Set Of Input Variables Research Articles

Modeling of local sea level rise and its future projection under climate change using regional information through EOF analysis

Sea level rise is one of the manifestations of climate change and may cause a threat to the coastal regions. Estimates from global circulation models (GCMs) are either not available on coastal locations due to their coarse spatial resolution or not reliable since the mismatch between (interpolated) GCM estimates at coastal locations and actual observation over historical period is significantly different. We propose a semi-empirical framework to model the local sea level rise (SLR) using the possibly existing relationship between local SLR and regional atmospheric/oceanic variables. Selection of set of input variables mostly based on the literature bears the signature of both atmospheric and oceanic variables that possibly have an effect on SLR. The proposed approach offers a method to extract the combined information hidden in the regional fields of atmospheric/oceanic variables for a specific target coastal location. Generality of the approach ensures the inclusion of more variables in the set of inputs depending on the geographical location of any coastal station. For demonstration, 14 coastal locations along the Indian coast and islands are considered and a set of regional atmospheric and oceanic variables are considered. After development and validation of the model at each coastal location with the historical data, the model is further used for future projection of local SLR up to the year 2100 for three different future emission scenarios represented by representative concentration pathways (RCPs)—RCP2.6, RCP4.5, and RCP8.5. The maximum projected SLR is found to vary from 260.65 to 393.16 mm (RCP8.5) by the end of 2100 among the locations considered. Outcome of the proposed approach is expected to be useful in regional coastal management and in developing mitigation strategies in a changing climate.

A modified importance sampling method for structural reliability and its global reliability sensitivity analysis

The importance sampling method is an extensively used numerical simulation method in reliability analysis. In this paper, a modification to the importance sampling method (ISM) is proposed, and the modified ISM divides the sample set of input variables into different subsets based on the contributive weight of the importance sample defined in this paper and the maximum super-sphere denoted by β-sphere in the safe domain defined by the truncated ISM. By this proposed modification, only samples with large contributive weight and locating outside of the β-sphere need to call the limit state function. This amelioration remarkably reduces the number of limit state function evaluations required in the simulation procedure, and it doesn’t sacrifice the precision of the results by controlling the level of relative error. Based on this modified ISM and the space-partition idea in variance-based sensitivity analysis, the global reliability sensitivity indices can be estimated as byproducts, which is especially useful for reliability-based design optimization. This process of estimating the global reliability sensitivity indices only needs the sample points used in reliability analysis and is independent of the dimensionality of input variables. A roof truss structure and a composite cantilever beam structure are analyzed by the modified ISM. The results demonstrate the efficiency, accuracy, and robustness of the proposed method.

Energy Management of Plug-in Hybrid Electric Vehicle Based on Fuzzy Logic Control

In order to optimize the energy distribution of plug-in hybrid electric vehicle (PHEV) and improve the fuel economy and discharge performance of PHEV, a double fuzzy controller composed of two sets of input variables which are accelerator opening and change rate, torque requirement and state of charge (SOC) is designed. Particle swarm optimization algorithm is used to optimize the fuzzy control strategy to obtain the global optimal. The simulation results show that the energy management strategy with optimized fuzzy logic control can effectively reduce the fuel consumption and exhaust emissions.

HYDROLOGICAL TIME SERIES FORECASTING USING ANFIS MODELS WITH AID OF WAVELET TRANSFORM

The precise and accurate models of hydrological time series that are embedded with high complexity, non-stationarity, and non-linearity in both spatial and temporal scales can provide important information for decision-making in water resources management and environmental related issues. Hybrid wavelet transform (WT) and adaptive neuro-fuzzy inference system (ANFIS) has been used in this study to improve the forecasting capability of ANFIS model by decomposing the time series into sub-time series (approximation and details) using wavelet transform then combining the effective and significant time lags of sub-time series to form a set of input variables. The present study attempts to add the effective and significant time lags of original time series as extra variables to the input variables set. In addition, different combinations of variables, 1-3, from the set of input variables as inputs to the ANFIS model were used to forecast the time series. To examine the potential of the approach for practical applications, the model is applied to forecast, one step-ahead, the monthly data of hydrological time series (rainfall, evaporation, minimum and maximum temperature, average wind speed and reservoir inflow) for Kirkuk, Sulaimani, Dokan and Darbandikhan meteorological stations in Iraq. The best fit models were selected using the coefficient of determination ( ) and root mean square error ( ). Based on the results, the proposed model has high performance in forecasting the monthly minimum and maximum temperature, evaporation and reservoir inflow with values ranged from 0.93 to 0.99 and relatively good performances in forecasting the monthly rainfall and average wind speed with values ranged from 0.77 to 0.93.

Development of an inverse simulation method for the analysis of train performance

Conventional methods of computer-based simulation allow prediction of output variables, often as a function of time, for a given model of a physical system for a given set of initial conditions and input variables. In the case of train performance simulation models, the possible output variables include train speed or distance travelled, both expressed as functions of time. The corresponding input variables, also expressed as functions of time, are the tractive force or power levels for given train characteristics and route information such as gradients, track curvature and speed restrictions. Inverse simulation methods, on the other hand, allow selected model variables (such as the tractive force at any time instant) to be found from other specified model variables applied as input (such as the train speed or distance travelled versus time) for a given set of route conditions and train characteristics. The specific inverse simulation method presented in the paper is based on feedback principles. Illustrative results are used to verify this inverse simulation approach for train performance applications, and further cases are used to show that the inverse formulation provides an insight that is different from that obtained using more conventional forward simulation techniques.

Exploring relations between EMG and biomechanical data recorded during a golf swing

A novel technique to explore relations between EMG and biomechanical data is presented.Statistically significant relations between EMG and biomechanical data were revealed.High prediction accuracy of peak angular velocity of thorax and pelvis was obtained.Insights concerning coordination of human body parts during a golf swing were obtained. Exploring relations between patterns of peak rotational speed of thorax, pelvis and arm, and patterns of EMG signals recorded from eight muscle regions of forearms and shoulders during the golf swing is the main objective of this article. The linear canonical correlation analysis, allowing studying relations between sets of variables, was the main technique applied. To get deeper insights, linear and nonlinear random forests-based prediction models relating a single output variable, e.g. a thorax peak rotational speed, with a set of input variables, e.g. an average intensity of EMG signals were used.The experimental investigations using data from 16 golfers revealed statistically significant relations between sets of input and output variables. A strong direct linear relation was observed between linear combinations of EMG averages and peak rotational speeds. The coefficient of determination values R2=0.958 and R2=0.943 obtained on unseen data by the random forest models designed to predict peak rotational speed of thorax and pelvis, indicate high modelling accuracy. However, predictions of peak rotational speed of arm were less accurate. This was expected, since peak rotational speed of arm played a minor role in the linear combination of peak speeds. The most important muscles to predict peak rotational speed of the body parts were identified. The investigations have shown that the canonical correlation analysis is a promising tool for studying relations between sets of biomechanical and EMG data. Better understanding of these relations will lead to guidelines concerning muscle engagement and coordination of thorax, pelvis and arms during a golf swing and will help golf coaches in providing substantiated advices.

Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs’ development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5–45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673–0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356–0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed that Ks exclusion from input variables list caused around 30 percent decrease in PTFs accuracy for all applied procedures. However, it seems that Ks exclusion resulted in more practical PTFs especially in the case of GMDH network applying input variables which are less time consuming than Ks. In general, it is concluded that GMDH provides more accurate and reliable estimates of cumulative infiltration (a non-readily available characteristic of soil) with a minimum set of input variables (2–4 input variables) and can be promising strategy to model soil infiltration combining the advantages of ANN and MLR methodologies.

Efficient probabilistic algorithm for estimating the algebraic properties of Boolean functions for large n

Although several methods for estimating the resistance of a random Boolean function against (fast) algebraic attacks were proposed, these methods are usually infeasible in practice for relatively large number of input variables n (for instance n ≥ 30) due to increased computational complexity. An efficient estimation of the resistance of Boolean functions, with relatively large number of inputs n, against (fast) algebraic attacks appears to be a rather difficult task. In this paper, the concept of partial linear relations decomposition is introduced, which decomposes any given nonlinear Boolean function into many linear (affine) subfunctions by using the disjoint sets of input variables. Based on this result, a general probabilistic decomposition algorithm for nonlinear Boolean functions is presented which gives a new framework for estimating the resistance of Boolean function against (fast) algebraic attacks. It is shown that our new probabilistic method gives very tight estimates (lower and upper bound) and it only requires about O(n22n) operations for a random Boolean function with n variables, thus having much less time complexity than previously known algorithms.

Application-Specific Computational Materials Design via Multiscale Modeling and the Inductive Design Exploration Method (IDEM).

The development of materials is a laborious, iterative, expensive, and intuitive process, often requiring decades to transition from early laboratory studies to commercial applications. This research seeks to address this issue by demonstrating a systematic process for linking process-structure-property-performance (PSPP) relations. We argue that the limitations on time for the material development process arise in large part due to lack of effective approaches for exploring the material design space that anticipates application requirements, objectives, and constraints. The material design process employed here utilizes hierarchical multiscale modeling, analytical models, and associated metamodels to construct a set of bottom-up deductive mappings, along with the inductive design exploration method (IDEM) to account for uncertainty in pursuing top-down inductive decision support problems that address application-specific design objectives. The demonstrated problem considers the simultaneous design of ultra-high-performance concrete material and a structural panel able to withstand a 1.5-MPa-ms reflected blast wave impulse. A set of PSPP mappings were constructed across micro-, meso-, and macro-length-scales using analytical expressions and a hierarchical multiscale finite element model at the single fiber, multiple fiber, and structural length scales. The set of PSPP deductive mappings considered seven design variables-panel thickness, fiber pitch, ratio of water to cementitious materials, curing temperature, and volume fractions of fibers, cement, and silica fume-across four hierarchical levels. After the set of deductive PSPP mappings were constructed and validated, ranged sets of feasible values for each design variable were determined via IDEM. Starting with the highest and next-to-higher hierarchical levels as the output and input spaces, respectively, IDEM was implemented via application of three steps-discretization of input variables, projection of discretized sets of input variables with account of uncertainty to a range in the output space, and determination of which sets of discrete input values satisfy the output space requirement(s). By recursively applying these three steps, the PSPP relations were robustly searched for properties, structures, and processes that satisfy the performance requirement(s). The advantages of this approach are the identification of ranged sets of values of design variables and the ability to account for propagated uncertainty. By defining additional mass and cost objectives, the feasible input space was then searched to find the preferred combination of values of design variables that minimized mass and minimized cost while maintaining a robust material and structural design.

Systematic Optimization of Boron Diffusion for Solar Cell Emitters

To achieve p–n junctions for n-type solar cells, we have studied BBr3 diffusion in an open tube furnace, varying parameters of the BBr3 diffusion process such as temperature, gas flows, and duration of individual process steps, i.e., predeposition and drive-in. Then, output parameters such as carrier lifetime, sheet resistance, and diffusion profile were measured and statistically analyzed to optimize the emitter characteristics. Statistical analysis (factorial design) was finally employed to systematically explore the effects of the set of input variables on the outputs. The effect of the interactions between inputs was also evaluated for each output, quantified using a two-level factorial method. Temperature and BBr3 flow were found to have the most significant effect on different outputs such as carrier lifetime, junction depth, sheet resistance, and final surface concentration.

Development and validation of a continuously age-adjusted measure of patient condition for hospitalized children using the electronic medical record

Awareness of a patient’s clinical status during hospitalization is a primary responsibility for hospital providers. One tool to assess status is the Rothman Index (RI), a validated measure of patient condition for adults, based on empirically derived relationships between 1-year post-discharge mortality and each of 26 clinical measurements available in the electronic medical record. However, such an approach cannot be used for pediatrics, where the relationships between risk and clinical variables are distinct functions of patient age, and sufficient 1-year mortality data for each age group simply do not exist. We report the development and validation of a new methodology to use adult mortality data to generate continuously age-adjusted acuity scores for pediatrics.Clinical data were extracted from EMRs at three pediatric hospitals covering 105,470 inpatient visits over a 3-year period.The RI input variable set was used as a starting point for the development of the pediatric Rothman Index (pRI). Age-dependence of continuous variables was determined by plotting mean values versus age. For variables determined to be age-dependent, polynomial functions of mean value and mean standard deviation versus age were constructed. Mean values and standard deviations for adult RI excess risk curves were separately estimated. Based on the “find the center of the channel” hypothesis, univariate pediatric risk was then computed by applying a z-score transform to adult mean and standard deviation values based on polynomial pediatric mean and standard deviation functions. Multivariate pediatric risk is estimated as the sum of univariate risk. Other age adjustments for categorical variables were also employed.Age-specific pediatric excess risk functions were compared to age-specific expert-derived functions and to in-hospital mortality. AUC for 24-h mortality and pRI scores prior to unplanned ICU transfers were computed. Age-adjusted risk functions correlated well with similar functions in Bedside PEWS and PAWS. Pediatric nursing data correlated well with risk as measured by mortality odds ratios. AUC for pRI for 24-h mortality was 0.93 (0.92, 0.94), 0.93 (0.93, 0.93) and 0.95 (0.95, 0.95) at the three pediatric hospitals. Unplanned ICU transfers correlated with lower pRI scores. Moreover, pRI scores declined prior to such events.A new methodology to continuously age-adjust patient acuity provides a tool to facilitate timely identification of physiologic deterioration in hospitalized children.

Airfoil Design Under Uncertainty Using Non-Intrusive Polynomial Chaos Theory and Utility Functions

Fast and accurate airfoil design under uncertainty using non-intrusive polynomial chaos (NIPC) expansions and utility functions is proposed. The NIPC expansions provide a means to efficiently and accurately compute statistical information for a given set of input variables with associated probability distribution. Utility functions provide a way to rigorously formulate the design problem. In this work, these two methods are integrated for the design of airfoil shapes under uncertainty. The proposed approach is illustrated on a numerical example of lift-constrained airfoil drag minimization in transonic viscous flow using the Mach number as an uncertain variable. The results show that compared with the standard problem formulation the proposed approach yields more robust designs. In other words, the designs obtained by the proposed approach are less sensitive to variations in the uncertain variables than those obtained with the standard problem formulation.

RSM Method in Probabilistic Analysis of the Foundation Plate

Many new approaches are currently used in the design and assessment of building structures. One of these approaches is the probabilistic analysis allowed by the European standards for the assessment of structural design. Input parameters are given as stochastic ones and they are characterized by different distribution types. During a probabilistic analysis, software executes multiple analysis loops to compute the random output parameters as functions of the set of random input variables. Uncertainties in input variables (load, material properties and dimensions structures) are reflected in the RSM method based on approximation of Monte Carlo simulations. Finally, some results of these probabilistic analyses are presented.

Applying the General Regression Neural Network to Ground Motion Prediction Equations of Induced Events in the Legnica-Głogów Copper District in Poland

This paper presents a study of the nonlinear estimation of the ground motion prediction equation (GMPE) using neural networks. The general regression neural network (GRNN) was chosen for its high learning rate. A separate GRNN was tested as well as a GRNN in cascade connection with linear regression (LR). Measurements of induced seis-micity in the Legnica-Glogow Copper District were used in this study. Various sets of input variables were tested. The basic variables used in every case were seismic energy and epicentral distance, while the additional variables were the location of the epicenter, the location of the seismic station, and the direction towards the epicenter. The GRNN improves the GMPE. The best results were obtained when the epicenter location was used as an additional input. The GRNN model was analysed for how it can improve the GMPE with respect to LR. The bootstrap resampling method was used for this purpose. It proved the statistical significance of the improvement of the GMPE. Additionally, this method allows the determination of smoothness parameters for the GRNN. Parameters derived through this method have better generalisation capabilities than the smoothness parameters estimated using the holdout method.

Plug-In Hybrid Electric Bus Energy Management Based on Dynamic Programming

Abstract An energy management strategy based on dynamic programming for a plug-in serial-parallel hybrid electric bus is proposed. The sets of comprehensive cost function, five control variables and mesh accuracies are illustrated. Then the fuel economy is discussed, followed by the sensitivity analysis of this strategy on different lengths of driving cycle input and dissimilar control variable settings. It can be concluded that scaling the length of driving cycle and battery capacity have little effect on control laws output while optimum control variable settings should combine exact characters of driving cycles and driving components of the bus.

A Fuzzy Logic Approach in the Definition of Risk Acceptance Boundaries in Occupational Safety and Health

Organizations need to make decisions about risk acceptance, to decide about the need of risk-reducing measures. In this process, the personal judgments of occupational safety and health (OSH) practitioners have great importance. If on one hand, they have the technical knowledge about risk; on the other hand, the decisions can be dependent on their level of risk acceptance. This paper analyzes judgments of OSH practitioners about the level of risk acceptance, using the fuzzy logic approach. A questionnaire to analyze the reported level of risk acceptance was applied. The questionnaire included 79 risk scenarios, each accounting for the frequency of an accident with more lost workdays than a given magnitude. Through the two-step cluster analysis, three groups of OSH practitioners were identified: unacceptable, tolerable, and realistic groups. A further analysis of the realistic group judgments about risk was performed, using the fuzzy logic approach. The fuzzy sets of input and output variables were determined, and the relationship between the variables was mapped through fuzzy rules. After that, the min–max fuzzy inference method was used. The obtained results show that the risk level is acceptable when input variables are at the lowest value and unacceptable when the risk level is high. The obtained results allow us to better understand the modeling of OSH practitioners’ judgments about risk acceptance, noting the uncertainty related to these judgments.

Regression shrinkage and neural models in predicting the results of 400-metres hurdles races.

This study presents the application of regression shrinkage and artificial neural networks in predicting the results of 400-metres hurdles races. The regression models predict the results for suggested training loads in the selected three-month training period. The material of the research was based on training data of 21 Polish hurdlers from the Polish National Athletics Team Association. The athletes were characterized by a high level of performance. To assess the predictive ability of the constructed models a method of leave-one-out cross-validation was used. The analysis showed that the method generating the smallest prediction error was the LASSO regression extended by quadratic terms. The optimal model generated the prediction error of 0.59 s. Otherwise the optimal set of input variables (by reducing 8 of the 27 predictors) was defined. The results obtained justify the use of regression shrinkage in predicting sports outcomes. The resulting model can be used as a tool to assist the coach in planning training loads in a selected training period.

Decision tree modeling using R.

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

Correlation analysis of the natural radionuclides in soil and indoor radon in Vojvodina, Province of Serbia

The most dominant source of indoor radon is the underlying soil, so the enhanced levels of radon are usually expected in mountain regions and geology units with high radium and uranium content in surface soils. Laboratory for radioactivity and dose measurement, Faculty of Sciences, University of Novi Sad has rich databases of natural radionuclides concentrations in Vojvodina soil and also of indoor radon concentrations for the region of Vojvodina, Northern Province of Serbia. In this paper we present the results of correlative and multivariate analysis of these results and soil characteristics in order to estimate the geogenic radon potential. The correlative and multivariate analysis were done using Toolkit for Multivariate Analysis software package TMVA package, within ROOT analysis framework, which uses several comparable multivariate methods for our analysis. The evaluation ranking results based on the best signal efficiency and purity, show that the Boosted Decision Trees (BDT) and Multi Layer Preceptor (MLP), based on Artificial Neural Network (ANN), are multivariate methods which give the best results in the analysis. The BDTG multivariate method shows that variables with the highest importance are radio-nuclides activity on 30 cm depth. Moreover, the multivariate regression methods give a good approximation of indoor radon activity using full set of input variables. On several locations in the city of Novi Sad the results of indoor radon concentrations, radon emanation from soil, gamma spectrometry measurements of underlying soil and geology characteristics of soil were analyzed in detail in order to verify previously obtained correlations for Vojvodina soil.

Dual-monitoring scheme for multivariate autocorrelated cascade processes with EWMA and MEWMA charts

This paper presents a dual monitoring scheme for multivariate autocorrelated cascade process control using principal components regressions. The autoregressive time series model is imposed on the time-correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and its process input variables under the autoregressive regression error model. A dual monitoring scheme consisting of residual-based EWMA control chart, applied to product characteristics, and the MEWMA chart, applied to the multivariate cascade process characteristics, is proposed. EWMA control chart is applied to increase the detection performance, especially to small mean shifts. The MEWMA is applied to a selected set of input variables from the first principal component to increase sensitivity to detecting process failures. The proposed dual scheme for product and process characteristics enhances both the detection and prediction performance of the monitoring system of multivariate autocorrelated cascade processes. The proposed dual monitoring scheme outperforms the conventional residual type control chart applied to the residuals of the principal component regression alone. Implementation of the proposed methodology is demonstrated through an example from a sugar beet pulp drying process.