Output Data Sets Research Articles

An artificial neural network-based response surface method for reliability analyses of c-φ slopes with spatially variable soil

This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-φ slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-φ slope reliability analyses.

Search Databases and Statistics: Pitfalls and Best Practices in Phosphoproteomics.

Advances in mass spectrometric instrumentation in the past 15 years have resulted in an explosion in the raw data yield from typical phosphoproteomics workflows. This poses the challenge of confidently identifying peptide sequences, localizing phosphosites to proteins and quantifying these from the vast amounts of raw data. This task is tackled by computational tools implementing algorithms that match the experimental data to databases, providing the user with lists for downstream analysis. Several platforms for such automated interpretation of mass spectrometric data have been developed, each having strengths and weaknesses that must be considered for the individual needs. These are reviewed in this chapter. Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here.

Dem Local Accuracy Patterns in Land-Use/Land-Cover Classification

AbstractGlobal and nation-wide DEM do not preserve the same height accuracy throughout the area of study. Instead of assuming a single RMSE value for the whole area, this study proposes a vario-model that divides the area into sub-regions depending on the land-use / landcover (LULC) classification, and assigns a local accuracy per each zone, as these areas share similar terrain formation and roughness, and tend to have similar DEM accuracies. A pilot study over Lebanon using the SRTM and ASTER DEMs, combined with a set of 1,105 randomly distributed ground control points (GCPs) showed that even though the inputDEMs have different spatial and temporal resolution, and were collected using difierent techniques, their accuracy varied similarly when changing over difierent LULC classes. Furthermore, validating the generated vario-models proved that they provide a closer representation of the accuracy to the validating GCPs than the conventional RMSE, by 94% and 86% for the SRTMand ASTER respectively. Geostatistical analysis of the input and output datasets showed that the results have a normal distribution, which support the generalization of the proven hypothesis, making this finding applicable to other input datasets anywhere around the world.

Stabilized adaptive controller based on direct IF–THEN knowledge of electronic systems for PWM drivers

This article represents an adaptive control scheme which can be implemented to electric machines driven by pulse-width modulation techniques. The controller is constructed under the human knowledge related to the machine behavior such as IF we need to move motor more speed on clock wise direction THEN the duty cycle should be more than 50 %. The set of IF–THEN rules is directly implemented by an adaptive network called fuzzy rules emulated network (FREN). Furthermore, the controlled plant can be considered as a class of nonlinear discrete-time systems and the boundary of pseudo partial derivative can be estimated by plant’s input–output data set. Thus, the mathematical model of overall systems can be neglected for all designing phase and running phase. The adaptive algorithm is developed to tune all adjustable parameters inside FREN and the convergence and closed-loop stability can be guaranteed using the time varying learning rate. The experiment setup with lab-scale DC-motor and generator coupling demonstrates the validation of proposed controller.

Sensor Calibration and Hysteresis Compensation With Heteroscedastic Gaussian Processes

We deal with the problem of estimating the true measured scalar quantity from the output signal of a sensor that is afflicted with hysteresis and noise. We use a probabilistic, nonparametric sensor model based on heteroscedastic Gaussian processes (GPs), which is trained using a data set of sensor output and ground truth pairs. The inference problem is formulated as state estimation in a dynamical system. We exploit the low dimensionality of the latent state space of the sensor to perform exact probabilistic inference of the measured quantity from a time series of the sensor’s output. Compared with the state-of-the-art assumed density filtering algorithm for GPs, which analytically approximates the posterior by a normal distribution during inference, our method reduces the prediction error by 33% on a data set obtained from a novel flexible tactile sensor based on carbon-black filled elastomers. The proposed model can be applied, but is not limited, to any sensor for which the Preisach model of hysteresis holds. The use of probabilistic modeling and inference not only provides a most likely estimate of the measured quantity but also the corresponding confidence interval.

Persim 3D: Context-Driven Simulation and Modeling of Human Activities in Smart Spaces

Automated understanding and recognition of human activities and behaviors in a smart space (e.g., smart house) is of paramount importance to many critical human-centered applications. Recognized activities are the input to the pervasive computer (the smart space) which intelligently interacts with the users to maintain the application's goal be it assistance, safety, child-development, entertainment or other goals. Research in this area is fascinating but severely lacks adequate validation which often relies on datasets that contain sensory data representing the activities. Availing adequate datasets that can be used in a large variety of spaces, for different user groups, and aiming at different goals is very challenging. This is due to the prohibitive cost and the human capital needed to instrument physical spaces and to recruit human subjects to perform the activities and generate data. Simulation of human activities in smart spaces has therefore emerged as an alternative approach to bridge this deficit. Traditional event-driven approaches have been proposed. However, the complexity of human activity simulation was proved to be challenging to these initial simulation efforts. In this paper, we present Persim 3D–an alternative context-driven approach to simulating human activities capable of supporting complex activity scenarios. We present the context-activity-action nexus and show how our approach combines modeling and visualization of actions with context and activity simulation. We present the Persim 3D architecture and algorithms, and describe a detailed validation study of our approach to verify the accuracy and realism of the simulation output (datasets and visualizations) and the scalability of the human effort in using Persim 3D to simulate complex scenarios. We show positive and promising results that validate our approach.

Bootstrap generated confidence interval for time averaged measure

In the simulation output analysis, there are some measures that should be calculated by time average concept such as the mean queue length. Especially, the confidence interval of those measures might be required for statistical analysis. In this situation, the traditional method that utilizes the central limit theorem (CLT) is inapplicable if the output data set has autocorrelation structure. The bootstrap is one of the most suitable methods which can reflect the autocorrelated phenomena in statistical analysis. Therefore, the confidence interval for a time averaged measure having autocorrelation structure can also be calculated by the bootstrap methods. This study introduces the method that constructs these confidence intervals applying the bootstraps. The bootstraps proposed are the threshold bootstrap (TB), the moving block bootstrap (MBB) and stationary bootstrap (SB). Finally, some numerical examples will be provided for verification.

Five algorithms that calculate cardiac output from the arterial waveform: a comparison with Doppler ultrasound

Different mathematical approaches are used to calculate arterial pulse pressure wave analysis (PPWA) cardiac output. The CardioQ-Combi is a research oesophageal Doppler (COODM) monitor that includes these five fundamental PPWA algorithms. We compared these PPWA cardiac output readings to COODM and suprasternal USCOM Doppler (COUS) over a range of cardiac output values induced by dopamine infusion in patients undergoing major surgery. USCOM acted as a control. Serial sets of cardiac output data were recorded at regular intervals as cardiac output increased. Formulae included: cardiac output calculated form systemic vascular resistance (COMAP), pulse pressure (COPP), Liljestrand-Zander formula (COLZ), alternating current power (COAC) and systolic area with Kouchoukos correction (COSA). The reference method for comparisons was COODM. Statistical methods included: Scatter plots (correlation), Bland-Altman (agreement) and concordance (trending) and polar (trending). From 20 patients 255 sets of cardiac output comparative data were collected. Mean cardiac output for each method ranged between 5.0 and 5.5 litre min(-1). For comparisons between COUS and the five PPWA algorithms with COODM: Correlation was best with COUS (R(2)=0.81) followed by COLZ (R(2)=0.72). Bias ranged between 0.1 and 0.5 litre min(-1). Percentage error was lowest with COUS (26.4%) followed by COLZ (35.2%), others (40.7 to 56.3%). Concordance was best with COUS (92%), followed by COLZ (71%), others (64 to 66%). Polar analysis (mean(standard deviation)) were best with COUS (-2.7 (21.1)), followed by COLZ (+4.7 (26.6). The Liljestrand-Zander PPWA formula was most reliable compared with oesophageal Doppler in major surgical patients under general anaesthesia, but not better than USCOM.

Drivers of cost reduction in solar photovoltaics

Using a new dataset of costs, output, sales, technical characteristics, and capital expenditures of firms in the solar industry during 2005–2012, this paper investigates the factors that have contributed to the decline in the cost of producing solar panels. While previous studies have attributed learning-by-doing and economies of scale as important drivers of cost reduction, these do not have any significant effect on cost once four other factors are taken into account, namely, (i) reduction in the cost of a principal raw material, (ii) increasing presence of solar panel manufacturers from China, (iii) technological innovations, and (iv) increase in investment at the industry level. These findings suggest that the upstream industries that supply the solar panel industry with raw materials and capital equipment have been important contributors to the reduction in the production cost of solar panels.

Identification and Online Validation of a pH Neutralization Process Using an Adaptive Network-Based Fuzzy Inference System

In this study, the application of adaptive neuro-fuzzy inference system (ANFIS) architecture to build prediction models that represent the pH neutralization process is proposed. The dataset used to identify the process was obtained experimentally in a bench scale plant. The prediction model attained was validated offline and online and demonstrated as able to precisely predict the one step-ahead value of effluent pH leaving the neutralization reactor. The input variables were the current and one past value of the acid and base flow rates and the current value of the output variable. Variance accounted for (VAF) indices greater than 99% were achieved by the model in experiments in which the disturbances in the acid and basic solutions flow rates were applied separately. For tests with simultaneous disturbances, conditions never seen in the training and suffering from reactor level oscillations, the prediction model VAF index was still approximately 96%. The validations demonstrated the capability of ANFIS to build precise fuzzy models from input–output datasets. R2 values achieved were always larger than 0.96.

Evaluating Observation Influence on Regional Water Budgets in Reanalyses

Abstract The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model or perhaps assimilating data from an inconsistent observing system. In the MERRA reanalysis, an area of long-term moisture flux divergence over land has been identified over the central United States. Here, the water vapor budget is evaluated in this region, taking advantage of two unique features of the MERRA diagnostic output: 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output dataset of the assimilated observations and their innovations (e.g., forecast departures). In the central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRA’s Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 0600 and 1800 UTC analysis cycles, when radiosonde information is not prevalent. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSU-A (mainly window channels) and Atmospheric Infrared Sounder (AIRS). This effort also shows the complexities of the observing system and the reactions of the regional water budgets in reanalyses to the assimilated observations.

Statistical downscaling of rainfall: a non-stationary and multi-resolution approach

A novel downscaling technique is proposed in this study whereby the original rainfall and reanalysis variables are first decomposed by wavelet transforms and rainfall is modelled using the semi-parametric additive model formulation of Generalized Additive Model in Location, Scale and Shape (GAMLSS). The flexibility of the GAMLSS model makes it feasible as a framework for non-stationary modelling. Decomposition of a rainfall series into different components is useful to separate the scale-dependent properties of the rainfall as this varies both temporally and spatially. The study was conducted at the Onkaparinga river catchment in South Australia. The model was calibrated over the period 1960 to 1990 and validated over the period 1991 to 2010. The model reproduced the monthly variability and statistics of the observed rainfall well with Nash-Sutcliffe efficiency (NSE) values of 0.66 and 0.65 for the calibration and validation periods, respectively. It also reproduced well the seasonal rainfall over the calibration (NSE = 0.37) and validation (NSE = 0.69) periods for all seasons. The proposed model was better than the tradition modelling approach (application of GAMLSS to the original rainfall series without decomposition) at reproducing the time-frequency properties of the observed rainfall, and yet it still preserved the statistics produced by the traditional modelling approach. When downscaling models were developed with general circulation model (GCM) historical output datasets, the proposed wavelet-based downscaling model outperformed the traditional downscaling model in terms of reproducing monthly rainfall for both the calibration and validation periods.

CGE model-based analysis of the neutralized hybrid carbon policy and its decomposed effects on economic growth, carbon reduction, and energy utilization costs

The hybrid policy is a flexible policy tool that combines features of carbon trading and carbon taxation. Its economic and environmental effects under China’s background are still not studied in detail. Given the exogenous carbon reduction targets, carbon prices, and carbon tax-rates, by computable general equilibrium modeling methods and factor decomposition methods, this article investigates direct and cascaded effects of the hybrid policy on economic growth, energy utilization, and carbon emission on the national level and the sector level, with China’s national input–output data-set. Stepwisely, policy scenarios with irrational estimated results are selectively excluded based on comprehensive evaluation among economic, carbon reduction and other policy targets. As a result, against national economic conditions in 2007, the hybrid policy, with a carbon reduction target of −10%, a carbon tax-rate of around ￥10, and a ceiling carbon price of ￥40, is highly recommended, because of its significant lower ec...

Multirate Partial Least Squares for Process Monitoring

In most chemical processes, variables are sampled at different rates which brings great challenges to traditional process monitoring methods that are built upon single sampling rate. In this paper, a multi-rate partial least squares algorithm is proposed. Compared to the traditional PLS method, the proposed algorithm takes use of the incomplete data samples through a modification of both of the covariance matrix of the input dataset and the covariance matrix between the input and output datasets. Iteration is used in the model training step to avoid the calculation of same parameters which requires complete training datasets. Then the fault detection and online prediction strategy is proposed based on this algorithm. A case study on TE process shows that the proposed method had an enhanced performance on both monitoring and online prediction, compared to the traditional PLS method.

Challenges and Solutions in Executing Numerical Weather Prediction in a Cloud Infrastructure

Abstract Cloud Computing has emerged as a solution to perform large-scale scientific computing. The elasticity of the cloud and its pay-as-you-go model present an interesting opportunity for applications commonly executed in clusters or supercomputers. This paper presents the challenges of migrating and performing a numerical weather prediction (NWP) application in a cloud computing infrastructure. We compared the execution of this High-Performance Computing (HPC) application in a local cluster and the cloud using different instance sizes. The experiments demonstrate that, although processing and networking create a limiting factor, storing input and output datasets in the cloud present an attractive option to share results and ease the deployment of a test-bed for a weather research platform. Results show that cloud infrastructure can be used as a viable HPC alternative for numerical weather prediction software.

Modeling of Magnetorheological Dampers under Various Impact Loads

Magnetorheological (MR) damper has received great attention from structural control engineering because it provides the best features of both passive and active control systems. However, many studies on the application of MR dampers to large civil structures have tended to center on the modeling of MR dampers under seismic excitations, while, to date, there has been minimal research regarding the MR damper model under impact loads. Hence, this paper investigates nonlinear models of MR dampers under a variety of impact loads and control signals. Two fuzzy models are proposed for modeling the nonlinear impact behavior of MR dampers. They are compared with mechanical models, the Bingham and Bouc-Wen models. Experimental studies are performed to generate sets of input and output data for training, validating, and testing the models: the deflection, acceleration, velocity, and current signals. It is demonstrated that the proposed fuzzy models are effective in predicting the complex nonlinear behavior of the MR damper subjected to a variety of impact loads and control signals. The proposed fuzzy model resulted in an accuracy of 99% to predict the impact forces of the MR damper.

Wave Prediction Using Neural Networks at New Mangalore Port along West Coast of India

Estimation of wave parameters is of great importance in coastal activities such as design studies for harbour, inshore and offshore structures, coastal erosion, sediment transport and wave energy estimation. Traditional methods like semi empirical formulations and numerical models have disadvantages of excessive data requirement, time consumption and are tedious to carry out.Artificial Intelligent methods like ANN (Artificial Neural Network) are powerful and flexible modeling tool is being widely used in coastal engineering field since last two decades for solving various problems related to time series forecasting of waves and tides; prediction of sea-bed liquefaction and scour depth; and estimation of design parameters of coastal engineering structures. It imbibes the qualities of exploiting non-linearity, adaptability to adjust free parameters by mapping input output data sets using various learning algorithms and fault tolerance.Using input and output information, intelligent systems enabled to estimate the hidden law behind the information and to find their relations among them. In the present study attempt has been made to predict waves at New Mangalore Port Trust (NMPT) located along the west coast of India using Feed Forward Back Propagation (FFBP) with LM algorithm and a recurrent network called Non-linear Auto Regressive with exogenous input(NARX) network. Field data of NMPT has been used to train and test the network performance, which are measured in terms of mean square error (mse) and correlation coefficient (r). Effect of network architecture on the performance of model has been studied.Correlation coefficient is found to be 0.94 in case of NARX predictions indicating better performance than FFBP network whose ‘r’ value is 0.9. It was found that for time series prediction NARX network outperform FFBP network not only in terms of accuracy but also in terms of time required for computation.

REGIONAL AND SECTORAL DISAGGREGATION OF MULTI-REGIONAL INPUT–OUTPUT TABLES – A FLEXIBLE ALGORITHM

A common shortcoming of available multi-regional input–output (MRIO) data sets is their lack of regional and sectoral detail required for many research questions (e.g. in the field of disaster impact analysis). We present a simple algorithm to refine MRIO tables regionally and/or sectorally. By the use of proxy data, each MRIO flow in question is disaggregated into the corresponding sub-flows. This downscaling procedure is complemented by an adjustment rule ensuring that the sub-flows match the superordinate flow in sum. The approximation improves along several iteration steps. The algorithm unfolds its strength through the flexible combination of multiple, possibly incomplete proxy data sources. It is also flexible in a sense that any target sector and region resolution can be chosen. As an exemplary case we apply the algorithm to a regional and sectoral refinement of the Eora MRIO database.

DEVELOPING A MULTI-SCALE MULTI-REGION INPUT–OUTPUT MODEL

Many efforts have recently been devoted to developing global multi-region input–output (GMRIO) models. Unfortunately, the scales of GMRIO models do not allow them to capture the heterogeneity of regions within a single country. Multi-scale models can provide more comprehensive analyses capable of capturing the interdependencies of the global economy while preserving regional differences. The primary objective of this research is to develop methods for integrating multi-region input–output data sets from multiple spatial scales into multi-scale multi-region input–output (MSMRIO) models. These methods result in models that may have unusual features such as non-square trade coefficient matrices and a mix of industry-by-industry and commodity-by-commodity technical coefficients. To demonstrate the feasibility of MSMRIO modelling, a Canada-centric model was developed. This model includes 47 countries and Canada's 13 subnational regions. A MSMRIO model provides a tool to analyse global issues with a more spatially detailed focus.

Adaboost.MRT: Boosting regression for multivariate estimation

Adaboost.RT is a well-known extension of Adaboost to regression problems, which achieves increased accuracy by iterativetraining of weak learners on different subsets of data. At each iteration, the prediction error is compared against a threshold,which is used to increase or decrease the weight of the sample for the next iteration. Adaboost.RT is susceptible to noiseand contains a singularity in its misclassification function, which results in reduced accuracy for output values near zero. Wepropose Adaboost.MRT, which extends Adaboost.RT to multivariate output, addresses the singularity in the misclassificationfunction and reduces noise sensitivity. A singularity-free, variance-scaled misclassification function is proposed that generatesdiversity in the training sets. Adaboost.MRT boosts multivariate regression by assigning each output variable a weight for eachsample in the training data. To avoid fitting to outliers, the sampling weights for the training sets are averaged across all outputvariables. The threshold parameter is extended to accommodate the multivariate output and experiments suggest that for smallamounts of output variables, the threshold can be tuned for each output variable individually. Comparisons on six singlevariateoutput datasets show that the proposed Adaboost.MRT outperforms Adaboost.RT on datasets with values near zero or with largenoise and displays a similar accuracy otherwise. Experiments with three multivariate output datasets show that Adaboost.MRTperforms similar or better than bagging and a simple averaging ensemble.