Granular Support Vector Machines Based on Granular Computing, Soft Computing and Statistical Learning

With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems. In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine statistical learning theory, granular computing theory and soft computing theory to address challenging predictive data modeling problems effectively and/or efficiently, with specific focus on binary classification problems. In general, GSVM works in 3 steps. Step 1 is granulation to build a sequence of information granules from the original dataset or from the original feature space. Step 2 is modeling Support Vector Machines (SVM) in some of these information granules when necessary. Finally, step 3 is aggregation to consolidate information in these granules at suitable abstract level. A good granulation method to find suitable granules is crucial for modeling a good GSVM. Under this framework, many different granulation algorithms including the GSVM-CMW (cumulative margin width) algorithm, the GSVM-AR (association rule mining) algorithm, a family of GSVM-RFE (recursive feature elimination) algorithms, the GSVM-DC (data cleaning) algorithm and the GSVM-RU (repetitive undersampling) algorithm are designed for binary classification problems with different characteristics. The empirical studies in biomedical domain and many other application domains demonstrate that the framework is promising. As a preliminary step, this dissertation work will be extended in the future to build a Granular Computing based Predictive Data Modeling framework (GrC-PDM) with which we can create hybrid adaptive intelligent data mining systems for high quality prediction

Predicting pharmaceutical particle size distributions using kernel mean embedding

In the pharmaceutical industry, the transition to continuous manufacturing of solid dosage forms is adopted by more and more companies. For these continuous processes, high-quality process models are needed. In pharmaceutical wet granulation, a unit operation in the ConsiGmaTM-25 continuous powder-to-tablet system (GEA Pharma systems, Collette, Wommelgem, Belgium), the product under study presents itself as a collection of particles that differ in shape and size. The measurement of this collection results in a particle size distribution. However, the theoretical basis to describe the physical phenomena leading to changes in this particle size distribution is lacking. It is essential to understand how the particle size distribution changes as a function of the unit operation's process settings, as it has a profound effect on the behavior of the fluid bed dryer. Therefore, we suggest a data-driven modeling framework that links the machine settings of the wet granulation unit operation and the output distribution of granules. We do this without making any assumptions on the nature of the distributions under study. A simulation of the granule size distribution could act as a soft sensor when in-line measurements are challenging to perform. The method of this work is a two-step procedure: first, the measured distributions are transformed into a high-dimensional feature space, where the relation between the machine settings and the distributions can be learnt. Second, the inverse transformation is performed, allowing an interpretation of the results in the original measurement space. Further, a comparison is made with previous work, which employs a more mechanistic framework for describing the granules. A reliable prediction of the granule size is vital in the assurance of quality in the production line, and is needed in the assessment of upstream (feeding) and downstream (drying, milling, and tableting) issues. Now that a validated data-driven framework for predicting pharmaceutical particle size distributions is available, it can be applied in settings such as model-based experimental design and, due to its fast computation, there is potential in real-time model predictive control

Initial Hydraulic modelling and Levee Stability Analysis of the Triple M Ranch Restoration Project

“Advanced Watershed Science and Policy (ESSP 660)” is a graduate class taught in the Master of Science in Coastal and Watershed Science & Policy program at California State University Monterey Bay (CSUMB). In 2007, the class was taught in four 4-week modules, each focusing on a local watershed issue. This report is one outcome of one of those 4-week modules taught in the fall 2007 session. (Document contains 32 pages

Parameters influencing calcium phosphate precipitation in granular sludge sequencing batch reactor

Parameters influencing calcium phosphate precipitation in Calcium phosphate precipitation inside microbial granules cultivated in a granular sequenced batch reactor (GSBR) has been demonstrated to contribute to phosphorus removal during wastewater treatment. Whereas hydroxyapatite (HAP) is proven to accumulate in the granule, the main calcium phosphate precursors that form prior to HAP are here investigated. A separate batch reactor was used to distinguish reactions involving biological phosphate removal from physicochemical reactions involving phosphateprecipitation in order to establish the kinetics and stoichiometry of calcium phosphate formation. Experiments and simulations with PHREEQC and AQUASIM software support the assumption that amorphous calciumphosphate (ACP) is the intermediary in HAP crystallization. The results provide the kinetic rate constants and thermodynamic constants of ACP. The formation of bioliths inside biological aggregates as well as the main parameters that drive their formations are discussed here. Finally, the influence of pH and calcium and phosphate concentrations in the influent was also assessed, in order to determine the contribution of precipitation in the different operating conditions

Data granulation by the principles of uncertainty

Researches in granular modeling produced a variety of mathematical models, such as intervals, (higher-order) fuzzy sets, rough sets, and shadowed sets, which are all suitable to characterize the so-called information granules. Modeling of the input data uncertainty is recognized as a crucial aspect in information granulation. Moreover, the uncertainty is a well-studied concept in many mathematical settings, such as those of probability theory, fuzzy set theory, and possibility theory. This fact suggests that an appropriate quantification of the uncertainty expressed by the information granule model could be used to define an invariant property, to be exploited in practical situations of information granulation. In this perspective, a procedure of information granulation is effective if the uncertainty conveyed by the synthesized information granule is in a monotonically increasing relation with the uncertainty of the input data. In this paper, we present a data granulation framework that elaborates over the principles of uncertainty introduced by Klir. Being the uncertainty a mesoscopic descriptor of systems and data, it is possible to apply such principles regardless of the input data type and the specific mathematical setting adopted for the information granules. The proposed framework is conceived (i) to offer a guideline for the synthesis of information granules and (ii) to build a groundwork to compare and quantitatively judge over different data granulation procedures. To provide a suitable case study, we introduce a new data granulation technique based on the minimum sum of distances, which is designed to generate type-2 fuzzy sets. We analyze the procedure by performing different experiments on two distinct data types: feature vectors and labeled graphs. Results show that the uncertainty of the input data is suitably conveyed by the generated type-2 fuzzy set models.Comment: 16 pages, 9 figures, 52 reference

Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules

Process behavior and product quality in fertilizer manufacturing using continuous hopper transfer pan granulation—Experimental investigations

Fertilizers are commonly used to improve the soil quality in both conventional and organic agriculture. One such fertilizer is dolomite for which soil application in granulated form is advantageous. These granules are commonly produced from ground dolomite powder in continuous pan transfer granulators. During production, the granulator’s operation parameters affect the granules’ properties and thereby also the overall performance of the fertilizer. To ensure product granules of certain specifications and an efficient overall production, process control and intensification approaches based on mathematical models can be applied. However, the latter require high-quality quantitative experimental data describing the effects of process operation parameters on the granule properties. Therefore, in this article, such data is presented for a lab-scale experimental setup. Investigations were carried out into how variations in binder spray rate, binder composition, feed powder flow rate, pan inclination angle, and angular velocity affect particle size distribution, mechanical stability, and humidity. Furthermore, in contrast to existing work samples from both, pan granules and product granules are analyzed. The influence of operation parameter variations on the differences between both, also known as trajectory separation, is described quantitatively. The results obtained indicate an increase in the average particle size with increasing binder flow rate to feed rate and increasing binder concentration and the inclination angle of the pan. Compressive strength varied significantly depending on the operating parameters. Significant differences in properties were observed for the product and the intermediate (pan) samples. In fact, for some operation parameters, e.g., binder feed rate, the magnitude of the separation effect strongly depends on the specific value of the operation parameter. The presented concise data will enable future mathematical modeling of the pan granulation process, e.g., using the framework of population balance equations