Committee Of Networks Research Articles

테이퍼 원형강관 캔틸레버 보의 결함탐지기법

A crack identification method using an equivalent bending stiffness for cracked beam and committee of neural networks is presented. Modal properties of tapered cantilever pipe-type beam is identified by applying the boundary conditions to a general solution for tapered beam. A bending stiffness for cracked beam based on an energy method is used to identify natural frequencies and mode shapes of tapered cantilever thin-walled pipe, which has a through-the-thickness crack, subjected to bending. The identified modal properties of the cracked beam are used in constructing training patterns of neural networks. Then crack location and size are identified using a committee of the neural networks. Crack detection was carried out for an example beam using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.

Medical Director Responsibilities to the ESRD Network.

The 18 regional ESRD Networks are established in legislation and contract with the Centers for Medicare and Medicaid Services to improve the quality and safety of dialysis, maximize patient rehabilitation, encourage collaboration among and between providers toward common quality goals, and improve the reliability and the use of data in pursuit of quality improvement. The Networks are funded by a $0.50 per treatment fee deducted from the reimbursement to dialysis providers, and their deliverables are determined by a statement of work, which is updated in a new contract every 3 years. The Conditions for Coverage require dialysis providers to participate in Network activities, and failure to do so can be the basis for sanctions against the provider. However, the Networks attempt to foster a collegial relationship with dialysis facilities by offering tools, educational activities, and other resources to assist the facilities in meeting the evolving requirements by the Centers for Medicare and Medicaid Services on the basis of national aims and domains for quality improvement in health care that transcend the ESRD program. Because of his/her responsibility for implementing the quality assessment and performance improvement activities in the facility, the medical director has much to gain by actively participating in Network activities, especially those focused on quality, safety, patient grievance, patient engagement, and coordination of care. Membership on Network committees can also foster the professional growth of the medical director through participation in quality improvement activity development and implementation, authorship of articles in peer-reviewed journals, creation of educational tools and presentations, and application of Network-sponsored materials to improve patient outcomes, engagement, and satisfaction in the medical director's facility. The improvement of care of patients on dialysis will be beneficial to the facility in achieving its goals of quality, safety, and financial viability.

Neural network committee to predict the AMEn of poultry feedstuffs

A committee of neural networks is the aggregation of two or more neural networks for making overall predictions that are supposedly more accurate than those obtained by the individual networks. The objective of this paper was to assign some uncertainty over the predictions of neural networks, using a network committee to estimate the nitrogen-corrected metabolizable energy (AMEn) values of the energetic and protein concentrate feedstuffs for broilers. The dataset used to implement each expert network contains 568 experimental results. Another dataset with 48 bioassay results was used as test data. From several implemented multilayer perceptrons, the networks that presented the best generalization performance were selected to constitute the committee. The percentage of correct predictions was used as the criterion to compare committees that contained different numbers of networks. The highest probability density intervals were obtained for each feedstuff in the test data in this comparison. The estimator that ensured more accurate predictions was selected. The highest accuracy for predicting the AMEn values of concentrate feedstuffs for broilers was achieved by a committee with 1,000 networks with the use of the mode of the empirical distribution obtained from 1,000 estimated values of the AMEn. The accuracy of the models was evaluated based on their values of error measures between the observed and predicted values, in which the mode of the empirical distribution presented lower values of mean squared error (MSE = 45,285.43), mean absolute deviation (MAD = 177.66) and mean absolute percentage error (MAPE = 5.97 %) compared to the mean and the median.

Pipe crack identification based on the energy method and committee of neural networks

A crack identification method using an equivalent bending stiffness for cracked beam and committee of neural networks is presented. The equivalent bending stiffness is constructed based on an energy method for a straight thin-walled pipe, which has a through-the-thickness crack, subjected to bending. Several numerical analysis for a steel cantilever pipe using the equivalent bending stiffness are carried out to extract the natural frequencies and mode shapes of the cracked beam. The extracted modal properties are used in constructing a training patterns of a neural network. The input to the neural network consists of the modal properties and the output is composed of the crack location and size. Multiple neural networks are constructed and each individual network is trained independently with the different initial synaptic weights. Then, the estimated crack locations and sizes from different neural networks are averaged. Crack detection is carried out for 16 damage cases using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.

Wi-Fi multi-floor indoor positioning considering architectural aspects and controlled computational complexity

This paper focuses on the positioning of Wi-Fi nodes in multi-floor indoor environments. A target radio-frequency (RF) fingerprint – measured by the MS to be localized – is compared with georeferenced RF fingerprints, previously stored in a correlation database (CDB). Therefore, this strategy lies within the so-called Database Correlation Methods (DCM) used to locate mobile stations (MS) in wireless networks. To obtain best matches in terms of architectural structures such as floors, doors, aisles, among others, the authors apply two combined techniques that improve localization accuracy: unsupervised clustering (K-medians and Kohonen layer) and majority voting committees of backpropagation artificial neural networks (ANNs). The unsupervised clustering is employed to allow collected data (the fingerprints) to group freely in their natural space, without precluding – through the imposition of architectural constraints – any natural arrangement of the collected fingerprints. The proposed combined strategy improves floor identification accuracy, which in indoor multi-floor positioning must be high. The effects of the proposed solution on the DCM positioning accuracy are experimentally evaluated using actual measured data. In the trial the floor identification accuracy ranged from 91% to 97%, and the average 2D positioning error ranged from 4.5 to 1.7m, depending on the size of the measurement window (from 1 to 25 samples).

Real-time Sequential Monte Carlo Sampling based on a Committee of Artificial Neural Networks for Residual Lifetime Prediction of a Component Subjected to Fatigue Crack Growth

Most of the studies available in the literature about sequential Monte-Carlo sampling algorithm assume that a sufficient number of process observations is available, to guarantee the convergence of the algorithm on the target process evolution. This requirement is remotely met if the process of fatigue crack growth is concerned, due to the costs of maintenance procedures, especially within the aeronautical field. A real-time diagnostic system is the enabler of the prognostic health monitoring methodology.This work is about the application of sequential Monte-Carlo sampling to estimate the probabilistic residual lifetime of a monitored structural component, subjected to fatigue crack propagation. A real-time diagnostic unit for crack detection and damage assessment, trained with Finite Element simulations of damage evolution, generates the information as input to the prognostic unit. A crack propagation model provides the knowledge of the residual lifetime prior to the application of fatigue loads. The prognostic unit updates in real-time the probability density functions of the component residual lifetime at each discrete time during fatigue crack evolution. The methodology is preliminarily applied in simulated environment to an aeronautical metallic panel and the overall performance of a fully autonomous prognostic health monitoring system based upon simulated strain measures is evaluated.0

An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes

networks are organized in committees to improve the correctness of the decisions created by artificial neural networks (ANN's). In the classification of human chromosomes, it is accustomed to use multilayer perceptrons with multiple (22-24) outputs. Because of the huge number of synaptic weights to be tuned, these classifiers cannot go beyond a level of 92% overall correctness. In this study we represent a special organized committee of 462 simple perceptrons to improve the rate of correct classification of 22 types of human chromosomes. Each of these simple perceptrons is trained to distinguish between two types of chromosomes. When a new data is entered, the votes of these 462 simple perceptrons and additional 22 dummy perceptrons create a decision matrix of the size 22×22. By a special assembling of these votes we get a higher rate of correct classification of 22 types of human chromosomes.

Comparative Assessment of the Artificial Neural Network and Response Surface Modelling Efficiencies for Biohydrogen Production on Sugar Cane Molasses

This study comparatively evaluates the modelling efficiency of the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN). Twenty-nine biohydrogen fermentation batches were carried out to generate the experimental data. The input parameters consisted of a concentration of molasses (50–150 g/l), pH (4–8), temperature (35–40 °C) and inoculum concentration (10–50 %). The obtained data were used to develop the RSM and ANN models. The ANN model was a committee of networks with a topology of 4-(6-10)-1 structured on multilayer perceptrons. RSM and ANN models gave R 2 values of 0.75 and 0.91, respectively, with predicted optimum conditions of 150 g/l, 8 and 35 °C for molasses, pH and temperature, respectively, with differences in inoculum concentrations (10.11 and 15 %) for RSM and ANN, respectively. Upon validation, 15.12 and 119.08 % prediction errors on hydrogen volume were found for ANN and RSM, respectively. These findings suggest that ANN has greater accuracy in modelling the relationships between the considered process inputs for fermentative biohydrogen production and thus, is more reliable to navigate the optimization space.

Old Keyam – A Framework for Examining Disproportionate Experience of Tuberculosis Among Aboriginal Peoples of the Canadian Prairies

On the Canadian Prairies, First Nations and Métis peoples are disproportionately affected by tuberculosis (TB) compared to other Canadians. Statistics show enduring transmission and high rates of active TB disease. Despite awareness of the social determinants of TB transmission—such as substance abuse, comorbidities, and basic needs being unmet—transmission and outbreaks continue to occur among Aboriginal people. The Determinants of Tuberculosis Transmission project is a mixed methods, interdisciplinary study that used quantitative questionnaires and qualitative interviews to look more closely at patients’ experiences of TB. Provincial Network Committees (PNCs) comprised of Elders, traditionalists, community-based TB workers, and health researchers in three participating provinces guided the project from inception through to data analysis, interpretation, and dissemination. The collaborative efforts of the patients, the research team, and the PNCs uncovered a continuing influence of colonization in TB transmission. Overwhelming feelings of apathy and despair for the hold that TB continues to have in the lives of patients, families, and communities is captured by the Cree word “keyam,” which may be translated as “to give up” or to ask, “What is the use?” This paper explores the concept of keyam in relation to TB transmission.

군집 신경망기법을 이용한 해상풍력발전기 지지구조물의 건전성 모니터링 기법

A damage estimation method for monopile support structure of offshore wind turbine using modal properties and committee of neural networks is presented for effective structural health monitoring. An analytical model for a monopile support structure is established, and the natural frequencies, mode shapes, and mode shape slopes for the support structure are calculated considering soil condition and added mass. The input to the neural networks consists of the modal properties and the output is composed of the stiffness indices of the support structure. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated stiffness indices from different neural networks are averaged. Ten damage cases are estimated using the proposed method, and the identified damage locations and severities agree reasonably well with the exact values. The accuracy of the estimation can be improved by applying the committee of neural networks which is a statistical approach averaging the damage indices in the functional space.

The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe

Abstract This paper aims to determine the influence of selected variables on residential property price indices for the European countries, with particular attention paid to Italy and Poland, using a rough set theory and an approach that uses a committee of artificial neural networks. Additionally, the overall analysis for each European country is presented. Quarterly time series data constituted the material for testing and empirical results. The developed models show that the economic and financial situation of European countries affects residential property markets. Residential property markets are connected, despite the fact that they are situated in different parts of Europe. The economic and financial crisis of countries has variable influence on prices of real estate. The results also suggest that methodology based on the rough set theory and a committee of artificial neural networks has the ability to learn, generalize, and converge the residential property prices index.

Quantile based decision making rule of the neural networks committee for ill-posed approximation problems

In this paper a multidimensional function approximation problem is stated. This problem is characterized by the strong influence of arguments measurement errors on the accuracy of the function estimation and a small amount of train data. A neural networks based solution is used for this problem.To improve the accuracy of the approximation model it is proposed to use a neural networks committee with an original decision making rule for the construction of the generalized function estimate. The developed rule is based on a specially introduced indirect accuracy measure and α-quantile calculation of its probability level. The decision making rule is trained on a set of patterns and uses statistical properties of each pattern's processing by the committee's networks.The computational scheme of the proposed approximation model and the effectiveness of the proposed approach are demonstrated on a simple model example. The developed method was successfully applied to a real industrial problem of metal's hardness characteristics estimation on the basis of kinetic indentation data. The results of modeling experiments are discussed.

The impact of macroeconomic factors on residential property prices indices in Europe

This paper aims to determine the influence of selected variables on residential property price indices for European countries, with particular attention paid to Italy and Poland, using a rough set theory and an approach that uses a committee of artificial neural networks. Additionally, the overall analysis for each European country is presented. Quarterly time series data constituted the material for testing and empirical results. The developed models show that the economic and financial situation of European countries affects residential property markets. Residential property markets are connected, despite the fact that they are situated in different parts of Europe. The economic and financial crisis of countries has variable influence on prices of real estate. The results also suggest that methodology based on the rough set theory and a committee of artificial neural networks has the ability to learn, generalize, and converge the residential property prices index.

Bayesian neural networks for the prediction of stochastic travel times in urban networks

Urban travel time prediction has received much less attention than predictions on freeways, perhaps because urban travel times show much larger variations and are therefore much harder to predict. However, urban travel time can form a substantial part of the total travel time of a road user and therefore effort should be taken to predict urban travel times. In this study, neural networks are used for urban travel time prediction because these have shown to be able to deal with noisy data. Bayesian techniques are used for training of the networks, resulting in committees with lower error and in confidence bounds. It is shown that the neural network committees are capable of predicting the ‘low-frequency trend’, which can be seen when the high-frequency component of travel time is removed using de-noising. The errors of the predictions on the low-frequency trend are in the same order as when predicting freeway travel times, and it is shown that the predicted confidence bounds are accurate.

Neural networks committee decision making for estimation of metal’s hardness properties from indentation data

In this paper the problem of metal's hardness properties estimation from indentation data is concerned. This problem belongs to a class of ill-posed vector function approximation problems and can't be solved by a single multilayered perceptron at the required precision level. A special neural networks committee architecture is developed in order to obtain precise estimates of metal's hardness properties. This method involves well-posed direct indentation task solution and a quantile based idea for best estimates selection. Studies have shown the estimates produced by the committee to be stable even in the case of noise presence that is similar to the true experimental one.

Wavelet energy signatures and robust Bayesian neural network for visual quality recognition of nonwovens

In this paper, the visual quality recognition of nonwovens is considered as a common problem of pattern recognition that will be solved by a joint approach by combining wavelet energy signatures, Bayesian neural network, and outlier detection. In this research, 625 nonwovens images of 5 different grades, 125 each grade, are decomposed at 4 levels with wavelet base sym6, then two energy signatures, norm-1 L 1 and norm-2 L 2 are calculated from wavelet coefficients of each high frequency subband to train and test Bayesian neural network. To detect the outlier of training set, scaled outlier probability of training set and outlier probability of each sample are introduced. The committees of networks and the evidence criterion are employed to select the ‘most suitable’ model, given a set of candidate networks which has different numbers of hidden neurons. However, in our research with the finite industrial data, we take both the evidence criterion and the actual performance into account to determine the structure of Bayesian neural network. When the nonwoven images are decomposed at level 4, with 500 samples to training the Bayesian neural network that has 3 hidden neurons, the average recognition accuracy of test set is 99.2%. Experimental results on the 625 nonwoven images indicate that the wavelet energy signatures are expressive and powerful in characterizing texture of nonwoven images and the robust Bayesian neural network has excellent recognition performance.

A process for the inclusion of Aboriginal People in health research: Lessons from the Determinants of TB Transmission project

The Determinants of TB Transmission (DTT) project, a federally-funded study covering the period April 1, 2006-March 31, 2013, and examining the determinants of TB transmission amongst the Canadian-born population (Aboriginal and non-Aboriginal) in the prairie provinces of Canada, took a novel approach to health research involving Aboriginal people. The methodology aligned itself with the recently published Canadian Institutes of Health Research (CIHR) Guidelines for Health Research Involving Aboriginal People and the established principles of Ownership, Control, Access, and Possession (OCAP). This article details the process by which collaboration with Aboriginal peoples was achieved, including the involvement of Aboriginal researchers, the development of Provincial Network Committees (PNCs), and communications with First Nations Chiefs and Council. Strengths of this methodology included Aboriginal organizational and community support with a high rate of participation; PNC leadership, which brought together Aboriginal stakeholders with provincial and federal TB program planners; and the exploration of both on and off-reserve transmission factors. Challenges of the methodology included meeting funding agency timelines and expectations given the gradual process of trust development and PNC-reviewed publication; respecting both community and individual participants' autonomy regarding study participation; and political discomfort with strong Aboriginal involvement. While the methodology required a dedicated investment from researchers and funding agencies alike, the process was worthwhile and achieved a high degree of support from its major collaborators: the Aboriginal peoples.

NFL Prediction using Committees of Artificial Neural Networks

This paper analyzes the ability of a neural network model to predict the outcome of NFL games. This model uses only readily available statistics, such as passing yards, rushing yards, fumbles lost, and scoring. A key component of this model is the use of statistical differentials to compare teams. For example, the offensive passing yards gained by one team are compared to the defensive passing yards allowed by an opposing team to create a data set of expected values for a given matchup. By using principal component analysis and derivative based analysis, we determined which statistics influence our model the most. We assessed the performance of the model by comparing its performance to that of published prediction algorithms and the Las Vegas oddsmakers over multiple seasons. Two novel aspects of this work include the use of multiple committees of machines for prediction and the use of our model to simulate virtual round-robin tournaments to establish an objective ranking of the teams.

Linear and non-linear T– S models for the eastern North Atlantic from Argo data: Role of surface salinity observations

Linear and non-linear empirical models for salinity (S) are estimated from the Argo temperature (T) and salinity (delayed) data. This study focuses on the reconstruction of salinity in the upper 1200 m of the eastern North Atlantic Ocean, a region characterized by the presence of many different water masses. While previous studies have found it necessary to split this region by boxes to fit different polynomial models in each box, a unique model valid for the entire region is fitted here. Argo profiles are randomly distributed on two sets: one for fitting the models and one for testing them. Non-linear regressions are built using neural networks with a single hidden layer and the fitting data set is further divided into two subsets: one for adjusting the coefficients (training data) and one for early stopping of the fitting (validation data). Our results indicate that linear regressions perform better than the climatologic T– S relationship, but that non-linear regressions perform better than the linear ones. Non-linear training using a three-data subsets strategy successfully prevents overfitting even when networks with 90 neurons in the hidden layer are being trained. While the presence of local minima may complicate the generalization of non-linear models to new data, network committees (created by training the same network from different random initial weights) are shown to better reproduce the test data. Several predictors are tested, and the results show that geographical, or surface, information does provide significant information. These results highlight the potential applications of future satellite missions measuring sea-surface salinity to reconstruct, when combined with temperature profiles, vertical salinity profiles.

Bayesian committee of neural networks to predict travel times with confidence intervals

Short-term prediction of travel time is one of the central topics in current transportation research and practice. Among the more successful travel time prediction approaches are neural networks and combined prediction models (a ‘committee’). However, both approaches have disadvantages. Usually many candidate neural networks are trained and the best performing one is selected. However, it is difficult and arbitrary to select the optimal network. In committee approaches a principled and mathematically sound framework to combine travel time predictions is lacking. This paper overcomes the drawbacks of both approaches by combining neural networks in a committee using Bayesian inference theory. An ‘evidence’ factor can be calculated for each model, which can be used as a stopping criterion during training, and as a tool to select and combine different neural networks. Along with higher prediction accuracy, this approach allows for accurate estimation of confidence intervals for the predictions. When comparing the committee predictions to single neural network predictions on the A12 motorway in the Netherlands it is concluded that the approach indeed leads to improved travel time prediction accuracy.