Discrete data clustering using finite mixture models

Finite mixture models have been applied for different computer vision, image processing and pattern recognition tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this paper, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel, well motivated mixture that we call the multinomial generalized Dirichlet mixture. The novel model is compared with other discrete mixtures. We designed experiments involving spatial color image databases modeling and summarization, and text classification to show the robustness, flexibility and merits of our approach.     

Robustness design of a dynamic output-feedback decentralized controller using <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> synthesis and LMI paradigm

This paper proposes developing a H _∞ dynamic output-feedback decentralized control design method for nonlinear interconnected systems subject to time-varying parameters and external disturbances. The designed controller is formulated as an optimization problem subject to linear matrix inequalities (LMIs) for the concurrent computation of the decentralized observation and control gains, and for the external disturbance mitigation by means of a H _∞ performance criterion minimization. The propounded optimization problem, designed in LMI conditions, is expeditiously resolved by a one-step procedure to override the conservatism generated by using many step-based procedures often used in the analysis and synthesis of interconnected systems. The effectiveness of the developed control scheme is demonstrated through simulation results of multimachine power systems.     

Positive vectors clustering using inverted Dirichlet finite mixture models

In this work we present an unsupervised algorithm for learning finite mixture models from multivariate positive data. Indeed, this kind of data appears naturally in many applications, yet it has not been adequately addressed in the past. This mixture model is based on the inverted Dirichlet distribution, which offers a good representation and modeling of positive non-Gaussian data. The proposed approach for estimating the parameters of an inverted Dirichlet mixture is based on the maximum likelihood (ML) using Newton Raphson method. We also develop an approach, based on the minimum message length (MML) criterion, to select the optimal number of clusters to represent the data using such a mixture. Experimental results are presented using artificial histograms and real data sets. The challenging problem of software modules classification is investigated within the proposed statistical framework, also.     

Novel approaches for synthesizing video textures

Video texture, a novel type of medium, can produce a new video with a continuously varying stream of images from a recorded video. A classic approach to generate video textures is to apply principal components analysis (PCA) for dimensionality reduction (i.e. extraction of frame signatures) and autoregressive (AR) process for prediction purposes. In this paper we investigate the use of other dimensionality reduction techniques to generate accurate video textures. Based on our experiments, the quality of video textures can be improved further. We also propose a new approach for generating video textures using probabilistic principal components analysis (PPCA) and Gaussian process dynamical model (GPDM) to synthesize video textures which contain frames that never appeared before and with similar motions as original videos. Furthermore, we propose two ways of generating online video textures by applying the incremental Isomap and incremental Spatio-temporal Isomap (IST-Isomap). Both approaches can produce good online video texture results. In particular, IST-Isomap, that we propose, is more suitable for sparse video data (e.g. cartoon).     

Optimization of friction stir welding process using NSGA-II and DEMO

In welding processes, the selection of optimal process parameter settings is very important to achieve best weld qualities. In this work, neuro-multi-objective evolutionary algorithms (EAs) are proposed to optimize the process parameters in friction stir welding process. Artificial neural network (ANN) models are developed for the simulation of the correlation between process parameters and mechanical properties of the weld using back-propagation algorithm. The weld qualities of the weld joint, such as ultimate tensile strength, yield stress, elongation, bending angle and hardness of the nugget zone, are considered. In order to optimize those quality characteristics, two multi-objective EAs that are non-dominated sorting genetic algorithm II and differential evolution for multi-objective are coupled with the developed ANN models. In the end, multi-criteria decision-making method which is technique for order preference by similarity to the ideal solution is applied on the Pareto front to extract the best solutions. Comparisons are conducted between results obtained from the proposed techniques, and confirmation experiments are performed to verify the simulated results.

     

A heuristic to solve the synchronized log-truck scheduling problem

We present a synchronized routing and scheduling problem that arises in the forest industry, as a variation of the log-truck scheduling problem. It combines routing and scheduling of trucks with specific constraints related to the Canadian forestry context. This problem includes aspects such as pick-up and delivery, multiple products, inventory stock, multiple supply points and multiple demand points. We developed a decomposition approach to solve the weekly problem in two phases. In the first phase we use a MIP solver to solve a tactical model that determines the destinations of full truckloads from forest areas to woodmills. In the second phase, we make use of two different methods to route and schedule the daily transportation of logs: the first one consists in using a constraint-based local search approach while the second one is a hybrid approach involving a constraint programming based model and a constraint-based local search model. These approaches have been implemented using COMET2.0. The method, was tested on two industrial cases from forest companies in Canada.     

Unexploited Thapsia garganica,Orlaya maritima,and Retama raetam Seeds: Potential Sources of Unsaturated Fatty Acid and Natural Antioxidants

Total lipid contents, fatty acid compositions, phenolic profiles and antioxidants activities of seeds from Thapsia garganica, Orlaya maritima, and Retama raetam were investigated. The oil values were more than 26 %, except seeds of R. raetam (ca. 3 %). Unsaturated fatty acids accounted for the majority of the fatty acids (more than 75 %). Oleic and linoleic acid were the predominant fatty acids. Total phenolic compounds (24–104 mg GAE g^?1 DR), total flavonoids (4–102 mg QE g^?1g DR), total tannins (28–85 mg GAE g^?1 DR) and condensed tannins (0.62–131 mg CE g^?1 DR) were also determined. The antioxidant activities using different assays were evaluated. The predominant detected classes were the phenolic acids (42–85 %) and the flavonoids (11–48 %). The major phenolic acids were caffeic, trans‐4‐hydroxy‐3‐methoxycinnamic, p‐coumaric, and gallic acid. The predominant flavonoids were quercetin, luteolin, naringin, apigenin, and kaempferol. This study brings attention to the medicinal importance of these species as a source of oil and antioxidant molecules.     

Novel multiplex PCR-RFLP assay discriminates bovine,porcine and fish gelatin substitution in Asian pharmaceuticals capsule shells

Gelatin is widely used in pharmaceuticals as a protective coating, such as soft and hard capsule shells. However, the animal source of gelatin is a sensitive issue because certain gelatins such as porcine and bovine gelatins are not welcome in Halal, Kosher and Hindus’ consumer goods. Recently, we have documented DNA barcoding and multiplex PCR platforms for discriminating porcine, bovine and fish gelatins in various fish and confectionary products; but those assays were not self-authenticating and also not tested in highly refined pharmaceutical products. To address this knowledge gap, here we report a self-authenticating multiplex PCR-restriction fragment length polymorphism (RFLP) assay to identify animal sources of various gelatin in pharmaceutical capsules. Three different restriction enzymes, BsaAI, Hpy188I and BcoDI were used to yield distinctive RFLP patterns for gelatin-based bovine (26, 94 bp), fish (97, 198 bp) and porcine (17, 70 bp) DNA in control experiments. The specificity was cross-tested against 16 non-target species and the optimised assay was used to screen gelatin sources in 30 halal-branded pharmaceuticals capsule shells. Bovine and porcine DNA was found in 27 and 3 of the 30 different capsules products. The assay was suitable for detecting 0.1 to 0.01 ng total DNA extracted from pure and mixed gelatins. The study might be useful to authenticate and monitor halal, kosher, vegetarian and Hindu compliant pharmaceuticals, foods and cosmetics.     

A GIS Database for Sustainable Management of Shallow Water Resources in the Tulul al Ashaqif Region, NE Jordan

The Tulul al Ashaqif region is an arid area in northeastern Jordan that contains renewable shallow perched aquifer water. The study of these aquifers has led to better understanding of the recharge process as well as other hydrological issues related to management of water resources in similar areas. The use of geographic information system (GIS)-based predictive mapping to locate areas of high potential for shallow perched aquifer sites is explored in this paper. Knowledge of the hydrologic, geologic and geomorphic variables influencing the development of shallow aquifer formation is used to produce GIS layers representing the spatial distribution of those variables. The GIS layers are then analyzed to identify locations where combinations of environmental variables match patterns observed at known sites. In addition, information can be deduced on the volume of water that is available and the best locations to site recharge facilities. Moreover, future development of these resources requires consideration of possible adverse affects of usage on these resources. The database developed can be used for this purpose as well.