RT Models Research Articles

Forecasting compressive strength of jute fiber reinforced concrete using ANFIS, ANN, RF and RT models

Forecasting compressive strength of jute fiber reinforced concrete using ANFIS, ANN, RF and RT models

Forecasting compressive strength of concrete with basalt and polypropylene fiber by using ANN, RF and RT models

Forecasting compressive strength of concrete with basalt and polypropylene fiber by using ANN, RF and RT models

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Research on force and position control performance of the tendon sheath system with time-varying parameters and flexible robotic arms.

The tendon-sheath-system (TSS) is an excellent medium for remote power transmission, which is widely used in laparoscopic surgery robots. Since the operation process requires the robot to move continuously, this time-varying characteristic further aggravates the force and position transmission loss caused by the nonlinear friction of TSS, which affects the control accuracy of the surgical robot. A time-varying tendon-sheath transmission model (RT model) is proposed. A feedforward control system is designed to improve tendon-sheath transmission accuracy. Furthermore, a tendon-sheath transmission model with velocity characteristics (RV model) is established. Force, position, and velocity experiments were carried out on the platform of TSS with a robotic arm. The results show that the R-square values of force and position compensation are at least 96.57% and 99.16%. The proposed RT and RV models are effective in compensating for the TSS transmission loss during the operation of the surgical robot.

A Forecasting Approach for IoT-Based Energy and Power Quality Monitoring in Buildings

This paper proposes a novel energy consumption and power quality forecasting technique based on the combination of poly-exponential (PE) and random forest (RF). This technique paves away seamlessly integrating the forecast to the Internet-of-things (IoT)-based monitoring system due to its capability of improving computational efficiency. For the experimental setup, the monitoring system in a building supplied the data for forecasting consisting of daily energy consumption and power quality parameters. The developed algorithm for efficient computational forecasting has already considered the space limitation in online real-time monitoring with a minor effect on the model prediction error (less than 0.3% in our experimental setup). The algorithm can alert the user once an indication of energy waste (more than the determined boundary) or the actual voltage violated the tolerance might occur. An experimental dataset taken from the Bappeda Pontianak building in West Kalimantan, Indonesia, shows that the proposed algorithm required fewer samples than the RF yet could predict the daily energy consumption and voltage inside a building accurately up to 96%. Note to Practitioners—As the energy and power quality monitoring using IoT is applied for buildings nowadays, how to handle the big data and forecast the energy and power for evaluation and strategy in buildings is significant. A standard solution is to apply sophisticated technology with a high computational resource. However, this will increase the cost either in the investment or maintenance. The algorithm in this paper is a new approach for IoT-based monitoring and forecasting of energy consumption and power quality by combining PE and RT models, which is relatively cheap in computational complexity yet does not require much space to store in an online setup. The proposed algorithm first creates the model with the RT algorithm and then tracks the residual generated by this algorithm with the PE model and extended Kalman filter. We then show that this model can predict the daily energy consumption accurately. The algorithm based on this model can be integrated into the monitoring system and advise the user when the current energy consumption has passed the “normal” energy consumption concerning the information from previous days. Experiments on a building located in Pontianak show that this algorithm is applicable and can precisely predict daily energy consumption and power quality with a limited amount of data. In future research, we plan to expand this technique further, such that it is not only for monitoring purposes but also for control.

System Identification–Based Adaptive Real-Time Simulators for Power Electronic Converters—Application to Three-Phase and NPC Inverters

This paper deals with the real-time simulation of power electronic converters. It discusses a new approach for designing embedded real-time simulators (eRTSs) that approximate the static and dynamic behavior of a power converter at the switching scale. The main concept is to approximate the voltage/current experimental characteristics of each switch using dedicated transfer functions obtained after a system identification process. The adaptive feature of such eRTS consists of developing varying and online reconfigurable coefficients transfer functions. The main potential of doing so is the possibility of reconfiguring the model according to the actual electrical/thermal environment where the power converter is used. Then, the latter is subdivided into independent switching cells, represented by dedicated RT models that are fully parallelized. Furthermore, using FPGA devices makes it possible to achieve very low latencies and, consequently, a short simulation time step. Previous work was published in this context, where this approach was deeply described and tested with half-bridge DC–DC, full-bridge DC–AC, and multi-level cascaded H-bridge (five-level and nine-level) power converters. This paper recalls the main basics and, more importantly, discusses additional case studies, namely a three-phase voltage source inverter, a half-bridge NPC (neutral-point clamped) inverter, and a three-phase NPC inverter.

Linear and areal surface roughness assessments for the study of tooth wear in human enamel.

To test 8 models of linear surface roughness assessment in characterizing surface profile description and to correlate these models with equivalent areal parameters over sound human enamel in vitro. Thirty enamel blocks were randomly selected. The roughness data (2D-Rp; Rv; Rz; Rc; Rt; Ra; Rq; Rsk; Rku/3D-Sp; Sv; Sz; Sa; Sq; Ssk; Sku) was obtained in duplicate in a non-contact 3D optical profilometer. The models were composed by 1 single vertical trace (model 1) until 8 traces (model 8 composed by three vertical traces, three horizontal traces, and two diagonal). The addition of linear sampling traces to the enamel blocks did not result in Rp, Rv, Rz, Rc, Rt, Ra, Rq, Rsk, and Rku value changes (low power-from 5 to 72%). Significant Spearman's correlation coefficients were obtained in most correlation analysis (Rp ↔ Sp; Rv ↔ Sv; Rz ↔ Sz; Ra ↔ Sa; Rq ↔ Sq; and Rku ↔ Sku). A single vertical trace in the middle of the sample was representative of the overall enamel surface roughness (Rp, Rv, Rz, Rc, Rt, Ra, Rq, Rsk, and Rku) models. The majority of the assessed models in the correlation evaluation presented significant and positive association. The findings highlight the applicable model for roughness analysis over human enamel recommended for research and in situ trials assessments.

Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques

Fiber-reinforced plastic (FRP) rebars can be the futuristic potential reinforcing material in place of mild steel (MS) rebars which are highly prone to corrosion. However, the bond properties of the FRP rebars are not consistent with those of mild steel rebars. Therefore, determination of bond strength properties of FRP rebars becomes essential. In this study, an investigation was conducted on 222 samples for bond strength data set for FRP rebars using various soft computing techniques such as multilinear regression, random forests, random tree, M5P, bagged-M5P tree, stochastic-M5P, and Gaussian process. Outcomes of accuracy assessment parameters, i.e., CC, MAE, and RMSE, suggest that bagged-M5P tree-based model is outperforming than other developed models CC, MAE, and RMSE whose values are 0.9530, 0.8970, and 1.2531, respectively, for testing stages. On assessing the data and the results, it was found that GP_PUK model is more appropriate than GP_RBF-based model for predicting the bond strength of FRP (MPa). On comparison of the RF and RT models, it was concluded that RF-based model performs better than RT models with CC, MAE, and RMSE values of 0.9427, 0.8674, and 1.3424, respectively, for testing stages. The results of the study also suggest that bagged-M5P model attains higher correlation with lesser RMSE values. Taylor diagram also verifies that bagged-M5P model performs better than other developed models. Sensitivity analysis suggests that bar embedment length to bar diameter (l/d) is the most influencing parameter for the prediction of bond strength of FRP.

Collector efficiency for hydrosol deep-bed filtration of non-Brownian particles at low and finite Reynolds numbers

Aqueous filtration systems with granular media are increasingly implemented as a unit operation for the treatment of urban waters. Many of these aqueous filtration systems are designed with coarse granular media and are therefore subject to finite granular Reynolds numbers (Reg). In contrast to the Reg conditions generated by such designs, current hydrosol filtration models, such as the Yao and RT models, rely on a flow solution that is derived within the Stokes limit at low Reg. In systems that are subject to these finite and higher Reg regimes, the collector efficiency has not been examined. Therefore, in this study, we develop a 3D periodic porosity-compensated face-centered cubic sphere (PCFCC) computational fluid dynamics (CFD) model, with the surface interactions incorporated, to investigate the collector efficiency for Reg ranging from 0.01 to 20. Particle filtration induced by interception and sedimentation is examined for non-Brownian particles ranging from 1 to 100 μm under favorable surface interactions for particle adhesion. The results from the CFD-based PCFCC model agreed well with those of the classical RT and Yao models for Reg < 1. Based on 3150 simulations from the PCFCC model, we developed a new correlation for vertical aqueous filtration based on a modified gravitation number, NG*, for the initial deep-bed filtration efficiency at lower yet finite (0.01 to 20) Reg. The proposed PCFCC model has low computational cost and is extensibile from vertical to horizontal filtration at low and finite Reg.

Comparative studies of helium behavior in copper and tungsten using molecular dynamics simulations

Helium (He) behavior in copper (Cu) and tungsten (W) bulk has been investigated and compared by molecular dynamics simulations. In contrast to the diffusion of He in W, interstitial He and He2 in Cu have higher diffusion coefficients and lower migration energies, which causes He atoms to rapidly coalesce into helium clusters. Once a Hen cluster (n ≥ 3) forms, trap mutation occurs in a short time. W atoms are less likely to be projected by Hen clusters. The binding energies are calculated to prove the results. For He2 in Cu, the dissociation of He2 and trap mutation both occur above 700 K. The total phenomenon exhibits Arrhenius behavior. The effective capture radii (ECRs) of an interstitial He trapping another one in Cu are obtained, which differ slightly from that in W. They decrease as the temperature increases, which deserves more attention, as the ECRs are required in MC and RT models.

Predicting Convergence Rate of Namaklan Twin Tunnels Using Machine Learning Methods

Convergence prediction of tunnels has always been one of the important issues of geotechnical projects. Developing prediction models is a good approach to predict convergence, when there is no knowledge of the possibility of convergence occurrence in the future. In this study, convergence rates of two tunnels from the Namaklan twin tunnel were predicted by different types of machine learning methods. Artificial neural networks (ANNs), multivariate linear regression (MLR), multivariate nonlinear regression (MNR), support vector regression (SVR), Gaussian process regression (GPR), regression trees and ensembles of trees (ET) were applied to predict the convergence rate (CR). The six parameters of cohesion (c), internal friction angle (Φ), uniaxial compressive strength of rock mass (σc), rock mass rating, overburden height (H) and the number of installed rock bolts (NB) were selected as predictor parameters. The dataset was collected via field investigations and laboratory experiments. The results showed that the MLP–ANN model can successfully predict the CR with the determination coefficient (R2) of 0.93. The RBF–ANN model is also successful in predicting the CR with R2 of 0.81. The SVR, MNR and MLR models were also constructed to obtain an empirical formula for predicting the CR. Comparing among the three models showed that the SVR model is more successful (R2 = 0.66) than the MNR and MLR models with R2 of 0.65 and 0.61, respectively. However, the SVR model is placed in the next rank of the ANN models. Among the rest models, except the ET model (R2 = 0.66), the RT and GPR models have no good capability for the prediction of the CR. In total, assessing the statistical indices indicated that the ANNs are superior to the other models in predicting the CR. However, the SVR model could be considered to be a reliable predictive model for convergence rate estimation.

Are model parameters linked to processing stages? An empirical investigation for the ex-Gaussian, ex-Wald, and EZ diffusion models.

In previous research, the parameters of the ex-Gaussian distribution have been subject to a wide variety of interpretations. The present study investigated whether the ex-Gaussian model is capable of distinguishing effects on separate processing stages (i.e., pre-motor vs. motor). In order to do so, we used datasets where the locus of effect was quite clear. Specifically, we analyzed data from experiments comparing hand vs. foot responses-presumably differing in the motor stage-and from experiments in which the lateralized readiness potential was used to localize experimental effects into premotor vs. motor processes. Moreover, we broadened the scope to two other descriptive RT models: the ex-Wald and EZ diffusion models. To the extent possible with each of these models, we reanalyzed the RT data of 19 clearly localized experimental effects from 12 separate experiments reported in seven previously published articles. Unfortunately, we did not find a clear pattern of results for any of the models, with no clear link between effects on one of the model's parameters and effects on different processing stages. The present results suggest that one should resist the temptation to associate specific processing stages with individual parameters of the ex-Gaussian, ex-Wald, and EZ diffusion models.

A vector radiative transfer model for sea-surface salinity retrieval from space: a non-raining case

ABSTRACTSea-surface salinity (SSS) can be measured from space using a microwave sensor. However, achieving the desired accuracy in SSS retrieval is challenging due to the lower sensitivity of the brightness temperature to SSS especially at low sea-surface temperature conditions. The retrieval accuracy can be further degraded due to the atmospheric and sea-surface effects (including emission and reflection), which require more accurate correction methods based on the radiative transfer model. In this article, a vector radiative transfer model (VRTM) was developed based on a matrix operator method that considers the ocean–atmosphere system under non-raining conditions. The results from this model were compared with measurement data provided by the Soil Moisture and Ocean Salinity (SMOS) satellite sensor and the results from two other RT models (RT4 model and a forward model of the European Space Agency, ESA). Statistical evaluation of these results revealed that estimation errors of top of atmosphere (TOA) radiance by the VRTM model was less than 0.3% as compared to the RT4 model results. The difference of the brightness temperatures predicted by the VRTM model and measured by the SMOS was within 1.5 K which was better than the ESA’s forward model predictions. These results suggest that the VRTM is relatively more accurate and has high computational efficiency for simulating the TOA brightness temperature for various scientific research and remote-sensing applications.

Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions

Users of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees - RT and Multiple Linear Regressions - MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km²) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in predictive models in order to develop the explanatory variables. The selection of sampling sites was based on the Latin Hypercube algorithm. A representative set of one hundred points with feasible field access was chosen. Different input databases were tested for prediction of mineral fraction content (harmonized and original data). The Spline algorithm was used to harmonize data according to the GlobalSoil.Net consortium standards. The results showed better performance from the RT models, using input from an average of six covariates; the simplest MLR model used twice as many input variables, creating more complex models without gaining precision. Furthermore, better R² values were obtained using RT models, irrespective of harmonization of soil data. The harmonized dataset from the 0.00-0.05 and 0.05-0.15 m layers, in general, presented better results for the clay and silt, with R2 values of 0.52 (0.00-0.05 m) and 0.69 (0.05-0.15 m), respectively. Prediction of sand content showed better results when the original depth data was used as an input, although all regression tree models had R2 values greater than 0.52. The RT models provided a better statistical index than MLR for all predicted properties; however, the variance between models suggests similarity of performance. Regarding harmonization of soil data, both input databases (harmonized or not) can be used to predict soil properties, since the variance of model performance was low and generalization of the soil maps showed similar trends. The products obtained from the digital soil mapping approach make it possible to integrate the factor of uncertainties, providing easier interpretation for soil management and land use decisions.

A comparative study of libRadtran and RTTOV predicted satellite images using the WRF forecasting output

ABSTRACTThe assimilation of cloudy radiances remains important in improving precipitation and severe weather forecasting. In practice, Numerical Weather Prediction (NWP) Models frequently do not predict meso-scale phenomena, so the phenomenon is either predicted but not realised, or is well predicted but not where it is observed. Radiative Transfer Models such as TIROS-Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOV) and libRadtran are the mathematical operators used in the simulation of satellite data. In the data assimilation process, an effective reproduction of the mesoscale convective phenomenon leads to high quality data analysis. Therefore, we are looking for an operator that reproduces the NWP model’s behaviour in a realistic way. Several cloud parameterisation schemes are available in RTTOV and libRadtran to simulate the satellite cloudy radiances. Each selected scheme may result in different simulated brightness temperature data compared to those observed by satellite. However, the source of errors is still unknown: are they generated by the RT model, are they coming from the predicted fields of the NWP Model used as input, or from both? This study aims to investigate the impact of libRadtran or RTTOV operators on the quality of the predicted satellite image. The same mandatory forecasted fields are used as input for both models and derive from the Weather Research and Forecasting (WRF) Limited-Area Model. In this study, we did not investigate the total capability of RT models, but we have focused on a standard and specific physical parameterisation scheme. As reference data, Meteosat Second Generation (MSG) images have been used to compute the deterministic and probabilistic scores. The results for the deterministic scores show that libRadtran reproduces the cold temperatures predicted by WRF well, but these are sometimes slightly distant from their geographical location. Conversely, for the RTTOV, there is a tendency to miss more cases of good detection of the events predicted by WRF. Probabilistic analyses confirm an improvement in libRadtran scores when the neighbourhood size is increased, and a boxplot analysis of the bootstrap method confirms the stability of scores for both models.

Implementation of Reciprocal Teaching Models to Increase The Capability of Writing Definition and Theorem in Simbolic Form

&lt;p&gt;This study aims to obtain objective information about increasing in writing skill about definitions and theorems in symbolic form students through RT models in the sets, all kind of relations and functions in set and logic courses. In addition, this study develop the ability of understanding of definitions and theorems, because the ability of students to write definitions and theorems in symbolic form will foster the ability of understanding of the definitions and theorems.The sample of this study are the first semester students in class A2 who follow sets and logic course in mathematics education program of PMIPA FKIP Untan 2016-2017. Implementation focus on subject, sets and all kind of relations and functions.The data collection conducted in providing achievement test after being given treatment. Form of test is Essai with 15 questions which consist of 4 questions from subject set, 8 questions from kind of relations and 3 questions from functions.The results showed if the Reciprocal Teaching Model is one of model that could improve the writing skill about definitions and theorems through symbolic form in the subject matter ; Sets, kind of relations, functions in Sets and Logic courses.&lt;/p&gt;

Non-local gravity and comparison with observational datasets. II. Updated results and Bayesian model comparison with ΛCDM

We present a comprehensive and updated comparison with cosmological observations of two non-local modifications of gravity previously introduced by our group, the so called RR and RT models. We implement the background evolution and the cosmological perturbations of the models in a modified Boltzmann code, using CLASS. We then test the non-local models against the Planck 2015 TT, TE, EE and Cosmic Microwave Background (CMB) lensing data, isotropic and anisotropic Baryonic Acoustic Oscillations (BAO) data, JLA supernovae, H0 measurements and growth rate data, and we perform Bayesian parameter estimation. We then compare the RR, RT and ΛCDM models, using the Savage-Dickey method. We find that the RT model and ΛCDM perform equally well, while the performance of the RR model with respect to ΛCDM depends on whether or not we include a prior on H0 based on local measurements.

Educational Data Mining: RT and RF Classification Models for Higher Education Professional Courses

Computer applications and business administrations have gained significant importance in higher education. The type of education, students get in these areas depend on the geo-economical and the social demography. The choice of a institution in these area of higher education dependent on several factors like economic condition of students, geographical area of the institution, quality of educational organizations etc. To have a strategic approach for the development of importing knowledge in this area requires understanding the behavior aspect of these parameters. The scientific understanding of these can be had from obtaining patterns or recognizing the attribute behavior from previous academic years. Further, applying data mining tool to the previous data on the attributes identified will throw better light on the behavioral aspects of the identified patterns. In this paper, an attempt has been made to use of some techniques of education data mining on the dataset of MBA and MCA admission for the academic year 2014- 15. The paper discusses the result obtained by applying RF and RT techniques. The results are analyzed for the knowledge discovery and are presented.

Pengaruh Model Reciprocal Teaching Dipadukan dengan Think Pair Share terhadap Hasil Belajar Mikrobiologi Mahasiswa IKIP Budi Utomo

Location of observation results were obtained preliminary data the value of the results obtained from the study of microbiology student class of 2012 a number of 297 students with a range (86-100) of 20% or 60 students, students with a range of values (70-85) by 23% or 68 students, while the value of students under 70 sebasar 57% or 169. The purpose of this research is as follows: Determine the influence learning model Think Pair Share Reciprocal Teaching combined with the learning outcomes of students. The study design is a quasi-experimental design (Quasi experiments). Research data analysis techniques performed by using analysis of covariance (Anacova). The results showed the average value of the lowest corrected is control by the conventional approach of 63 102 followed by 68. 923 polling stations, amounting to 71. 587 the next Reciprocal Teaching and TPS + RT amounted to 81. 644. Test homogeneity of variance between different groups or homogeneous data. An average of 4 models fell from above that showed significant are: the integration of TPS + RT RT significantly different, real different to the polling stations and significantly different to conventional (control). But for TPS and RT models are not significantly different.

Efficient simulation of diffusion-based choice RT models on CPU and GPU.

In this paper, we present software for the efficient simulation of a broad class of linear and nonlinear diffusion models for choice RT, using either CPU or graphical processing unit (GPU) technology. The software is readily accessible from the popular scripting languages MATLAB and R (both 64-bit). The speed obtained on a single high-end GPU is comparable to that of a small CPU cluster, bringing standard statistical inference of complex diffusion models to the desktop platform.