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In recent times we have seen a trend of having the ideologies of the two dominant political parties in the U.S. growing further and further apart. Simultaneously we have entered the age of big data raising enormous interest in computational approaches to solve problems in many domains such as political elections. However, an overlooked problem lies in predicting what happens once our elected officials take office, more specifically, predicting the congressional votes, which are perhaps the most influential decisions being made in the U.S. This, nevertheless, is far from a trivial task, since the congressional system is highly complex and heavily influenced by both ideological and social factors. Thus, dedicated efforts are required to first effectively identify and represent these factors, then furthermore capture the interactions between them. To this end, we proposed a robust end-to-end framework Multi-Factor Congressional Vote Prediction (MFCVP) that defines and encodes features from indicative ideological factors while also extracting novel social features. This allows for a principled expressive representation of the complex system, which ultimately leads to MFCVP making accurate vote predictions. Experimental results on a dataset from the U.S. House of Representatives shows the superiority of MFCVP to several representatives approaches when predicting votes for individual representatives and also the overall outcome of the bill voted on. Finally, we perform a factor analysis to understand the effectiveness and interplay between the different factors.

Adaptive dynamic programming (ADP) technique is adopted in this work to investigate the optimal control problem of Markovian jump systems. By utilizing Bellman’s optimality principle, a discrete Hamilton Jacobi Bellman (HJB) equation is established to design the optimal controller for the system under consideration. Then, based on value iteration, a new ADP algorithm is proposed for finding the solution of the established HJB equation. It is proven that the iterative solution sequence generated by the developed ADP iterative approach under zero initial values is monotonically convergent. Neural networks are constructed to accomplish the presented value iteration ADP algorithm. At last, simulation researches for two Markovian jump systems demonstrate the effectiveness of the proposed optimal control method.

The problem of robust reliable tracking control on the omnidirectional rehabilitative training walker is examined. The new nonlinear redundant input method is proposed when one wheel actuator fault occurs. The aim of the study is to design an asymptotically stable controller that can guarantee the safety of the user and ensure tracking on a training path planned by a physical therapist. The redundant degrees of freedom safety control and the asymptotically zero state detectable concept of the walker are presented, the model of redundant degree is constructed, and the property of center of gravity constant shift is obtained. A controller that can satisfy asymptotic stability is obtained using a common Lyapunov function for admissible uncertainties resulting from an actuator fault. Simulation results confirm the effectiveness of the proposed method and verify that the walker can provide safe sequential motion when one wheel actuator is at fault.

Current signal monitoring (CSM) can be used as an effective tool for diagnosing broken rotor bars fault in induction motors. In this paper, fault diagnosis and classification based on artificial neural networks (ANNs) is done in two stages. In the first stage, a filter is designed to remove irrelevant fault components (such as noise) of current signals. The coefficients of the filter are obtained by least square (LS) algorithm. Then by extracting suitable time domain features from filter's output, a neural network is trained for fault classification. The output vector of this network is represented in one of four categories that includes healthy mode, a 5 mm crack on a bar, one broken bar, and two broken bar modes. An optimum structure of the neural network is obtained via particle swarm optimization (PSO) algorithm.

In this paper, we deal with the issue of robust delay-independent asymptotic stability and robust disturbance attenuation problem for linear parameter-dependent systems. Using Hamiltonian-Jacoby-Isaac approach, a parameter-dependent LMI optimization is obtained. It is shown that by utilizing polynomial parameter-dependent quadratic Lyapunov functions, a parameter-dependent LMI optimization problem is derived. Therefore, state feedback control is determined by solving a parameter-independent LMI. Finally, the applicability of the proposed design is illustrated on a simple example

This paper describes a new sufficient condition for the Dynamic Output Feedback (DOF) stabilisation of linear time invariant parameter-dependent systems in the presence of norm-bounded non-linear uncertainties. The required sufficient conditions with high precision are expressed as Linear Matrix Inequalities (LMIs) using the idea of Polynomial Parameter-Dependent Quadratic (PPDQ) functions. It is shown that the parameter-dependent DOF control ensures robust asymptotically stability and robust disturbance attenuation, simultaneously, based on the Hamiltonian-Jacobi-Isaac method. The applicability of the proposed method is illustrated with a simple example.

The aim of this article is to develop a soft approach for a real-time cell temperature prediction in the aluminum electrolysis reduction. Under the limited labeled data constraint, Laplacian semi-supervised learning methods, which can fully utilize the underlying structure of the data distribution and further extract information contained in all available data, has recently received extensive attention in the field of soft sensor modeling. Since the Laplacian underlying manifold is a constant, it remains a challenging task to improve the extrapolating ability for the case that only a few labeled samples are available. This study presents a soft modeling method based on a semi-supervised deep learning structure, which was developed from the hierarchical autoencoders with extreme learning machine. Furthermore, a Laplacian–Hessian semi-supervised extreme learning machine is built to learn all the geometric distribution information. The Laplacian–Hessian semi-supervised extreme learning machine method is applied to estimate the cell temperature in an aluminum reduction process. The experimental results demonstrate the performance and robustness of the proposed algorithm are superior to those of the existing state-of-the-art methods.

Landing gear suspension systems fulfill the tasks of absorbing the vertical energy of the touch-down as well as providing passenger and crew comfort with a smooth ground ride before take-off and after landing. They are also designed to have optimal performance in the case of a hard landing. In general, the tasks of aircraft landing gears are complex and sometimes lead to a number of contradictory requirements. Although there are existing modifications of aircraft shock absorbers to reduce the problem, the basic design conflict between the requirements for landing and for rolling cannot be fully overcome by a passive suspension layout. Active and semiactive suspension techniques are a solution to this problem and are capable of reducing fuselage vibrations effectively. In order to get satisfactory damping performance with active and semiactive devices, appropriate control laws must be employed. In this paper, we study the use of an adaptive backstepping control with H∞ performance to cope with disturbances, uncertainties and nonlinearities, typical of suspension systems and damping devices. A comparison between active and semiactive strategies is provided through the analysis of simulation results.

Existing results on the generalized dissipativity of digital filters are limited to 1-D cases. This brief presents some novel results on the generalized dissipativity of 2-D filters in the discrete Fornasini–Marchesini second (FMS) form. More specifically, we propose a criterion to ascertain that single 2-D filters in the discrete FMS form are 2-D generalized dissipative. Using this result, a condition is established such that the interconnected 2-D filters in the discrete FMS form are 2-D generalized dissipative. Furthermore, the asymptotic stability of unforced interconnected 2-D filters is examined. It is shown that the results developed in our recent work for 1-D filters and in this brief for 2-D filters serve as an overall framework for the generalized dissipativity of digital filters.

The problem of event-triggered neural adaptive fault-tolerant finite-time control is investigated for a class of nonstrict feedback nonlinear systems in the presence of nonaffine nonlinear faults. The event-triggered signal is designed by using a relative-threshold to reduce communication burden. The dynamic surface control method is used to relax the assumption of the reference signal and deal with the computational complexity issue. Based on the finite-time stability, a new neural adaptive backstepping design method is developed. The event-triggered neural adaptive fault-tolerant control law is developed for the closed-loop system so that not only the semi-global practical finite-time stability is ensured, but also the tracking performance with a small residual set is guaranteed. Finally, the effectiveness of the proposed control law is verified via simulation results.

Nowadays, each newly produced car must conform to the appropriate safety standards and norms. The most direct way to observe how a car behaves during a collision and to assess its crashworthiness is to perform a crash test. This paper deals with the wavelet-based performance analysis of the safety barrier for use in a full-scale test. The test involves a vehicle, a Ford Fiesta, which strikes the safety barrier at a prescribed angle and speed. The vehicle speed before the collision was measured. Vehicle accelerations in three directions at the centre of gravity were measured during the collision. The yaw rate was measured with a gyro meter. Using normal speed and high-speed video cameras, the behavior of the safety barrier and the test vehicle during the collision was recorded. Based upon the results obtained, the tested safety barrier, has proved to satisfy the requirements for an impact severity level. By taking into account the Haar wavelets, the property of integral operational matrix is utilized to find an algebraic representation form for calculate of wavelet coefficients of acceleration signals. It is shown that Haar wavelets can construct the acceleration signals well.

This paper is concerned with the problem of load-dependent H2 control for vehicle semi-active suspension. A quarter-car model equipped with a magnetorheological (MR)-damper, which captures essential features of a real car suspension, is considered in this study. H2-norm measures the root-mean-square (RMS) value of output to white noise input. Considering the fact that road roughness is often modelled as white noise, H2-norm is used to quantify control objectives of ride comfort and safety as well as suspension deflection and control effort. To guarantee system performance against parameter variations, a linear matrix inequality (LMI)-based design framework has been utilised and a linear parameter-varying (LPV) controller is synthesised. The design procedure of the semi-active suspension requires the inverse dynamics of MR damper, which is obtained through a locally linear neuro-fuzzy (LLNF) network. To illustrate the effectiveness of the proposed approach, the system outputs for both impulse and real road inputs are compared with H∞ controller in terms of performances.

Aerial image registration is one of the bases in many aerospace applications, such as aerial reconnaissance and aerial mapping. In this paper, we propose a novel aerial image registration algorithm which is based on Gaussian mixture models. First of all, considering the characters of the aerial images, the work uses a shape feature detector which computes the boundaries of regions with nearly the same gray-value to extract invariant feature. Then, a Gaussian mixture models (GMM) based image registration model is built and solved to estimate the transformation matrix between two aerial images. Furthermore, the proposed method is applied on real aerial images, and the results demonstrate the improved performance of the proposed algorithm.

In this paper, the problem of Soft-stall power control design for a small wind turbine is considered. Passive stalling and furling methods are widely used to limit the output power of small wind turbines at above-rated wind speed conditions. However, these methods have substantial limitations, for instance, related to tracking the maximum power at some wind speed levels, limited variable speed operation and introducing unbalanced forces on wind turbine blades. Soft-stall power control is a promising technique to overcome above limitations and improve the performance of small wind turbines. Small wind turbines have a comparatively low moment of inertia value, and it is possible to make fast speed changes by generator torque control which is essential to the successful implementation of proposed control method. A sliding-mode controller is developed as the wind turbine speed controller. Furthermore, two sliding-mode current controllers are utilized in the field oriented control system of the generator. A simulation study is illustrated to validate the proposed soft-stall power control technique and results are compared with two other speed control strategies which confirm the applicability of the proposed control technique for small wind turbines.