD exchange rate trading and the usefulness of the algorithm for chemotaxis leading to training networks thereby maximising an objective function re predicting a trader’s profits. JEL: F310, C450.

  17. Optimization of the Production of Extracellular Polysaccharide from the Shiitake Medicinal Mushroom Lentinus edodes (Agaricomycetes) Using Mutation and a Genetic Algorithm-Coupled Artificial Neural Network (GA-ANN).

    Science.gov (United States)

    Adeeyo, Adeyemi Ojutalayo; Lateef, Agbaje; Gueguim-Kana, Evariste Bosco

    2016-01-01

    Exopolysaccharide (EPS) production by a strain of Lentinus edodes was studied via the effects of treatments with ultraviolet (UV) irradiation and acridine orange. Furthermore, optimization of EPS production was studied using a genetic algorithm coupled with an artificial neural network in submerged fermentation. Exposure to irradiation and acridine orange resulted in improved EPS production (2.783 and 5.548 g/L, respectively) when compared with the wild strain (1.044 g/L), whereas optimization led to improved productivity (23.21 g/L). The EPS produced by various strains also demonstrated good DPPH scavenging activities of 45.40-88.90%, and also inhibited the growth of Escherichia coli and Klebsiella pneumoniae. This study shows that multistep optimization schemes involving physical-chemical mutation and media optimization can be an attractive strategy for improving the yield of bioactives from medicinal mushrooms. To the best of our knowledge, this report presents the first reference of a multistep approach to optimizing EPS production in L. edodes.

  18. A constructive algorithm for unsupervised learning with incremental neural network

    OpenAIRE

    Wang, Jenq-Haur; Wang, Hsin-Yang; Chen, Yen-Lin; Liu, Chuan-Ming

    2015-01-01

    Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neur...

  19. Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network.

    Science.gov (United States)

    Mbakwe, Anthony C; Saka, Anthony A; Choi, Keechoo; Lee, Young-Jae

    2016-08-01

    Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Threshold selection in gene co-expression networks using spectral graph theory techniques.

    Science.gov (United States)

    Perkins, Andy D; Langston, Michael A

    2009-10-08

    Gene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff. We apply spectral graph theory methods to develop a systematic method for threshold selection. Eigenvalues and eigenvectors are computed for a transformation of the adjacency matrix of the network constructed at various threshold values. From these, we use a basic spectral clustering method to examine the set of gene-gene relationships and select a threshold dependent upon the community structure of the data. This approach is applied to two well-studied microarray data sets from Homo sapiens and Saccharomyces cerevisiae. This method presents a systematic, data-based alternative to using more artificial cutoff values and results in a more conservative approach to threshold selection than some other popular techniques such as retaining only statistically-significant relationships or setting a cutoff to include a percentage of the highest correlations.

  1. Designing and Implementation of Solar Street Lighting Management System Using Wide Area Network Technique

    Directory of Open Access Journals (Sweden)

    Ala'a Imran Al-Mutairi

    2016-03-01

    Full Text Available In this work, an innovate system is designed and implemented to manage solar street lighting units which are distributed in urban. The designed system overcomes wireless sensor network's essential drawback used to automate these units. This drawback is represented by multi-hops networking technique. The system constructs from several clusters, each one is represented by single-hop topology of Wide Area Network (WAN. Each cluster has three type of nodes namely: lamppost stations, local station, and optionally master station. All designed stations are attached to SX1272 modem from Semtech company in order to establish the WAN. This module provides ultra-long transmission range up to 1.4 Km with high interference immunity. The system tested practically in real urban environment. It gives an excellent results with respect to maximum communication range and monitoring of vital operation parameters related to solar panel and other unit equipment's. In addition, the system performed on/off command to controlling lamp. During the test period, the designed system was very efficient in a manner it enabled operator to identify the possible system malfunctions and increase solar lighting units lifespan, reduce maintenance costs consequently it ensures the continues the service from these units.

  2. Impedance measurement techniques for one-port and two-port networks.

    Science.gov (United States)

    Bai, Mingsian R; Lo, Yi-Yang; Chen, You Siang

    2015-10-01

    A microphone array impedance matrix measurement technique is presented for linear and passive acoustic two-port networks. Two impedance tubes fitted with three non-uniformly spaced microphones are required in the measurement. The non-uniform spacing is intended to avoid ill-posedness problems in calculating two plane-wave components traveling in opposite directions. Based on the one-port measurement, acoustic two-port networks modeled with the source and the load connected are examined. Three experimental procedures, the two-load measurement method (TLMM), the reciprocal-constrained method (RCM), and the reciprocity-symmetry-constrained method (RSCM), are developed to measure the acoustic impedance matrix. Experiments are conducted for several acoustic two-port systems to verify the proposed techniques. The results demonstrate the efficacy of the three experimental procedures when applied to symmetrical and reciprocal systems. For asymmetrical systems, the TLMM and RCM are preferred over the RSCM for measuring the impedance matrix. On top of that, the non-uniform array in conjunction with TLMM is extended to a general electroacoustic two-port system, which can be regarded as a unique contribution of the present work.

  3. Cross-Layer Techniques for Adaptive Video Streaming over Wireless Networks

    Directory of Open Access Journals (Sweden)

    Yufeng Shan

    2005-02-01

    Full Text Available Real-time streaming media over wireless networks is a challenging proposition due to the characteristics of video data and wireless channels. In this paper, we propose a set of cross-layer techniques for adaptive real-time video streaming over wireless networks. The adaptation is done with respect to both channel and data. The proposed novel packetization scheme constructs the application layer packet in such a way that it is decomposed exactly into an integer number of equal-sized radio link protocol (RLP packets. FEC codes are applied within an application packet at the RLP packet level rather than across different application packets and thus reduce delay at the receiver. A priority-based ARQ, together with a scheduling algorithm, is applied at the application layer to retransmit only the corrupted RLP packets within an application layer packet. Our approach combines the flexibility and programmability of application layer adaptations, with low delay and bandwidth efficiency of link layer techniques. Socket-level simulations are presented to verify the effectiveness of our approach.

  4. Cross-Layer Techniques for Adaptive Video Streaming over Wireless Networks

    Science.gov (United States)

    Shan, Yufeng

    2005-12-01

    Real-time streaming media over wireless networks is a challenging proposition due to the characteristics of video data and wireless channels. In this paper, we propose a set of cross-layer techniques for adaptive real-time video streaming over wireless networks. The adaptation is done with respect to both channel and data. The proposed novel packetization scheme constructs the application layer packet in such a way that it is decomposed exactly into an integer number of equal-sized radio link protocol (RLP) packets. FEC codes are applied within an application packet at the RLP packet level rather than across different application packets and thus reduce delay at the receiver. A priority-based ARQ, together with a scheduling algorithm, is applied at the application layer to retransmit only the corrupted RLP packets within an application layer packet. Our approach combines the flexibility and programmability of application layer adaptations, with low delay and bandwidth efficiency of link layer techniques. Socket-level simulations are presented to verify the effectiveness of our approach.

  5. Vehicle Assisted Data Delievery Technique To Control Data Dissemination In Vehicular AD - HOC Networks Vanets

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar

    2015-08-01

    Full Text Available Abstract Multi-hop data delivery through vehicular ad hoc networks is complicated by the fact that vehicular networks are highly mobile and frequently disconnected. To address this issue the idea of helper node is opted where a moving vehicles carries the packet until a new vehicle moves into its vicinity and forwards the packet. Different from existing helper node solution use of the predicable vehicle mobility is made which is limited by the traffic pattern and the road layout. Based on the existing traffic pattern a vehicle can find the next road to forward packet a vehicle can find the next road to forward the packet to reduce the delay. Several vehicle-assisted date delievery VADD protocol is proposed to forward the packet to the best road with the road with the lowest data delivery delay. Experiment results are used to evaluate the proposed solutions. Results show that the proposed VADD protocol outperform existing solution in terms of packet delivery ratio data packet delay and protocol overhead. Among the proposed VADD protocols the Hybrid probe HVADD protocol has much better performance. In this Solution the helper node technique is provider with which the helper node will contain destination node path and the path in routine table continuously changes with the help of helper node technique.

  6. Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413 Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique

    Directory of Open Access Journals (Sweden)

    R. Soundararajan

    2015-01-01

    Full Text Available Artificial Neural Network (ANN approach was used for predicting and analyzing the mechanical properties of A413 aluminum alloy produced by squeeze casting route. The experiments are carried out with different controlled input variables such as squeeze pressure, die preheating temperature, and melt temperature as per Full Factorial Design (FFD. The accounted absolute process variables produce a casting with pore-free and ideal fine grain dendritic structure resulting in good mechanical properties such as hardness, ultimate tensile strength, and yield strength. As a primary objective, a feed forward back propagation ANN model has been developed with different architectures for ensuring the definiteness of the values. The developed model along with its predicted data was in good agreement with the experimental data, inferring the valuable performance of the optimal model. From the work it was ascertained that, for castings produced by squeeze casting route, the ANN is an alternative method for predicting the mechanical properties and appropriate results can be estimated rather than measured, thereby reducing the testing time and cost. As a secondary objective, quantitative and statistical analysis was performed in order to evaluate the effect of process parameters on the mechanical properties of the castings.

  7. Predicting Subsurface Soil Layering and Landslide Risk with Artificial Neural Networks

    DEFF Research Database (Denmark)

    Farrokhzad, Farzad; Barari, Amin; Ibsen, Lars Bo

    2011-01-01

    the investigation of study area. The quality of the modeling is further improved by the application of some controlling techniques involved in ANN. Based on the obtained results and considering that the test data were not presented to the network in the training process, it can be stated that the trained neural...... networks are capable of predicting variations in the soil profile and assessing the landslide hazard with an acceptable level of confidence....

  8. Forecasting of Turkey inflation with hybrid of feed forward and recurrent artifical neural networks

    OpenAIRE

    Erilli, N. Alp; Eğrioğlu, Erol; Yolcu, Ufuk; Aladağ, Ç. Hakan; Uslu, V. Rezzan

    2010-01-01

    Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved ...

  9. Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction

    Directory of Open Access Journals (Sweden)

    Ting Zhou

    2017-10-01

    Full Text Available Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT preprocess and support vector machine (SVM was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN, regular SVM, and wavelet preprocessed artificial neural networks (WANN models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE, Pearson correlation coefficient (r, root mean square error (RMSE, and Nash-Sutcliffe efficiency (NSE were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

  10. Optical burst add-drop multiplexing technique for sub-wavelength granularity in wavelength multiplexed ring networks.

    Science.gov (United States)

    Cho, Jeong Sik; Seo, Young Kwang; Yoo, Hark; Park, Paul K; Rhee, June-Koo; Won, Yong Hyub; Kang, Min Ho

    2007-10-01

    We demonstrate optical burst add-drop multiplexing as a practical application of the optical burst switching technology in a wavelength-division-multiplexed ring network. To control optical bursts in the network, a burst identifier (BI) for delivering control information, and a BI processor for handling the BI, were designed. Optical bursts of 10- to 100-mus in length were optically multiplexed or demultiplexed in an intermediate node of the ring network. The demonstration shows that the optical burst add-drop multiplexing technique provides sub-wavelength granularity to a ring network.

  11. Modeling of local scour depth downstream hydraulic structures in trapezoidal channel using GEP and ANNs

    Directory of Open Access Journals (Sweden)

    Yasser Abdallah Mohamed Moussa

    2013-12-01

    Full Text Available Local scour downstream stilling basins is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour depth. Lack estimation of local scour can endanger to stability of hydraulic structure and can cause risk of failure. This paper presents Gene expression program (GEP and artificial neural network (ANNs, to simulate local scour depth downstream hydraulic structures. The experimental data is collected from the literature for the scour depth downstream the stilling basin through a trapezoidal channel. Using GEP approach gives satisfactory results compared with artificial neural network (ANN and multiple linear regression (MLR modeling in predicting the scour depth downstream of hydraulic structures.

  12. 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.

  13. Adaptive-Compression Based Congestion Control Technique for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Joa-Hyoung Lee

    2010-03-01

    Full Text Available Congestion in a wireless sensor network causes an increase in the amount of data loss and delays in data transmission. In this paper, we propose a new congestion control technique (ACT, Adaptive Compression-based congestion control Technique based on an adaptive compression scheme for packet reduction in case of congestion. The compression techniques used in the ACT are Discrete Wavelet Transform (DWT, Adaptive Differential Pulse Code Modulation (ADPCM, and Run-Length Coding (RLC. The ACT first transforms the data from the time domain to the frequency domain, reduces the range of data by using ADPCM, and then reduces the number of packets with the help of RLC before transferring the data to the source node. It introduces the DWT for priority-based congestion control because the DWT classifies the data into four groups with different frequencies. The ACT assigns priorities to these data groups in an inverse proportion to the respective frequencies of the data groups and defines the quantization step size of ADPCM in an inverse proportion to the priorities. RLC generates a smaller number of packets for a data group with a low priority. In the relaying node, the ACT reduces the amount of packets by increasing the quantization step size of ADPCM in case of congestion. Moreover, in order to facilitate the back pressure, the queue is controlled adaptively according to the congestion state. We experimentally demonstrate that the ACT increases the network efficiency and guarantees fairness to sensor nodes, as compared with the existing methods. Moreover, it exhibits a very high ratio of the available data in the sink.

  14. Passenger Flows Estimation of Light Rail Transit (LRT System in Izmir, Turkey Using Multiple Regression and ANN Methods

    Directory of Open Access Journals (Sweden)

    Mustafa Özuysal

    2012-01-01

    Full Text Available Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN. The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.   Keywords: light rail transit, multiple regression, artificial neural networks, public transportation

  15. Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations.

    Science.gov (United States)

    León Blanco, José M; González-R, Pedro L; Arroyo García, Carmen Martina; Cózar-Bernal, María José; Calle Suárez, Marcos; Canca Ortiz, David; Rabasco Álvarez, Antonio María; González Rodríguez, María Luisa

    2018-01-01

    This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.

  16. Intelligent reservoir operation system based on evolving artificial neural networks

    Science.gov (United States)

    Chaves, Paulo; Chang, Fi-John

    2008-06-01

    We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

  17. A Hybrid ANN-GA Model to Prediction of Bivariate Binary Responses: Application to Joint Prediction of Occurrence of Heart Block and Death in Patients with Myocardial Infarction.

    Science.gov (United States)

    Mirian, Negin-Sadat; Sedehi, Morteza; Kheiri, Soleiman; Ahmadi, Ali

    2016-01-01

    In medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. Due to the limitations of usual statistical models, other methods such as Artificial Neural Network (ANN) and hybrid models could be used. In this paper, we propose a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model to prediction the occurrence of heart block and death in myocardial infarction (MI) patients simultaneously. For fitting and comparing the models, 263 new patients with definite diagnosis of MI hospitalized in Cardiology Ward of Hajar Hospital, Shahrekord, Iran, from March, 2014 to March, 2016 were enrolled. Occurrence of heart block and death were employed as bivariate binary outcomes. Bivariate Logistic Regression (BLR), ANN and hybrid ANN-GA models were fitted to data. Prediction accuracy was used to compare the models. The codes were written in Matlab 2013a and Zelig package in R3.2.2. The prediction accuracy of BLR, ANN and hybrid ANN-GA models was obtained 77.7%, 83.69% and 93.85% for the training and 78.48%, 84.81% and 96.2% for the test data, respectively. In both training and test data set, hybrid ANN-GA model had better accuracy. ANN model could be a suitable alternative for modeling and predicting bivariate binary responses when the presuppositions of statistical models are not met in actual data. In addition, using optimization methods, such as hybrid ANN-GA model, could improve precision of ANN model.

  18. ANN reconstruction of geoelectrical parameters of the Minou fault zone by scalar CSAMT data

    Science.gov (United States)

    Spichak, V.; Fukuoka, K.; Kobayashi, T.; Mogi, T.; Popova, I.; Shima, H.

    2002-01-01

    Scalar controlled source AMT data collected in a northern part of the Minou fault area (Kyushu Island, Japan) are interpreted by means of the ANN Expert System MT-NET in terms of 3-D earth macro-parameters. A number of synthetic responses created in advance by means of forward modeling in typical 3-D geoelectrical models (conductive and resistive local bodies, fault, dyke, etc.) formed sequences for teaching an artificial neural network (ANN). MT-NET, once taught to the correspondence between the data images and the model parameters, is able to recognize unknown parameters given even incomplete and noisy data. The results of ANN reconstruction are compared with the resistivity distribution obtained for the same area using fast 3-D imaging based on synthesis of 1-D Bostick transforms of the apparent resistivities beneath each site as well as on 2-D TM mode inversion along four profiles. The best-fitting model reconstructed by ANN belongs to the guessed model class formed by "dykes buried in the two-layered earth", on the one hand, and to the equivalence class formed by all models giving rms misfit less than the noise level in the data, on the other hand.

  19. 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.

  20. Fundamental study of interpretation technique for 3-D magnetotelluric data using neural networks; Neural network wo mochiita sanjigen MT ho data kaishaku gijutsu no kisoteki kenkyu

    Energy Technology Data Exchange (ETDEWEB)

    Kobayashi, T.; Fukuoka, K.; Shima, H. [Oyo Corp., Tokyo (Japan); Mogi, T. [Kyushu University, Fukuoka (Japan). Faculty of Engineering; Spichak, V.

    1997-05-27

    The research and development have been conducted to apply neural networks to interpretation technique for 3-D MT data. In this study, a data base of various data was made from the numerical modeling of 3-D fault model, and the data base management system was constructed. In addition, an unsupervised neural network for treating noise and a supervised neural network for estimating fault parameters such as dip, strike and specific resistance were made, and a basic neural network system was constructed. As a result of the application to the various data, basically sufficient performance for estimating the fault parameters was confirmed. Thus, the optimum MT data for this system were selected. In future, it is necessary to investigate the optimum model and the number of models for learning these neural networks. 3 refs., 5 figs., 2 tabs.

  1. On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing

    Science.gov (United States)

    Hafiane, Mohamed Lamine; Dibi, Zohir; Manck, Otto

    2009-01-01

    An intelligent sensor for light wavelength readout, suitable for visible range optical applications, has been developed. Using buried triple photo-junction as basic pixel sensing element in combination with artificial neural network (ANN), the wavelength readout with a full-scale error of less than 1.5% over the range of 400 to 780 nm can be achieved. Through this work, the applicability of the ANN approach in optical sensing is investigated and compared with conventional methods, and a good compromise between accuracy and the possibility for on-chip implementation was thus found. Indeed, this technique can serve different purposes and may replace conventional methods. PMID:22574051

  2. An Alternative to Optimize the Indonesian’s Airport Network Design: An Application of Minimum Spanning Tree (MST Technique

    Directory of Open Access Journals (Sweden)

    Luluk Lusiantoro

    2012-09-01

    Full Text Available Using minimum spanning tree technique (MST, this exploratory research was done to optimize the interrelation and hierarchical network design of Indonesian’s airports. This research also identifies the position of the Indonesian’s airports regionally based on the ASEAN Open Sky Policy 2015. The secondary data containing distance between airports (both in Indonesia and in ASEAN, flight frequency, and correlation of Gross Domestic Regional Product (GDRP for each region in Indonesia are used as inputs to form MST networks. The result analysis is done by comparing the MST networks with the existing network in Indonesia. This research found that the existing airport network in Indonesia does not depict the optimal network connecting all airports with the shortest distance and maximizing the correlation of regional economic potential in the country. This research then suggests the optimal networks and identifies the airports and regions as hubs and spokes formed by the networks. Lastly, this research indicates that the Indonesian airports have no strategic position in the ASEAN Open Sky network, but they have an opportunity to get strategic positions if 33 airports in 33 regions in Indonesia are included in the network.

  3. Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

    Directory of Open Access Journals (Sweden)

    Anup Kumar Paul

    2017-10-01

    Full Text Available Localization is an important aspect in the field of wireless sensor networks (WSNs that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

  4. Dynamic Beamforming for Three-Dimensional MIMO Technique in LTE-Advanced Networks

    Directory of Open Access Journals (Sweden)

    Yan Li

    2013-01-01

    Full Text Available MIMO system with large number of antennas, referred to as large MIMO or massive MIMO, has drawn increased attention as they enable significant throughput and coverage improvement in LTE-Advanced networks. However, deploying huge number of antennas in both transmitters and receivers was a great challenge in the past few years. Three-dimensional MIMO (3D MIMO is introduced as a promising technique in massive MIMO networks to enhance the cellular performance by deploying antenna elements in both horizontal and vertical dimensions. Radio propagation of user equipments (UE is considered only in horizontal domain by applying 2D beamforming. In this paper, a dynamic beamforming algorithm is proposed where vertical domain of antenna is fully considered and beamforming vector can be obtained according to UEs’ horizontal and vertical directions. Compared with the conventional 2D beamforming algorithm, throughput of cell edge UEs and cell center UEs can be improved by the proposed algorithm. System level simulation is performed to evaluate the proposed algorithm. In addition, the impacts of downtilt and intersite distance (ISD on spectral efficiency and cell coverage are explored.

  5. A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks.

    Science.gov (United States)

    Gholami, Mohammad; Brennan, Robert W

    2016-01-06

    In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.

  6. Sonu Shamdasani interviewed by Ann Casement.

    Science.gov (United States)

    Shamdasani, Sonu

    2010-02-01

    Sonu Shamdasani interviewed by Ann Casement about Jung's The Red Book: Liber Novus in the course of which they range over issues to do with what drew Shamdasani to Jung; how he came to be involved in editing, translating and publishing Liber Novus; why he is so passionate about it; where it stands in relation to Jung's other work; some of the central figures that appear in the book such as Philemon and Izdubar; what Liber Novus might offer training candidates and succeeding generations of Jungians; how it has changed Shamdasani's own impression of Jung and what he hopes this enormous project will achieve; why Jung did not publish it in his own lifetime and whether he was mistaken in not doing so; and what impact the publication of Liber Novus will have on Jung's reputation worldwide as well as within the Jungian community.

  7. Noise Reduction Technique for Images using Radial Basis Function Neural Networks

    Directory of Open Access Journals (Sweden)

    Sander Ali Khowaja

    2014-07-01

    Full Text Available This paper presents a NN (Neural Network based model for reducing the noise from images. This is a RBF (Radial Basis Function network which is used to reduce the effect of noise and blurring from the captured images. The proposed network calculates the mean MSE (Mean Square Error and PSNR (Peak Signal to Noise Ratio of the noisy images. The proposed network has also been successfully applied to medical images. The performance of the trained RBF network has been compared with the MLP (Multilayer Perceptron Network and it has been demonstrated that the performance of the RBF network is better than the MLP network.

  8. Application of MIMO Techniques in sky-surface wave hybrid networking sea-state radar system

    Science.gov (United States)

    Zhang, L.; Wu, X.; Yue, X.; Liu, J.; Li, C.

    2016-12-01

    The sky-surface wave hybrid networking sea-state radar system contains of the sky wave transmission stations at different sites and several surface wave radar stations. The subject comes from the national 863 High-tech Project of China. The hybrid sky-surface wave system and the HF surface wave system work simultaneously and the HF surface wave radar (HFSWR) can work in multi-static and surface-wave networking mode. Compared with the single mode radar system, this system has advantages of better detection performance at the far ranges in ocean dynamics parameters inversion. We have applied multiple-input multiple-output(MIMO) techniques in this sea-state radar system. Based on the multiple channel and non-causal transmit beam-forming techniques, the MIMO radar architecture can reduce the size of the receiving antennas and simplify antenna installation. Besides, by efficiently utilizing the system's available degrees of freedom, it can provide a feasible approach for mitigating multipath effect and Doppler-spread clutter in Over-the-horizon Radar. In this radar, slow-time phase-coded MIMO method is used. The transmitting waveforms are phase-coded in slow-time so as to be orthogonal after Doppler processing at the receiver. So the MIMO method can be easily implemented without the need to modify the receiver hardware. After the radar system design, the MIMO experiments of this system have been completed by Wuhan University during 2015 and 2016. The experiment used Wuhan multi-channel ionospheric sounding system(WMISS) as sky-wave transmitting source and three dual-frequency HFSWR developed by the Oceanography Laboratory of Wuhan University. The transmitter system located at Chongyang with five element linear equi-spaced antenna array and Wuhan with one log-periodic antenna. The RF signals are generated by synchronized, but independent digital waveform generators - providing complete flexibility in element phase and amplitude control, and waveform type and parameters

  9. Beam orientation in stereotactic radiosurgery using an artificial neural network.

    Science.gov (United States)

    Skrobala, Agnieszka; Malicki, Julian

    2014-05-01

    To investigate the feasibility of using an artificial neural network (ANN) to generate beam orientations in stereotactic radiosurgery (SRS). A dataset of 669 intracranial lesions was used to build, train, and validate three ANNs. In ANN1, Cartesian coordinates described the localization of the PTV and OARs. In ANN2, a genetic algorithm was used to optimize the model. In ANN3, vectors were used to define the distance between the PTV and OARs. In all ANNs, inputs consisted of the treatment plan parameters plus the patient's particular geometric parameters; outputs were beam and table angles. The ANN- and human-generated plans were then compared using dose-volume histograms, root-mean-square (RMS) and Gamma index methods. The mean volume of PTV covered by the 95% isodose was 99.2% in the MP's plan vs. 99.3%, 98.5% and 99.2% for ANN1, ANN2, and ANN3, respectively. No significant differences were observed between the plans. ANN1 showed the best agreement (Gamma index) with the human planner. While RMS errors in the three ANN models were comparable, ANN1 showed the lowest (best) values. ANN models were able to determine beam orientation in SRS. ANN-generated treatment plans were comparable to human-designed plans. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. A signal combining technique based on channel shortening for cooperative sensor networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2010-06-01

    The cooperative relaying process needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems, e.g. wireless sensor networks where the nodes are equipped with very basic communication hardware. In this paper, we consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination can capture the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. ©2010 IEEE.

  11. Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

    Directory of Open Access Journals (Sweden)

    Vladimir Krasnopolsky

    2016-01-01

    Full Text Available A neural network (NN technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived “ocean color” (OC data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013, and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN’s generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.

  12. Improved hyperspectral vegetation detection using neural networks with spectral angle mapper

    Science.gov (United States)

    Özdemir, Okan Bilge; Yardımcı ćetin, Yasemin

    2017-05-01

    Hyperspectral images have been used in many areas including city planning, mining and military decision support systems. Hyperspectral image analysis techniques have a great potential for vegetation detection and classification with their capability to identify the spectral differences across the electromagnetic spectrum due to their ability to provide information about the chemical compositions of materials. This study introduces a vegetation detection method employing Artificial Neural Network (ANN) over hyperspectral imaging. The algorithm employed backpropagation MLP algorithm for training neural networks. The performance of ANN is improved by the joint use with Spectral Angle Mapper(SAM). The algorithm first obtains the certainty measure from ANN, following the completion of this process, every pixels' angular distance is computed by SAM. The certainty measure is divided by angular distance. Results from ANN, SAM and Support Vector Machine (SVM) algorithms are compared and evaluated with the result of the algorithm. Limited number of training samples are used for training. The results demonstrate that joint use of ANN and SAM significantly improves classification accuracy for smaller training samples.

  13. An artificial neural network method for lumen and media-adventitia border detection in IVUS.

    Science.gov (United States)

    Su, Shengran; Hu, Zhenghui; Lin, Qiang; Hau, William Kongto; Gao, Zhifan; Zhang, Heye

    2017-04-01

    Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Artificial Neural Network System for Thyroid Diagnosis

    Directory of Open Access Journals (Sweden)

    Mazin Abdulrasool Hameed

    2017-05-01

    Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance

  15. On The Comparison of Artificial Neural Network (ANN) and ...

    African Journals Online (AJOL)

    West African Journal of Industrial and Academic Research. Journal Home · ABOUT · Advanced Search · Current Issue · Archives · Journal Home > Vol 13, No 1 (2015) >. Log in or Register to get access to full text downloads.

  16. artificial neural network (ann) approach to electrical load

    African Journals Online (AJOL)

    2004-08-18

    Aug 18, 2004 ... validation, pattern recognition, prediction and multivariable quality applications. Some of its benefits are: - Reduced maintenance costs. Minimized chances of catastrophic failures. : Early error detection and trend analysis. • Significant reduction in data analysis tasks/time. Robust, accurate, and operate in ...

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

    Indian Academy of Sciences (India)

    Tokar and Johnson (1999) developed ANN model to predict daily streamflow from daily rainfall, evaporation, temperature and snowmelt for watershed. ANN can also be applied to streamflow forecasting (Shivakumar et al. 2002;. Sinha Jitendra et al. 2013), reservoir inflow fore- casting (Jain and Srivastava 1999), sediment ...

  18. ImpNet: Programming Software-Defied Networks Using Imperative Techniques

    OpenAIRE

    El-Zawawy, Mohamed A.; AlSalem, Adel I.

    2014-01-01

    Software and hardware components are basic parts of modern networks. However the software compo- nent is typical sealed and function-oriented. Therefore it is very difficult to modify these components. This badly affected networking innovations. Moreover, this resulted in network policies having complex interfaces that are not user-friendly and hence resulted in huge and complicated flow tables on physical switches of networks. This greatly degrades the network performance in many cases. Soft...

  19. Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh

    Directory of Open Access Journals (Sweden)

    Ahmad Hasan Nury

    2017-01-01

    Full Text Available Time-series analyses of temperature data are important for investigating temperature variation and predicting temperature change. Here, Mann–Kendall (M–K analyses of temperature time-series data in northeastern Bangladesh indicated increasing trends (Sen’s slope of maximum and minimum yearly temperature at Sylhet of 0.03 °C and 0.026 °C, respectively, and a minimum temperature at Sreemangal of 0.024 °C except for the maximum temperature at Sreemangal. The linear trends showed that the maximum temperature is increasing by 2.97 °C and 0.59 °C per hundred years, and the minimum, by 2.17 °C and 2.73 °C per hundred years at the Sylhet and Sreemangal stations, indicating that climate change is affecting temperature in this area. This paper presents an alternative method for temperature prediction by combining the wavelet technique with an autoregressive integrated moving average (ARIMA model and an artificial neural network (ANN applied to monthly maximum and minimum temperature data. The data are divided into a training dataset (1957–2000 to construct the models and a testing dataset (2001–2012 to estimate their performance. The calibration and validation performance of the models is evaluated statistically, and the relative performance based on the predictive capability of out-of-sample forecasts is assessed. The results indicate that the wavelet-ARIMA model is more effective than the wavelet-ANN model.

  20. A parallel way of data decomposition approach for ANN based image reconstruction in e-MRI on a multi-core computer system

    Directory of Open Access Journals (Sweden)

    Subramanian Kartheeswaran

    2017-01-01

    Full Text Available This paper presents the performance of sequential data decomposition and parallel data decomposition strategies applied on a Back-Propagation Artificial Neural Network (BP-ANN algorithm. The application system is developed for reconstruction of two-dimensional spatial images from continuous wave electron magnetic resonance imaging (CW-EMRI tomography data, on a multi-core computer. The BP-ANN learns the relationship between the ‘ideal’ images that are reconstructed using filtered back projection (FBP technique and the corresponding projection data from various temporal data of in vivo objects. In an earlier work, it has been reported that as the exemplar sizes are too large, the training time is too long and image PSNR (Peak Signal to Noise Ratio values are too low. Hence, in the present work, we propose that the exemplar datasets are decomposed into subsets. Using these subsets, artificial sub neural nets (subnets are constructed and training is carried out on a multi-core system. Consequently, the sequential approach of the proposed method yields better PSNR images. However, it consumes more training time. But when the parallel approach is applied the computational training time becomes reduced. The parallel approach of BP-ANN is able to simplify reconstruction tasks and is seen improving both in accuracy and efficiency. The performance results are tabulated for different exemplar subset sizes, different subnet sizes and the number of multi-core processors. The parallel approach is further explored for image reconstruction from ‘noisy’ and ‘limited-angle’ datasets.

  1. Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Ameli

    2012-01-01

    Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.

  2. Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

    Directory of Open Access Journals (Sweden)

    Tian Han

    2006-01-01

    Full Text Available This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT, feature extraction, genetic algorithm (GA, and neural network (ANN techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

  3. Prediction of SEM–X-ray images’ data of cement-based materials using artificial neural network algorithm

    Directory of Open Access Journals (Sweden)

    Ashraf Ragab Mohamed

    2014-09-01

    Full Text Available Recent advances of computational capabilities have motivated the development of more sophisticated models to simulate cement-based hydration. However, the input parameters for such models, obtained from SEM–X-ray image analyses, are quite complicated and hinder their versatile application. This paper addresses the utilization of the artificial neural networks (ANNs to predict the SEM–X-ray images’ data of cement-based materials (surface area fraction and the cement phases’ correlation functions. ANNs have been used to correlate these data, already obtained for 21 types of cement, to basic cement data (cement compounds and fineness. Two approaches have been proposed; the ANN, and the ANN-regression method. Comparisons have shown that the ANN proves effectiveness in predicting the surface area fraction, while the ANN-regression is more computationally suitable for the correlation functions. Results have shown good agreement between the proposed techniques and the actual data with respect to hydration products, degree of hydration, and simulated images.

  4. 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.

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

    Science.gov (United States)

    Djemal, Ridha; AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah

    2017-01-01

    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.

  6. ANN based Estimation of Ultra High Energy (UHE) Shower Size using Radio Data

    Science.gov (United States)

    Sinha, Kalpana Roy; Datta, Pranayee; Sarma, Kandarpa Kumar

    2013-02-01

    Size estimation is a challenging area in the field of Ultra High Energy (UHE) showers where actual measurements are always associated with uncertainty of events and imperfections in detection mechanisms. The subtle variations resulting out of such factors incorporate certain random behaviour in the readings provided by shower detectors for subsequent processing. Field strength recorded by radio detectors may also be affected by this statistical nature. Hence there is a necessity of development of a system which can remain immune to such random behaviour and provide resilient readings to subsequent stages. Here, we propose a system based on Artificial Neural Network (ANN) which accepts radio field strength recorded by radio detectors and provides estimates of shower sizes in the UHE region. The ANN in feed-forward form is trained with a range of shower events with which it can effectively handle the randomness observed in the detector reading due to imperfections in the experimental apparatus and related set-up.

  7. Developing a secured social networking site using information security awareness techniques

    Directory of Open Access Journals (Sweden)

    Julius O. Okesola

    2014-03-01

    Full Text Available Background: Ever since social network sites (SNS became a global phenomenon in almost every industry, security has become a major concern to many SNS stakeholders. Several security techniques have been invented towards addressing SNS security, but information security awareness (ISA remains a critical point. Whilst very few users have used social circles and applications because of a lack of users’ awareness, the majority have found it difficult to determine the basis of categorising friends in a meaningful way for privacy and security policies settings. This has confirmed that technical control is just part of the security solutions and not necessarily a total solution. Changing human behaviour on SNSs is essential; hence the need for a privately enhanced ISA SNS.Objective: This article presented sOcialistOnline – a newly developed SNS, duly secured and platform independent with various ISA techniques fully implemented.Method: Following a detailed literature review of the related works, the SNS was developed on the basis of Object Oriented Programming (OOP approach, using PhP as the coding language with the MySQL database engine at the back end.Result: This study addressed the SNS requirements of privacy, security and services, and attributed them as the basis of architectural design for sOcialistOnline. SNS users are more aware of potential risk and the possible consequences of unsecured behaviours.Conclusion: ISA is focussed on the users who are often the greatest security risk on SNSs, regardless of technical securities implemented. Therefore SNSs are required to incorporate effective ISA into their platform and ensure users are motivated to embrace it.

  8. Developing a secured social networking site using information security awareness techniques

    Directory of Open Access Journals (Sweden)

    Julius O. Okesola

    2014-11-01

    Full Text Available Background: Ever since social network sites (SNS became a global phenomenon in almost every industry, security has become a major concern to many SNS stakeholders. Several security techniques have been invented towards addressing SNS security, but information security awareness (ISA remains a critical point. Whilst very few users have used social circles and applications because of a lack of users’ awareness, the majority have found it difficult to determine the basis of categorising friends in a meaningful way for privacy and security policies settings. This has confirmed that technical control is just part of the security solutions and not necessarily a total solution. Changing human behaviour on SNSs is essential; hence the need for a privately enhanced ISA SNS. Objective: This article presented sOcialistOnline – a newly developed SNS, duly secured and platform independent with various ISA techniques fully implemented. Method: Following a detailed literature review of the related works, the SNS was developed on the basis of Object Oriented Programming (OOP approach, using PhP as the coding language with the MySQL database engine at the back end. Result: This study addressed the SNS requirements of privacy, security and services, and attributed them as the basis of architectural design for sOcialistOnline. SNS users are more aware of potential risk and the possible consequences of unsecured behaviours. Conclusion: ISA is focussed on the users who are often the greatest security risk on SNSs, regardless of technical securities implemented. Therefore SNSs are required to incorporate effective ISA into their platform and ensure users are motivated to embrace it.

  9. Alice-Anne Martin (1926 - 2016)

    CERN Multimedia

    2016-01-01

    Alice-Anne Martin, known as “Schu” from her maiden name Schubert, passed away on 8 January 2016.   (Image: Gérard Bertin) Hired the year CERN was founded, 1954, when the construction of the Laboratory had not even begun, Schu first worked at the Villa de Cointrin (a historic building now within the grounds of Geneva airport) as a secretary. In this role, she typed the convention between CERN and the Swiss Confederation, prepared by Stéphanie Tixier, as well as some of the "Yellow Reports" that have marked key points in the Laboratory’s history. For example, using a special typewriter with two keyboards – Latin and Greek – she typed the Yellow Report on the KAM theorem by Rolf Hagedorn. Schu also worked with Felix Bloch, the first Director-General of CERN, and later became the secretary of Herbert Coblenz, the first CERN librarian. She was head of the team that edited the proceedings of the ...

  10. Digital Family History Data Mining with Neural Networks: A Pilot Study.

    Science.gov (United States)

    Hoyt, Robert; Linnville, Steven; Thaler, Stephen; Moore, Jeffrey

    2016-01-01

    Following the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, electronic health records were widely adopted by eligible physicians and hospitals in the United States. Stage 2 meaningful use menu objectives include a digital family history but no stipulation as to how that information should be used. A variety of data mining techniques now exist for these data, which include artificial neural networks (ANNs) for supervised or unsupervised machine learning. In this pilot study, we applied an ANN-based simulation to a previously reported digital family history to mine the database for trends. A graphical user interface was created to display the input of multiple conditions in the parents and output as the likelihood of diabetes, hypertension, and coronary artery disease in male and female offspring. The results of this pilot study show promise in using ANNs to data mine digital family histories for clinical and research purposes.

  11. Simulation and prediction for energy dissipaters and stilling basins design using artificial intelligence technique

    Directory of Open Access Journals (Sweden)

    Mostafa Ahmed Moawad Abdeen

    2015-12-01

    Full Text Available Water with large velocities can cause considerable damage to channels whose beds are composed of natural earth materials. Several stilling basins and energy dissipating devices have been designed in conjunction with spillways and outlet works to avoid damages in canals’ structures. In addition, lots of experimental and traditional mathematical numerical works have been performed to profoundly investigate the accurate design of these stilling basins and energy dissipaters. The current study is aimed toward introducing the artificial intelligence technique as new modeling tool in the prediction of the accurate design of stilling basins. Specifically, artificial neural networks (ANNs are utilized in the current study in conjunction with experimental data to predict the length of the hydraulic jumps occurred in spillways and consequently the stilling basin dimensions can be designed for adequate energy dissipation. The current study showed, in a detailed fashion, the development process of different ANN models to accurately predict the hydraulic jump lengths acquired from different experimental studies. The results obtained from implementing these models showed that ANN technique was very successful in simulating the hydraulic jump characteristics occurred in stilling basins. Therefore, it can be safely utilized in the design of these basins as ANN involves minimum computational and financial efforts and requirements compared with experimental work and traditional numerical techniques such as finite difference or finite elements.

  12. Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques

    Directory of Open Access Journals (Sweden)

    Diksha Kaur

    2015-01-01

    Full Text Available The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT along with the Auto Regressive Moving Average (ARMA is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE. A simulation study has been conducted by comparing the forecasting results using the Wavelet-ARMA with the ARMA and Artificial Neural Network (ANN-Ensemble Kalman Filter (EnKF hybrid technique to verify the effectiveness of the proposed hybrid method. Results of the proposed hybrid show significant improvements in the forecasting error.

  13. Compression and Combining Based on Channel Shortening and Rank Reduction Technique for Cooperative Wireless Sensor Networks

    KAUST Repository

    Ahmed, Qasim Zeeshan

    2013-12-18

    This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of L sensors. As the destination has the capacity of processing only U out of these L signals, the strongest U signals are selected while the remaining (L?U) signals are suppressed. A preprocessing block similar to channel-shortening is proposed in this contribution. However, this preprocessing block employs a rank-reduction technique instead of channel-shortening. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit error rate (BER) performance. From our simulations, it can be shown that these schemes outperform the channel-shortening schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared to channel-shortening schemes when sensors employ fixed gain amplification. However, for sensors which employ variable gain amplification, a tradeoff exists in terms of BER performance between the channel-shortening and these schemes. These schemes outperform channel-shortening scheme for lower signal-to-noise ratio.

  14. A novel neural network-based technique for smart gas sensors operating in a dynamic environment.

    Science.gov (United States)

    Baha, Hakim; Dibi, Zohir

    2009-01-01

    Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX) are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects…). We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor's response characteristics and eliminate its dependency on the environmental parameters. The corrector's responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration). The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering). The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor's responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.

  15. Low Latency Network-on-Chip Router Microarchitecture Using Request Masking Technique

    Directory of Open Access Journals (Sweden)

    Alireza Monemi

    2015-01-01

    Full Text Available Network-on-Chip (NoC is fast emerging as an on-chip communication alternative for many-core System-on-Chips (SoCs. However, designing a high performance low latency NoC with low area overhead has remained a challenge. In this paper, we present a two-clock-cycle latency NoC microarchitecture. An efficient request masking technique is proposed to combine virtual channel (VC allocation with switch allocation nonspeculatively. Our proposed NoC architecture is optimized in terms of area overhead, operating frequency, and quality-of-service (QoS. We evaluate our NoC against CONNECT, an open source low latency NoC design targeted for field-programmable gate array (FPGA. The experimental results on several FPGA devices show that our NoC router outperforms CONNECT with 50% reduction of logic cells (LCs utilization, while it works with 100% and 35%~20% higher operating frequency compared to the one- and two-clock-cycle latency CONNECT NoC routers, respectively. Moreover, the proposed NoC router achieves 2.3 times better performance compared to CONNECT.

  16. Vibration control of a class of semiactive suspension system using neural network and backstepping techniques

    Science.gov (United States)

    Zapateiro, M.; Luo, N.; Karimi, H. R.; Vehí, J.

    2009-08-01

    In this paper, we address the problem of designing the semiactive controller for a class of vehicle suspension system that employs a magnetorheological (MR) damper as the actuator. As the first step, an adequate model of the MR damper must be developed. Most of the models found in literature are based on the mechanical behavior of the device, with the Bingham and Bouc-Wen models being the most popular ones. These models can estimate the damping force of the device taking the control voltage and velocity inputs as variables. However, the inverse model, i.e., the model that computes the control variable (generally the voltage) is even more difficult to find due to the numerical complexity that implies the inverse of the nonlinear forward model. In our case, we develop a neural network being able to estimate the control voltage input to the MR damper, which is necessary for producing the optimal force predicted by the controller so as to reduce the vibrations. The controller is designed following the standard backstepping technique. The performance of the control system is evaluated by means of simulations in MATLAB/Simulink.

  17. A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment

    Directory of Open Access Journals (Sweden)

    Zohir Dibi

    2009-11-01

    Full Text Available Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects…. We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor’s response characteristics and eliminate its dependency on the environmental parameters. The corrector’s responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration. The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering. The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor’s responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.

  18. A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

    Directory of Open Access Journals (Sweden)

    H.H. Soliman

    2012-11-01

    Full Text Available An explosive growth in the field of wireless sensor networks (WSNs has been achieved in the past few years. Due to its important wide range of applications especially military applications, environments monitoring, health care application, home automation, etc., they are exposed to security threats. Intrusion detection system (IDS is one of the major and efficient defensive methods against attacks in WSN. Therefore, developing IDS for WSN have attracted much attention recently and thus, there are many publications proposing new IDS techniques or enhancement to the existing ones. This paper evaluates and compares the most prominent anomaly-based IDS systems for hierarchical WSNs and identifying their strengths and weaknesses. For each IDS, the architecture and the related functionality are briefly introduced, discussed, and compared, focusing on both the operational strengths and weakness. In addition, a comparison of the studied IDSs is carried out using a set of critical evaluation metrics that are divided into two groups; the first one related to performance and the second related to security. Finally based on the carried evaluation and comparison, a set of design principles are concluded, which have to be addressed and satisfied in future research of designing and implementing IDS for WSNs.

  19. General Dentists’ Use of Isolation Techniques During Root Canal Treatment: from the National Dental Practice-Based Research Network

    Science.gov (United States)

    Lawson, Nathaniel C.; Gilbert, Gregg H.; Funkhouser, Ellen; Eleazer, Paul D.; Benjamin, Paul L.; Worley, Donald C.

    2015-01-01

    Introduction A preliminary study done by a National Dental Practice-Based Research Network precursor observed that 44% of general dentists (GDs) reported always using a rubber dam (RD) during root canal treatment (RCT). This full-scale study quantified use of all isolation techniques, including RD use. Methods Network practitioners completed a questionnaire about isolation techniques used during RCT. Network Enrollment Questionnaire data provided practitioner characteristics. Results 1,490 of 1,716 eligible GDs participated (87%); 697 (47%) reported always using a RD. This percentage varied by tooth type. These GDs were more likely to always use a RD: do not own a private practice; perform less than 10 RCT/month; have postgraduate training. Conclusions Most GDs do not use a RD all the time. Ironically, RDs are used more frequently by GDs who do not perform molar RCT. RD use varies with tooth type and certain dentist, practice, and patient characteristics. PMID:26015159

  20. Artificial Neural Networks and Instructional Technology.

    Science.gov (United States)

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  1. An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots

    CSIR Research Space (South Africa)

    Machaka, P

    2015-01-01

    Full Text Available . The Neural Networks, Bayesian Networks and Artificial Immune Systems were used for this experiment. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP; we integrated algorithms into a data collection system to detect...

  2. Bandwidth re-distribution techniques for extended epon based multi-wavelength networks

    NARCIS (Netherlands)

    Roy, R.; Manhoudt, Gert; van Etten, Wim

    2007-01-01

    The broadband photonics project (BBP) under the Freehand consortium of projects looks into the design of an extended access network. The network is a photonic network which can be dynamically reconfigured to distribute bandwidth in an optimised manner. This paper presents linear programming based

  3. Investigation of an HMM/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals.

    Science.gov (United States)

    Polur, Prasad D; Miller, Gerald E

    2006-10-01

    Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients requires a robust technique that can handle conditions of very high variability and limited training data. In this study, application of a 10 state ergodic hidden Markov model (HMM)/artificial neural network (ANN) hybrid structure for a dysarthric speech (isolated word) recognition system, intended to act as an assistive tool, was investigated. A small size vocabulary spoken by three cerebral palsy subjects was chosen. The effect of such a structure on the recognition rate of the system was investigated by comparing it with an ergodic hidden Markov model as a control tool. This was done in order to determine if this modified technique contributed to enhanced recognition of dysarthric speech. The speech was sampled at 11 kHz. Mel frequency cepstral coefficients were extracted from them using 15 ms frames and served as training input to the hybrid model setup. The subsequent results demonstrated that the hybrid model structure was quite robust in its ability to handle the large variability and non-conformity of dysarthric speech. The level of variability in input dysarthric speech patterns sometimes limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor impaired individuals holds sufficient promise.

  4. Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling

    Science.gov (United States)

    Rogers, Leah L.; Dowla, Farid U.

    1994-02-01

    A new approach to nonlinear groundwater management methodology is presented which optimizes aquifer remediation with the aid of artificial neural networks (ANNs). The methodology allows solute transport simulations, usually the main computational component of management models, to be run in parallel. The ANN technology, inspired by neurobiological theories of massive interconnection and parallelism, has been successfully applied to a variety of optimization problems. In this new approach, optimal management solutions are found by (1) first training an ANN to predict the outcome of the flow and transport code, and (2) then using the trained ANN to search through many pumping realizations to find an optimal one for successful remediation. The behavior of complex groundwater scenarios with spatially variable transport parameters and multiple contaminant plumes is simulated with a two-dimensional hybrid finite-difference/finite-element flow and transport code. The flow and transport code develops the set of examples upon which the network is trained. The input of the ANN characterizes the different realizations of pumping, with each input indicating the pumping level of a well. The output is capable of characterizing the objectives and constraints of the optimization, such as attainment of regulatory goals, value of cost functions and cleanup time, and mass of contaminant removal. The supervised learning algorithm of back propagation was used to train the network. The conjugate gradient method and weight elimination procedures are used to speed convergence and improve performance, respectively. Once trained, the ANN begins a search through various realizations of pumping patterns to determine whether or not they will be successful. The search is directed by a simple genetic algorithm. The resulting management solutions are consistent with those resulting from a more conventional optimization technique, which combines solute transport modeling and nonlinear programming

  5. Driver drowsiness detection using ANN image processing

    Science.gov (United States)

    Vesselenyi, T.; Moca, S.; Rus, A.; Mitran, T.; Tătaru, B.

    2017-10-01

    The paper presents a study regarding the possibility to develop a drowsiness detection system for car drivers based on three types of methods: EEG and EOG signal processing and driver image analysis. In previous works the authors have described the researches on the first two methods. In this paper the authors have studied the possibility to detect the drowsy or alert state of the driver based on the images taken during driving and by analyzing the state of the driver’s eyes: opened, half-opened and closed. For this purpose two kinds of artificial neural networks were employed: a 1 hidden layer network and an autoencoder network.

  6. 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.

  7. Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

    Directory of Open Access Journals (Sweden)

    Nattawat Khamphakdee

    2015-07-01

    Full Text Available The intrusion detection system (IDS is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS  can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks.  We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS.  The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved.

  8. Stability analysis of rubblemound breakwater using ANN

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Rao, S.; Manjunath, Y.R.; Kim, D.H.

    1993, Nielsen 1988). Networks have an ability to recognize the hidden pattern in the data and accordingly estimate the values. The biggest merit is its ability to deal with fuzzy information whose interrelation us ambiguous or whose functional...

  9. DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.

    Science.gov (United States)

    Singh, A; Quek, C; Cho, S Y

    2008-04-01

    Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into uniform fuzzy sets (Gaussian, triangular, or trapezoidal), the clustering may not be exact and some amount of information may be lost. In this paper, two clustering techniques using a Kohonen-like self-organizing neural network architecture, namely, the unsupervised discrete clustering technique (UDCT) and the supervised discrete clustering technique (SDCT), are proposed. The UDCT and SDCT algorithms reduce this data loss by introducing nonuniform, normal fuzzy sets that are not necessarily convex. The training data range is divided into discrete points at equal intervals, and the membership value corresponding to each discrete point is generated. Hence, the fuzzy sets obtained contain pairs of values, each pair corresponding to a discrete point and its membership grade. Thus, it can be argued that fuzzy membership functions generated using this kind of a discrete methodology provide a more accurate representation of the actual input data. This fact has been demonstrated by comparing the membership functions generated by the UDCT and SDCT algorithms against those generated by the MLVQ, FKP, and pseudofuzzy Kohonen partitioning (PFKP) algorithms. In addition to these clustering techniques, a novel pattern classifying network called the Yager fuzzy neural network (FNN) is proposed in this paper. This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities. A modified version of the pseudo-outer product (POP)-Yager FNN called the modified Yager FNN is introduced that eliminates the drawbacks of the earlier network and yi- elds

  10. 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.

  11. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation.

    Science.gov (United States)

    Tahmasebi, Pejman; Hezarkhani, Ardeshir

    2012-05-01

    The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

  12. Finding the Hole in the Wreck: Shamanic practice in the Poetry of Adrienne Rich and Anne Sexton

    DEFF Research Database (Denmark)

    Elias, Camelia

    2013-01-01

    to argue that both Adrienne Rich and Anne Sexton manipulate with visualization techniques in their symbolic imagery in order to create an atmosphere that is akin to a shamanic journey. The poetic examples that I want to discuss demonstrate how the “language of the suicides” (Sexton) and “the thing itself...

  13. Error Control Techniques for Efficient Multicast Streaming in UMTS Networks: Proposals andPerformance Evaluation

    Directory of Open Access Journals (Sweden)

    Michele Rossi

    2004-06-01

    Full Text Available In this paper we introduce techniques for efficient multicast video streaming in UMTS networks where a video content has to be conveyed to multiple users in the same cell. Efficient multicast data delivery in UMTS is still an open issue. In particular, suitable solutions have to be found to cope with wireless channel errors, while maintaining both an acceptable channel utilization and a controlled delivery delay over the wireless link between the serving base station and the mobile terminals. Here, we first highlight that standard solutions such as unequal error protection (UEP of the video flow are ineffective in the UMTS systems due to its inherent large feedback delay at the link layer (Radio Link Control, RLC. Subsequently, we propose a local approach to solve errors directly at the UMTS link layer while keeping a reasonably high channel efficiency and saving, as much as possible, system resources. The solution that we propose in this paper is based on the usage of the common channel to serve all the interested users in a cell. In this way, we can save resources with respect to the case where multiple dedicated channels are allocated for every user. In addition to that, we present a hybrid ARQ (HARQ proactive protocol that, at the cost of some redundancy (added to the link layer flow, is able to consistently improve the channel efficiency with respect to the plain ARQ case, by therefore making the use of a single common channel for multicast data delivery feasible. In the last part of the paper we give some hints for future research, by envisioning the usage of the aforementioned error control protocols with suitably encoded video streams.

  14. Simulating GPS radio signal to synchronize network--a new technique for redundant timing.

    Science.gov (United States)

    Shan, Qingxiao; Jun, Yang; Le Floch, Jean-Michel; Fan, Yaohui; Ivanov, Eugene N; Tobar, Michael E

    2014-07-01

    Currently, many distributed systems such as 3G mobile communications and power systems are time synchronized with a Global Positioning System (GPS) signal. If there is a GPS failure, it is difficult to realize redundant timing, and thus time-synchronized devices may fail. In this work, we develop time transfer by simulating GPS signals, which promises no extra modification to original GPS-synchronized devices. This is achieved by applying a simplified GPS simulator for synchronization purposes only. Navigation data are calculated based on a pre-assigned time at a fixed position. Pseudo-range data which describes the distance change between the space vehicle (SV) and users are calculated. Because real-time simulation requires heavy-duty computations, we use self-developed software optimized on a PC to generate data, and save the data onto memory disks while the simulator is operating. The radio signal generation is similar to the SV at an initial position, and the frequency synthesis of the simulator is locked to a pre-assigned time. A filtering group technique is used to simulate the signal transmission delay corresponding to the SV displacement. Each SV generates a digital baseband signal, where a unique identifying code is added to the signal and up-converted to generate the output radio signal at the centered frequency of 1575.42 MHz (L1 band). A prototype with a field-programmable gate array (FPGA) has been built and experiments have been conducted to prove that we can realize time transfer. The prototype has been applied to the CDMA network for a three-month long experiment. Its precision has been verified and can meet the requirements of most telecommunication systems.

  15. Türkiye’de Enflasyonun İleri ve Geri Beslemeli Yapay Sinir Ağlarının Melez Yaklaşımı ile Öngörüsü = Forecasting of Turkey Inflation with Hybrid of Feed Forward and Recurrent Artifical Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Rezan USLU

    2010-01-01

    Full Text Available Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI. A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data.

  16. voltage compensation using artificial neural network

    African Journals Online (AJOL)

    Offor Theophilos

    VOLTAGE COMPENSATION USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY OF. RUMUOLA ... using artificial neural network (ANN) controller based dynamic voltage restorer (DVR). ... substation by simulating with sample of average voltage for Omerelu, Waterlines, Rumuola, Shell Industrial and Barracks.

  17. Hubane hambaravi / Ann-Liis Ojaots, Arina Palm-Lillepea

    Index Scriptorium Estoniae

    Ojaots, Ann-Liis

    2014-01-01

    Intervjuu KliinikPluss hambaravikliiniku omaniku Arina Palm-Lillepea ja psühholoogi Ann-Liis Ojaotsaga Sõpruse Ärimajas asuva hambaravi erilisest lähenemisest hambaravile ja teistele toitumisega seotud haigustele nagu nt buliimia

  18. Anne-Marie Sargueil: ilu on kasulik / intervjueerinud Emilie Toomela

    Index Scriptorium Estoniae

    Sargueil, Anne-Marie

    2015-01-01

    Prantsuse Disainiinstituudi juht Anne-Marie Sargueil rääkis prantsuse ja skandinaavia disainist, prantslaste disainieelistustest, uutest suundadest disaini valdkonnas, Eesti Tarbekunsti- ja Disainimuuseumis avatud näitusest "20 prantsuse disainiikooni"

  19. 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.

  20. Forecasting Water Levels Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Shreenivas N. Londhe

    2011-06-01

    Full Text Available For all Ocean related activities it is necessary to predict the actual water levels as accurate as possible. The present work aims at predicting the water levels with a lead time of few hours to a day using the technique of artificial neural networks. Instead of using the previous and current values of observed water level time series directly as input and output the water level anomaly (difference between the observed water level and harmonically predicted tidal level is calculated for each hour and the ANN model is developed using this time series. The network predicted anomaly is then added to harmonic tidal level to predict the water levels. The exercise is carried out at six locations, two in The Gulf of Mexico, two in The Gulf of Maine and two in The Gulf of Alaska along the USA coastline. The ANN models performed reasonably well for all forecasting intervals at all the locations. The ANN models were also run in real time mode for a period of eight months. Considering the hurricane season in Gulf of Mexico the models were also tested particularly during hurricanes.

  1. In memoriam dr. Anne van Wijngaarden (1925-2004)

    OpenAIRE

    Broekhuizen, S.; Laar, van, V.

    2005-01-01

    Op 4 oktober 2004 overleed Dr. Anne van Wijngaarden op 78-jarige leeftijd in zijn huis bij Millac- Carlux, Frankrijk. Hij was een van de Nederlandse oprichters van de Vereniging voor Zoogdierkunde en Zoogdierbescherming. Nadat zijn eindexamen op de middelbare school wilde Anne aanvankelijk geologie studeren, maar toen de verplichte excursies te duur bleken, werd het biologie. Nog voordat hij doctoraal-examen had gedaan, kreeg hij een baan bij de Plantenziektenkundige Dienst. Zijn eerste opdra...

  2. Impression Techniques Used for Single-Unit Crowns: Findings from the National Dental Practice-Based Research Network.

    Science.gov (United States)

    McCracken, Michael S; Louis, David R; Litaker, Mark S; Minyé, Helena M; Oates, Thomas; Gordan, Valeria V; Marshall, Don G; Meyerowitz, Cyril; Gilbert, Gregg H

    2017-01-11

    To: (1) determine which impression and gingival displacement techniques practitioners use for single-unit crowns on natural teeth; and (2) test whether certain dentist and practice characteristics are significantly associated with the use of these techniques. Dentists participating in the National Dental Practice-Based Research Network were eligible for this survey study. The study used a questionnaire developed by clinicians, statisticians, laboratory technicians, and survey experts. The questionnaire was pretested via cognitive interviewing with a regionally diverse group of practitioners. The survey included questions regarding gingival displacement and impression techniques. Survey responses were compared by dentist and practice characteristics using ANOVA. The response rate was 1777 of 2132 eligible dentists (83%). Regarding gingival displacement, most clinicians reported using either a single cord (35%) or dual cord (35%) technique. About 16% of respondents preferred an injectable retraction technique. For making impressions, the most frequently used techniques and materials are: poly(vinyl siloxane), 77%; polyether, 12%; optical/digital, 9%. A dental auxiliary or assistant made the final impression 2% of the time. Regarding dual-arch impression trays, 23% of practitioners report they typically use a metal frame tray, 60% use a plastic frame, and 16% do not use a dual-arch tray. Clinicians using optical impression techniques were more likely to be private practice owners or associates. This study documents current techniques for gingival displacement and making impressions for crowns. Certain dentist and practice characteristics are significantly associated with these techniques. © 2017 by the American College of Prosthodontists.

  3. Application of neural network in market segmentation: A review on recent trends

    Directory of Open Access Journals (Sweden)

    Manojit Chattopadhyay

    2012-04-01

    Full Text Available Despite the significance of Artificial Neural Network (ANN algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000–2010 and proposed a classification scheme for the articles. One thousands (1000 articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.

  4. Use of genetic algorithms and neural networks to optimize well locations and reduce well requirements

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, V.M.; Rogers, L.L.

    1994-09-01

    A goal common to both the environmental and petroleum industries is the reduction of costs and/or enhancement of profits by the optimal placement of extraction/production and injection wells. Formal optimization techniques facilitate this goal by searching among the potentially infinite number of possible well patterns for ones that best meet engineering and economic objectives. However, if a flow and transport model or reservoir simulator is being used to evaluate the effectiveness of each network of wells, the computational resources required to apply most optimization techniques to real field problems become prohibitively expensive. This paper describes a new approach to field-scale, nonlinear optimization of well patterns that is intended to make such searches tractable on conventional computer equipment. Artificial neural networks (ANNs) are trained to predict selected information that would normally be calculated by the simulator. The ANNs are then embedded in a variant of the genetic algorithm (GA), which drives the search for increasingly effective well patterns and uses the ANNs, rather than the original simulator, to evaluate the effectiveness of each pattern. Once the search is complete, the ANNs are reused in sensitivity studies to give additional information on the performance of individual or clusters of wells.

  5. Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN

    Directory of Open Access Journals (Sweden)

    Umut Bulucu

    2008-09-01

    Full Text Available Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs. Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN.

  6. Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Metin Ertunc, H. [Department of Mechatronics Engineering, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey); Hosoz, Murat [Department of Mechanical Education, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey)

    2008-12-15

    This study deals with predicting the performance of an evaporative condenser using both artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. For this aim, an experimental evaporative condenser consisting of a copper tube condensing coil along with air and water circuit elements was developed and equipped with instruments used for temperature, pressure and flow rate measurements. After the condenser was connected to an R134a vapour-compression refrigeration circuit, it was operated at steady state conditions, while varying both dry and wet bulb temperatures of the air stream entering the condenser, air and water flow rates as well as pressure, temperature and flow rate of the entering refrigerant. Using some of the experimental data for training, ANN and ANFIS models for the evaporative condenser were developed. These models were used for predicting the condenser heat rejection rate, refrigerant temperature leaving the condenser along with dry and wet bulb temperatures of the leaving air stream. Although it was observed that both ANN and ANFIS models yielded a good statistical prediction performance in terms of correlation coefficient, mean relative error, root mean square error and absolute fraction of variance, the accuracies of ANFIS predictions were usually slightly better than those of ANN predictions. This study reveals that, having an extended prediction capability compared to ANN, the ANFIS technique can also be used for predicting the performance of evaporative condensers. (author)

  7. Soft computing techniques in voltage security analysis

    CERN Document Server

    Chakraborty, Kabir

    2015-01-01

    This book focuses on soft computing techniques for enhancing voltage security in electrical power networks. Artificial neural networks (ANNs) have been chosen as a soft computing tool, since such networks are eminently suitable for the study of voltage security. The different architectures of the ANNs used in this book are selected on the basis of intelligent criteria rather than by a “brute force” method of trial and error. The fundamental aim of this book is to present a comprehensive treatise on power system security and the simulation of power system security. The core concepts are substantiated by suitable illustrations and computer methods. The book describes analytical aspects of operation and characteristics of power systems from the viewpoint of voltage security. The text is self-contained and thorough. It is intended for senior undergraduate students and postgraduate students in electrical engineering. Practicing engineers, Electrical Control Center (ECC) operators and researchers will also...

  8. Resource allocation using ANN in LTE

    Science.gov (United States)

    Yigit, Tuncay; Ersoy, Mevlut

    2017-07-01

    LTE is the 4th generation wireless network technology, which provides flexible bandwidth, higher data speeds and lower delay. Difficulties may be experienced upon an increase in the number of users in LTE. The objective of this study is to ensure a faster solution to any such resource allocation problems which might arise upon an increase in the number of users. A fast and effective solution has been obtained by making use of Artificial Neural Network. As a result, fast working artificial intelligence methods may be used in resource allocation problems during operation.

  9. Neutron spectrometry and dosimetry with ANNs

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H. R.; Hernandez D, V. M. [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Cipres 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Departamento de Ingenieria Nuclear, Universidad Politecnica de Madrid, Jose Gutierrez Abascal 2, 28006 Madrid (Spain)], e-mail: fermineutron@yahoo.com

    2009-10-15

    Artificial neural networks technology has been applied to unfold the neutron spectra and to calculate the effective dose, the ambient equivalent dose, and the personal dose equivalent for {sup 252}Cf and {sup 241}AmBe neutron sources. A Bonner sphere spectrometry with a {sup 6}LiI(Eu) scintillator was utilized to measure the count rates of the spheres that were utilized as input in two artificial neural networks, one for spectrometry and another for dosimetry. Spectra and the ambient dose equivalent were also obtained with BUNKIUT code and the UTA4 response matrix. With both procedures spectra and ambient dose equivalent agrees in less than 10%. (author)

  10. 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.

  11. Accuracy assessment of the scalar network analyzer using sliding termination techniques

    DEFF Research Database (Denmark)

    Knudsen, Bent; Engen, Glenn F.; Guldbrandsen, Birthe

    1989-01-01

    In the absence of phase response the major, if not the primary, sources of error in the scalar network analyzer are the imperfect directivity, etc., associated with its internal and frequently inaccessible test set or measurement network. An explicit expression is obtained for this error in terms...

  12. Practical network design techniques a complete guide for WANs and LANs

    CERN Document Server

    Held, Gilbert

    2004-01-01

    This new edition has two parts. The first part focuses on wide area networks; the second, which is entirely new, focuses on local area networks. Because Ethernet emerged victorious in the LAN war, the second section pays particular attention to Ethernet design and performance characteristics.

  13. Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at

  14. Forecasting macroeconomic variables using neural network models and three automated model selection techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2016-01-01

    When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet...

  15. A study on Optical Labelling Techniques for All-Optical Networks

    DEFF Research Database (Denmark)

    Holm-Nielsen, Pablo Villanueva

    2005-01-01

    Optical switching has been proposed as an effective solution to overcoming the potential electronic bottleneck in all-optical network nodes carrying IP over WDM. The solution builds on the use of optical labelling as a mean to route packets or bursts of packets through the network. In addition to...

  16. Non-linear Bio-geophysical and Remote Sensing Relations Revealed in Neural Network Training for Fractional Snow Cover Estimation

    Science.gov (United States)

    Czyzowska-Wisniewski, E. H.; Van Leeuwen, W. J. D.; Marsh, S. E.; Hirschboeck, K. K.; Wisniewski, W. T.

    2014-12-01

    Accurate estimation of Fractional Snow Cover (FSC) in complex alpine-forested terrain is now possible with appropriate remote sensing data and analysis techniques. This research examines what minimum combination of input variables are required to obtain state-of-the-art FSC estimates for heterogeneous alpine-forested terrains. Currently, one of the most accurate FSC estimators for alpine regions is based on training an Artificial Neural Network (ANN) that can deconvolve the relationships between numerous compounded and possibly non-linear bio-geophysical relations encountered in rugged terrain. Under the assumption that the ANN optimally extracts available information from its input data, we can exploit the ANN as a tool to assess the contributions toward FSC estimation of each of the data sources, and combinations thereof. By assessing the quality of the modeled FSC estimates versus ground equivalent data, suitable combinations of input variables can be identified. High spatial resolution imagery from IKONOS are used to estimate snow cover for ANN training and validation, and also for error assessment of the ANN FSC results. Input variables are initially chosen representing information already incorporated into leading snow cover estimators. Additional variables such as topographic slope, aspect, and shadow distribution are evaluated to observe the ANN as it accounts for illumination incidence and directional reflectance of surfaces affecting the viewed radiance in complex terrain. Snow usually covers vegetation and underlying geology partially, therefore the ANN also has to resolve spectral mixtures of unobscured surfaces surrounded by snow. Multispectral imagery if therefore acquired in the fall prior to the first snow of the season and are included in the ANN analyses for assessing the baseline reflectance values of the environment that later become modified by the snow. The best ANN FSC model performance was achieved when all 15 pre-selected inputs were used

  17. Combined application of mixture experimental design and artificial neural networks in the solid dispersion development.

    Science.gov (United States)

    Medarević, Djordje P; Kleinebudde, Peter; Djuriš, Jelena; Djurić, Zorica; Ibrić, Svetlana

    2016-01-01

    This study for the first time demonstrates combined application of mixture experimental design and artificial neural networks (ANNs) in the solid dispersions (SDs) development. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs were prepared by solvent casting method to improve carbamazepine dissolution rate. The influence of the composition of prepared SDs on carbamazepine dissolution rate was evaluated using d-optimal mixture experimental design and multilayer perceptron ANNs. Physicochemical characterization proved the presence of the most stable carbamazepine polymorph III within the SD matrix. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs significantly improved carbamazepine dissolution rate compared to pure drug. Models developed by ANNs and mixture experimental design well described the relationship between proportions of SD components and percentage of carbamazepine released after 10 (Q10) and 20 (Q20) min, wherein ANN model exhibit better predictability on test data set. Proportions of carbamazepine and poloxamer 188 exhibited the highest influence on carbamazepine release rate. The highest carbamazepine release rate was observed for SDs with the lowest proportions of carbamazepine and the highest proportions of poloxamer 188. ANNs and mixture experimental design can be used as powerful data modeling tools in the systematic development of SDs. Taking into account advantages and disadvantages of both techniques, their combined application should be encouraged.

  18. Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

    Science.gov (United States)

    Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

    2016-01-01

    According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

  19. Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

    Science.gov (United States)

    Maleki, E.

    2015-12-01

    Friction stir welding (FSW) is a relatively new solid-state joining technique that is widely adopted in manufacturing and industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a very complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints makes it difficult to develop an overall governing equations system for theoretical behavior analyse of the friction stir welded joints. Weld quality is predominantly affected by welding effective parameters, and the experiments are often time consuming and costly. On the other hand, employing artificial intelligence (AI) systems such as artificial neural networks (ANNs) as an efficient approach to solve the science and engineering problems is considerable. In present study modeling of FSW effective parameters by ANNs is investigated. To train the networks, experimental test results on thirty AA-7075-T6 specimens are considered, and the networks are developed based on back propagation (BP) algorithm. ANNs testing are carried out using different experimental data that they are not used during networks training. In this paper, rotational speed of tool, welding speed, axial force, shoulder diameter, pin diameter and tool hardness are regarded as inputs of the ANNs. Yield strength, tensile strength, notch-tensile strength and hardness of welding zone are gathered as outputs of neural networks. According to the obtained results, predicted values for the hardness of welding zone, yield strength, tensile strength and notch-tensile strength have the least mean relative error (MRE), respectively. Comparison of the predicted and the experimental results confirms that the networks are adjusted carefully, and the ANN can be used for modeling of FSW effective parameters.

  20. Forecasting of nonlinear time series using ANN

    Directory of Open Access Journals (Sweden)

    Ahmed Tealab

    2017-06-01

    Full Text Available When forecasting time series, it is important to classify them according linearity behavior that the linear time series remains at the forefront of academic and applied research, it has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life with its autoregressive and inherited moving average terms issue the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.

  1. Analysis of Drug Design for a Selection of G Protein-Coupled Neuro-Receptors Using Neural Network Techniques

    DEFF Research Database (Denmark)

    Agerskov, Claus; Mortensen, Rasmus M.; Bohr, Henrik G.

    2015-01-01

    mu-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drug...... networks, trained with greedy learning algorithms, showed superior performance in prediction over the simple feedback NNs. The best networks obtained scores of more than 90 % accuracy in predicting the degree of binding drug molecules to the mentioned receptors and with a maximal Matthew's coefficient of 0...... computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques....

  2. A new XML-aware compression technique for improving performance of healthcare information systems over hospital networks.

    Science.gov (United States)

    Al-Shammary, Dhiah; Khalil, Ibrahim

    2010-01-01

    Most organizations exchange, collect, store and process data over the Internet. Many hospital networks deploy Web services to send and receive patient information. SOAP (Simple Object Access Protocol) is the most usable communication protocol for Web services. XML is the standard encoding language of SOAP messages. However, the major drawback of XML messages is the high network traffic caused by large overheads. In this paper, two XML-aware compressors are suggested to compress patient messages stemming from any data transactions between Web clients and servers. The proposed compression techniques are based on the XML structure concepts and use both fixed-length and Huffman encoding methods for translating the XML message tree. Experiments show that they outperform all the conventional compression methods and can save tremendous amount of network bandwidth.

  3. A New Stochastic Technique for Painlevé Equation-I Using Neural Network Optimized with Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Muhammad Asif Zahoor Raja

    2012-01-01

    Full Text Available A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method.

  4. Artificial Neural Networks for reconstruction of energy losses in dead materials between barrel LAr and Tile calorimeters exploration and results

    CERN Document Server

    Budagov, Yu A; Kulchitskii, Yu A; Rusakovitch, N A; Shigaev, V N; Tsiareshka, P V

    2008-01-01

    In the course of computational experiments with Monte-Carlo events for ATLAS Combined Test Beam 2004 setup Artificial Neural Networks (ANN) technique was applied for reconstruction of energy losses in dead materials between barrel LAr and Tile calorimeters (Edm). The constructed ANN procedures exploit as their input vectors the information content of different sets of variables (parameters) which describe particular features of the hadronic shower of an event in ATLAS calorimeters. It was shown that application of ANN procedures allows one to reach 40% reduction of the Edm reconstruction error compared to the conventional procedure used in ATLAS collaboration. Impact of various features of a shower on the precision of $Edm$ reconstruction is presented in detail. It was found that longitudinal shower profile information brings greater improvement in $Edm$ reconstruction accuracy than cell energies information in LAr3 and Tile1 samplings.

  5. MIMO wireless networks channels, techniques and standards for multi-antenna, multi-user and multi-cell systems

    CERN Document Server

    Clerckx, Bruno

    2013-01-01

    This book is unique in presenting channels, techniques and standards for the next generation of MIMO wireless networks. Through a unified framework, it emphasizes how propagation mechanisms impact the system performance under realistic power constraints. Combining a solid mathematical analysis with a physical and intuitive approach to space-time signal processing, the book progressively derives innovative designs for space-time coding and precoding as well as multi-user and multi-cell techniques, taking into consideration that MIMO channels are often far from ideal. Reflecting developments

  6. Use of Dynamic Time Warping and Network Based Techniques for Mapping Trends and Patterns in Vegetation Using MODIS Data.

    Science.gov (United States)

    Singh, N.; Lucey, R.; Lunga, D.

    2016-12-01

    Normalized Difference Vegetation Index (NDVI) can be used as an indicator of healthy, green vegetation. NDVI values can be used to categorize the characteristics of land features. Similarly, a time-series analysis of NDVI can be used to monitor trends of vegetation patterns on the land surface. However vegetation has a natural phenology cycle, which varies from location to location based on various parameters like rainfall, temperature, soil etc. which makes it difficult to identify changes in land cover changes using conventional change detection techniques. Changes in vegetation pattern can be detected by using high temporal resolution satellite data but it can be computationally challenging to apply change detection over large areas and normal classification techniques don't perform well. In this study, we use annual stacks of NDVI time-series data at 8-day temporal resolution obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to classify and monitor land cover change. We show how dynamic time warping (DTW) and deep recurrent network based techniques perform better in classifying land cover and detecting changes using noisy high temporal resolution satellite data which is not possible by conventional remote sensing classification techniques. We present the application and results of the DTW and network based techniques and compare the results with standard supervised classification methods.

  7. Mitigating Handoff Call Dropping in Wireless Cellular Networks: A Call Admission Control Technique

    Science.gov (United States)

    Ekpenyong, Moses Effiong; Udoh, Victoria Idia; Bassey, Udoma James

    2016-06-01

    Handoff management has been an important but challenging issue in the field of wireless communication. It seeks to maintain seamless connectivity of mobile users changing their points of attachment from one base station to another. This paper derives a call admission control model and establishes an optimal step-size coefficient (k) that regulates the admission probability of handoff calls. An operational CDMA network carrier was investigated through the analysis of empirical data collected over a period of 1 month, to verify the performance of the network. Our findings revealed that approximately 23 % of calls in the existing system were lost, while 40 % of the calls (on the average) were successfully admitted. A simulation of the proposed model was then carried out under ideal network conditions to study the relationship between the various network parameters and validate our claim. Simulation results showed that increasing the step-size coefficient degrades the network performance. Even at optimum step-size (k), the network could still be compromised in the presence of severe network crises, but our model was able to recover from these problems and still functions normally.

  8. Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

    Science.gov (United States)

    Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

    2001-03-01

    This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

  9. 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.

  10. Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    In this paper we consider the forecasting performance of a well-defined class of flexible models, the so-called single hidden-layer feedforward neural network models. A major aim of our study is to find out whether they, due to their flexibility, are as useful tools in economic forecasting as some...... previous studies have indicated. When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. In fact, their parameters are not even globally...... on the linearisation idea: the Marginal Bridge Estimator and Autometrics. Second, one must decide whether forecasting should be carried out recursively or directly. Comparisons of these two methodss exist for linear models and here these comparisons are extended to neural networks. Finally, a nonlinear model...

  11. [Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

    Science.gov (United States)

    Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

    2006-08-01

    In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

  12. Improving Air Force Active Network Defense Systems through an Analysis of Intrusion Detection Techniques

    National Research Council Canada - National Science Library

    Dunklee, David R

    2007-01-01

    .... The research then presents four recommendations to improve DCC operations. These include: Transition or improve the current signature-based IDS systems to include the capability to query and visualize network flows to detect malicious traffic...

  13. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  14. ANN based Performance Evaluation of BDI for Condition Monitoring of Induction Motor Bearings

    Science.gov (United States)

    Patel, Raj Kumar; Giri, V. K.

    2017-06-01

    One of the critical parts in rotating machines is bearings and most of the failure arises from the defective bearings. Bearing failure leads to failure of a machine and the unpredicted productivity loss in the performance. Therefore, bearing fault detection and prognosis is an integral part of the preventive maintenance procedures. In this paper vibration signal for four conditions of a deep groove ball bearing; normal (N), inner race defect (IRD), ball defect (BD) and outer race defect (ORD) were acquired from a customized bearing test rig, under four different conditions and three different fault sizes. Two approaches have been opted for statistical feature extraction from the vibration signal. In the first approach, raw signal is used for statistical feature extraction and in the second approach statistical features extracted are based on bearing damage index (BDI). The proposed BDI technique uses wavelet packet node energy coefficients analysis method. Both the features are used as inputs to an ANN classifier to evaluate its performance. A comparison of ANN performance is made based on raw vibration data and data chosen by using BDI. The ANN performance has been found to be fairly higher when BDI based signals were used as inputs to the classifier.

  15. Monitoring soil moisture patterns in alpine meadows using ground sensor networks and remote sensing techniques

    Science.gov (United States)

    Bertoldi, Giacomo; Brenner, Johannes; Notarnicola, Claudia; Greifeneder, Felix; Nicolini, Irene; Della Chiesa, Stefano; Niedrist, Georg; Tappeiner, Ulrike

    2015-04-01

    Soil moisture content (SMC) is a key factor for numerous processes, including runoff generation, groundwater recharge, evapotranspiration, soil respiration, and biological productivity. Understanding the controls on the spatial and temporal variability of SMC in mountain catchments is an essential step towards improving quantitative predictions of catchment hydrological processes and related ecosystem services. The interacting influences of precipitation, soil properties, vegetation, and topography on SMC and the influence of SMC patterns on runoff generation processes have been extensively investigated (Vereecken et al., 2014). However, in mountain areas, obtaining reliable SMC estimations is still challenging, because of the high variability in topography, soil and vegetation properties. In the last few years, there has been an increasing interest in the estimation of surface SMC at local scales. On the one hand, low cost wireless sensor networks provide high-resolution SMC time series. On the other hand, active remote sensing microwave techniques, such as Synthetic Aperture Radars (SARs), show promising results (Bertoldi et al. 2014). As these data provide continuous coverage of large spatial extents with high spatial resolution (10-20 m), they are particularly in demand for mountain areas. However, there are still limitations related to the fact that the SAR signal can penetrate only a few centimeters in the soil. Moreover, the signal is strongly influenced by vegetation, surface roughness and topography. In this contribution, we analyse the spatial and temporal dynamics of surface and root-zone SMC (2.5 - 5 - 25 cm depth) of alpine meadows and pastures in the Long Term Ecological Research (LTER) Area Mazia Valley (South Tyrol - Italy) with different techniques: (I) a network of 18 stations; (II) field campaigns with mobile ground sensors; (III) 20-m resolution RADARSAT2 SAR images; (IV) numerical simulations using the GEOtop hydrological model (Rigon et al

  16. Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

    Science.gov (United States)

    Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

    2017-08-01

    In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

  17. Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

    Science.gov (United States)

    Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

    2017-09-01

    In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

  18. Ocean wave forecasting using recurrent neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    to the biological neurons, works on the input and output passing through a hidden layer. The ANN used here is a data- oriented modeling technique to find relations between input and output patterns by self learning and without any fixed mathematical form assumed... = 1/p ? Ep (2) Where, Ep = ? ? (Tk ?Ok)2 (3) p is the total number of training patterns; Tk is the actual output and Ok is the predicted output at kth output node. In the learning process of backpropagation neural network...

  19. 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

  20. Neural network for regression problems with reduced training sets.

    Science.gov (United States)

    Bataineh, Mohammad; Marler, Timothy

    2017-11-01

    Although they are powerful and successful in many applications, artificial neural networks (ANNs) typically do not perform well with complex problems that have a limited number of training cases. Often, collecting additional training data may not be feasible or may be costly. Thus, this work presents a new radial-basis network (RBN) design that overcomes the limitations of using ANNs to accurately model regression problems with minimal training data. This new design involves a multi-stage training process that couples an orthogonal least squares (OLS) technique with gradient-based optimization. New termination criteria are also introduced to improve accuracy. In addition, the algorithms are designed to require minimal heuristic parameters, thus improving ease of use and consistency in performance. The proposed approach is tested with experimental and practical regression problems, and the results are compared with those from typical network models. The results show that the new design demonstrates improved accuracy with reduced dependence on the amount of training data. As demonstrated, this new ANN provides a platform for approximating potentially slow but high-fidelity computational models, and thus fostering inter-model connectivity and multi-scale modeling. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Representation of Female Poetics in Anne Finch’s Poe

    Directory of Open Access Journals (Sweden)

    Dilek Sarikaya

    2009-11-01

    Full Text Available Anne Kingsmill Finch, the Countess of Winchelsea (1661-1720, holds an undeniably significant position in the history of women's writing. Although she was among the well-known poets during the 18th century, Anne Finch has only recently received literary appreciation which she deserves. Instead of complying with the conventions of masculine tradition, and prefers to follow a different path from her contemporaries. She concentrates mainly on the negative stereotypes of women and questions the assumptions of 18th century masculine poetic conventions. Undermining the socially constructed ideas about female identity, Anne Finch is very outspoken in her critique of male resistance to the poetry of women. In her poetry, Finch courageously demonstrates a challenging attitude to the prejudiced opinions about women's creative poetic capabilities. Hence, the poetry of Anne Finch which seem to be personal in its exploration of the experiences of a woman poet in the 18th century, it is in fact, fully embedded with ideological arguments trying to change strongly established conventions of writing in her own time. Therefore, through a detailed analysis of her poetry, the major concern of this paper will be to demonstrate that Anne Finch's poetry is specifically committed to the creation of a consciousness for women writers, distinguishing herself and her poetry from the general trend of her own period which attempts to eliminate women writers from the domain of male writing tradition.

  2. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

    Science.gov (United States)

    Martínez-Romero, Marcos; Vázquez-Naya, José M; Rabuñal, Juan R; Pita-Fernández, Salvador; Macenlle, Ramiro; Castro-Alvariño, Javier; López-Roses, Leopoldo; Ulla, José L; Martínez-Calvo, Antonio V; Vázquez, Santiago; Pereira, Javier; Porto-Pazos, Ana B; Dorado, Julián; Pazos, Alejandro; Munteanu, Cristian R

    2010-05-01

    Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

  3. Applying Fuzzy Logic and Data Mining Techniques in Wireless Sensor Network for Determination Residential Fire Confidence

    Directory of Open Access Journals (Sweden)

    Mirjana Maksimović

    2014-09-01

    Full Text Available The main goal of soft computing technologies (fuzzy logic, neural networks, fuzzy rule-based systems, data mining techniques… is to find and describe the structural patterns in the data in order to try to explain connections between data and on their basis create predictive or descriptive models. Integration of these technologies in sensor nodes seems to be a good idea because it can significantly lead to network performances improvements, above all to reduce the energy consumption and enhance the lifetime of the network. The purpose of this paper is to analyze different algorithms in the case of fire confidence determination in order to see which of the methods and parameter values work best for the given problem. Hence, an analysis between different classification algorithms in a case of nominal and numerical d

  4. Efficient Pricing Technique for Resource Allocation Problem in Downlink OFDM Cognitive Radio Networks

    Science.gov (United States)

    Abdulghafoor, O. B.; Shaat, M. M. R.; Ismail, M.; Nordin, R.; Yuwono, T.; Alwahedy, O. N. A.

    2017-05-01

    In this paper, the problem of resource allocation in OFDM-based downlink cognitive radio (CR) networks has been proposed. The purpose of this research is to decrease the computational complexity of the resource allocation algorithm for downlink CR network while concerning the interference constraint of primary network. The objective has been secured by adopting pricing scheme to develop power allocation algorithm with the following concerns: (i) reducing the complexity of the proposed algorithm and (ii) providing firm power control to the interference introduced to primary users (PUs). The performance of the proposed algorithm is tested for OFDM- CRNs. The simulation results show that the performance of the proposed algorithm approached the performance of the optimal algorithm at a lower computational complexity, i.e., O(NlogN), which makes the proposed algorithm suitable for more practical applications.

  5. The Royal Summer Palace, Ferdinand I and Anne

    Directory of Open Access Journals (Sweden)

    Sylva Dobalová

    2015-12-01

    Full Text Available This essay examines the iconography of the best-known relief from the renaissance Royal Summer Palace at the Prague Castle, depicting Ferdinand I of Habsburg and his wife Anne Jagiello. It highlights its marriage symbolism and the question of the dowry. In the relief Anne, heiress to the Czech Lands, gives her husband an olive branch symbolising peace. In the context of the political significance of the palace’s decoration the relief expresses Ferdinand’s view of his claim to the Bohemian throne, based on his marriage to the heiress. Due to opposition from the Bohemian Estates, this finally became his lawful right in 1545, 24 years after the royal wedding. The Italian sculptor Paolo della Stella expressed a search for a peaceful solution to Ferdinand’s succession. The relief was carved between 1540 and 1550. The interpretations do not rule out the possibility that it was made after Anne had died (1547.

  6. Quantitative recognition of flammable and toxic gases with artificial neural network using metal oxide gas sensors in embedded platform

    Directory of Open Access Journals (Sweden)

    B. Mondal

    2015-06-01

    Full Text Available Artificial Neural Network (ANN based pattern recognition technique is used for ensuring the reliable evaluation of responses from an array of Zinc Oxide (ZnO based sensors comprising of pure ZnO nano-rods and composites of ZnO–SnO2. All the sensors were fabricated in the lab. The paper first reports the development of an artificial neural network based model for successfully recognizing different concentration of hydrogen, methane and carbon mono-oxide. Feed forward back propagation neural network was used for the classification of the gases at critical concentrations. The optimized ANN algorithm is then embedded in the microcontroller based circuit and finally verified under lab conditions.

  7. Estimation of MHD boundary layer slip flow over a permeable stretching cylinder in the presence of chemical reaction through numerical and artificial neural network modeling

    Directory of Open Access Journals (Sweden)

    P. Bala Anki Reddy

    2016-09-01

    Full Text Available In this paper, the prediction of the magnetohydrodynamic boundary layer slip flow over a permeable stretched cylinder with chemical reaction is investigated by using some mathematical techniques, namely Runge–Kutta fourth order method along with shooting technique and artificial neural network (ANN. A numerical method is implemented to approximate the flow of heat and mass transfer characteristics as a function of some input parameters, explicitly the curvature parameter, magnetic parameter, permeability parameter, velocity slip, Grashof number, solutal Grashof number, Prandtl number, temperature exponent, Schmidt number, concentration exponent and chemical reaction parameter. The non-linear partial differential equations of the governing flow are converted into a system of highly non-linear ordinary differential equations by using the suitable similarity transformations, which are then solved numerically by a Runge–Kutta fourth order along with shooting technique and then ANN is applied to them. The Back Propagation Neural Network is applied for forecasting the desired outputs. The reported numerical values and the ANN values are in good agreement than those published works on various special cases. According to the findings of this study, the ANN approach is reliable, effective and easily applicable for simulating heat and mass transfer flow over a stretched cylinder.

  8. Deriving Frequency-Dependent Spatial Patterns in MEG-Derived Resting State Sensorimotor Network: A Novel Multiband ICA Technique

    Science.gov (United States)

    Nugent, Allison C.; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E.; Coppola, Richard; Zarate, Carlos A.

    2016-01-01

    Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. PMID:27770478

  9. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes.

    Science.gov (United States)

    Arulsudar, N; Subramanian, N; Muthy, R S R

    2005-08-05

    We planned to optimize the effect of formulation variables on the percent drug entrapment (PDE) of the liposomes encapsulating leuprolide acetate by reverse phase evaporation method using Artificial neural network (ANN) and Multiple linear regression (MLR). Twenty seven formulations were prepared based on 3x3 factorial design. The volume of aqueous phase (X(1)), HSPC/DSPG [negative charge] (X(2)), and HSPC/Cholesterol (X(3)) were selected as the causal factors. Potential variables such as concentration of lipid: drug and hydration medium were kept constant in experimental design. The PDE (dependent variable) and the transformed values of independent variables were subjected to multiple regression analysis to establish a second order polynomial equation (full model). A set of PDE and causal factors was used as tutorial data for the ANN and fed into a computer. The feed forward back propagation (bp) method was optimized. The ANN model and MLR were validated for accurate prediction of PDE. To simplify the polynomial equation, F-statistic was applied to reduce polynomial equation (reduced model) by neglecting non-significant (Pexperimental data were compared with predicted data by paired "t" test, no statistically significant difference was observed. ANN showed less error compared to MLR. These findings demonstrate that the ANN model provides more accurate prediction and is quite useful in the optimization of pharmaceutical formulations when compared to multiple regression analysis method. The normalized error (NE) value observed with the optimal ANN model was 0.0211 while it was 0.0658 for the full model in the case of second-order polynomial equation composed of the combination of causal factors (X(1), X(2) and X(3)). Thus the derived equation, contour plots and ANN helps in predicting the values of the independent variables for maximum PDE in the preparation of leuprolide acetate liposomes by reverse phase evaporation technique.

  10. A simulator to assess energy-saving techniques in content distribution networks

    NARCIS (Netherlands)

    Bostoen, T.; Napper, J.; Mullender, Sape J.; Berbers, Y.

    2013-01-01

    The scalable and bandwidth-efficient delivery of IPTV services to an increasingly diverse set of screens requires the deployment of telco content distribution networks (CDNs). These CDNs are composed of cache servers located in the telco's data centers close to the end user. The additional cache

  11. Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique

    DEFF Research Database (Denmark)

    Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.

    All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs. One of the major factors in transaction costs theory is information. Firm networks can catalyse the interpersonal information exchange and hence, increase the access to n...

  12. Application of an artificial neural network and morphing techniques in the redesign of dysplastic trochlea.

    Science.gov (United States)

    Cho, Kyung Jin; Müller, Jacobus H; Erasmus, Pieter J; DeJour, David; Scheffer, Cornie

    2014-01-01

    Segmentation and computer assisted design tools have the potential to test the validity of simulated surgical procedures, e.g., trochleoplasty. A repeatable measurement method for three dimensional femur models that enables quantification of knee parameters of the distal femur is presented. Fifteen healthy knees are analysed using the method to provide a training set for an artificial neural network. The aim is to use this artificial neural network for the prediction of parameter values that describe the shape of a normal trochlear groove geometry. This is achieved by feeding the artificial neural network with the unaffected parameters of a dysplastic knee. Four dysplastic knees (Type A through D) are virtually redesigned by way of morphing the groove geometries based on the suggested shape from the artificial neural network. Each of the four resulting shapes is analysed and compared to its initial dysplastic shape in terms of three anteroposterior dimensions: lateral, central and medial. For the four knees the trochlear depth is increased, the ventral trochlear prominence reduced and the sulcus angle corrected to within published normal ranges. The results show a lateral facet elevation inadequate, with a sulcus deepening or a depression trochleoplasty more beneficial to correct trochlear dysplasia.

  13. Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research

  14. Ultra low power and interference robust transceiver techniques for wireless sensor networks

    NARCIS (Netherlands)

    Dutta, R.

    2016-01-01

    Wireless sensor networks (WSNs) have the potential to build breakthrough technologies for a variety of applications to improve human life. Some of the important applications are prevention, prediction and rescue of disasters, medical study and cure, improve the energy efficiency of homes and

  15. Partial Least Squares and Neural Networks for Quantitative Calibration of Laser-induced Breakdown Spectroscopy (LIBs) of Geologic Samples

    Science.gov (United States)

    Anderson, R. B.; Morris, Richard V.; Clegg, S. M.; Humphries, S. D.; Wiens, R. C.; Bell, J. F., III; Mertzman, S. A.

    2010-01-01

    The ChemCam instrument [1] on the Mars Science Laboratory (MSL) rover will be used to obtain the chemical composition of surface targets within 7 m of the rover using Laser Induced Breakdown Spectroscopy (LIBS). ChemCam analyzes atomic emission spectra (240-800 nm) from a plasma created by a pulsed Nd:KGW 1067 nm laser. The LIBS spectra can be used in a semiquantitative way to rapidly classify targets (e.g., basalt, andesite, carbonate, sulfate, etc.) and in a quantitative way to estimate their major and minor element chemical compositions. Quantitative chemical analysis from LIBS spectra is complicated by a number of factors, including chemical matrix effects [2]. Recent work has shown promising results using multivariate techniques such as partial least squares (PLS) regression and artificial neural networks (ANN) to predict elemental abundances in samples [e.g. 2-6]. To develop, refine, and evaluate analysis schemes for LIBS spectra of geologic materials, we collected spectra of a diverse set of well-characterized natural geologic samples and are comparing the predictive abilities of PLS, cascade correlation ANN (CC-ANN) and multilayer perceptron ANN (MLP-ANN) analysis procedures.

  16. Development and validation of a new PCR optimization method by combining experimental design and artificial neural network.

    Science.gov (United States)

    Li, Ye; Du, Xueling; Yuan, Qipeng; Lv, Xinhua

    2010-01-01

    Polymerase chain reaction (PCR) is one of the most powerful techniques in a variety of clinical and biological research fields. In this paper, a chemometrics approach, combining experimental design (ED) and artificial neural network (ANN), was proposed for optimization of PCR amplification of lycopene cyclase gene carRA in Blakeslea Trispora. Five-level star design was carried out to obtain experimental information and provide data source for ANN modeling. Nine variables were used as inputs in ANN, including the added amount of template, primer, dNTP, polymerase and magnesium ion, the temperature of denaturating, annealing and extension, and the number of cycles. The output variable was the efficiency (yield) of the PCR. Based on the developed model, the effects of each parameter on PCR efficiency were predicted and the most suitable operation condition for present system was determined. At last, the validation experiment was performed under the optimized condition, and the expectant results were produced. The results obtained in this paper showed that the combination of ANN and ED provided a satisfactory optimization model with good descriptive and predictive abilities, indicating that the method of combining ANN and ED can be a useful tool in PCR optimization and other biological applications.

  17. Forwarding Techniques for IP Fragmented Packets in a Real 6LoWPAN Network

    Directory of Open Access Journals (Sweden)

    Jordi Casademont

    2011-01-01

    Full Text Available Wireless Sensor Networks (WSNs are attracting more and more interest since they offer a low-cost solution to the problem of providing a means to deploy large sensor networks in a number of application domains. We believe that a crucial aspect to facilitate WSN diffusion is to make them interoperable with external IP networks. This can be achieved by using the 6LoWPAN protocol stack. 6LoWPAN enables the transmission of IPv6 packets over WSNs based on the IEEE 802.15.4 standard. IPv6 packet size is considerably larger than that of IEEE 802.15.4 data frame. To overcome this problem, 6LoWPAN introduces an adaptation layer between the network and data link layers, allowing IPv6 packets to be adapted to the lower layer constraints. This adaptation layer provides fragmentation and header compression of IP packets. Furthermore, it also can be involved in routing decisions. Depending on which layer is responsible for routing decisions, 6LoWPAN divides routing in two categories: mesh under if the layer concerned is the adaptation layer and route over if it is the network layer. In this paper we analyze different routing solutions (route over, mesh under and enhanced route over focusing on how they forward fragments. We evaluate their performance in terms of latency and energy consumption when transmitting IP fragmented packets. All the tests have been performed in a real 6LoWPAN implementation. After consideration of the main problems in forwarding of mesh frames in WSN, we propose and analyze a new alternative scheme based on mesh under, which we call controlled mesh under.

  18. Estimation of the volumetric oxygen tranfer coefficient (KLa from the gas balance and using a neural network technique

    Directory of Open Access Journals (Sweden)

    CRUZ A. J. G.

    1999-01-01

    Full Text Available This paper reports on the use of the gas balance and dynamic methods to obtain an estimate of the volumetric oxygen transfer coefficient (kLa in a conventional reactor during the growth phase of the microorganism Cephalosporium acremonium. A new way of calculating kLa by the dynamic method employing an electrode with a slow response, is proposed. The calculated values of kLa were used in the training of a feedforward neural network, for which the inputs were the parameter measurements of the related variables. The neural network technique proved effective, predicting values of kLa accurately from input data not used during the training phase. In contrast, the gas balance method was shown to be less useful. This could be attributed to the poor data obtained with the apparatus used to measure the oxygen in the exhaust gas, explained by the low rate of oxygen consumption by the microorganism.

  19. 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.

  20. Beating the bookmakers - Using artificial neural networks to profit from football betting

    OpenAIRE

    Borøy-Johnsen, Simon

    2017-01-01

    Artificial Neural Networks (ANNs) have throughout the years been used for several different purposes. Problems spanning from image classification to text generation have all been subject to ANNs. In this report, ANNs were used in order to predict the outcomes of football matches. Using data from the football statistics web site www.whoscored.com, ANNs were constructed in order to predict the outcomes of matches from two successive seasons of the English Premier League. The predictions wer...

  1. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    user

    Consequently, many structures based on artificial neural network (ANN) have been developed in the literature, The most significant ... Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion. 1. ..... and pure shunt active fitters, IEEE 38th Conf on Industry Applications, Vol. 2, pp.

  2. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    National Research Council Canada - National Science Library

    Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

    2016-01-01

    ...) 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...

  3. Recent advances on artificial intelligence and learning techniques in cognitive radio networks

    National Research Council Canada - National Science Library

    Abbas, Nadine; Nasser, Youssef; Ahmad, Karim El

    2015-01-01

    ... of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved...

  4. Application of ANN and PCA to two-phase flow evaluation using radioisotopes

    Directory of Open Access Journals (Sweden)

    Hanus Robert

    2017-01-01

    Full Text Available In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug – bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA was used. It was found that the reduction of signals features allows for building a network with better performance.

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

    OpenAIRE

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

    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...

  6. Information Exchange Between Resilient and High-Threat Networks: Techniques for Threat Mitigation

    Science.gov (United States)

    2004-11-01

    details; e-mail arriving from the Internet carrying viruses that infect a business ’ Intranet; or more recently, the emergence of ‘phishing’ where...unavoidable business requirement to share information. RTO-MP-IST-041 16 - 11 Information Exchange between Resilient and High-Threat Networks...December 1999 on a Community framework for electronic signatures. Official Journal L 013, 19/01/2000 p. 0012 – 0020. http://europa.eu.int/ISPO/ ecommerce

  7. A Partnership Training Program in Breast Cancer Diagnosis: Concept Development of the Next Generation Diagnostic Breast Imaging Using Digital Image Library and Networking Techniques

    National Research Council Canada - National Science Library

    Chouikha, Mohamed F

    2004-01-01

    ...); and Georgetown University (Image Science and Information Systems, ISIS). In this partnership training program, we will train faculty and students in breast cancer imaging, digital image database library techniques and network communication strategy...

  8. WDM Optical Access Network for Full-Duplex and Reconfigurable Capacity Assignment Based on PolMUX Technique

    Directory of Open Access Journals (Sweden)

    Jose Mora

    2014-12-01

    Full Text Available We present a novel bidirectional WDM-based optical access network featuring reconfigurable capacity assignment. The architecture relies on the PolMUX technique allowing a compact, flexible, and bandwidth-efficient router in addition to source-free ONUs and color-less ONUs for cost/complexity minimization. Moreover, the centralized architecture contemplates remote management and control of polarization. High-quality transmission of digital signals is demonstrated through different routing scenarios where all channels are dynamically assigned in both downlink and uplink directions.

  9. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  10. Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques

    Directory of Open Access Journals (Sweden)

    Wei Kong

    2014-01-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA, which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.

  11. Optimum coagulant forecasting by modeling jar test experiments using ANNs

    Directory of Open Access Journals (Sweden)

    S. Haghiri

    2018-01-01

    Full Text Available Currently, the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are the common ways through which the use of coagulants leads to instability of particles and the formation of larger and heavier particles, resulting in improvement of sedimentation and filtration processes. Determination of the optimum dose of such a coagulant is of particular significance. A high dose, in addition to adding costs, can cause the sediment to remain in the filtrate, a dangerous condition according to the standards, while a sub-adequate dose of coagulants can result in the reducing the required quality and acceptable performance of the coagulation process. Although jar tests are used for testing coagulants, such experiments face many constraints with respect to evaluating the results produced by sudden changes in input water because of their significant costs, long time requirements, and complex relationships among the many factors (turbidity, temperature, pH, alkalinity, etc. that can influence the efficiency of coagulant and test results. Modeling can be used to overcome these limitations; in this research study, an artificial neural network (ANN multi-layer perceptron (MLP with one hidden layer has been used for modeling the jar test to determine the dosage level of used coagulant in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in Ardabil province in Iran. To evaluate the performance of the model, the mean squared error (MSE and correlation coefficient (R2 parameters have been used. The obtained values are within an acceptable range that demonstrates the high accuracy of the models with respect to the estimation of water-quality characteristics and the optimal dosages of coagulants; so using these models will allow operators to not only reduce costs and time taken to perform

  12. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Vinay Chandwani

    2014-01-01

    Full Text Available Artificial neural networks (ANNs have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.

  13. Fuel economy and torque tracking in camless engines through optimization of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, Moh' d Sami S. [Department of Mechanical Engineering, The Hashemite University, Zarqa 13115 (Jordan)

    2008-02-15

    The feed forward controller of a camless internal combustion engine is modeled by inverting a multi-input multi-output feed forward artificial neural network (ANN) model of the engine. The engine outputs, pumping loss and cylinder air charge, are related to the inputs, intake valve lift and closing timing, by the artificial neural network model, which is trained with historical input-output data. The controller selects the intake valve lift and closing timing that will mimimize the pumping loss and achieve engine torque tracking. Lower pumping loss means better fuel economy, whereas engine torque tracking guarantees the driver's torque demand. The inversion of the ANN is performed with the complex method constrained optimization. How the camless engine inverse controller can be augmented with adaptive techniques to maintain accuracy even when the engine parts degrade is discussed. The simulation results demonstrate the effectiveness of the developed camless engine controller. (author)

  14. Pole discontinuity removal using artificial neural networks for microstrip antenna design

    Science.gov (United States)

    Kulshrestha, Sanjeev; Chheda, Deven J.; Chakrabarty, S. B.; Jyoti, Rajeev; Sharma, S. B.

    2011-12-01

    This article presents the use of artificial neural networks for the evaluation of integrals with finite number of pole singularities while formulating the integral equation for the electric surface current density. A feed-forward single-layer back-propagated artificial neural network (ANN) model has been trained to approximate the discontinuous integrand function. Generation of a soft continuous function obtained from the ANN model and closed-loop expressions for the evaluation of the integrals are presented. The proposed technique is applied to compute the input impedance of microstrip antenna and results have been compared with IE3D. Integration has been performed using n-point Gaussian quadrature rule for evaluating the reaction matrix.

  15. Comparison of artificial neural network and regression models in the prediction of urban stormwater quality.

    Science.gov (United States)

    May, D; Sivakumar, M

    2008-01-01

    Urban stormwater quality is influenced by many interrelated processes. However, the site-specific nature of these complex processes makes stormwater quality difficult to predict using physically based process models. This has resulted in the need for more empirical techniques. In this study, artificial neural networks (ANN) were used to model urban stormwater quality. A total of 5 different constituents were analyzed-chemical oxygen demand, lead, suspended solids, total Kjeldahl nitrogen, and total phosphorus. Input variables were selected using stepwise linear regression models, calibrated on logarithmically transformed data. Artificial neural networks models were then developed and compared with the regression models. The results from the analyses indicate that multiple linear regression models were more applicable for predicting urban stormwater quality than ANN models.

  16. Quantitative comparison of performance analysis techniques for modular and generic network-on-chip

    Directory of Open Access Journals (Sweden)

    M. C. Neuenhahn

    2009-05-01

    Full Text Available NoC-specific parameters feature a huge impact on performance and implementation costs of NoC. Hence, performance and cost evaluation of these parameter-dependent NoC is crucial in different design-stages but the requirements on performance analysis differ from stage to stage. In an early design-stage an analysis technique featuring reduced complexity and limited accuracy can be applied, whereas in subsequent design-stages more accurate techniques are required.

    In this work several performance analysis techniques at different levels of abstraction are presented and quantitatively compared. These techniques include a static performance analysis using timing-models, a Colored Petri Net-based approach, VHDL- and SystemC-based simulators and an FPGA-based emulator. Conducting NoC-experiments with NoC-sizes from 9 to 36 functional units and various traffic patterns, characteristics of these experiments concerning accuracy, complexity and effort are derived.

    The performance analysis techniques discussed here are quantitatively evaluated and finally assigned to the appropriate design-stages in an automated NoC-design-flow.

  17. Mobility based key management technique for multicast security in mobile ad hoc networks.

    Science.gov (United States)

    Madhusudhanan, B; Chitra, S; Rajan, C

    2015-01-01

    In MANET multicasting, forward and backward secrecy result in increased packet drop rate owing to mobility. Frequent rekeying causes large message overhead which increases energy consumption and end-to-end delay. Particularly, the prevailing group key management techniques cause frequent mobility and disconnections. So there is a need to design a multicast key management technique to overcome these problems. In this paper, we propose the mobility based key management technique for multicast security in MANET. Initially, the nodes are categorized according to their stability index which is estimated based on the link availability and mobility. A multicast tree is constructed such that for every weak node, there is a strong parent node. A session key-based encryption technique is utilized to transmit a multicast data. The rekeying process is performed periodically by the initiator node. The rekeying interval is fixed depending on the node category so that this technique greatly minimizes the rekeying overhead. By simulation results, we show that our proposed approach reduces the packet drop rate and improves the data confidentiality.

  18. Modeling the adsorption of benzeneacetic acid on CaO2 nanoparticles using artificial neural network

    Directory of Open Access Journals (Sweden)

    Sapana S. Madan

    2016-12-01

    Full Text Available The present work reported a method for removal of benzeneacetic acid from water solution using CaO2 nanoparticle as adsorbent and modeling the adsorption process using artificial neural network (ANN. CaO2 nanoparticles were synthesized by a chemical precipitation technique. The characterization and confirmation of nanoparticles have been done by using different techniques such as X-ray powder diffraction (XRD, high resolution field emission scanning electron microscope (HR-FESEM,transmittance electron microscopy (TEM and high-resolution TEM (HRTEM analysis. ANN model was developed by using elite-ANN software. The network was trained using experimental data at optimum temperature and time with different CaO2 nanoparticle dosage (0.002–0.05 g and initial benzeneacetic acid concentration (0.03–0.099 mol/L. Root mean square error (RMS of 3.432, average percentage error (APE of 5.813 and coefficient of determination (R2 of 0.989 were found for prediction and modeling of benzeneacetic acid removal. The trained artificial neural network is employed to predict the output of the given set of input parameters. The single-stage batch adsorber design of the adsorption of benzeneacetic acid onto CaO2 nanoparticles has been studied with well fitted Langmuir isotherm equation which is homogeneous and has monolayer sorption capacity.

  19. Partitioning and interpolation based hybrid ARIMA–ANN model for ...

    Indian Academy of Sciences (India)

    Time series forecasting; ARIMA; ANN; partitioning and interpolation; Box–Jenkins methodology ... Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all ...

  20. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

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

    Beata Bialic

    Date(s):. 2016-08-14 to 2016-08-23. Destination(s):. Peru/Colombia. Airfare: $3,484.87. Other. Transportation: $0.00. Accommodation: $1,942.21. Meals and. Incidentals: $395.27. Other: $75.50. Total: $5,897.85. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson of the.