The research presented in this thesis was focused on the use of PMS2 data for the calibration of flexible pavement performance models coefficients for Ontario as a case study Performance model coefficients were created for application with the Mechanistic-Empirical Pavement Design Guide (MEPDG), now known as AASHTOWare®, and were calibrated using statistical tools through a series of analyses of historical pavement condition data that were collected in the field. The data were classified according to pavement type and annual average daily traffic. For this study, three categories were examined and calibrated: low traffic volume (AADT < 10,000), high traffic volume (AADT > 10,000), and overall network. The spilt in data was Eighty-five percent to be used in calibration development of the performance model calibration coefficients and the remaining fifteen percent of the data were employed for validating the performance models using a variety of statistical tools. A comparison of the results with the field measurements revealed that rutting model coefficients should be locally calibrated for each category. For the low-volume, high-volume, and overall network categories, localcalibration produced significant reductions in the rutting root-mean-square error (RMSE) of 30, 37, and 37 %, respectively, and in the IRI showed there was no significant correlation.
The data collection process was the major hurdle during the research with the amount of data, number of data sources (different units of NCDOT, i.e., Traffic unit, Pavement Management unit, Geotechnical Engineering unit, Pavement Construction unit), and the time constraints involved. Also the quality of the data plays a major role in the calibration process and hence more efforts and time were expended in obtaining the data from the NCDOT and LTPP database. LTPP database (FHWA, 2007) provided complete data set for 30 flexible pavement sections (16 new and 14 rehabilitated sections) whereas NCDOT provided the data for 23 flexible pavement sections to be used in this localcalibration and validation process. It was noted that there are significant differences in the data collection methods by LTPP and the NCDOT. For example, the pavement performance data collection methodology for the alligator cracking distress; LTPP directly measures (FHWA, 2003) the area cracked under the distress whereas NCDOT measures (NCDOT, 2006) the length of the crack with the appropriate severity rating. Hence NCDOT monitored performance data is converted into the MEPDG format before even the calibration is begun.
For those agencies who want to utilize local PMS database to calibrate MEPDG, comparison between data in LTPP and in PMS should be conducted to see if differences exist. Kang (2007) prepared a regional pavement performance database for a Midwest implementation of the MEPDG from Michigan, Ohio, Iowa and Wisconsin State transportation agencies. They suggested a data cleaning process be conducted before applied to MEPDG calibration. They also found that the default national calibration values do not predict the distresses observed in the Midwest. Mamlouk and Zapata (2010) found the differences between the Arizona Department of Transportation (ADOT) PMS data and the LTPP database used in the original development and national calibration of the MEPDG distress models including rut measurements, asphalt cracking, IRI, and all layer backcalculated moduli found from NDT measurements done by ADOT and those of the LTPP.
Abstract. This work explores the potential of the distributed GEM-Hydro runoff modeling platform, developed at Envi- ronment and Climate Change Canada (ECCC) over the last decade. More precisely, the aim is to develop a robust imple- mentation methodology to perform reliable streamflow sim- ulations with a distributed model over large and partly un- gauged basins, in an efficient manner. The latest version of GEM-Hydro combines the SVS (Soil, Vegetation and Snow) land-surface scheme and the WATROUTE routing scheme. SVS has never been evaluated from a hydrological point of view, which is done here for all major rivers flowing into Lake Ontario. Two established hydrological models are con- fronted to GEM-Hydro, namely MESH and WATFLOOD, which share the same routing scheme (WATROUTE) but rely on different land-surface schemes. All models are calibrated using the same meteorological forcings, objective function, calibration algorithm, and basin delineation. GEM-Hydro is shown to be competitive with MESH and WATFLOOD: the NSE √ (Nash–Sutcliffe criterion computed on the square root of the flows) is for example equal to 0.83 for MESH and GEM-Hydro in validation on the Moira River basin, and to 0.68 for WATFLOOD. A computationally efficient strategy is proposed to calibrate SVS: a simple unit hydro- graph is used for routing instead of WATROUTE. Global and localcalibration strategies are compared in order to esti- mate runoff for ungauged portions of the Lake Ontario basin.
The effectiveness of highly localcalibration is likely due to some combination of: (1) restricting the range of ecological variability, thus simplifying the prediction task; and (2) ensuring that when models revert to prediction of the mean of the training data they at least revert to a more locally representative mean. This experiment randomly drew 100 neighbors to create separate pixel-level rh98 models at each scale of calibration. This ensured comparability across scales, but would not be operationally efficient for creating a global map. Extension to mapping applications would likely involve using a local tiling system, where all constituent clear footprints were used to train a tile-level model. The results presented here suggest that tiling should be as local as possible. Operational mapping may also include imagery from other sensors or stationary predictors related to topography or ownership class. Texture metrics applied to the Landsat-like Copernicus Sentinel-2 platform have also proven effective in training multispectral imagery from LiDAR returns .
The method for FDC calibration developed by Westerberg et al. (2011b) was here tested for a wider range of basins and resulted in a high reliability in the localcalibration in basins where the data screening indicated that the data had good quality. An assessment of the performance for differ- ent hydrograph aspects and of different ways of choosing the EPs on the FDCs, as in the previous study, was not made here but would be useful to assess the performance of the FDC calibration for the wider range of hydrological condi- tions in this study. It could be seen that in arid basins the dis- charge was often more constrained in recession periods com- pared to in humid basins (which could be a result of the more non-linear FDC shape), indicating that recession information (e.g. Winsemius et al., 2009; McMillan et al., 2013) might be useful to further constrain the uncertainty bounds in the lat- ter case. Further conclusions on the strengths and weaknesses of the FDC calibration for this wider range of basins could also be drawn through the use of different model structures, e.g. different conceptualisations of groundwater storage and runoff generation in groundwater-dominated basins. The par- simonious model structure used here might be overly simple in many cases even if it showed good results previously at Paso La Ceiba (Westerberg et al., 2011b). Compared to those results, the average reliability was lower here (86 %, com- pared to 95 % previously), with the main difference between the simulations being the precipitation data. The CRN073 precipitation used here had a correlation of only 0.77 with the locally interpolated precipitation in that study. It might also be possible to estimate the prior parameter ranges based on catchment and climate characteristics, however such an analysis was outside the scope of this paper and would also be affected by disinformation in the regionalisation data.
The two main features missing from the Black-Scholes model are non-Gaussianity and time-varying volatility of the log re- turns. To realize the non-Gaussianity, various asset price mod- els have been proposed. Among such well-known models are the Heston model  and the SABR model , both of which are of stochastic volatility type. With those models, the plain vanilla premium or the implied volatility are given in nearly closed form. Those explicit formulas prove quite useful in calibration to market quotes. Other successful candidates are models involving jumps, in particular L´evy processes, such as the variance gamma model of Madan and Seneta , the NIG model of Barndorff-Nielsen , the Meixner model of Schoutens and Teugels , and the CGMY model of Carr et al. . All those models have attracted much attention among market practitioners for the main purpose to interpolate or pre- dict prices of contracts of the same type as the calibration in- struments.
3. If the calibration is being performed by a metrology laboratory or another facility that maintains traceable standards, call the Copy_Cal function in the ni6704Cal . dll . Calling this function copies the calibration values to the factory calibration area and updates the calibration date to match the system clock of the calibration computer. The board is now calibrated. You can verify the analog input and output operation by repeating the Verifying Your Board’s Operation section.
The calibration system of cylindrical sensor of radial force is composed of eccentric axial flow fan, sensor unit, speed measuring unit and data recovery unit. The sensor unit is used in data acquisition and storage. Speed measurement of axial flow fan is completed by the hall sensor and oscilloscope. The data recovery unit is that reading out of data stored in the flash module through the serial port, and it is conveyed to the computer analysis. The calibration system of cylindrical sensor of radial force is shown in Figure 2.
• After the laser or CCD camera has been realigned/replaced by a service engineer • If you see a decrease in spectral separation (pull-up and/or pull-down peaks) • If you alter any condition (dye set, array type, array length, or polymer type) Note: Life Technologies recommends that you run a spectral calibration each time a new capillary array is installed. In the 3730 Series Data Collection Software, if you install an array that is the same length as the previously installed array, the active spectral calibration still persists. For optimal data quality, perform a new spectral calibration before you perform regular runs.
To create a better benchmarking comparison than with just the initial parameter estimation (see Chapter 4), we also execute random search. Since it is commonly known that random search is a poor-performing auto- matic search algorithm (Schrijver, 2003), it functions as a lower bound for algorithm convergence. If any of the other calibration algorithms achieves worse results than random search, we know that an error must have happened in our coding. Next to the benchmark algorithm, we test LHS and SA alone as well, so that we can get an indication whether the combination LHS/SA performs better in comparison to their single usage. We conducted a couple of initial test scenario runs and realized that many of the distribution settings require at least 10 consecutive replications, i.e., 10 working days to count as one valid simulation run. We determined this through the application of Formula (6), p. 41. Since these replications are time-consuming to run, we de- cide to execute our experiments in a two-leveled way. We evaluate all four calibration schemes with 1500 runs for each statistical test distribution (see Table 6) and later, we repeat the same with 300 runs. We do this to assess the influence of the run-length regarding the convergence speed and quality of a calibration scheme. The reason we choose 1500 simulation runs is that this number of runs is still conductible in an acceptable amount of time. Since 300 runs are significantly less than 1500, we should be able to see any changing behav- ioral algorithm pattern due to the simulation runs length (i.e., allowed iterations). We conduct 3 replications of these 1500/300 run lengths to approximate algorithm convergence with an averaged regression formula. Since already one replication, of e.g., 1500 runs, is rather long, we chose to conduct only these three replications. Of course, since this is a regression approximation, randomness is incurred. The more replications are conducted the better the quality of the approximation finally is. However, computational time is a restricting issue in our research. Thus, in total, we execute simulation runs at 2.5 seconds per run. This adds up to an uninterrupted computation time of about 25 days.
To keep balance between calibration accuracy and time complexity, an improved calibration method is proposed to decrease the complexity of the calibration procedure by simplifying the extrinsic calibration of the structured light system. A white plate with a matrix of hollow black ring markers is used to calibrate the dual-camera structured light system. The system calibration process can be di- vided into three steps. 1) Calibrating the right camera and the structured light system with the left camera by estab- lishing corresponding point pairs between projector pixel coordinate and left camera pixel coordinate of discrete markers on a plate surface. The corresponding projector pixel coordinate of each marker is determined by measur- ing the absolute phase from projected vertical and hori- zontal sinusoidal fringe patterns on the plate surface. 2) Computing the transformation between left camera and right camera using intrinsic parameters of two cameras, the center of each marker coordinates in two camera im- ages and world coordinates of the center of each marker. 3) Calculating extrinsic parameters of the structured light system with the right camera using the aforementioned obtained parameters. 3D cloud data sets for the two projector-camera pairs obtained by the calibrated system are matched based on the variant ICP (iterative closest point) algorithm . We simplified the system calibra- tion and achieved the high measurement accuracy by using the variant ICP algorithm. The rest of this paper is organized as follows. The principle and details of the proposed calibration method are described in Section “Theories”. Experimental results are presented in Section “ Experiments and Results ”. Section “Discussions” presents the conclusion and remarks about future work.
Abstract. Absolute profiles of atomic oxygen number den- sities with high vertical resolution have been determined in the mesosphere–lower thermosphere (MLT) region from in situ measurements by several rocket-borne solid electrolyte sensors. The amperometric sensors were operated in both controlled and uncontrolled modes and with various orienta- tions on the foredeck and aft deck of the payload. Calibration was based on mass spectrometry in a molecular beam con- taining atomic oxygen produced in a microwave discharge. The sensor signal is proportional to the number flux onto the electrodes, and the mass flow rate in the molecular beam was additionally measured to derive this quantity from the spectrometer reading. Numerical simulations provided aero- dynamic correction factors to derive the atmospheric number density of atomic oxygen from the sensor data. The flight results indicate a preferable orientation of the electrode sur- face perpendicular to the rocket axis. While unstable dur- ing the upleg, the density profiles measured by these sensors show an excellent agreement with the atmospheric models and photometer results during the downleg of the trajectory. The high spatial resolution of the measurements allows for the identification of small-scale variations in the atomic oxy- gen concentration.
Two peptides from the haptoglobin beta chain were used in the SRM assay: VGYVSGWGR and VTSIQDWVQK (Additional file 2). These peptides were selected using the following process. Three aliquots of serum from a patient sample with a haptoglobin concentration mea- sured at 2.66 g/L by immunonephelometry were digested as described in the Sample Preparation section. Digested samples were analyzed by RP-LC-MS/MS on a LTQ Orbitrap velos Pro (Thermo Electron, San Jose, CA, USA) equipped with a NanoAcquity system (Waters, Milford, MA, USA). Of the 13 peptides identified in common from the three samples, 7 were found to be proteotypic using SRMAtlas (www.srmatlas.org/), Pep- tideAtlas (www.peptideatlas.org/), and BLAST (http:// blast.ncbi.nlm.nih.gov/Blast.cgi). The two peptides used in the SRM assay were selected from this list of proteo- typic peptides based on the following criteria: reproduci- bility of the retention time, peak shape, absence of matrix interferences, limit of detection, calibration curve linearity, and the consistency of the collision energy for peptide fragmentation. Isotope-labeled peptides used as internal standards were obtained from JPT Peptide Technologies (Berlin, Germany) (Additional file 2). Ly- ophilized heavy peptides (26 nmol) were dissolved in 100 μl of 5% acetonitrile (ACN), 0.1% formic acid (FA). A volume of 5 μL of this stock solution was diluted then in 200 μL 5% ACN, 0.1% FA.
Our single-maturity interpolation does not depend on the shape of the dis- crete volatilities and applies to index, equity, forex and interest rate options. It takes seconds to calibrate a 10 × 10 volatilities matrix on a 800 Mhz processor and the quality of the fit is excellent. While our interpolation is of indepen- dent interest one of its main applications is the calibration of the local volatil- ity model (Derman and Kani 1994, Dupire 1994, Rubinstein 1994), where the volatility of the spot is a deterministic function of the spot and time. The local volatilities can be calculated from the implied volatility surface via Dupire’s formula (Dupire 1994) which is very sensitive to the interpolation used. It is well known (Avellaneda, Friedman, Holmes and Samperi 1997) that, for stan- dard interpolation methods, Dupire’s formula often leads to instabilities in the local volatilities. Our interpolated volatility surface has been designed to cali- brate Dupire’s model. Numerical experiments show that prices of plain options calculated via our local volatility surface and deterministic schemes or Monte Carlo simulation are very close to input prices. This allows the pricing of exotic options, including options on several assets, in a way consistent with the smile. An alternative approach (Achdou and Pironneau 2002, Avellaneda, Fried- man, Holmes and Samperi 1997, Coleman, Li and Verma 1999, Cr´epey 2003a, Cr´epey 2003b, Lagnado and Osher 1997) to calibrating Dupire’s model is to cal- culate directly a local volatility surface that satisfies a regularity condition and produces prices close to input prices. This approach generates an arbitrage-free implied volatility surface but is more time-consuming than ours.
The first part printed is the Calibration Cube of Figure 5. Four samples of the cube are printed from each location. Tables 6 and 7 compare key attributes of the South Carolina and Australia based controllers, respectively. For all four trials, the South Carolina based controller ran hitch free, without any pauses due to latency. As a result, it returned prints with consistent printing times and similar surface quality as the corresponding part printed using dSPACE. Interestingly, the printing times of the South Carolina based controller are about one minute shorter than that from dSPACE (see Tables 2 and 3). This is due to slight differences in the implementation of the algorithms on dSPACE and the VMs. dSPACE is a real-time computer which is run at a fixed 40 kHz clock frequency. The clock frequency is down-sampled by a factor of 3 to create each step for the stepper motors in real time at 13.33 kHz, and by a factor of 40 to generate the outputs of the JLMCG and LPFBS algorithms in real time at 1 kHz. On the other hand, the VMs are non-real-time computers. They run the JLMCG and LPFBS algorithms at a sampling frequency of 1 kHz (non-real-time) and then up-sample the output to 20 kHz stepping frequency. The non-real-time nature of the VMs provides more flexibility in the implementation of the algorithms (allowing up-sampling rather than down-sampling), leading to fewer approximations hence reductions in motion time.
Remote measurements of GHGs first started in the 1950s at the Mauna Loa Observatory, Hawaii, USA. Remote back- ground locations were chosen as to avoid strong anthro- pogenic sources encountered at stations close to populated regions which made data interpretation more difficult at the time (Keeling et al., 1976; Popa et al., 2010). Other back- ground stations followed in the decades after Mauna Loa was set up, such as at Baring Head, New Zealand, in 1970 (Brailsford et al., 2012) and the Atmospheric Life Experi- ment (ALE, a predecessor to the current Advanced Global Atmospheric Gases Experiment, AGAGE) in 1978 (Prinn et al., 2000). Measurements from these background stations only constrained estimations of global or hemispheric-scale fluxes within inverse models and were not able to capture local to regional scales (Gloor et al., 2001). Tall tower mea- surements in conjunction with transport models were pro- posed as a means to estimate local to regional-scale GHG fluxes (Tans, 1993). GHG measurements from tall towers be- gan in the 1990s (Haszpra et al., 2001; Popa et al., 2010) and have been expanded in the 2000s as part of a number of national and international measurement campaigns (Ver- meulen, 2007; Kozlova et al., 2008; Thompson et al., 2009; Popa et al., 2010). Measurements made from ground level at terrestrial sites often display complex atmospheric signals with source and sink interactions visible. Sampling from tall towers reduces the influence of these local effects (Gerbig et al., 2003, 2009).
Numerous studies have investigated the hydrological im- pacts of climate change in the US using process-based mod- els (Mote et al., 2005; Christensen et al., 2004; Payne et al., 2004; Maurer et al., 2002; McCabe and Hay, 1995; Hamlet and Lettenmaier, 1999; Wolock and McCabe, 1999; Ashfaq et al., 2010). Output from global climate models (GCMs) is usually downscaled, bias corrected, and used in conjunction with hydrologic models to assess future water availability. However, owing to resource limitations, many hydro-climate impact assessments either are focused on smaller US re- gions or provide lower spatial resolution (Christensen et al., 2004; Payne et al., 2004; Maurer et al., 2002; McCabe and Hay, 1995). Repeated efforts may be needed for fundamen- tal data processing and model calibration, and these may un- avoidably reduce the amount of computing time available for the more challenging task of assessing climate change im- pacts. To extend geographical coverage, refine spatial reso- lution, and make hydro-climate impact assessment more ef- ficient, a comprehensive set of calibrated physical parame- ters is desired that can provide the most up-to-date, high- resolution watershed soil, vegetation, elevation, and other hydrologic characteristics. If a fine-resolution hydrological model parameter data set could be pre-organized, generally calibrated, and constantly updated, it would enable numerous researchers to easily extend hydro-climate impact assessment efforts to different watersheds.