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Toggle navigation. Gen Link Twitter E-Mail. External Reports VirusTotal. Risk Assessment. Related Sandbox Artifacts. Associated SHAs f5bb29a11e94aa3ace93dcefd06fdaae. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.
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 experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other. The highest R"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"2 values prove that modeling the adsorption process with ANN is a satisfactory approach. Many models have been proposed to predict it. The MRTD testing is a psychophysical task, for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time.
Especially when measuring imagers of the same type, the test is time consuming. So an automated and intelligent measurement method should be discussed. Firstly, we use frame grabber to capture the 4-bar target image data. Then according to image gray scale, we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability.
Actually it is a nonlinear classification of input dimensions of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. This method can reduce the uncertainties between observers and long time dependent factors by standardization.
This paper will introduce the feature extraction algorithm, demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement. Meals and. Comments: From residence in Thornhill, Ontario. Anne Chambers, Governor, Chairperson of the. Human Resources Committee. The former is a dramatic monologue of a dolphin, which is exploited by people, and the latter is a dramatic monologue of an omnipotent observer in a restaurant.
A close reading of the two poems from the eco-feminist perspective helps the reader understand why Carol Ann Duffy is honored as the first woman poet laureate in British history, and better understand vegetarian eco-feminism and its influence in British society.
Keywords: eco-feminism; consciousness, species-ism, vegetarian, animal, diet. The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: i determining the phase subspace of the system, or embedding, from available time series and ii constructing an evolution operator acting in this reduced subspace.
In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network ANN with special topology. The proposed ANN -based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold.
Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on.
The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed.
The study was supported by the Russian Science Foundation grant Date s :. Destination s :. Comments: Travel Expense Reports for Mary. Mapping brain circuits of reward and motivation: in the footsteps of Ann Kelley. Ann Kelley was a scientific pioneer in reward neuroscience.
Ann Kelley's accomplishments inspired other researchers to follow in her footsteps, including our own laboratory group. Here we describe results from several lines of our research that sprang in part from earlier findings by Kelley and colleagues. We describe hedonic hotspots for generating intense pleasure 'liking', separate identities of 'wanting' versus 'liking' systems, a novel role for dorsal neostriatum in generating motivation to eat, a limbic keyboard mechanism in nucleus accumbens for generating intense desire versus intense dread, and dynamic limbic transformations of learned memories into motivation.
We describe how origins for each of these themes can be traced to fundamental contributions by Ann Kelley. All rights reserved. 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.
Comments: Travel Expense Reports for. Margaret Ann Biggs, Chairperson. Purpose: Board meetings. Anne Chambers, Governor This paper describes ANN -Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms.
It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several ANN -Benchmarks aims to provide a constantly updated overview of the current state of the art of k-NN algorithms. In the short term, this overview allows users to choose the correct k-NN algorithm and parameters The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work.
Interestingly, very different Padari vastusest. Full Text Available The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables.
But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition SVD technique based local characteristic-scale decomposition LCD was developed to extract the feature variables.
The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability. The present study focusses on development of models using ANN and fuzzy logic FL algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used.
In the present study, 20 years Anne Chambers, Governor, Chairperson Aspekte van die outeursfunksie in Antjie Krog se Lady Anne Lady Anne It is an attempt to show how the notion of the death of the author Barthes links up with this theorisation of Foucault. Thirteen transects associated This paper presents an extended version of study already undertaken on development of an artificial neural networks ANNs model for assigning workforce into virtual cells under virtual cellular manufacturing systems VCMS environments.
Previously, the same authors have introduced this concept and applied it to virtual cells of two-cell configuration and the results demonstrated that ANNs could be a worth applying tool for carrying out workforce assignments.
In this attempt, three-cell configurations problems are considered for worker assignment task. Virtual cells are formed under dual resource constraint DRC context in which the number of available workers is less than the total number of machines available. Since worker assignment tasks are quite non-linear and highly dynamic in nature under varying inputs and conditions and, in parallel, ANNs have the ability to model complex relationships between inputs and outputs and find similar patterns effectively, an attempt was earlier made to employ ANNs into the above task.
In this paper, the multilayered perceptron with feed forward MLP-FF neural network model has been reused for worker assignment tasks of three-cell configurations under DRC context and its performance at different time periods has been analyzed. The previously proposed worker assignment model has been reconfigured and cell formation solutions available for three-cell configuration in the literature are used in combination to generate datasets for training ANNs framework.
Finally, results of the study have been presented and discussed. Ellipsis in English Literature: Signs of Omission. For years the thesis topic kept me entertained as an example of how narrowly focused literary study could become.
Full Text Available Offer preparation has always been a specific part of a building process which has significant impact on company business. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Estimation of works duration has proved to be more difficult.
The best MAPEs were Why philosophy and history matter : A conversation with Ann Taves. The article picks up some ideas that Ann Taves presents in her book Religious Experience Reconsidered, and looks at possible conversations that are not fleshed out in detail in Taves' book.
In particular, it is argued that the disciplinary confrontation with philosophy and with historiography is of. Artificial neural network ANN -based prediction of depth filter loading capacity for filter sizing. 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 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient R 2 of 0. 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.
The data used in this work are global solar radiation GSR , sunshine duration, maximum and minimum air temperature and relative humidity. These data are available from Jordanian meteorological station over a period of two years. Different configurations patterns were tested using available observed data. It was found that the model using mainly sunshine duration and air temperature as inputs gives accurate results. The ANN model efficiency and the mean square error values show that the prediction model is accurate.
It is found that the effect of the three learning algorithms on the accuracy of the prediction model at the training and testing stages for each time scale is mostly within the same accuracy range. Measurement and ANN prediction of pH-dependent solubility of nitrogen-heterocyclic compounds. Based on the solubility of 25 nitrogen-heterocyclic compounds NHCs measured by saturation shake-flask method, artificial neural network ANN was employed to the study of the quantitative relationship between the structure and pH-dependent solubility of NHCs.
With genetic algorithm-multivariate linear regression GA-MLR approach, five out of the molecular descriptors computed by Dragon software were selected to describe the molecular structures of NHCs. Standard Penetration Resistance N value is used in many empirical geotechnical engineering formulas. For a particular site, usually, only a limited N value data are available.
In contrast, resistivity data can be obtained extensively. Moreover, previous studies showed evidence of a correlation between N value and resistivity value. Yet, no existing method is able to interpret resistivity data for estimation of N value. Thus, the aim is to develop a method for estimating N-value using resistivity data.
The performance metrics used were the coefficient of determination, R2 and mean absolute error, MAE. 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.
Currently we are improving the results using cross validation. 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. 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. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.
The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. 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. Anne Oja. Diagramm: Finantsinvesteeringute maht.
Dividendisaajate esirinnas. Kommenteerib Annika Matson. 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.
Among elderlies under Bagbazar UHTC, were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale.
WEKA 3. Prevalence of depression among the study population was 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 Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes. Modelling and automatic reactive power control of isolated wind-diesel hybrid power systems using ANN. This paper presents an artificial neural network ANN based approach to tune the parameters of the static var compensator SVC reactive power controller over a wide range of typical load model parameters.
The gains of PI proportional integral based SVC are optimised for typical values of the load voltage characteristics n q by conventional techniques. Using the generated data, the method of multi-layer feed forward ANN with error back propagation training is employed to tune the parameters of the SVC.
It is observed that the maximum deviations of all parameters are more for larger values of n q. Bansal, R. Application of ann -based decision making pattern recognition to fishing operations. Akhlaghinia, M. Faculty of Engineering. Torabi uregina. Decision making is a crucial part of fishing operations. Proper decisions should be made to prevent wasted time and associated costs on unsuccessful operations.
This paper presents a novel model to help drilling managers decide when to commence and when to quit a fishing operation. A decision making model based on Artificial Neural Network ANN has been developed that utilizes Pattern Recognition based on fishing incidents from one of the most fish-prone fields of the southwest of Iran. All parameters chosen to train the ANN -Based Pattern Recognition Tool are assumed to play a role in the success of the fishing operation and are therefore used to decide whether a fishing operation should be performed or not.
If the tool deems the operation suitable for consideration, a cost analysis of the fishing operation can then be performed to justify its overall cost. My analysis of this corpus is based on four approaches: a comparison between Carter's and Rice's works, supported by their common use of vampire characters; an investigation of how this use con The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index RI with a good degree of accuracy.
This paper deals with the identification of material parameters using an inverse strategy. In the classical methods, the inverse technique is generally coupled with a finite element code which leads to a long computing time.
In this work an inverse strategy coupled with an ANN procedure is proposed. This method has the advantage of being faster than the classical one. To validate this approach an experimental plane tensile and bulge tests are used in order to identify material behavior. The ANN model is trained from finite element simulations of the two tests.
In order to reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure to identify material parameters for the AISI The identified material parameters are the hardening curve and the anisotropic coefficients.
Full Text Available Dr. Anne Brennan Malec is the founder and managing partner of Symmetry Counseling, a counseling, coaching, and psychotherapy group practice located in downtown Chicago. 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. Effectiveness of ANN for seismic behaviour prediction considering geometric configuration effect in concrete gravity dams. 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 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. This paper presents the use of both the Water Erosion Prediction Project WEPP and the artificial neural network ANN for the prediction of runoff and soil loss in the central highland mountainous of the Palestinian territories.
Analyses show that the soil erosion is highly dependent on both the rainfall depth and the rainfall event duration rather than on the rainfall intensity as mostly mentioned in the literature. The results obtained from the WEPP model for the soil loss and runoff disagree with the field data. The WEPP underestimates both the runoff and soil loss. Analyses conducted with the ANN agree well with the observation. In addition, the global network models developed using the data of all the land use type show a relatively unbiased estimation for both runoff and soil loss.
The study showed that the ANN model could be used as a management tool for predicting runoff and soil loss. 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.
Realising the need of the hour, the neckbands are made from food-grade silicone, making them both smart and sustainable. Primed to offer immersive and clear sound across your calls; the Noise Flair comes with Environmental Noise Cancellation to drown out all distractions - so that you focus exclusively on the audio itself. Dual microphones ensure that you get lag-free and consistent sound on your calls. This is further backed by a smart vibration call alert feature, which ensures that you never miss an important call.
The first-ever neckband that features complete touch access - the Noise Flair is our smartest neckband yet. Packing several industry-leading features, the neckband has a Qualcomm powered chipset ensuring seamless audio operation. Its full touch capabilities are a consequence of its magnetic power controls which make do without any flimsy buttons.
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