RISK FINANCING TECHNIQUES

Abstract. This research studies the evaluation of risk financing techniques. The management function isbased on the modified model.Using the financial statements of 110 companies listed in Tehran Stock Exchange during the period of 2013 to 2017, and using multivariate linear regression analysis with SPSS19 software, the results of the research showed that there is a positive and significant relationship between different financing methods and risk.Keywords: Risk, financing techniques, cash flow, investor

Sensor fault diagnosis of singular delayed LPV systems with inexact parameters: an uncertain system approach

In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H8 performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.Peer ReviewedPostprint (author's final draft

Actuator fault diagnosis of singular delayed LPV systems with inexact measured parameters via PI unknown input observer

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this study, actuator fault diagnosis of singular delayed linear parameter varying (SDLPV) systems is considered. The considered system has a time-varying state delay and its matrices are dependent on some parameters that are measurable online. It is assumed that the measured parameters are inexact due to the existence of noise in real situations. The system with inexact measured parameters is converted to an uncertain system. Actuator fault diagnosis is carried out based on fault size estimation. For this purpose, the system is transformed to a polytopic representation and then a polytopic proportional integral unknown input observer (PI-UIO) is designed. The proposed observer provides simultaneous state and actuator fault estimation while attenuating, in the H8H8 sense, the effects of input disturbance, output noise and the uncertainty caused by inexact measured parameters. The design procedure of PI-UIO is formulated as a convex optimisation problem with a set of Linear Matrix Inequality (LMI) constraints in the vertices of the parameter domain, guaranteeing robust exponential convergence of the PI-UIO. The efficiency of the proposed method is illustrated with an electrical circuit example modelled as an SDLPV system.Peer ReviewedPostprint (author's final draft

Evaluation of micro shear bonding strength of two universal dentin bondings to superficial dentin by self etch and etch-and-rinse strategies

Universal bondings can be used either with the etch-and-rinse or self-etch technique. Thus, the present study was done to evaluate the micro-shear bonding strength of two types of Universal Bondings to superficial dentin i.e self-etch and etch and rinse. The samples included 70 tooth blocks taken from 35 extracted sound premolar teeth. The superficial dentin was exposed to grinding by 800 grit silicon carbide Disk. The samples were randomly divided into 5 equal groups (14 samples in each group). Scotch bond universal (3M/USA) and All bond universal (BISCO/USA) were applied by self-etch and etch and rinse technique in group 1-4 and Adper Single bond 2 (3M/USA) was used in group 5 as etch and rinse for the control group. Z250 XT (3M/USA) resin composite was bonded in tygon tube on surfaces of samples and were cured. Specimens were stored in distilled water at 37ºC for 24 h and then subjected to the micro shear bond strength test in a universal testing machine at a crosshead speed of 0.5 mm/min. The data were analyzed by two-way ANOVA and Tukey test. Failure mode was determined using a stereomicroscope under 20X magnification. Significance level was considered 0/05. The mean of micro-shear bonding strength and Standard Deviation of groups in Mega Pascal are respectively: 35.74 (6.21), 29.50 (3.89), 24.60 (3.53), 31.47 (4.73), 18.09 (3.87). The self-etch technique for Scotch bond Universal and the etch and rinse technique for All bond Universal showed higher micro shear bonding strength. Adper single bond 2 showed the lowest bond strength to a significant level in comparison to other groups (p<0.05). Failure mode was predominantly adhesive. The micro shear bonding strength of universal adhesives was highly bonding-dependent. Universal bondings had higher micro-shear bonding strength than Adper single bond 2

A Robust Multi Response Surface Approach for Optimization of Multistage Processes

Purpose: In a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but also is dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. One of the most efficient statistical approaches used to model the multistage problems is the response surface method (RSM). However, it is necessary to optimize each response in all stages so to achieve the best solution for the whole problem. Robust optimization can produce very accurate solutions in this case. Design/methodology/approach: In order to model a multistage problem, the RSM is often used by the researchers. A classical approach to estimate response surfaces is the ordinary least squares (OLS) method. However, this method is very sensitive to outliers. To overcome this drawback, some robust estimation methods have been presented in the literature. In optimization phase, the global criterion (GC) method is used to optimize the response surfaces estimated by the robust approach in a multistage problem. Findings: The results of a numerical study show that our proposed robust optimization approach, considering both the sum of square error (SSE) index in model estimation and also global criterion (GC) index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method. Originality/value: To the best of the authors’ knowledge, there are few papers focusing on quality oriented designs in the multistage problem by means of RSM. Development of robust approaches for the response surface estimation and also optimization of the estimated response surfaces are the main novelties in this study. The proposed approach will produce more robust and accurate solutions for multistage problems rather than classical approaches

Ensembles of Random Projections for Nonlinear Dimensionality Reduction

Dimensionality reduction methods are widely used in informationprocessing systems to better understand the underlying structuresof datasets, and to improve the efficiency of algorithms for bigdata applications. Methods such as linear random projections haveproven to be simple and highly efficient in this regard, however,there is limited theoretical and experimental analysis for nonlinearrandom projections. In this study, we review the theoretical frameworkfor random projections and nonlinear rectified random projections,and introduce ensemble of nonlinear maximum random projections.We empirically evaluate the embedding performance on 3commonly used natural datasets and compare with linear randomprojections and traditional techniques such as PCA, highlightingthe superior generalization performance and stable embedding ofthe proposed method

Microscale investigation of phase transformation and plasticity in multi-crystalline shape memory alloy using discrete dislocation–transformation method

Martensitic phase transformation and plasticity are two primary mechanisms of deformation in shape memory alloys (SMAs) and the interaction between them influences the behaviour of SMA during cyclic loading, specifically the pseudoelasticity behaviour and the shape memory effect. This interaction, which occurs in microscale, affects the reversibility and eventually the actuation capacity of SMAs. In order to capture this interaction in microscale, a discrete dislocation–transformation model was developed in Sakhaei et al. (Mech Mater 97:1–18, 2016) and was applied to simulate the single-crystalline NiTi samples under thermo-mechanical loads. In this study, the microscale coupling between phase transformation and plasticity as well as grain size and orientation effects is investigated in multi-crystalline shape memory alloys under thermal and mechanical loading by using the discrete dislocation–transformation framework through the representative numerical simulations. The results illustrated the dependency of dislocation slip and martensitic transformation to crystalline orientations as well as grain size and grain boundary densities in the multi-crystalline SMAs

Simultaneous actuator and sensor fault reconstruction of singular delayed linear parameter varying systems in the presence of unknown time varying delays and inexact parameters

In this article, robust fault diagnosis of a class of singular delayed linear parameter varying systems is considered. The considered system has delayed dynamics with unknown time varying delays and also it is affected by noise, disturbance and faults in both actuators and sensors. Moreover, in addition to the aforementioned unknown inputs and uncertainty, another source of uncertainty related to inexact measures of the scheduling parameters is present in the system. Making use of the descriptor system approach, sensor faults in the system are added as additional states into the original state vector to obtain an augmented system. Then, by designing a suitable proportional double integral unknown input observer (PDIUIO), the states, actuator, and sensor faults are estimated. The uncertainty due to the mismatch between the inexact parameters that schedule the observer and the real parameters that schedule the original system is formulated with an uncertain system approach. In the PDIUIO, the uncertainty induced by unknown inputs (disturbance, noise and actuator, and sensor faults), unknown delays, and inexact parameter measures are attenuated in H8 sense with different weights. The constraints regarding the existence and the robust stability of the designed PDIUIO are formulated using linear matrix inequalities. The efficiency of the proposed method is verified using an application example based on an electrical circuit.Peer ReviewedPostprint (author's final draft