Modeling Diesel and Hybrid Bus Fuel Consumption with Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model enhancements and Calibration issues

2015 ◽  
Vol 2533 (1) ◽  
pp. 100-108 ◽  
Author(s):  
William Edwardes ◽  
Hesham Rakha
Keyword(s):  
Fuel Consumption ◽  
Field Data ◽  
Mean Squared Error ◽  
Carbon Dioxide Emission ◽  
Calibration Procedure ◽  
Virginia Tech ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Model Parameters ◽  
Consumption Model

The goal of this paper was to develop a calibration procedure and use it to estimate diesel bus fuel consumption and carbon dioxide emission levels. There are few models for estimating those values. Available models require dynamometer data to calibrate model parameters and produce a bang-bang control system (optimum control entails maximum throttle and braking input). The only diesel fuel consumption model that does not suffer from these deficiencies is the Virginia Tech comprehensive power-based fuel consumption model (VT-CPFM). VT-CPFM can be calibrated with publicly available data from the Altoona Bus Research and Testing Center. However, each bus is slightly different because it is built and tuned for the specific transit agency. Consequently, research presented in this paper enhanced the VT-CPFM for modeling diesel buses and developed a procedure for calibrating bus fuel consumption models by using in-field data. All models produced a good fit to the in-field data with a coefficient of determination ( R2) greater than .936, and the sum of the mean squared error for each quarter of a second was less than 0.002. Validation found an average error of 17.55% in total fuel consumed during the validation portion of the test. However, for tests with air-conditioning on, the average error was 10.82%.

Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model development and testing

2011 ◽  
Vol 16 (7) ◽  
pp. 492-503 ◽  
Author(s):  
Hesham A. Rakha ◽  
Kyoungho Ahn ◽  
Kevin Moran ◽  
Bart Saerens ◽  
Eric Van den Bulck
Keyword(s):  
Fuel Consumption ◽  
Model Development ◽  
Virginia Tech ◽  
Consumption Model

scholarly journals Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

2019 ◽  
Vol 4 (2) ◽  
pp. 26 ◽  
Author(s):  
Danial Mohammadzadeh S. ◽  
Seyed-Farzan Kazemi ◽  
Amir Mosavi ◽  
Ehsan Nasseralshariati ◽  
Joseph H. M. Tah
Keyword(s):  
Gene Expression ◽  
Mean Squared Error ◽  
Programming Model ◽  
Gene Expression Programming ◽  
Liquid Limit ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Soil Parameters ◽  
Compression Index ◽  
Fine Grained

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.

scholarly journals Raman Calibration Models for Chemical Species Determination in CO2-Loaded Aqueous MEA Solutions Using PLS and ANN Techniques

2021 ◽  
Vol 5 (4) ◽  
pp. 87
Author(s):  
Ahmad Syukri Hanafiah ◽  
Abdulhalim Shah Maulud ◽  
Muhammad Zubair Shahid ◽  
Humbul Suleman ◽  
Azizul Buang
Keyword(s):  
Carbon Capture ◽  
Mean Squared Error ◽  
Chemical Species ◽  
Operating Conditions ◽  
In Situ Monitoring ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Species Determination ◽  
Species Concentration ◽  
Calibration Models

The improvement in energy efficiency is recognized as one of the significant parameters for achieving our net-zero emissions target by 2050. One exciting area for development is conventional carbon capture technologies. Current amine absorption-based systems for carbon capture operate at suboptimal conditions resulting in an efficiency loss, causing a high operational expenditure. Knowledge of qualitative and quantitative speciation of CO2-loaded alkanolamine systems and their interactions can improve the equipment design and define optimal operating conditions. This work investigates the potential of Raman spectroscopy as an in situ monitoring tool for determining chemical species concentration in the CO2-loaded aqueous monoethanolamine (MEA) solutions. Experimental information on chemical speciation and vapour-liquid equilibrium was collected at a range of process parameters. Then, partial least squares (PLS) regression and an artificial neural network (ANN) were applied separately to develop two Raman species calibration models where the Kent–Eisenberg model correlated the species concentrations. The data were paired and randomly distributed into calibration and test datasets. A quantitative analysis based on the coefficient of determination (R2) and root mean squared error (RMSE) was performed to select the optimal model parameters for the PLS and ANN approach. The R2 values of above 0.90 are observed for both cases indicating that both regression techniques can satisfactorily predict species concentration. ANN models are slightly more accurate than PLS. However, PLS (being a white box model) allows the analysis of spectral variables using a weight plot.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals A Raman algorithm to estimate human age from protein structural variations in autopsy skin samples: a protein biological clock

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daisuke Miyamori ◽  
Takeshi Uemura ◽  
Wenliang Zhu ◽  
Kei Fujikawa ◽  
Takaaki Nakaya ◽  
...  
Keyword(s):  
Protein Folding ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
Biological Clock ◽  
Estimation Method ◽  
Forensic Identification ◽  
Random Coil ◽  
Coefficient Of Determination ◽  
Skin Samples

AbstractThe recent increase of the number of unidentified cadavers has become a serious problem throughout the world. As a simple and objective method for age estimation, we attempted to utilize Raman spectrometry for forensic identification. Raman spectroscopy is an optical-based vibrational spectroscopic technique that provides detailed information regarding a sample’s molecular composition and structures. Building upon our previous proof-of-concept study, we measured the Raman spectra of abdominal skin samples from 132 autopsy cases and the protein-folding intensity ratio, RPF, defined as the ratio between the Raman signals from a random coil an α-helix. There was a strong negative correlation between age and RPF with a Pearson correlation coefficient of r = 0.878. Four models, based on linear (RPF), squared (RPF2), sex, and RPF by sex interaction terms, were examined. The results of cross validation suggested that the second model including linear and squared terms was the best model with the lowest root mean squared error (11.3 years of age) and the highest coefficient of determination (0.743). Our results indicate that the there was a high correlation between the age and RPF and the Raman biological clock of protein folding can be used as a simple and objective forensic age estimation method for unidentified cadavers.

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