Application of the Heterogeneous System Prediction Method to Pattern Recognition Problem

* This work was financially supported by RFBR-04-01-00858.

Detection of Logical-and-Probabilistic Correlation in Time Series

An application of the heterogeneous variables system prediction method to solving the time series analysis problem with respect to the sample size is considered in this work. It is created a logical-and-probabilistic correlation from the logical decision function class. Two ways is considered. When the information about event is kept safe in the process, and when it is kept safe in depending process.

DIFFUSE-INTERFACE FIELD APPROACH TO MODELING SELF-ASSEMBLY OF HETEROGENEOUS COLLOIDAL SYSTEMS AND RELATED DIPOLE-DIPOLE INTERACTION PHENOMENA

Colloid self-assembly under external control is a new route to fabrication of advanced materials with novel microstructures and appealing functionalities. The kinetic processes of colloidal self-assembly have attracted great interests also because they are similar to many atomic level kinetic processes of materials. In the past decades, rapid technological progresses have been achieved on producing shape-anisotropic, patchy, core-shell structured particles and particles with electric/magnetic charges/dipoles, which greatly enriched the self-assembled structures. Multi-phase carrier liquids offer new route to controlling colloidal self-assembly. Therefore, heterogeneity is the essential characteristics of colloid system, while so far there still lacks a model that is able to efficiently incorporate these possible heterogeneities. This thesis is mainly devoted to development of a model and computational study on the complex colloid system through a diffuse-interface field approach (DIFA), recently developed by Wang et al. This meso-scale model is able to describe arbitrary particle shape and arbitrary charge/dipole distribution on the surface or body of particles. Within the framework of DIFA, a Gibbs-Duhem-type formula is introduced to treat Laplace pressure in multi-liquid-phase colloidal system and it obeys Young-Laplace equation. The model is thus capable to quantitatively study important capillarity related phenomena. Extensive computer simulations are performed to study the fundamental behavior of heterogeneous colloidal system. The role of Laplace pressure is revealed in determining the mechanical equilibrium of shape-anisotropic particles at fluid interfaces. In particular, it is found that the Laplace pressure plays a critical role in maintaining the stability of capillary bridges between close particles, which sheds light on a novel route to in situ firming compact but fragile colloidal microstructures via capillary bridges. Simulation results also show that competition between like-charge repulsion, dipole-dipole interaction and Brownian motion dictates the degree of aggregation of heterogeneously charged particles. Assembly and alignment of particles with magnetic dipoles under external field is studied. Finally, extended studies on the role of dipole-dipole interaction are performed for ferromagnetic and ferroelectric domain phenomena. The results reveal that the internal field generated by dipoles competes with external field to determine the dipole-domain evolution in ferroic materials.

Final Report : learning system for life prediction of infrastructure

The project has further developed two programs for the industry partners related to service life prediction and salt deposition. The program for Queensland Department of Main Roads which predicts salt deposition on different bridge structures at any point in Queensland has been further refined by looking at more variables. It was found that the height of the bridge significantly affects the salt deposition levels only when very close to the coast. However the effect of natural cleaning of salt by rainfall was incorporated into the program. The user interface allows selection of a location in Queensland, followed by a bridge component. The program then predicts the annual salt deposition rate and rates the likely severity of the environment. The service life prediction program for the Queensland Department of Public Works has been expanded to include 10 common building components, in a variety of environments. Data mining procedures have been used to develop the program and increase the usefulness of the application. A Query Based Learning System (QBLS) has been developed which is based on a data-centric model with extensions to provide support for user interaction. The program is based on number of sources of information about the service life of building components. These include the Delphi survey, the CSIRO Holistic model and a school survey. During the project, the Holistic model was modified for each building component and databases generated for the locations of all Queensland schools. Experiments were carried out to verify and provide parameters for the modelling. These included instrumentation of a downpipe, measurements on pH and chloride levels in leaf litter, EIS measurements and chromate leaching from Colorbond materials and dose tests to measure corrosion rates of new materials. A further database was also generated for inclusion in the program through a large school survey. Over 30 schools in a range of environments from tropical coastal to temperate inland were visited and the condition of the building components rated on a scale of 0-5. The data was analysed and used to calculate an average service life for each component/material combination in the environments, where sufficient examples were available.

UNSTEADY DIFFUSION KINETICS OF ZIEGLER-NATTA HETEROGENEOUS CATALYST POLYMERIZATION .4. PREDICTION OF EXPERIMENTAL RESULTS OF PROPYLENE ETHYLENE 1-HEXADECENE TERPOLYMERIZATION

The prediction, based on unsteady diffusion kinetics, of the enhancement of reactivity and incorporation of 1-hexadecene in its copolymerization with propylene on adding a small amount of ethylene (increase from 5,2 mol-% to 10,8 mol-% when 2% of ethylene was added, and to 16,1 mol-% when 5% was added) was verified in the terpolymerization of propylene/1-hexadecene/ethylene on a commercial Solvay-type delta-TiCl3 catalyst. The catalyst efficiency was thus also increased. These augmentations originate from the increase in diffusion coefficient of 1-hexadecene at the catalyst surface when the PP crystallinity decreases on introduction of ethylene. Calculation based on unsteady diffusion kinetics showed that the order of diffusion coefficients ethylene > propylene > 1-hexadecene is reversed as the monomer concentration increases when the monomers are not at their equilibrium concentration. Sequence distribution as determined by means of C-13 NMR revealed a tendency of blocky structure rather than a Bernoullian one. The terpolymer compositions obtained by means of an IR method developed in this work conform rather well with the NMR results. Results in this work not only support the unsteady diffusion kinetics but also provide a new route to prepare olefinic copolymer rubbers with heterogeneous titanium catalysts.

Development of a post-mitotic, neuronal-astrocytic cell system, for the prediction of acute neurotoxicity in humans

The human NT2.D1 cell line was differentiated to form both a 1:2 co-culture of post-mitotic NT2 neuronal and NT2 astrocytic (NT2.N/A) cells and a pure NT2.N culture. The respective sensitivities to several test chemicals of the NT2.N/A, the NT2.N, and the NT2.D1 cells were evaluated and compared with the CCF-STTG1 astrocytoma cell line, using a combination of basal cytotoxicity and biochemical endpoints. Using the MTT assay, the basal cytotoxicity data estimated the comparative toxicities of the test chemicals (chronic neurotoxin 2,5-hexanedione, cytotoxins 2,3- and 3,4-hexanedione and acute neurotoxins tributyltin- and trimethyltin- chloride) and also provided the non-cytotoxic concentration-range for each compound. Biochemical endpoints examined over the non-cytotoxic range included assays for ATP levels, oxidative status (H2O2 and GSH levels) and caspase-3 levels as an indicator of apoptosis. although the endpoints did not demonstrate the known neurotoxicants to be consistently more toxic to the cell systems with the greatest number of neuronal properties, the NT2 astrocytes appeared to contribute positively to NT2 neuronal health following exposure to all the test chemicals. The NT2.N/A co-culture generally maintained superior ATP and GSH levels and reduced H2O2 levels in comparison with the NT2.N mono-culture. In addition, the pure NT2.N culture showed a significantly lower level of caspase-3 activation compared with the co-culture, suggesting NT2 astrocytes may be important in modulating the mode of cell death following toxic insult. Overall, these studies provide evidence that an in vitro integrated population of post-mitotic human neurons and astrocytes may offer significant relevance to the human in vivo heterogeneous nervous system, when initially screening compounds for acute neurotoxic potential.

The user query based learning system for lifetime prediction of metallic components

Real-World Data Mining Applications generally do not end up with the creation of the models. The use of the model is the final purpose especially in prediction tasks. The problem arises when the model is built based on much more information than that the user can provide in using the model. As a result, the performance of model reduces drastically due to many missing attributes values. This paper develops a new learning system framework, called as User Query Based Learning System (UQBLS), for building data mining models best suitable for users use. We demonstrate its deployment in a real-world application of the lifetime prediction of metallic components in buildings

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Influence of environmental and instrumental variables on the non-invasive prediction of Brix in pineapple using near infrared spectroscopy

The Brix content of pineapple fruit can be non-invasively predicted from the second derivative of near infrared reflectance spectra. Correlations obtained using a NIRSystems 6500 spectrophotometer through multiple linear regression and modified partial least squares analyses using a post-dispersive configuration were comparable with that from a pre-dispersive configuration in terms of accuracy (e.g. coefficient of determination, R2, 0.73; standard error of cross validation, SECV, 1.01°Brix). The effective depth of sample assessed was slightly greater using the post-dispersive technique (about 20 mm for pineapple fruit), as expected in relation to the higher incident light intensity, relative to the pre-dispersive configuration. The effect of such environmental variables as temperature, humidity and external light, and instrumental variables such as the number of scans averaged to form a spectrum, were considered with respect to the accuracy and precision of the measurement of absorbance at 876 nm, as a key term in the calibration for Brix, and predicted Brix. The application of post-dispersive near infrared technology to in-line assessment of intact fruit in a packing shed environment is discussed.