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The present study examines the impact of managerial remuneration and its severe cut on audit fees by considering the moderating role of audit quality in companies listed on the Tehran stock exchange. To measure audit quality, three criteria were used, including auditor size, tenure, and expertise. The statistical population includes 92 firms, and the study was done from 2007 to 2016. Testing the hypotheses is conducted in Stata14 software through panel data with the fixed-effect method. The results showed that auditor size and tenure mitigate the relationship between managerial remuneration and audit fees. In return, the results also suggested that auditor expertise does not affect the relationship between managerial remuneration and audit fees. The obtained results also indicated that audit quality criteria such as size, tenure, and expertise do not affect the relationship between severe managerial remuneration cut and audit fees. In other words, audit quality does not moderate the positive relationship between severe managerial remuneration cut and audit fees.
Our intention, in this brief note, is to investigate what influence the viscoelastic models' unloading properties have on models' accuracy of representing vehicle crash event. Two types of simple spring-mass-damper systems (Kelvin models) such underdamped and critically damped conditions are analyzed. Subsequently, two different unloading scenarios are specified: elastic rebound in which only the damper is an energy dissipating element and plastic collision in which the model's maximum achievable displacement is at the same time its constant deflection at the zero-force level. By comparing models' behavior not only in terms of their time responses but also in terms of their force-deflection characteristics, it is concluded which of them is the most suitable to represent vehicle to pole collision.
This paper aims at providing new design approaches for positive observers of discrete-time positive linear systems based on a construction method of linear copositive Lyapunov function for positive systems. First, an efficient positive observer design approach is proposed by using linear programming such that the observer error system is exponentially stable. Furthermore, an interval observer design is proposed for uncertain positive systems. Then, the results are extended to positive time delay systems. In contrast with the previous design approaches, the new design method provides a general observer design with lower computational burden. Finally, three comparison examples are given to show the merit of the new design approach.
This paper intends to analyse the disturbance rejection and output tracking control for a class of nonlinear control systems with disturbances. In this connection, we present a method that combines a proportional-integral observer and nonlinear-equivalent-input-disturbance estimator for superior disturbance rejection performance. Specifically, the nonlinear-equivalent-input-disturbance estimator comprises of equivalent-input-disturbance estimator and nonlinear feedback term, which is employed to estimate and reject the disturbances from the nonlinear system. Notably, the proportional-integral loop in the proportional-integral observer reduces the estimation inaccuracy of the nonlinear-equivalent-input-disturbance. Then the estimated disturbance is intertwined into the repetitive control input to compensate it efficiently. In order to obtain the required results, the proposed control system is converted into a two-dimensional modified repetitive control system to describe the learning and control actions. In particular, the proposed controller enables to adjust the gains directly to improve the learning and control performance and as a result, the tracking accuracy increases. Using a general Lyapunov–Krasovskii functional, singular value decomposition technique and linear matrix inequalities approach, a design algorithm for establishing proportional-integral observer and feedback gains is developed for the system under consideration. Finally, simulation results are given to illustrate the developed method's validity and superiority.
It is evident that deep text classification models trained on human data could be biased.In particular, they produce biased outcomes for texts that explicitly include identity terms of certain demographic groups.We refer to this type of bias as explicit bias, which has been extensively studied.However, deep text classification models can also produce biased outcomes for texts written by authors of certain demographic groups.We refer to such bias as implicit bias, of which we still have a rather limited understanding.In this paper, we first demonstrate that implicit bias exists in different text classification tasks for different demographic groups.Then, we build a learningbased interpretation method to deepen our knowledge of implicit bias.Specifically, we verify that classifiers learn to make predictions based on language features that are related to the demographic attributes of the authors.Next, we propose a framework Debiased-TC to train deep text classifiers to make predictions on the right features and consequently mitigate implicit bias.We conduct extensive experiments on three real-world datasets.The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.
Because of the fact that vehicle crash tests are complex and complicated experiments it is advisable to establish their mathematical models. This paper contains an overview of the kinematic and dynamic relationships of a vehicle in a collision. There is also presented basic mathematical model representing a collision together with its analysis. The main part of this paper is devoted to methods of establishing parameters of the vehicle crash model and to real crash data investigation i.e. - creation of a Kelvin model for a real experiment, its analysis and validation. After model's parameters extraction a quick assessment of an occupant crash severity is done.
A robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering algorithm is proposed for two-dimensional (2-D) linear uncertain time-varying systems in this study. The considered 2-D systems are subject to unknown inputs, measurement noises, and time-varying modeling uncertainties. To appropriately deal with the system uncertainties, a new problem formulation of robust H∞ filter design for 2-D uncertain systems is first presented by introducing an alternative indefinite quadratic performance function in lieu of the standard H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance index. Then, the robust 2-D H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter design is converted to an optimization problem of finding the positive minimum of the new indefinite quadratic performance function under certain conditions. By relating this quadratic form to a specific 2-D state-space model in Krein space, the Krein space estimation theory is used to solve the reformulated optimization problem. A recursive robust 2-D time-varying H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter and its explicit condition for the existence are obtained through projection techniques in 2-D Krein space. One thermal process plant is used to illustrate the effectiveness of the proposed filter.
We present an artificial neural network model to predict hourly A-weighted equivalent sound pressure levels (LA eq,1h) for roads in Tehran at distances less than 4 m from the nearside carriageway edge. Our model uses the UK Calculation of Road Traffic Noise (CORTN) approach. Data were obtained from 50 sampling locations near five roads in Tehran at nearside carriageway edge distances of less than 4 m. The data were randomly assigned to training, testing, and holdout subsets. Model training was carried out using the training and testing subsets and comprised 60% and 20% of the data, respectively. Model validation was performed using the remaining 20% of data as a holdout subset. We examine the overall model efficiency using non-parametric tests, such as the Wilcoxon matched-pairs signed-rank test for the training step and the Kolmogorov–Smirnov test for two independent samples for the validation step. Our results indicate that a neural network approach can be applied for traffic noise prediction in Tehran in a statistically sound manner. The Wilcoxon matched-pairs signed-ranks test detects no significant difference between the absolute testing set errors of the developed neural network and a calibrated version of the CORTN model.
In this paper, the decentralized optimal control problem is addressed for a class of large-scale systems subject to injection attacks. All subsystem matrices are considered to be unavailable to the designer. A model-free decentralized sliding mode control (SMC) scheme for each subsystem is designed via just utilizing its own state information and the known bounds of the interconnections and the injection attacks. Moreover, the adaptive dynamic programming (ADP) approach is incorporated to deal with the infinite horizon optimal control problem for the sliding mode dynamics, which is equivalent to the solution of a set of parallel algebraic Riccati equations. Furthermore, a novel parallel policy iteration algorithm is developed to implement the proposed decentralized SMC scheme without using all subsystems dynamics matrices. Specifically, it is shown that during the whole policy iteration steps, the reachability of each sliding variable and the stability of each sliding mode dynamics are guaranteed simultaneously by the online updating decentralized SMC scheme. Finally, the applicability of the proposed novel ADP-based decentralized SMC strategy is illustrated by a two-machine power system subject to three different injection attacks.
This paper investigates the delay-dependent adaptive synchronization problem of the master and slave structure of linear systems with both constant neutral and time-varying discrete time-delays and nonlinear perturbations based on the Barbalat lemma and matching conditions. An adaption law which includes the master-slave parameters is obtained by using the Lyapunov functional method and inequality techniques to synchronize the master-slave systems without the knowledge of upper bounds of perturbation terms. Particularly, it is shown that the synchronization speed can be controlled by adjusting the update gain of the synchronization signal. A numerical example has been given to show the effectiveness of the method.
This brief studies the stability of switched systems in which all the subsystems may be unstable. In addition, some of the switching behaviors of the systems are destabilizing. By using the piecewise Lyapunov function method and taking a tradeoff between the increasing scale and the decreasing scale of the Lyapunov function at switching times, the maximum dwell time for admissible switching signals is obtained and the extended stability results for switched systems in a nonlinear setting are first derived. Then, based on the discretized Lyapunov function method, the switching stabilization problem for linear context is solved. By contrasting with the contributions available in the literature, we do not require that all the switching behaviors of the switching system under consideration are stabilizing. More specifically, even if all the subsystems governing the continuous dynamics are not stable and some of the switching behaviors are destabilizing, the stability of the switched system can still be retained. A numerical example is given to illustrate the validity of the proposed results.
This paper presents a new technique to design a parameter-dependent adaptive H ∞ control for a class of linear parameter-varying (LPV) systems. It is assumed that the statespace matrices affinely depend on parameters that are not measurable in real-time for the control process. By introducing a Hamiltonian-Jacobi-Isaac (HJI) function and using the vector projection method, a sufficient condition is first established for the stability analysis problem in terms of a parameter-dependent linear matrix inequality (LMI). Then, by means of the polynomially parameter-dependent quadratic (PPDQ) functions, a parameter-independent LMI-based condition is derived, which enables an explicit expression to be found of the parameter-dependent adaptive H ∞ control that guarantees both robust asymptotic stability and a prescribed level of disturbance attenuation for the system. Two numerical examples are given to illustrate the applicability of the proposed design approach.
