ON THE IDENTIFICATION OF SOME BILINEAR TIME SERIES MODELS

Abstract. In their book on bilinear time series models Granger and Andersen (1978, p. 43) dismiss the use of third order moments for identifying models on the grounds that for some bilinear models they will all be zero and hence are of no use in discriminating between true white noise and some bilinear models. However, in this paper it is shown that some of the third order moments do not vanish for some superdiagonal and diagonal bilinear models and the pattern of non zero moments can be used to discriminate between true white noise and these bilinear models and also between different bilinear models. Simulation experiments are used to study the applicability of theoretical results.     

Sonochemical effect induced by hydrodynamic cavitation: Comparison of venturi/orifice flow geometries

This study presents comparative assessment of four cavitation devices (three venturis and an orifice) in terms of cavitational yield. A fourfold approach was adopted for assessment, viz. CFD simulations of cavitating flow, simulations of individual cavitation bubble dynamics, high speed photographs of cavitating flow and model reaction of potassium iodide oxidation. Influence of design parameters of cavitation devices on nature of cavitation produced in the flow was studied. Number density of cavitation bubbles in the flow and interactions among bubbles had critical influence on cavitation yield. Orifice gave the highest cavitational yield per unit energy dissipation in flow (despite lower working inlet pressure) due to low density of cavitation bubbles in flow. On contrary, occurrence of large cavitation bubble clouds in venturi flow had adverse effect on cavitational yield due to high interactions among cavitation bubbles resulting in interbubble coalescence and recombination of oxidizing radicals generated from cavitation bubbles. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4705–4716, 2017     

On computing minimal independent support and its applications to sampling and counting

Constrained sampling and counting are two fundamental problems arising in domains ranging from artificial intelligence and security, to hardware and software testing. Recent approaches to approximate solutions for these problems rely on employing SAT solvers and universal hash functions that are typically encoded as XOR constraints of length n/2 for an input formula with n variables. As the runtime performance of SAT solvers heavily depends on the length of XOR constraints, recent research effort has been focused on reduction of length of XOR constraints. Consequently, a notion of Independent Support was proposed, and it was shown that constructing XORs over independent support (if known) can lead to a significant reduction in the length of XOR constraints without losing the theoretical guarantees of sampling and counting algorithms. In this paper, we present the first algorithmic procedure (and a corresponding tool, called MIS) to determine minimal independent support for a given CNF formula by employing a reduction to group minimal unsatisfiable subsets (GMUS). By utilizing minimal independent supports computed by MIS, we provide new tighter bounds on the length of XOR constraints for constrained counting and sampling. Furthermore, the universal hash functions constructed from independent supports computed by MIS provide two to three orders of magnitude performance improvement in state-of-the-art constrained sampling and counting tools, while still retaining theoretical guarantees.     

Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling

Random surface defects occur during the hot bar rolling of steels and are identified either by manual or by automated inspection techniques. Manual inspection techniques are purely based on the process knowledge of the inspector such as the location, type and kind of defects, and the primary sources of these defects. The automated techniques, to identify and classify the defects, rely on machine vision technologies and image processing algorithms based on support vector machines, wavelets, image processing and statistical inference. Both these approaches have their own advantages and limitations. To improve the accuracy of classification of these defects a process knowledge based support vector classification scheme is proposed (called PK-MSVM) which combines feature extraction task of automated inspection with the process knowledge. The defect observation data from the imaging sensor is transformed to include this process knowledge. Three attributes of the defects – length to width ratio, longitudinal location and transverse location- are used for this transformation are they are closely related to the thermo-mechanics of the rolling process. Different formulations of the multi-class support vector machines (MSVMs) are compared for this classification with or without process knowledge based transformation: one-against-one, one-against-all and Hastie’s algorithm of multi class SVM. It is found that the new approach (PK-MSVM) performs better than traditional MSVM for all the three formulations. For the best case, the performance sees a jump of more than 100%. Thus incorporating process knowledge in identification and classification does increase the reliability of inspection considerably.     

Nanoencapsulation of betamethasone valerate using high pressure homogenization–solvent evaporation technique: optimization of formulation and process parameters for efficient dermal targeting

Betamethsone valerate (BMV), a medium potency topical corticosteroid, is one of the most commonly employed pharmacological agents for the management of atopic dermatitis in both adults and children. Despite having remarkable pharmacological efficacy, these agents have limited clinical implication due to poor penetration across the startum cornum (SC). To mitigate issues related to targeted delivery, stability, and solubility as well as to potentiate therapeutic and clinical implication, the nanodelivery systems have gained remarkable recognition. Therefore, this study was aimed to encapsulate BMV into the chitosan nanoparticles (CS-NPs) for optimum dermal targeting and improved penetration across the SC. The prepared NPs were characterized for particle size, zeta potential, polydispersity index, entrapment efficiency, loading capacity, crystallinity, thermal behavior, morphology, in vitro release kinetics, drug permeation across the SC, and percentage of drug retained into various skin layers. Results showed that optimized BMV-CS-NPs exhibited optimum physicochemical characteristics including small particle size (< 250?±?28?nm), higher zeta potential (+58?±?8?mV), and high entrapment efficiency (86?±?5.6%) and loading capacity (34?±?7.2%). The in vitro release study revealed that BMV-CS-NPs displayed Fickian-diffusion type mechanism of release in simulated skin surface (pH 5.5). Drug permeation efficiency and the amount of BMV retained into the epidermis and the dermis were comparatively higher in case of BMV-CS-NPs compared to BMV solution. Conclusively, we anticipated that BMV-CS-NPs could be a promising nanodelivery system for efficient dermal targeting of BMV and improved anti-AD efficacy.     

On Inhomogeneous Deformations in ES Materials

Electro-sensitive (ES) elastomers or electro-rheological (ER) elastomers deform under the application of an electric field and their mechanical properties can be changed rapidly under the influence of electric field. The present paper deals with inhomogeneous deformations in ES elastomers. We have solved two problems namely inhomogeneous shearing deformation of a slab and inhomogeneous radial expansion and longitudinal shearing of an isotropic incompressible elastic circular cylindrical shell in the presence of electric field.     

Interaction Study of Anionic Surfactant with Aqueous Non‐Ionic Polymers from Conductivity,Density and Speed of Sound Measurements

In the present study, we have investigated in detail the interactions of the anionic surfactant sodium dodecyl sulfate (SDS) with aqueous polyethylene glycol (PEG), polyvinyl pyrrolidone (PVP) and various PEG + PVP mixtures at 293.15, 303.15 and 313.15 K by applying conductivity, density and speed of sound techniques. From experimentally measured data, the critical micelle concentration (CMC) values, standard free energy of micellization (ΔG_m^o), standard enthalpy of micellization (ΔH_m^o), standard entropy of micellization (ΔS_m^o), isentropic compressibilities (κ_s), apparent molar volumes (?_v) and apparent molar isentropic compressions (?_k) of SDS in aqueous polymer mixtures have been calculated. The nature of the process of micellization has been evidenced from the magnitude of ΔG_m^o, ΔH_m^o and ΔS_m^o values. The trends of variations obtained in the various parameters have been explained in terms of the electrostatic as well as hydrophobic interactions pertaining in SDS?PEG/PVP?water systems.     

A cascaded deep learning framework for iris centre localization in facial image

Accurate iris centre localization is crucial in many computer vision and facial biometric applications such as gaze estimation, human–computer interaction, iris recognition, and liveness detection. However, it is challenging in an uncontrolled environment due to variations like pose, scale, rotation, specular reflection, and image quality. Therefore, a cascaded deep learning framework for iris centre localization in facial images is proposed that is robust to the abovementioned variations. The proposed approach consists of (i) YOLOv3 for eye detection, (ii) UNet for iris segmentation, and (iii) statistical modelling for iris centre localization. The eyes are first detected using the YOLOv3, and subsequently, iris segmentation is performed within the detected eyes using the UNet. Following iris segmentation, statistical modelling is employed to enhance the localization accuracy of the iris centre. Experiments were performed on benchmark databases, resulting in a standardized error measure S_ED of 3.405 pixels for BioID and 3.259 pixels for GI4E databases. In addition, the robustness of the proposed eye detection model was further evaluated on the Yale B for illumination variations and the CAS-PEAL for pose variations.     

Optimum design of cyclone separator

Cyclone separators are one of the most widely used gas-solid separators. Although the optimal design of cyclone separators has been suggested earlier, the earlier works do not include all the critical parameters responsible for minimizing the pressure drop which is quite decisive to obtain a correct optimal design. In this article, the optimal design of the cyclone separator has been formulated as a geometric programming with a single degree of difficulty. The solution of the problem yields the optimum values of the number of cyclones to be used in parallel, and the inside diameter of cyclone shell and exit pipe, when a specified flow rate of gas is to be separated from solid particles, when the cut diameter is already specified. © 2009 American Institute of Chemical Engineers AIChE J, 2009