Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

Organotypic Modeling Platform for Adverse Outcome Pathways of Male Reproductive and Developmental Processes

Thesis (Master's)--University of Washington, 2016-06

Modeling of hardening and softening processes in Mg alloys

The tensile deformation behavior of a range of supersaturated Mg-Al solid solutions and an as-cast magnesium alloy AM60 has been studied. The Mg-Al alloys were tested at room temperature while the alloy AM60 was tested in the temperature range 293-573 K. The differences in the deformation behavior of the alloys is discussed in terms of hardening and softening processes. In order to identify which processes were active, the stress dependence of the strain-hardening coefficient was assessed using Lukac and Balik's model of hardening and softening. The analysis indicates that hardening involves solid solution hardening and interaction with forest dislocations and non-dislocation obstacles such as second phase particles. Cross slip is not a significant recovery process in the temperature range 293-423 K. At temperatures between 473 and 523 K the analysis suggests that softening is controlled by cross slip and climb of dislocations. At temperatures above 523 K softening seems to be controlled by dynamic recrystallisation. (C) 2004 Elsevier B.V. All rights reserved.

Modeling high-speed network traffic with truncated α-stable processes

It has been reported that high-speed communication network traffic exhibits both long-range dependence (LRD) and burstiness, which posed new challenges in network engineering. While many models have been studied in capturing the traffic LRD, they are not capable of capturing efficiently the traffic impulsiveness. It is desirable to develop a model that can capture both LRD and burstiness. In this letter, we propose a truncated a-stable LRD process model for this purpose, which can characterize both LRD and burstiness accurately. A procedure is developed further to estimate the model parameters from real traffic. Simulations demonstrate that our proposed model has a higher accuracy compared to existing models and is flexible in capturing the characteristics of high-speed network traffic. © 2012 Springer-Verlag GmbH.

Mathematical Modeling for Studying Microbial Processes – Some Examples

Mathematical modeling may have different purposes in chemical and biochemical engineering sciences. One of them is to confirm or to reject kinetic models for certain processes, or to evaluate the importance of some transport phenomena on the net chemical or biochemical reaction rate. In the present paper different microbial processes are considered and modeled for evaluation of kinetic constants for batch and continuous processes accomplished by free and immobilized microbial cells. The practical examples are from the field of wastewater treatment and biosynthesis of products, like enzymes, lactic acid, gluconic acid, etc. By the aid of mathematical modeling the kinetics and the type of inhibition are specified for microbial wastewater denitrification and biodegradation of halogenated hydrocarbons. The importance of free and immobilized cells and their separate contribution to the overall microbial process is also evaluated for some fermentation processes: gluconic acid production, dichloroethane biodegradation, lactic acid fermentation and monochloroacetic acid biodegradation.

Multi-impurity adsorption model for modeling crystal purity and shape evolution during crystallization processes in impure media

Peer reviewed

A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format

Topographic variation, the spatial variation in elevation and terrain features, underpins a myriad of patterns and processes in geography and ecology and is key to understanding the variation of life on the planet. The characterization of this variation is scale-dependent, i.e. it varies with the distance over which features are assessed and with the spatial grain (grid cell resolution) of analysis. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale basic research and analytical applications, however to date, such technique is unavailable. Here we used the digital elevation model products of global 250 m GMTED and near-global 90 m SRTM to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile and tangential curvature, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches (median, average, minimum, maximum, standard deviation, percent cover, count, majority, Shannon Index, entropy, uniformity). While a global cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at http://www.earthenv.org and can serve as a basis for standardized hydrological, environmental and biodiversity modeling at a global extent.

Experimental Modeling of Twin-Screw Extrusion Processes to Predict Properties of Extruded Composites

Twin-screw extrusion is used to compound fillers into a polymer matrix in order to improve the properties of the final product. The resultant properties of the composite are determined by the operating conditions used during extrusion processing. Changes in the operating conditions affect the physics of the melt flow, inducing unique composite properties. In the following work, the Residence Stress Distribution methodology has been applied to model both the stress behavior and the property response of a twin-screw compounding process as a function of the operating conditions. The compounding of a pigment into a polymer melt has been investigated to determine the effect of stress on the degree of mixing, which will affect the properties of the composite. In addition, the pharmaceutical properties resulting from the compounding of an active pharmaceutical ingredient are modeled as a function of the operating conditions, indicating the physical behavior inducing the property responses.

Study of the development and evolution of seasonal thermocline and turbulent processes in the northern parts of Persian Gulf using numerical modeling

The Persian Gulf (PG) is a semi-enclosed shallow sea which is connected to open ocean through the Strait of Hormuz. Thermocline as a suddenly decrease of temperature in subsurface layer in water column leading to stratification happens in the PG seasonally. The forcing comprise tide, river inflow, solar radiation, evaporation, northwesterly wind and water exchange with the Oman Sea that influence on this process. In this research, analysis of the field data and a numerical (Princeton Ocean Model, POM) study on the summer thermocline development in the PG are presented. The Mt. Mitchell cruise 1992 salinity and temperature observations show that the thermocline is effectively removed due to strong wind mixing and lower solar radiation in winter but is gradually formed and developed during spring and summer; in fact as a result of an increase in vertical convection through the water in winter, vertical gradient of temperature is decreased and thermocline is effectively removed. Thermocline development that evolves from east to west is studied using numerical simulation and some existing observations. Results show that as the northwesterly wind in winter, at summer transition period, weakens the fresher inflow from Oman Sea, solar radiation increases in this time interval; such these factors have been caused the thermocline to be formed and developed from winter to summer even over the northwestern part of the PG. The model results show that for the more realistic monthly averaged wind experiments the thermocline develops as is indicated by summer observations. The formation of thermocline also seems to decrease the dissolved oxygen in water column due to lack of mixing as a result of induced stratification. Over most of PG the temperature difference between surface and subsurface increases exponentially from March until May. Similar variations for salinity differences are also predicted, although with smaller values than observed. Indeed thermocline development happens more rapidly in the Persian Gulf from spring to summer. Vertical difference of temperature increases to 9 centigrade degrees in some parts of the case study zone from surface to bottom in summer. Correlation coefficients of temperature and salinity between the model results and measurements have been obtained 0.85 and 0.8 respectively. The rate of thermcline development was found to be between 0.1 to 0.2 meter per day in the Persian Gulf during the 6 months from winter to early summer. Also it is resulted from the used model that turbulence kinetic energy increases in the northwestern part of the PG from winter to early summer that could be due to increase in internal waves activities and stability intensified through water column during this time.

Probabilistic Imaging and Dynamic Modeling of Earthquake Source Processes

Investigation of large, destructive earthquakes is challenged by their infrequent occurrence and the remote nature of geophysical observations. This thesis sheds light on the source processes of large earthquakes from two perspectives: robust and quantitative observational constraints through Bayesian inference for earthquake source models, and physical insights on the interconnections of seismic and aseismic fault behavior from elastodynamic modeling of earthquake ruptures and aseismic processes.

To constrain the shallow deformation during megathrust events, we develop semi-analytical and numerical Bayesian approaches to explore the maximum resolution of the tsunami data, with a focus on incorporating the uncertainty in the forward modeling. These methodologies are then applied to invert for the coseismic seafloor displacement field in the 2011 Mw 9.0 Tohoku-Oki earthquake using near-field tsunami waveforms and for the coseismic fault slip models in the 2010 Mw 8.8 Maule earthquake with complementary tsunami and geodetic observations. From posterior estimates of model parameters and their uncertainties, we are able to quantitatively constrain the near-trench profiles of seafloor displacement and fault slip. Similar characteristic patterns emerge during both events, featuring the peak of uplift near the edge of the accretionary wedge with a decay toward the trench axis, with implications for fault failure and tsunamigenic mechanisms of megathrust earthquakes.

To understand the behavior of earthquakes at the base of the seismogenic zone on continental strike-slip faults, we simulate the interactions of dynamic earthquake rupture, aseismic slip, and heterogeneity in rate-and-state fault models coupled with shear heating. Our study explains the long-standing enigma of seismic quiescence on major fault segments known to have hosted large earthquakes by deeper penetration of large earthquakes below the seismogenic zone, where mature faults have well-localized creeping extensions. This conclusion is supported by the simulated relationship between seismicity and large earthquakes as well as by observations from recent large events. We also use the modeling to connect the geodetic observables of fault locking with the behavior of seismicity in numerical models, investigating how a combination of interseismic geodetic and seismological estimates could constrain the locked-creeping transition of faults and potentially their co- and post-seismic behavior.

Challenges in imaging and predictive modeling of rhizosphere processes

Background Plant-soil interaction is central to human food production and ecosystem function. Thus, it is essential to not only understand, but also to develop predictive mathematical models which can be used to assess how climate and soil management practices will affect these interactions. Scope In this paper we review the current developments in structural and chemical imaging of rhizosphere processes within the context of multiscale mathematical image based modeling. We outline areas that need more research and areas which would benefit from more detailed understanding. Conclusions We conclude that the combination of structural and chemical imaging with modeling is an incredibly powerful tool which is fundamental for understanding how plant roots interact with soil. We emphasize the need for more researchers to be attracted to this area that is so fertile for future discoveries. Finally, model building must go hand in hand with experiments. In particular, there is a real need to integrate rhizosphere structural and chemical imaging with modeling for better understanding of the rhizosphere processes leading to models which explicitly account for pore scale processes.

Innovation in aquaculture. Modeling the adoption and implementation processes of innovations in the Italian aquaculture sector

Since the turn of the century, fisheries have maintained a steady growth rate, while aquaculture has experienced a more rapid expansion. Aquaculture can offer EU consumers more diverse, healthy, and sustainable food options, some of which are more popular elsewhere. To develop the sector, the EU is investing heavily. The EU supports innovative projects that promote the sustainable development of seafood sectors and food security. Priority 3 promotes sector development through innovation dissemination. This doctoral dissertation examined innovation transfer in the Italian aquaculture sector, specifically the adoption of innovative tools, using a theoretical model to better understand the complexity of these processes. The work focused on innovation adoption, emphasising that it is the end of a well-defined process. The Awareness Knowledge Adoption Implementation Effectiveness (AKAIE) model was created to better analyse post-adoption phases and evaluate technology adoption implementation and impact. To identify AKAIE drivers and barriers, aquaculture actors were consulted. "Perceived complexity"—barriers to adoption that are strongly influenced by contextual factors—has been used to examine their perspectives (i.e. socio-economic, institutional, cultural ones). The new model will contextualise the sequence based on technologies, entrepreneur traits, corporate and institutional contexts, and complexity perception, the sequence's central node. Technology adoption can also be studied by examining complexity perceptions along the AKAIE sequence. This study proposes a new model to evaluate the diffusion of a given technology, offering the policy maker the possibility to be able to act promptly across the process. The development of responsible policies for evaluating the effectiveness of innovation is more necessary than ever, especially to orient strategies and interventions in the face of major scenarios of change.

Modeling The Effects Of Temperature And Ph On The Resistance Of Alicyclobacillus Acidoterrestris In Conventional Heat-treated Fruit Beverages Through A Meta-analysis Approach.

In this work, all publicly-accessible published findings on Alicyclobacillus acidoterrestris heat resistance in fruit beverages as affected by temperature and pH were compiled. Then, study characteristics (protocols, fruit and variety, °Brix, pH, temperature, heating medium, culture medium, inactivation method, strains, etc.) were extracted from the primary studies, and some of them incorporated to a meta-analysis mixed-effects linear model based on the basic Bigelow equation describing the heat resistance parameters of this bacterium. The model estimated mean D* values (time needed for one log reduction at a temperature of 95 °C and a pH of 3.5) of Alicyclobacillus in beverages of different fruits, two different concentration types, with and without bacteriocins, and with and without clarification. The zT (temperature change needed to cause one log reduction in D-values) estimated by the meta-analysis model were compared to those ('observed' zT values) reported in the primary studies, and in all cases they were within the confidence intervals of the model. The model was capable of predicting the heat resistance parameters of Alicyclobacillus in fruit beverages beyond the types available in the meta-analytical data. It is expected that the compilation of the thermal resistance of Alicyclobacillus in fruit beverages, carried out in this study, will be of utility to food quality managers in the determination or validation of the lethality of their current heat treatment processes.

Random perturbations of stochastic processes with unbounded variable length memory

We consider binary infinite order stochastic chains perturbed by a random noise. This means that at each time step, the value assumed by the chain can be randomly and independently flipped with a small fixed probability. We show that the transition probabilities of the perturbed chain are uniformly close to the corresponding transition probabilities of the original chain. As a consequence, in the case of stochastic chains with unbounded but otherwise finite variable length memory, we show that it is possible to recover the context tree of the original chain, using a suitable version of the algorithm Context, provided that the noise is small enough.

Hybrid modeling of convective laminar flow in a permeable tube associated with the cross-flow process

The confined flows in tubes with permeable surfaces arc associated to tangential filtration processes (microfiltration or ultrafiltration). The complexity of the phenomena do not allow for the development of exact analytical solutions, however, approximate solutions are of great interest for the calculation of the transmembrane outflow and estimate of the concentration, polarization phenomenon. In the present work, the generalized integral transform technique (GITT) was employed in solving the laminar and permanent flow in permeable tubes of Newtonian and incompressible fluid. The mathematical formulation employed the parabolic differential equation of chemical species conservation (convective-diffusive equation). The velocity profiles for the entrance region flow, which are found in the connective terms of the equation, were assessed by solutions obtained from literature. The velocity at the permeable wall was considered uniform, with the concentration at the tube wall regarded as variable with an axial position. A computational methodology using global error control was applied to determine the concentration in the wall and concentration boundary layer thickness. The results obtained for the local transmembrane flux and the concentration boundary layer thickness were compared against others in literature. (C) 2007 Elsevier B.V. All rights reserved.