Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Parameter estimation and use of gamma interferon assay for the diagnosis of bovine tuberculosis in Brazil

This study aimed to evaluate the interference of tuberculin test on the gamma-interferon (INFg) assay, to estimate the sensitivity and specificity of the INFg assay in Brazilian conditions, and to simulate multiple testing using the comparative tuberculin test and the INFg assay. Three hundred-fifty cattle from two TB-free and two TB-infected herds were submitted to the comparative tuberculin test and the INFg assay. The comparative tuberculin test was performed using avian and bovine PPD. The INFg assay was performed by the BovigamTM kit (CSL Veterinary, Australia), according to the manufacturer's specifications. Sensitivity and specificity of the INFg assay were assessed by a Bayesian latent class model. These diagnostic parameters were also estimate for multiple testing. The results of INFg assay on D0 and D3 after the comparative tuberculin test were compared by the McNemar's test and kappa statistics. Results of mean optical density from INFg assay on both days were similar. Sensitivity and specificity of the INFg assay showed results varying (95% confidence intervals) from 72 to 100% and 74 to 100% respectively. Sensitivity of parallel testing was over 97.5%, while specificity of serial testing was over 99.7%. The INFg assay proved to be a very useful diagnostic method.

Parameter optimization of AC rectangular wave outputs for aluminum cold wire GTAW

The objective of this work was to optimize the parameter setup for GTAW of aluminum using an AC rectangular wave output and continuous feeding. A series of welds was carried-out in an industrial joint, with variation of the negative and positive current amplitude, the negative and positive duration time, the travel speed and the feeding speed. Another series was carried out to investigate the isolate effect of the negative duration time and travel speed. Bead geometry aspects were assessed, such as reinforcement, penetration, incomplete fusion and joint wall bridging. The results showed that currents at both polarities are remarkably more significant than the respective duration times. It was also shown that there is a straight relationship between welding speed and feeding speed and this relationship must be followed for obtaining sound beads. A very short positive duration time is enough for arc stability achievement and when the negative duration time is longer than 5 ms its effect on geometry appears. The possibility of optimizing the parameter selection, despite the high inter-correlation amongst them, was demonstrate through a computer program. An approach to reduce the number of variables in this process is also presented.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Modeling and Parameter Estimation of Double-Star Permanent Magnet Synchronous Machines

The power rating of wind turbines is constantly increasing; however, keeping the voltage rating at the low-voltage level results in high kilo-ampere currents. An alternative for increasing the power levels without raising the voltage level is provided by multiphase machines. Multiphase machines are used for instance in ship propulsion systems, aerospace applications, electric vehicles, and in other high-power applications including wind energy conversion systems. A machine model in an appropriate reference frame is required in order to design an efficient control for the electric drive. Modeling of multiphase machines poses a challenge because of the mutual couplings between the phases. Mutual couplings degrade the drive performance unless they are properly considered. In certain multiphase machines there is also a problem of high current harmonics, which are easily generated because of the small current path impedance of the harmonic components. However, multiphase machines provide special characteristics compared with the three-phase counterparts: Multiphase machines have a better fault tolerance, and are thus more robust. In addition, the controlled power can be divided among more inverter legs by increasing the number of phases. Moreover, the torque pulsation can be decreased and the harmonic frequency of the torque ripple increased by an appropriate multiphase configuration. By increasing the number of phases it is also possible to obtain more torque per RMS ampere for the same volume, and thus, increase the power density. In this doctoral thesis, a decoupled d–q model of double-star permanent-magnet (PM) synchronous machines is derived based on the inductance matrix diagonalization. The double-star machine is a special type of multiphase machines. Its armature consists of two three-phase winding sets, which are commonly displaced by 30 electrical degrees. In this study, the displacement angle between the sets is considered a parameter. The diagonalization of the inductance matrix results in a simplified model structure, in which the mutual couplings between the reference frames are eliminated. Moreover, the current harmonics are mapped into a reference frame, in which they can be easily controlled. The work also presents methods to determine the machine inductances by a finite-element analysis and by voltage-source inverters on-site. The derived model is validated by experimental results obtained with an example double-star interior PM (IPM) synchronous machine having the sets displaced by 30 electrical degrees. The derived transformation, and consequently, the decoupled d–q machine model, are shown to model the behavior of an actual machine with an acceptable accuracy. Thus, the proposed model is suitable to be used for the model-based control design of electric drives consisting of double-star IPM synchronous machines.

The use and misuse of the "impact factor" as a parameter for evaluation of scientific publication quality: a proposal to rationalize its application

We present a critical analysis of the generalized use of the "impact factor". By means of the Kruskal-Wallis test, it was shown that it is not possible to compare distinct disciplines using the impact factor without adjustments. After assigning the median journal the value of one (1.000), the impact factor value for each journal was calculated by the rule of three. The adjusted values were homogeneous, thus permitting comparison among distinct disciplines.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Intensification of hemicellulose hot-water extraction from spruce wood by parameter tuning

The growing population on earth along with diminishing fossil deposits and the climate change debate calls out for a better utilization of renewable, bio-based materials. In a biorefinery perspective, the renewable biomass is converted into many different products such as fuels, chemicals, and materials, quite similar to the petroleum refinery industry. Since forests cover about one third of the land surface on earth, ligno-cellulosic biomass is the most abundant renewable resource available. The natural first step in a biorefinery is separation and isolation of the different compounds the biomass is comprised of. The major components in wood are cellulose, hemicellulose, and lignin, all of which can be made into various end-products. Today, focus normally lies on utilizing only one component, e.g., the cellulose in the Kraft pulping process. It would be highly desirable to utilize all the different compounds, both from an economical and environmental point of view. The separation process should therefore be optimized. Hemicelluloses can partly be extracted with hot-water prior to pulping. Depending in the severity of the extraction, the hemicelluloses are degraded to various degrees. In order to be able to choose from a variety of different end-products, the hemicelluloses should be as intact as possible after the extraction. The main focus of this work has been on preserving the hemicellulose molar mass throughout the extraction at a high yield by actively controlling the extraction pH at the high temperatures used. Since it has not been possible to measure pH during an extraction due to the high temperatures, the extraction pH has remained a “black box”. Therefore, a high-temperature in-line pH measuring system was developed, validated, and tested for hot-water wood extractions. One crucial step in the measurements is calibration, therefore extensive efforts was put on developing a reliable calibration procedure. Initial extractions with wood showed that the actual extraction pH was ~0.35 pH units higher than previously believed. The measuring system was also equipped with a controller connected to a pump. With this addition it was possible to control the extraction to any desired pH set point. When the pH dropped below the set point, the controller started pumping in alkali and by that the desired set point was maintained very accurately. Analyses of the extracted hemicelluloses showed that less hemicelluloses were extracted at higher pH but with a higher molar-mass. Monomer formation could, at a certain pH level, be completely inhibited. Increasing the temperature, but maintaining a specific pH set point, would speed up the extraction without degrading the molar-mass of the hemicelluloses and thereby intensifying the extraction. The diffusion of the dissolved hemicelluloses from the wood particle is a major part of the extraction process. Therefore, a particle size study ranging from 0.5 mm wood particles to industrial size wood chips was conducted to investigate the internal mass transfer of the hemicelluloses. Unsurprisingly, it showed that hemicelluloses were extracted faster from smaller wood particles than larger although it did not seem to have a substantial effect on the average molar mass of the extracted hemicelluloses. However, smaller particle sizes require more energy to manufacture and thus increases the economic cost. Since bark comprises 10 – 15 % of a tree, it is important to also consider it in a biorefinery concept. Spruce inner and outer bark was hot-water extracted separately to investigate the possibility to isolate the bark hemicelluloses. It was showed that the bark hemicelluloses comprised mostly of pectic material and differed considerably from the wood hemicelluloses. The bark hemicelluloses, or pectins, could be extracted at lower temperatures than the wood hemicelluloses. A chemical characterization, done separately on inner and outer bark, showed that inner bark contained over 10 % stilbene glucosides that could be extracted already at 100 °C with aqueous acetone.

Dendritic thickness: a morphometric parameter to classify mouse retinal ganglion cells

To study the dendritic morphology of retinal ganglion cells in wild-type mice we intracellularly injected these cells with Lucifer yellow in an in vitro preparation of the retina. Subsequently, quantified values of dendritic thickness, number of branching points and level of stratification of 73 Lucifer yellow-filled ganglion cells were analyzed by statistical methods, resulting in a classification into 9 groups. The variables dendritic thickness, number of branching points per cell and level of stratification were independent of each other. Number of branching points and level of stratification were independent of eccentricity, whereas dendritic thickness was positively dependent (r = 0.37) on it. The frequency distribution of dendritic thickness tended to be multimodal, indicating the presence of at least two cell populations composed of neurons with dendritic diameters either smaller or larger than 1.8 µm ("thin" or "thick" dendrites, respectively). Three cells (4.5%) were bistratified, having thick dendrites, and the others (95.5%) were monostratified. Using k-means cluster analysis, monostratified cells with either thin or thick dendrites were further subdivided according to level of stratification and number of branching points: cells with thin dendrites were divided into 2 groups with outer stratification (0-40%) and 2 groups with inner (50-100%) stratification, whereas cells with thick dendrites were divided into one group with outer and 3 groups with inner stratification. We postulate, that one group of cells with thin dendrites resembles cat ß-cells, whereas one group of cells with thick dendrites includes cells that resemble cat a-cells.

HIGH PRESSURE PHASE EQUILIBRIUM: PREDICTION OF ESSENTIAL OIL SOLUBILITY

This work describes a method to predict the solubility of essential oils in supercritical carbon dioxide. The method is based on the formulation proposed in 1979 by Asselineau, Bogdanic and Vidal. The Peng-Robinson and Soave-Redlich-Kwong cubic equations of state were used with the van der Waals mixing rules with two interaction parameters. Method validation was accomplished calculating orange essential oil solubility in pressurized carbon dioxide. The solubility of orange essential oil in carbon dioxide calculated at 308.15 K for pressures of 50 to 70 bar varied from 1.7± 0.1 to 3.6± 0.1 mg/g. For same the range of conditions, experimental solubility varied from 1.7± 0.1 to 3.6± 0.1 mg/g. Predicted values were not very sensitive to initial oil composition.

Solubility and Seeded Metastable Zone width of functional sugar L-arabinose

L-arabinose is widely used in food, medicine, chemistry, and biology fields; however, solubility and seeded metastable zone width (MSZW) of L-arabinose have not been reported in the literature. In this paper, solubility and MSZW of L-arabinose in aqueous solution were determined. Solubility of L-arabinose was measured in the range of 20-68 °C by a conventional equilibrium solubility method and quantitation was determined using the ion chromatography technique. Seeded MSZW was determined in the range of 51-73% by the calorimetric method. The effect of two salts (potassium chloride and calcium chloride) on the solubility and MSZW of L-arabinose were also evaluated. Results showed that both potassium chloride and calcium chloride increased the solubility of L-arabinose, and this increase was intensified with temperature rise. The MSZW of L-arabinose was not constant but a spread. Potassium chloride increased the MSZW of L-arabinose. However, the effect of calcium chloride on MSZW of L-arabinose was concentration dependent. Conclusion: the L-arabinose solubility increased with the increase in temperature, and both potassium chloride and calcium chloride increased the solubility of L-arabinose in aqueous solution. The seeded MSZW of L-arabinose is not a constant; it increases in the presence of potassium chloride and varies with the change in calcium chloride concentration.

Production, solubility and antioxidant activity of curcumin nanosuspension

Curcumin is a powerful bioactive agent and natural antioxidant, but it is practically water-insoluble and has low bioavailability; a possible solution to this obstacle would be formulations of curcumin nanoparticles. Surfactants such as tween 80 can be used to stabilize low-solubility molecules preventing particle aggregation. The objectives of this study were the preparation of a suspension with curcumin nanoparticles in tween 80, the testing of pure curcumin solubility and of a simple mixture of curcumin with tween 80 and nanosuspension in water and ethanol as solvents, and finally the assessment of the antioxidant activity. We prepared the nanosuspension by injecting a curcumin solution in dichloromethane at low flow in water with tween 80 under heating and ultrasound. The analysis of particles size was conducted through dynamic light scattering; the non-degradation of curcumin was verified through thin-layer chromatography. The analyses of antioxidant activity were carried out according to the DPPH method. The method applied to reduce the particles size was efficient. Both the curcumin suspension and nanosuspension in tween 80 increased its solubility. Curcumin and the formulations presented antioxidant activity.

Chemical changes in biomass during torrefaction

Torrefaction is moderate thermal treatment (~200-300 °C) of biomass in an inert atmosphere. The torrefied fuel offers advantages to traditional biomass, such as higher heating value, reduced hydrophilic nature, increased its resistance to biological decay, and improved grindability. These factors could, for instance, lead to better handling and storage of biomass and increased use of biomass in pulverized combustors. In this work, we look at several aspects of changes in the biomass during torrefaction. We investigate the fate of carboxylic groups during torrefaction and its dependency to equilibrium moisture content. The changes in the wood components including carbohydrates, lignin, extractable materials and ashforming matters are also studied. And at last, the effect of K on torrefaction is investigated and then modeled. In biomass, carboxylic sites are partially responsible for its hydrophilic characteristic. These sites are degraded to varying extents during torrefaction. In this work, methylene blue sorption and potentiometric titration were applied to measure the concentration of carboxylic groups in torrefied spruce wood. The results from both methods were applicable and the values agreed well. A decrease in the equilibrium moisture content at different humidity was also measured for the torrefied wood samples, which is in good agreement with the decrease in carboxylic group contents. Thus, both methods offer a means of directly measuring the decomposition of carboxylic groups in biomass during torrefaction as a valuable parameter in evaluating the extent of torrefaction. This provides new information to the chemical changes occurring during torrefaction. The effect of torrefaction temperature on the chemistry of birch wood was investigated. The samples were from a pilot plant at Energy research Center of the Netherlands (ECN). And in that way they were representative of industrially produced samples. Sugar analysis was applied to analyze the hemicellulose and cellulose content during torrefaction. The results show a significant degradation of hemicellulose already at 240 °C, while cellulose degradation becomes significant above 270 °C torrefaction. Several methods including Klason lignin method, solid state NMR and Py-GC-MS analyses were applied to measure the changes in lignin during torrefaction. The changes in the ratio of phenyl, guaiacyl and syringyl units show that lignin degrades already at 240 °C to a small extent. To investigate the changes in the extractives from acetone extraction during torrefaction, gravimetric method, HP-SEC and GC-FID followed by GC-MS analysis were performed. The content of acetone-extractable material increases already at 240 °C torrefaction through the degradation of carbohydrate and lignin. The molecular weight of the acetone-extractable material decreases with increasing the torrefaction temperature. The formation of some valuable materials like syringaresinol or vanillin is also observed which is important from biorefinery perspective. To investigate the change in the chemical association of ash-forming elements in birch wood during torrefaction, chemical fractionation was performed on the original and torrefied birch samples. These results give a first understanding of the changes in the association of ashforming elements during torrefaction. The most significant changes can be seen in the distribution of calcium, magnesium and manganese, with some change in water solubility seen in potassium. These changes may in part be due to the destruction of carboxylic groups. In addition to some changes in water and acid solubility of phosphorous, a clear decrease in the concentration of both chlorine and sulfur was observed. This would be a significant additional benefit for the combustion of torrefied biomass. Another objective of this work is studying the impact of organically bound K, Na, Ca and Mn on mass loss of biomass during torrefaction. These elements were of interest because they have been shown to be catalytically active in solid fuels during pyrolysis and/or gasification. The biomasses were first acid washed to remove the ash-forming matters and then organic sites were doped with K, Na, Ca or Mn. The results show that K and Na bound to organic sites can significantly increase the mass loss during torrefaction. It is also seen that Mn bound to organic sites increases the mass loss and Ca addition does not influence the mass loss rate on torrefaction. This increase in mass loss during torrefaction with alkali addition is unlike what has been found in the case of pyrolysis where alkali addition resulted in a reduced mass loss. These results are important for the future operation of torrefaction plants, which will likely be designed to handle various biomasses with significantly different contents of K. The results imply that shorter retention times are possible for high K-containing biomasses. The mass loss of spruce wood with different content of K was modeled using a two-step reaction model based on four kinetic rate constants. The results show that it is possible to model the mass loss of spruce wood doped with different levels of K using the same activation energies but different pre-exponential factors for the rate constants. Three of the pre-exponential factors increased linearly with increasing K content, while one of the preexponential factors decreased with increasing K content. Therefore, a new torrefaction model was formulated using the hemicellulose and cellulose content and K content. The new torrefaction model was validated against the mass loss during the torrefaction of aspen, miscanthus, straw and bark. There is good agreement between the model and the experimental data for the other biomasses, except bark. For bark, the mass loss of acetone extractable material is also needed to be taken into account. The new model can describe the kinetics of mass loss during torrefaction of different types of biomass. This is important for considering fuel flexibility in torrefaction plants.

Parameter estimation for Chaotic or Stochastic Dynamics

Since its discovery, chaos has been a very interesting and challenging topic of research. Many great minds spent their entire lives trying to give some rules to it. Nowadays, thanks to the research of last century and the advent of computers, it is possible to predict chaotic phenomena of nature for a certain limited amount of time. The aim of this study is to present a recently discovered method for the parameter estimation of the chaotic dynamical system models via the correlation integral likelihood, and give some hints for a more optimized use of it, together with a possible application to the industry. The main part of our study concerned two chaotic attractors whose general behaviour is diff erent, in order to capture eventual di fferences in the results. In the various simulations that we performed, the initial conditions have been changed in a quite exhaustive way. The results obtained show that, under certain conditions, this method works very well in all the case. In particular, it came out that the most important aspect is to be very careful while creating the training set and the empirical likelihood, since a lack of information in this part of the procedure leads to low quality results.