The central idea of individual fairness is based on an auspicious yet intuitive assertion: similar individuals should be treated similarly. Nevertheless, the fulfillment of individual fairness is hindered by three major obstacles. First, one needs to determine individuals who should receive similar treatment. Second, seamlessly formulating the notion of individual fairness in an ML learning process is another challenge. Third, effectively evaluating the notion of similar treatment in probabilistic classifiers is another challenge. To overcome these challenges, we propose a novel framework called FairMatch. Our proposed framework offers a new approach to pairing similar and dissimilar individuals using a causal analysis method called propensity score matching. Moreover, we formulate individual fairness as a representation learning problem where we incorporate similar and dissimilar pairs in a triplet-based loss function. Eventually, we devise a novel metric to evaluate individual fairness that captures the notion of similar treatment in probabilistic classifiers in a better way. Experimental results on four real-world datasets verify the superiority of FairMatch to existing solutions where we demonstrate it can deliver fairer decisions without scarifying the predictive performance.
We address the problem of downlink throughput improvement for IEEE 802.11a/g systems by using a modified access point (AP) equipped with multiple antennas. The main restriction is that standard terminals should not be modified in any way. An alternating time-offset space division multiple access (SDMA) solution is proposed to overcome restrictions imposed by the legacy terminals requirement. In this paper we concentrate on channel estimation over acknowledgement (ACK) bursts and effect of imperfections such as non-ideal channel reciprocity and delayed channel estimates. Simulations based on channel models approved by the IEEE 802.11 Standard Group demonstrate that a near doubling of downlink capacity can be achieved in a conference room environment in the case of low levels of channel reciprocity errors at the AP.
Rolling bearings are crucial for ensuring the safe and stable operation of electromechanical systems. Although deep learning has been widely used in fault diagnosis of rolling bearings, it is unable to accurately diagnose faults when the system operates under multiple working conditions. Therefore, it is essential to conduct research on fault diagnosis of rolling bearings under multiple working conditions to ensure the reliable operation of electromechanical systems. The potential features related to working conditions may be reflected in the different layers of the deep neural network (DNN). However, information loss during the process of layer-by-layer feature extraction may result in the loss of potential features related to changes in working conditions, which in turn affects the fault diagnosis results. This study focused on developing a multiscale recursive fusion strategy for a DNN by designing a new attention model with a lower computational burden. The proposed multiscale recursive fusion strategy guided by the attention mechanism can help correctly characterize the potential features related to variations in working conditions by allocating more attention to useful information and less attention to useless information on the adjacent layers of the DNN. Experimental tests for fault diagnosis of rolling bearings verified that the proposed method is superior to existing methods for fault diagnosis when the system is operated under multiple working conditions.
This paper proposes an observer-based robust guaranteed cost control method for thrust-limited rendezvous in near-circular orbits. Treating the noncircularity of the target orbit as a parametric uncertainty, a linearized motion model derived from the two-body problem is adopted as the controlled plant. Based on this model, a robust guaranteed cost observer-controller is synthesized with a less conservative saturation control law, and sufficient condition for the existence of this observer-based rendezvous controller is derived. Finally, an illustrative example with immeasurable velocity states is presented to demonstrate the advantages and effectiveness of the control scheme.
This paper considers the problem of adaptive fuzzy backstepping-based output-feedback controller design for a class of uncertain switched nonlinear stochastic systems in lower-triangular form without the measurements of the system states. By combining fuzzy logic systems' universal approximation ability and dynamic surface control technique in the adaptive backstepping recursive design with a modified average dwell-time scheme, a new adaptive fuzzy control approach is presented for the switched system. More specifically, a switched observer is constructed to reduce the conservativeness aroused by the employ of a common observer, and individual coordinate transformations for subsystems are given up by adopting a common coordinate transformation of all subsystems. It is proved that the overall closed-loop system is stable in the sense of semi-globally uniformly ultimately bounded in mean square, and the output of the switched system converges to a small neighborhood of the origin with appropriate choice of design parameters. Finally, simulation studies are provided to demonstrate the validity of the proposed control method.
This paper investigates the problems of stability and <svg style="vertical-align:-3.27605pt;width:12.15px;" id="M2" height="17.424999" version="1.1" viewBox="0 0 12.15 17.424999" width="12.15" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"> <g transform="matrix(.017,-0,0,-.017,.625,12.725)"><use xlink:href="#x1D459"/></g> <g transform="matrix(.012,-0,0,-.012,5.2,16.8)"><use xlink:href="#x31"/></g> </svg>-gain controller design for positive switched systems with time-varying delays via delta operator approach. The purpose is to design a switching signal and a state feedback controller such that the resulting closed-loop system is exponentially stable with <svg style="vertical-align:-3.27605pt;width:12.15px;" id="M3" height="17.424999" version="1.1" viewBox="0 0 12.15 17.424999" width="12.15" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"> <g transform="matrix(.017,-0,0,-.017,.625,12.725)"><use xlink:href="#x1D459"/></g> <g transform="matrix(.012,-0,0,-.012,5.2,16.8)"><use xlink:href="#x31"/></g> </svg>-gain performance. Based on the average dwell time approach, a sufficient condition for the existence of an <svg style="vertical-align:-3.27605pt;width:12.15px;" id="M4" height="17.424999" version="1.1" viewBox="0 0 12.15 17.424999" width="12.15" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"> <g transform="matrix(.017,-0,0,-.017,.625,12.725)"><use xlink:href="#x1D459"/></g> <g transform="matrix(.012,-0,0,-.012,5.2,16.8)"><use xlink:href="#x31"/></g> </svg>-gain controller for the considered system is established by constructing an appropriate copositive type Lyapunov-Krasovskii functional in delta domain. Moreover, the obtained conditions can unify some previously suggested relevant methods in the literature of both continuous- and discrete-time systems into the delta operator framework. Finally, a numerical example is presented to explicitly demonstrate the effectiveness and feasibility of the proposed method.
This paper is concerned with the stabilization problem for a class of uncertain discrete impulsive switched delay systems under asynchronous switching. The so-called asynchronous switching means that the switches between the candidate controllers and system modes are asynchronous. By using the average dwell time (ADT) approach, sufficient conditions for the existence of an asynchronously switched controller is derived such that the resulting closed-loop system is exponentially stable. The desired controller gains and the admissible switching signals are obtained in terms of a set of matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed method.
This paper will entail the work of modeling, testing and verification of the Dielectric Electro Active Polymers (DEAP's) material. Whereby mathematical model will be derived to describe the statics and the dynamics of the DEAP's sheet, parameters of the models are determined using experimental/simulation results which will be used to verify and determine the precision of the derived dynamic model. The testing for finding the material properties is requested before doing the dynamic analysis, however this will not be addressed thoroughly in this paper. Both material characterization and dynamic analysis tests are performed using the developed testing rig specially designed for this purpose. The results of this study highlight the dependency of the material dynamics on the mechanical fixture of the material as well as the material creep properties, whereby the range of operation can be reduced to lower frequencies or expanded to higher frequencies when the mechanical fixture is designed for a certain application. The test results for the material shows an offset error between 0–20 Hz mainly because of the material creep but the error diverge after this range stimulated when exceeding the natural frequency of the system which lead to non stable state effecting the controllability of the actuated material, the offset error due to the creep is found to be eliminated using faster discharging circuits.
No abstract is provided for this article.
In this paper, we propose a method of modeling for vehicle crash systems based on viscous and elastic properties of the materials. This paper covers an influence of different arrangement of spring and damper on the models' response. Differences in simulating vehicle - to - rigid barrier collision and vehicle - to - pole collision are explained. Comparison of the models obtained from wideband (unfiltered) acceleration and filtered acceleration is done. At the end we propose a model which is suitable for localized collisions simulation.
No abstract is provided for this article.
In response to the increasingly severe traffic congestion problem, this paper proposes a novel method based on Double Deep Q-Learning Network to enhance the performance of adaptive traffic signal control agents in alleviating traffic congestion and delays. By designing a novel state space model and reward function, the proposed method can minimize vehicle queue lengths and reduce vehicle delay duration when dealing with complex intersections or segments with significant traffic fluctuations. To evaluate the performance of this method, the paper utilizes the Simulation of Urban MObility software to set up environments for complex intersections. Simulation results demonstrate that compared to previous works and current mainstream algorithms, the proposed method can efficiently control signals in complex traffic environments, effectively addressing congestion and improving traffic efficiency.
In this paper, a non-fragile fuzzy control design is proposed for a class of nonlinear systems with mixed discrete and distributed time delays. The Takagi and Sugeno (T-S) fuzzy set approach is applied to the modelling of the nonlinear dynamics, and a T-S fuzzy model is constructed, which can represent the nonlinear system. Then, based on the fuzzy linear model, a fuzzy linear controller is developed to stabilize the nonlinear system. The control law is obtained to ensure stochastically exponentially stability in the mean square. The sufficient conditions for the existence of such a control are proposed in terms of certain linear matrix inequalities.
The paper is concerned with the application of wavelet-based neural networks for optimal control of robotic manipulators motion. The model of robotic manipulators with regard to frictions and disturbances is nonlinear and uncertain. Optimal control law is found by the optimization of the Hamilton–Jacobi–Bellman (H-J-B) equation and it shows how wavelet-based neural networks can overcome nonlinearities through optimization without preliminary off-line learning phase. The neural network is learned as on-line and an adaptive learning algorithm is derived from the Lyapunov theory. This is so that both tracking stability and error convergence of the estimation for the nonlinear function can be guaranteed in the closed-loop system. The Lyapunov function for the nonlinear analysis is derived from the user input in terms of a specified quadratic performance index. Simulation results on a three-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances. Furthermore, it is shown that the tracking error for wavelet neural networks is less than conventional neural networks.
No abstract is provided for this article.
This paper investigates the problem of delay-dependent H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> memory filtering for continuous-time semi-Markovian jump linear systems (MJLSs) with time-varying delay in an input-output framework. Differing from the constant transition rates (TRs) in the conventional MJLSs, the TRs of the semi-MJLSs depend on the random sojourn-time and are thus with time-varying characteristics. By utilizing a two-term approximation for the terms with time-varying delay, it is first shown that the filtering error system (FES) can be reformulated into a feedback interconnection form and the stability and performance analysis problem of the FES can be recast as the scaled small gain (SSG) problem of an interconnected system. Then, based on a semi-Markovian Lyapunov-Krasovskii formulation of SSG condition combined with projection lemma, the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter synthesis for the underlying semi-MJLSs is formulated in terms of linear matrix inequalities. Finally, simulation studies are provided to evaluate the effectiveness and superiority of the proposed design method.
